Approximately 60% of businesses struggle to effectively execute their pricing strategies (Forrester®, Intelligent Pricing Research, Dec 2021). Pricing has already become a true leadership topic in many companies, yet it has not fully taken root in the heart of organisations as desired. Developing a pricing and value mind-set within an organisation requires more than an agreed strategy and cascaded directives. Fostering a deep understanding and commitment to pricing and value needs to be supported with bottom-up actions. What practical steps can a pricing leader take to embed the pricing and value mind-set into the DNA of the team and organisation?

Introduction

We are in an age of unprecedented technological progress. This, combined with the fact that we are just emerging from an unexpected economic recession, is evidence that businesses must evaluate what it looks like to have sustainable growth and success. In this realm, price optimisation is one paramount area.
Recent years have brought forth significant data challenges to make the price elasticity model accurate. This article provides a unique approach to combining human expertise and machine learning to assess products on price sensitivity to give companies new insights and actions on how to improve profitability.

A B2B distribution company we know made an attempt to optimise its margins. The software system they use helps the company to reset price targets in real-time at a product level based upon cost-, stock- and margin levels, but the company desires to move towards a more value-driven approach. Therefore, the management also studies price elasticity to identify and segregate products that are sensitive and insensitive to demand. Management notices that historical data is less relevant to measure price elasticity. Cost price changes mainly drove the past 12 months' price changes. Measuring these as new price points is statistically not correct.

The management ran into a few issues with price elasticity:

• historical data was not clean for optimisation;
• no competitive data was available.
• there are more factors that drive the demand for the product than just the price;
• the sales teams didn’t trust the pricing recommendations features and so wouldn’t use them. 

This situation is all too familiar to many companies. Therefore, new optimisation methods have to be developed that take more factors into account and are easier to adopt by sales teams.

Companies require a system that ensures precise and accurate pricing strategies based on the relevant macro to micro parameters and advanced datasets to brace for and combat such downturns. A system that frees up significant time for human judgement towards thoughtful decisions instead of mundane and repetitive ones. In short, the pricing decisions in the future call for the synergy between Machine Learning and human expertise to ensure profitable and sustainable business growth.

Price Optimisation and its Potential

A McKinsey & Company study reports that a 1% rise in price, with stable sales volume, led to an 8% rise in operating profits. More and more intelligent companies are using scientific algorithms and analytics to find optimal prices. This process involves accounting for competitors and price sensitivity. When using these scientific algorithms, it’s crucial to include multiple factors and use platforms and systems which allow them to change prices dynamically in real time.

Case in point: Amazon "reprices millions of items as frequently as every few minutes". Furthermore, according to a study by Forrester Consulting, enterprises using data management systems are 58% more likely to hit their revenue goals and 162% more likely to exceed them. Price optimisation allows companies to transform this data into actionable insights, driving smarter pricing decisions.

Likewise, during uncertain times too, companies can capitalise on such power of technology to enhance profitability, cater to customer needs, and gain a competitive edge- all in a sustainable way.

Why Traditional Pricing Methods Don’t Cut it

The COVID-19 pandemic has been responsible for major changes in consumer behaviour and purchasing patterns. McKinsey and Company report an overall trend in consumers becoming more mindful and spending less on expensive products. 
 

McKinsey's hypothesis on which changes could stick or dissapate

According to a McKinsey & Company report, in the UK alone, 44% of respondents became more mindful of where they spent their money, and 32% shifted to less expensive products to save money.

This along with the disruption of e-commerce and the shift to digital methods has led to the need for more dynamic pricing.  Deloitte highlights that FMCG companies sitting "on a treasure-trove of data [ . . . ] can highly benefit from using AI-driven dynamic (portfolio) price setting…”

"The pandemic essentially squeezed ten years of digital sales penetration into three months." - McKinsey & Company.

We also see that large volumes of historical data pre-COVID-19 have been rendered unusable and/or unreliable due to the marked changes seen in the market during and post COVID19.  

Falling back on the traditional or manual ways to make those adjustments breeds inaccuracy due to human errors. The procedure currently conjures time and energy and needs to be updated. Therefore, traditional price management methods, based on historical data and fixed pricing models, often fall short in adapting to these rapid changes. From the above data, we can deduce that those enterprises slow to adjust to the crisis with new services, offers and customer-specific pricing strategies had to suffer the blow.  

Multiple factors can lead us to label items/services as price sensitive including continuous shifts in buyer purchase behaviour towards specific products, depending on seasonality, current economic health, region, and other aspects alike.

With time and new technological advancements, consumer sensitivity to product prices shifts. Hence, with such changes, it is imperative to research new probable drivers that trigger price sensitivity and experiment to fine-tune the strategy. Likewise, such a step is vital to maintain revenue flow and healthy profit margins regardless of new advancements with time.

At SYMSON, we saw the accuracy of our current price elasticity models decrease, and we saw the need to develop a new method. In response, we developed a 5-step approach to test a new aspect of our pricing strategies.

5-Step Approach to Assess the Price Sensitivity of Your Products

Summary: Here we look at SYMSON’s discovery of critical drivers that affect price sensitivity. The selection of these drivers was based on market research and customer surveys. They include but are not limited to:

• Brand value
• Product Lifecycle
• Price Level (as compared to competitors)
• Purchase Price changes (sudden increases or decreases in price)
• Competitive Intensity

Following this, a regression model using the relevant datasets was built to predict the price sensitivity of each product in response to a price change. Using this information, we can now group these products based on their sensitivity and apply differing price strategies to capture margin better or increase sales. For example, products in the price-sensitive group can be offered at discounts which may likely lead to higher sales. On the contrary, the products in the price-insensitive groupcan be priced higher. In this case, the sales may stay the same or slightly drop, but we can expect that they will be compensated for their higher prices. SYMSON is currently in the phase of testing the regression model and running experiments to determine the validity and accuracy of the model.

Assessing new key drivers for price sensitivity involves human expertise and judgement. This combined with the computational and automatization power of Machine Learning can help build a hyperlearning organisation.

Step 1: Identifying Drivers for Price Sensitivity

Engaging in market research and customer surveys helps companies gain accurate insights into customer preferences, perceptions, and purchasing decisions. At SYMSON, we discussed with a few of our clients as well as analysed data to gauge a combined perspective on the parameters that regulate their sensitivity towards purchasing a product. Of all the aspects we uncovered, here are a few drivers that could be of help to most businesses:

• Basket size:
  
  The number of products selected in the basket size or the first product selected in the basket can impact the price sensitivity.
  
  
• Stock:
  
  If the product is in stock or not can impact the price sensitivity. For example, whether the customer can choose an alternative if the product is not in stock can affect the price sensitivity.
  
  
• Price Change Frequency:
  
  The number of historical price changes can impact the price sensitivity of each product.
  
  
• Brand Value:
  
  Consumers often associate higher prices with better quality, leading to decreased sensitivity for well-established brands and vice versa for lesser-known brands.
  
  
• Product Lifecycle: 
  
  When the product is in its introductory phase, the price sensitivity may be relatively low, as customers may be willing to pay a premium for a novel or innovative product. In the growth stage, when the product gains popularity, its sales increase rapidly. The price sensitivity tends to decrease as the product becomes more established. Customers tend to be less sensitive to price changes due to increased demand and perceived value.
  
  When the product matures reaching its peak sales and market saturation, the price sensitivity becomes typically high as the market becomes more competitive with growing alternatives to choose from. Finally, the product experiences a decline in demand as newer and better alternatives emerge. Price sensitivity can vary during this stage, but generally, customers become more price-sensitive as they may seek lower-priced alternatives or switch to newer products.
  
  
• Price Level:
  
  This refers to whether a price is categorised as cheap or expensive. This grouping is based on customer input and sales data. A higher price may increase sensitivity. Additionally, assessing the product's price sensitivity elasticity can provide insights into the price level's impact on purchasing decisions.
  
  
• Price Change Frequency Driver:
  
  Price Change Frequency determines the number of times a client changes the product’s price in the selected time period. High frequency suggests the price strategy follows the market dynamics closely and thus can be correlated with higher sensitivity.
  
  
• Competitive Intensity:
  
  In a market with numerous alternatives, consumers are likelier to exhibit heightened sensitivity and seek lower-priced options. Conducting competitor analyses and staying abreast of market dynamics can help businesses gauge competitive intensity and its effect on price sensitivity.
  
  Assessing new key drivers for price sensitivity involves human expertise and judgement. This combined with the computational and automatization power of Machine Learning can help build a hyperlearning organisation.

Step 2: Build a Regression Model including Price, Quantity, and Drivers

During a certain period, we calculated the average price, quantity, and other numerical or categorical factors for each product to build a data frame for the customer. Using this dataset, we built a regression model. This model is critical to understand the relationship between price, quantity sold, and the identified drivers of price sensitivity.

Studying these datasets enables companies to quantify the impact of each driver on price sensitivity and sales volume. Leveraging a regression analysis helped us gain a more nuanced view of how customers respond to changes in price.

Step 3: Aggregate Relevant Coefficients from the Regression Model to Predict Price Sensitivity

With this regression model in place, businesses can aggregate the relevant coefficients to predict the price sensitivity of those products. To break it down, this step uses the regression output to compute the price sensitivity scores of individual products. The equation would show a price sensitivity prediction which represents how a 1% change in price impacts the % change in quantity sold or demanded. For e.g. a price sensitivity of -4% means that a 1% rise in price results in a 4% drop in the quantity sold.

Step 4: Interpret Price Sensitivity Scores

A price sensitivity score indicates the magnitude of customer response to changes in price. Based on the estimation of the coefficients from the regression model, we give different weights to different coefficients to finally calculate the sensitivity score.

A higher sensitivity value indicates that customers are more responsive to price changes, while a lower score indicates a less significant response.

To do this, performing a test analysis on real-time price changes would provide a clear understanding of the price sensitivity of one product across multiple platforms. As our experimentation goes on, we have seen a marked difference between the price sensitivity scores over two specific platforms, one scoring significantly higher.

Step 5: Act upon Price Sensitivity to increase Margin or Sales

The final step is to translate insights from the price sensitivity analysis to increase margin. At this stage, we test it for practice to understand its performance. To do this, we segmented the product assortment into a controlled experiment group and a test group. After the sensitivity score calculation, we further divided the products within the test group into a price-sensitive group and a price-insensitive group.

Now, most of the products in the price-sensitive group are offered at discounts as it may potentially lead to higher sales due to their lower prices. On the contrary, the products in the price-insensitive group will be priced at a higher scale. The sales can stay the same or slightly drop, but we can expect the higher prices to compensate for the drop.

Likewise, by closely testing our price sensitivity scores, we can set strategies for a product group and balance sales growth/revenue generation and profit margin. We have elaborated on the experiment in the next section.

The Impact of the Price Sensitivity Assessment Approach on Business Outcomes

Summary:

Before adopting this approach, we encountered multiple issues with price elasticity and data quality. This would have led to inaccuracy in finding correct price recommendations had we continued without it. However, we see our roadblocks get solved seamlessly after adopting the price sensitivity approach. Let’s have a closer look at the outcome we witnessed and how it helped us combat the lingering issues.

• Historical Data was more organised and ready to be used for the Optimisation
  
  With the implementation of the price sensitivity assessment approach, SYMSON experienced a remarkable transformation in historical data management. Earlier, we found our historical datasets in an unorganised manner which made it difficult to use them for price optimisation. Now, with the price sensitivity assessment approach, we could include more price factors and use them to make more accurate price suggestions.
  
  
• Availability of Competitive Pricing Data
  
  The 5-step approach not only considered internal data but also helped equip us with accurate datasets about the price sensitivity of competitor products. Further, it provides a direction to either set a discount or mark up. In the case that your competitor is not available, this approach can offer you a sense of price optimisation.
  
  
• Increased Accuracy in Finding an Optimal Balance between Profit Optimisation and Risk Management
  
  As we researched (combined customer and data perspectives) to find additional drivers for sensitivity, we can understand the demand level, of the products in question, with more precision. Of course, price isn’t a lone factor that makes demand fluctuate significantly. Hence, the newly researched drivers are proving to ensure accurate demand trends, customer behaviour and enhanced demand predictions. That said, it will help us gauge the optimal price recommendations for increased revenue and profit growth.
  
  This method allows for risk management as well. For example, very high discounts or markups can affect sales too heavily. By setting conservative discounts and markups the price sensitivity predictions can be tested and measured in impact.
  
  
• Sales Teams gained Confidence in the Outcomes to discuss them with Customers.
  
  Incorporating this clear and logical approach into the pricing strategy that included several practical drivers, we saw our sales team gain confidence in the sensitivity outcomes. Previously, the word wasn’t presented to the customers as we lacked clarity in the datasets and elasticity predictions.
  
  To get a buy-in from the sales team, we interviewed them regarding the factors to include in the model. Below is a heatmap, that we created after making the model, to make the outcomes understandable and explainable. It shows how each factor affects the sensitivity per product. Furthermore, it proved to be more extensive and accessible than a price elasticity approach.

5 Benefits of Embracing Price Sensitivity for Price Optimisation

As markets transform into dynamic spaces, businesses need to refine their price optimisation tactics to keep pace. Identifying new parameters that regulate purchase decisions and understanding consumer behaviour towards a specific product category help maintain margin growth and brand identity in the long run.

While there are several other profitable benefits of implementing price sensitivity for price optimisation, we present the top five.

1. Enable Demand Forecasting with Precision

Conducting new driver-research exercises helps ensure better accuracy in identifying product demand. With additional practical parameters in place, the final price recommendation outcome would be precise. Furthermore, it creates future demand predictions that prepare organisations to set strategies for correct pricing suggestions.

2. Optimal Pricing Strategies for Sales and Margin Growth

As price sensitivity includes several new drivers, the pricing strategy will provide recommendations that are the closest to reality. This approach refines the price optimisation process ensuring accurate suggestions that are accurately feasible for customers and also provide optimal profit for the company.
With proper identification of the nature of the products and demarcation into their feature groups, businesses can not only assign discount and markup strategies but also combine additional strategies with respect to the market and competitive scenario.

3. Capitalise on Price-insensitive Products to improve Margins

With the clarity of the product assortment’s price sensitivity, companies can identify items with unresponsive demand during price changes. This opens up the ground for raising prices to a certain level that not only increases profit margins but also is within the range of customer’s willingness to pay. The method frees up the pressure to increase sales volume while also boosting profitability.

4. Strengthen Customer Loyalty with Personalised Pricing

Businesses offering value-based pricing and personalised promotions that align with buyer preferences make customers perceive it positively. Moreover, consistency in this approach leads to brand loyalty and eventually, advocacy. Lasting customer relationships also save businesses the costs of acquiring new customers.

5. Stay ahead of the Competition with regular Pricing Analysis

With comprehensive pricing strategies in place, brands can beat competitors in dynamic markets. Continuous analysis of customer behaviour and competitors' prices by intelligent pricing tools helps set new prices with respect to the minimum margin and cap adjustment set in business rules.
Implementing price sensitivity assessment in smart price optimisation tools like SYMSON paves the way for a successful pricing journey, positioning businesses to thrive in ever-evolving market landscapes.

HyperlearningTM: The Future of AI Adoption

In the article Building a More Intelligent Enterprise, Paul Schoemaker shows us a visualisation of the differing areas of strengths for humans vs computers and a new category of areas where humans and computers are stronger together. He explains that these categories are grouped based on the familiarity of problems and the available density of data.

In the price sensitivity approach, human expertise excels in assessing price sensitivity drivers and prioritizing them according to their importance. Computers are outperforming them in finding patterns, building the algorithms and computing all scores. However, in the end, computers and humans must work together to assess which products are practically sensitive.

Business managers must evaluate the Machine Learning outcomes so that the model can continuously improve and enhance the price sensitivity scores with precision. Moreover, this is a continuous process where the business experts learn from data insights, and the model is updated. This is what we call HyperlearningTM where both worlds (business and AI) operate with fine cooperation and an awareness of each strength.

Combining Machine Learning with Human Expertise

Price optimisation serves as a prime example of the potential impact of combining human expertise and machine learning. Price sensitivity analysis, conducted through regression models and machine learning, allows organisations to understand customer behaviour patterns, facilitating more accurate price-setting decisions. By integrating historical data on customer preferences, buying behaviours, and market trends, businesses can optimise prices to maximise profitability and customer satisfaction.

AI-powered pricing algorithms can rapidly analyse vast amounts of data, including competitor pricing, customer demographics, and market trends, to adjust prices in real-time dynamically. By combining AI's data-driven decision-making capabilities with human expertise in understanding consumer psychology and market dynamics, businesses can strike a delicate balance between competitive pricing and maintaining profit margins.

In other words, combining human expertise and machine learning is the cornerstone of building a more intelligent enterprise.
 

Conclusion

It is a well-known phenomenon that in many companies the data of recent years is insufficient and inaccurate to measure price elasticity and thus predict prices.
Machine learning's ability to handle high-dimensional data, uncover complex patterns, enhances accuracy and provides a competitive advantage.

This article provides insight into this approach in which more variables and limited recent data can be included to map price sensitivity.

The human judgment adds a unique perspective and understanding of customer behaviour and market dynamics. The combination of both these elements creates a more intelligent enterprise capable of making informed decisions, adapting pricing strategies, and maintaining a competitive edge.

This method is based on a close collaboration between the human expertise in the company and machine learning technology. This leads to greater adoption by sales, better risk management of price adjustments and high accuracy.

The fusion of human expertise and machine learning in price optimisation emerges as a pivotal factor for businesses seeking sustainable growth and success in the data-driven business landscape.

The increasing power of artificial intelligence and machine learning now enables companies to experiment and “run the numbers” on any range of prices they wish. They can look for an optimized uniform price for everyone or pursue near-complete price personalization in real time.

This awesome power, however, raises many questions which roll up into two central ones: how do you determine the right extent of price differentiation, and what is the right way to implement it?  These questions sound like exciting challenges for a team of data scientists, so you might be surprised to learn that the profit-maximizing answer to that question has an ethical basis, in addition to a mathematical one.

Extensive global research conducted by BCG’s Henderson Institute (BHI), supplemented with additional analyses and backed by years of pricing experience, have led my colleagues and me to a controversial but powerful conclusion: the fairest price – not the highest price – optimizes profits for a business. In other words, the way to determine the right extent of price differentiation and the right way to implement is fairness.

That assertion, of course, hinges on the definition of what a fairness means, and by extension, of a “fair price.” In this paper I will answer that question and then connect the ethics to the math by providing some high-level quantitative guidance on how to implement a fair (and profitable) price.

What is a fair price?

Defining fairness recall the comments that US Supreme Court Justice Potter Stewart made when he was asked to define obscenity: “I know it when I see it.”

You can see fairness in action in the research of Dutch primatologist Frans de Waal. He documented how two capuchin monkeys are very content to receive pieces of cucumber as rewards when they bring a stone to a researcher. That peace lasts until one monkey notices that her partner just earned a grape for the same task, but she still receives stones. Grapes are apparently more valuable than cucumbers, because the “underpaid” monkey goes increasingly berserk when the unfair treatment persists. She even refuses the cucumber slices, meaning she would rather have no reward than an inferior one.

Primates have an innate sense of fairness. But a precise definition of fair price – whether for a simple stone or in the intricacies of day-to-day human commerce – seems elusive. Attempts to define it often lead to paradoxes, as you will see when I rule out several common definitions below.

• Is a fair price the same price for everyone? That definition makes intuitive sense. But the BHI research revealed that people perceive it to be fair when different customers pay different prices in many real‐world scenarios. The heat map in Figure 1 shows the extent to which people think it is fair to offer discounts to certain groups (e.g., seniors or students) or under certain circumstances (e.g., time of day). People in the US – like most people around the world – feel it is fair for seniors to receive discounts when they stay at a hotel. The French do not. People in India and Japan feel that time of day is a fair rationale for offering discounts for gasoline, while the Germans, French, and Americans consider such discounts to be quite unfair.

Figure1: Perceptions of the fairness of differentiated prices vary widely around the world

Figure 1 shows that perceptions of fairness are literally all over the map, but on average, there are more green areas than red. That highlights that people often perceive price differentiation as fair. The BHI research also showed that perceptions of fairness differ by age, by income level, and even by political affiliation. Amidst all those complications, let’s now rule out two more definitions of a fair price.

• Is a fair price one that is accepted by both parties? That definition makes sense as long as the context of the transaction remains constant. An acceptable price on one day will seem unfair the next day if a buyer learns that their next-door neighbor or the toughest competitor bought the same product from the same seller at a much lower price.
  
  
• Is a fair price one that is set by the free market? The idea that the law of supply and demand leads to fair outcomes continues to permeate economic thinking. But the free market often leads to uniform prices or to regressive prices that entrench biases. Minority and female car buyers tend to pay higher prices, and poor patients tend to pay higher prices for medications.

The research and analyses ultimately led us to this definition of a fair price: one that shares value equitably between parties.One prerequisite is that the buyer and seller need to estimate and agree on value. We have seen that this is neither easy nor always possible. But our fairness research confirmed that respondents find cost variations to be a good rationale for price variations, because that rationale depends the least on other circumstances. We therefore define the value that buyers and sellers can share equitably as the difference between customer value and variable costs. The position of the price within that range defines how the two parties will share that value. The buyer earns the surplus, which is the difference between customer value and the price. The seller earns a gross profit, the difference between the price and the variable cost, as Figure 2 shows.

Figure 2: The basis for equitable sharing of value

A price that shares value equally across buyers and equitably between buyers and sellers may seem counterintuitive under classical economics. But it turns out to be a more effective way to serve the goal of classical economics – higher profits – than conventional pricing approaches.

To implement these insights, an organization needs to answer a question that will sound heretical to many pricing professionals, never mind the business leaders in their organizations: how much money should we leave on the table?

The Heretical Question: How much money should you leave on the table?

One of the mantras of pricing is that a company should never leave money on the table. But business leaders seeking to charge fair prices – which are more profitable prices – should view “leaving money on the table” as an opportunity to seize, not a mistake to avoid. Granted, leaving money on the table inadvertently may still be a mistake. We recommend doing it intentionally. Companies that share value through fair prices – reinforced with a strong and transparent rationale – are rewarded with increased sales and higher profits over time.

To answer that heretical question robustly, we used the extensive BHI research – complemented by our own experience – to estimate how the perception of price fairness changes as a function of the value shared. Through these analyses we concluded that the margin‐optimizing share of seller value is around 50%, as we show in Figure 3.

Figure 3: Profit as a function of the seller’s share of the value in a transaction

But this is an average for basic guidance. The actual optimum varies by product, by country, and by whether the transaction is B2C or B2B. The seller's optimal share of value is higher in countries such as India, China, or Brazil, which have higher average levels of perceived fairness. The seller’s fair proportion of the value decreases as the absolute value at stake increases, but an exception to this guideline are products with both high absolute value and a unique value proposition.

The seller’s optimal share of optimum is closer to 20% for B2B products, because customers tend to be much more sensitive to the amount of value the seller retains. In practice we have found these optimal proportions to be even lower, often closer to 10%. We also found that technology companies often choose to retain only 10% of the available value, thus sharing 90% or more of the value with customers and channel partners. One motivation behind this conscious choice to gain market adoption as fast as possible.

Implementation: The fairest price – not the highest price – optimizes profits

To implement fair prices, we recommend the following approach:

• Focus on value sharing instead of willingness to pay: Each allows companies to set prices, but knowing the level of value sharing gives you more options to influence customer behavior and align your pricing with the drivers of price variation in your market.
  
  
• Understand your market characteristics: The levels of value sharing and the rationales behind them differ from market to market. Figure 2 is an aggregate view, and the optimal amount can be much different from market to market.
  
  
• Decide how much money to leave on the table: That may sound like heresy to those who advocate the maximization of profit in a transaction, but fair and profitable pricing depends on how much money a company decides to leave in the table.

Fairness is the basis for profitable differentiated pricing, but fairness must respect all the subtle, significant differences across countries, populations, and industries that allow multiple, often conflicting definitions of fair prices to coexist. Customers become angry and even react irrationally – such as opting for no value over some value – when a company crosses the lines.

Despite those caveats, it is clear that price differentiation – based on fairness – is a strong and significant pricing lever that business leaders should use more confidently.

• Royal FrieslandCampina (RFC) as a cooperative is owned by its farmers and receives its farmer’s milk “no matter what”
• The milk price paid to the farmer is both the main source of income for our farmers, but also the main cost for the company FrieslandCampina
• One of the main goals of RFC is to get the most value out of its milk as possible
• This should be achieved by allocating the right amount or milk to the highest valorizing products
• Meanwhile, pricing of these different products is aimed at optimizing value by offering the right price for the right (sub)product to the right customer at the right time
• There is especially a lot of focus on timing, as the dairy sector is a very volatile sector with market prices fluctuating significantly over time

Digitization is taking over the corporate world by storm. However, pricing remains in the pen-and-pencil era. Many companies still use static elasticities and Excel models, which fail to capture the complexity of real-life challenges. Worse yet, better technology is available but is not being leveraged sufficiently. In this webinar, we will explain how new technology can help unleash value for your team. Join us to learn more about using AI and machine learning for pricing and revenue management and how you can leverage them for better, faster outcomes.

If you are lucky enough to have read Danilo Zatta’s excellent book “The Pricing Model Revolution: How Pricing Will Change the Way We Sell and Buy On and Offline”(1) , you would know that Pricing is the strongest profitability driver, affecting profits much more than increasing sales volume or reducing costs. 

Admittedly, getting your pricing right has a strong impact on your profits, demand, supply, and overall business performance. However, the task itself is one of the most challenging since you have to consider multiple internal and external factors(2)  while setting your prices:

• BUSINESS ENVIRONMENT:First, you need to understand the environment where you operate and sell your products. This includes the Macroeconomic environmentwith inflation, spending capacity, exchange rates, or other macro indicators, as well as Market conditions with competitors’ pricing, new technologies, supply shortages, or other market conditions.
  
  
• PRODUCT-MARKET FIT: Then you need to consider how well your Product fits the intended customer audience's needs. This includes the Product with all its features, specifications, bundles or packs, perceived quality, or other product-related parameters, as well as Customers with relevant customer segments, best-fit characteristics, or other customer-related factors. 

Despite the complexity of the pricing premise, you know some ground rules2, for example, that your chances of success increase with more competitive prices, combined with higher perceived quality, the right target audience, and favorable macro, country, or market conditions. But what you don’t know is how much these factors affect your pricing and sales performance, and this is exactly the point where AI gets into the picture to help quantify these dynamics.

AI is the talk of the town today and for a good reason. Following the wide release of ChatGPT in late 2022, AI hit the mainstream market, reaching a broader audience beyond technology enthusiasts and visionaries. Generative AI and well-known platforms like ChatGPT, BARD, Midjourney, and many other tools took the world by storm as people realized how valuable and user-friendly AI can be and started discovering its numerous use cases and applications.

But AI comes in many forms, and Generative AI, despite its current hype, is still at the beginning of its journey, capturing less than 5% of AI equity investments . The biggest slice of AI investments goes to more mature and proven AI technologies, including 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗜, which is the most relevant to Pricing.

In the following, we will explore the numerous applications of Predictive AI on Pricing, including a real-life customer case , but also refer to some relevant Generative AI applications.

Using Predictive AI in Pricing (5)

Predictive AI, also referred to as Predictive Analytics, involves the use of artificial intelligence and machine learning methods to predict and/or optimize future events or outcomes based on actual data and patterns.

The main application of Predictive AI in business and the economy is to:

• Apply statistical or econometric models on available information,including historical data (e.g., actual sales) or prospective data (e.g., pricing research).
  
  
• Recognize important patterns, such as estimating the demand curve (Fig. 1) or a sales trend.
  
  
• Generate important predictive insights, including sales forecasts, market trends, or optimized suggestions (Fig. 2), such as the best price for maximum revenue or profitability.

Predictive AI can help you make better or optimized pricing decisions with significant gains for growth, profitability, or other vital KPIs of your business. Evidence suggests that Predictive AI, incorporating econometric modeling, can help optimize pricing and increase revenue and other important KPIs even 2-fold while improving strategic business foresight with great accuracy, reaching 97% .

Let’s now explore some of the most important pricing use cases for Predictive AI and a real-life customer case(4).

Estimating the Probability to win and the Demand curve to optimize pricing(5)

Pricing significantly impacts sales performance by affecting the customer’s probability to buy, but the question is how much. As anyone even slightly involved in Pricing knows, the answer lies in the Demand curve (Fig. 1), a chart with the price on one axis and the probability to buy or demand (probability to buy x opportunity size in units) on the other.

Fig. 1 The Demand curve

But how can Predictive AI help in estimating the Demand curve?

It does so by simulating the demand curve using a specialized AI-based Demand model and fitting it to actual data. “Fitting,” for the non-data science familiars, means that the Predictive AI system tries to change the model (usually its key parameters) repeatedly until its output resembles the actual data as much as possible. And how can you know if your model is good and you can trust it? By testing its prediction accuracy or how close the model’s estimate is to actual data with the smallest possible margin of error. There are multiple error metrics and testing methods, such as in & out of sample and cross-validation.

Once you have discovered the right Predictive AI model that adequately simulates your demand curve, this opens the door to generating multiple valuable insights (Fig. 2). So, you can use the model to estimate your probability to sell, your sales performance, and your profitability (assuming you have cost information as well) at any given price level. In addition, you can identify Optimal Prices that maximize your revenue or profits or the Penetration Price that allows you to maximize your success rates under certain restrictions of profitability or other KPIs. 

Fig. 2 Predictive Insights using the Demand curve

As already mentioned, Price is not the only aspect affecting sales performance. Several other factors are crucial in determining the Demand curve and should be analyzed when making pricing or other business decisions (Fig. 3). For example, when considering a specific country, inflation and changes in the average spending capacity can alter the perception of what is considered expensive or not. When competitors change their prices, this may influence your chances of success over new or existing customers. Also, the perceived quality of the product and the best-fit customer characteristics matter a lot. All these factors affect the customer’s probability to buy at any price point. In addition, sales performance can also be influenced by the target country and market size, product bundling, and customer size or type, just to mention a few critical factors.

Fig. 3 Multiple Factors affecting sales performance and pricing

A more advanced Predictive AI model can take into account some or all of these factors and shift the Demand curve to reflect their impact. This enables the creation of multiple what-if scenarios to reveal new predictive insights for varying country, market, product, or customer conditions (Fig. 4). 

Fig. 4 Multiple what-if scenarios for varying country, market, product, or customer conditions

One of the benefits of having a Predictive AI model that understands, among other things, country dynamics is that you can get differentiated price suggestions and sales performance estimates across countries.

Fig. 5 AI-powered Price or other KPI Heat Maps

For instance, you can create heat maps (Fig. 5) indicating the differences among countries in terms of suggested best prices, expected revenue, penetration levels, or other vital indicators. And you can update these maps automatically when something significant changes, like inflation or the average spending capacity per country.

Real-life case: Combining Predictive AI with Neuroscience to identify the Best Price across countries for a new product launch(7)

This is the story of the launch of a new product, Scent Camera, a revolutionary device created by a team of scientists in Lithuania. The extraordinary device will forever change the way we experience the digital world. Scent Camera is a technological marvel that allows you to capture and experience smells based on a novel gadget. It’s now possible to witness the captivating aroma of freshly baked cookies or the invigorating scent of a tropical paradise right from the comfort of your own home. With Scent Camera, you can fully communicate a world of fragrances like never before.

The pricing challenge

Apart from the technological breakthrough needed for such an accomplishment, Scent Camera faced another challenge, i.e., how to price such a unique device, especially when this is a new market with no past sales, no competition, or any other point of reference to use. Even more, how to differentiate pricing per country, considering the relative strength and potential of local markets, and prioritize product launch accordingly.

The solution

To discover the Ideal Go-To-Market Price, FutureUP and Neurensics jointly followed a two-step approach to help them address these challenges (Fig. 6):

• Pricing research data: First, Neurensics, armed with their groundbreaking NeuroPricing™ approach, delved deep into the subconscious responses of consumers in Germany, the first go-to-market. The technology is driven by assessing the subconscious mind via a brain scan-validated reaction time test where pricing decisions are made without us even realizing it.
  
  
• Predictive AI insights: Then, FutureUP combined the results with its innovative Predictive AI technology to generate price suggestions for other countries where no research data existed. Behind FutureUP’s technology lies a specialized econometric AI model, which considers various country or market conditions, like spending capacity, inflation, or other macro indicators, and predicts their impact on pricing and sales performance.

Fig. 6 The solution powered by Predictive AI

The result & benefit

By combining Predictive AI with Neuroscience, FutureUP and Neurensics jointly delivered:

• A set of suggested Optimum Prices, maximizing revenue, for 40 countries (Fig. 7)
  
  
• A set of suggested Safe/Penetration Prices, ensuring more than 90% win probability, for 40 countries
  
  
• Expected Revenue and Penetration Levels (probability to win/buy) for the suggested prices
  
  
• What-if scenarios that automatically adjust price suggestions and probability to win for inflation and spending income for each country (see an example for Germany in Fig. 8)

Fig. 7 Optimum Price to maximize revenue across countries

Fig. 8 Example what-if scenario for Germany in 2024 with expected inflation at 3% and income growth at 1% (New expected demand/revenue in 2024 vs. Initial demand/revenue in 2023)

The accuracy of predictions regarding the probability to buy at each price level was extremely high, with less than 2.5% error, for in & out-of-sample testing, even with a sample as small as 15%.

The result provided Scent Camera with a pricing and penetration roadmap for numerous countries around the world, thus facilitating its go-to-market strategy for the new product.

Supply – Demand dynamics and Pricing Trends

The Demand/Customer dynamics we explored so far is one side of the coin. This will suffice if supply dynamics are not so important, for instance, when there is no threat of supply shortages and the variable cost is low and, therefore, suppliers are more flexible with their pricing. 
Although this may be true to some extent in several cases, there are many markets like commodities or the energy sector where supply dynamics play an important role in pricing and overall sales performance. A recent example is what happened in 2022 when supply stresses in energy and certain commodities led to a price increase and the biggest inflation of the past ten years.

But what initiates these dynamics?

Every day, buyers interact with sellers, and the result is a potentially successful sales transaction. The seller is happier when prices increase and seeks to improve or optimize a KPI, for instance, to maximize revenue or profitability. The buyer, on the other side, is happier when prices decrease and seeks to maximize the value derived from the purchased product or offering at the lowest cost possible. All these dynamics are famously reflected in the Supply-Demand curve (Fig. 9).

Fig. 9 Supply vs. Demand curve

The role of Predictive AI here is to simulate the Supply curve on top of the Demand curve and generate additional predictive insights, and more precisely, what will happen if demand is higher than supply (supply shortage) or lower than supply (supply surplus).

In many cases, it’s not only the demand that is unknown, but on top, neither the price nor the supply is given or known in advance. It is all part of a dynamic process where each side makes decisions and acts and the other responds following the supply or demand curve until an equilibrium is reached.

In its simplest form, you can use Predictive AI to estimate volume and revenue sales separately and then divide them to get an indication of the pricing trend. In its more sophisticated form, Predictive AI would simulate demand & supply dynamics (like in Fig. 9) and estimate price and volume shifts and their respective equilibriums using more advanced econometric predictive models.

Estimating pricing trends can be very useful, for instance, when we want to estimate the average price in the market and how this may change as a response to rising inflation, an economic slowdown, or a competitor changing prices.

Using Generative AI in Pricing

One of the most critical challenges in pricing is collecting relevant data found in an unstructured form. Anyone with the slightest selling experience knows that salespeople include internal comments on their CRM indicating, for instance, that they lost a case to a particular competitor at a specific price. Similar data can be found online when customers or potential customers post reviews, product assessments, prices, or information about competitors. The problem is that this information is not structured and, therefore, is challenging to get and analyze properly to aid decision-making. 
But this is where Generative AI is strong, as anyone who has used ChatGPT can confirm. You can apply Generative AI models to unstructured content from internal or external sources and ask to look for specific information and structure it. Granted, you’ll face certain challenges since you’ll need to train the AI model for your specific business case and test it thoroughly to your satisfaction, but still, it is worth your time to obtain a structured and cleansed data set for your sales and pricing decision-making.

Generative or other types of AI can also be used to automate pricing processes and workflows or to enhance user experience while performing price-related tasks.

Final remarks

Evidence suggests that Predictive AI, incorporating econometric modeling, can help optimize pricing and increase revenue and other important KPIs even 2-fold while improving strategic business foresight with great accuracy, reaching 97%6.

The simple reason you cannot beat this performance by using your instinct, experience, or base data analysis is the complexity of the Pricing premise involving so many different parameters at the macro, market, product, and customer levels. Using Predictive AI can help quantify pricing and sales dynamics and answer many important questions:

• Macro environment impact: What will happen if inflation, spending capacity, exchange rates, or other macro indicators change?  
  
  
• Market impact: What will happen if competitors' pricing, market size, or other market conditions change?  
  
  
• Product impact: What will happen if you offer more value with your products, package features differently, or improve the perceived quality of your offering?
  
  
• Customer impact: What will happen if you target customers of different market segments based on geography, size, type, or other customer characteristics?
  
  
• Key drivers:Which of the above are the most important drivers of growth, profit, or other important KPIs?  
  
  
• Price optimization:What is the best price to optimize revenue, profits, or other KPIs, considering all or some of these factors?
  
  
• Price discrimination: Should you differentiate prices across countries, different market segments, or per customer?
  
  
• Dynamic pricing: How should you adjust pricing in real-time to reflect changes in demand vs. supply, competitors' intelligence, customer profile or history, or other parameters?
  
  
• Competition-based pricing:How should you adjust pricing in response to competitors' pricing?
  
  
• Pricing and packaging:Should you pack or bundle your offering in a certain way, including certain specifications or characteristics, to boost sales performance and product acceptance?
  
  
• Pricing trends:How does the average price in the market evolve, and are there any emerging trends that you should be aware of?
  
  
• Market intelligence: What is the value and trend of key market indicators like churn or market shares against key competitors?

AI, especially Predictive AI, can help you boost your business performance and get the most out of your pricing while improving strategic foresight. The right AI technologies and predictive models already exist, creating value for large or small businesses across industries worldwide. So, don’t let yourself fall behind; take the lead by embracing the AI revolution and seize this excellent opportunity to supercharge your Pricing!

(1) Danilo Zatta, “The Pricing Model Revolution: How Pricing Will Change the Way We Sell and Buy On and Offline”, 2022, Wiley

(2) Based either on material or reproduced partly or fully from: https://www.futureup.io/post/please-pay-attention-to-your-pricing

(3) McKinsey Technology Trends Outlook 2023: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-top-trends-in-tech

(4)You can see more information regarding this customer case here: https://www.futureup.io/post/combining-predictive-ai-with-neuroscience-to-identify-the-best-price-across-countries 

(5) Based either on material or reproduced partly or fully from: https://www.futureup.io/predict

(6) You can check out more AI success stories and evidence of Predictive AI’s positive contribution here: https://www.futureup.io/news/categories/success-stories

(7) https://www.neurensics.com/en/discover-why-neuropricing-is-the-key-to-uncover-the-ideal-price 

(8) You can find more information here: https://www.futureup.io/post/pricing-inflation 

Disclaimer

All content included in this article is reproduced with the explicit permission of the author, George Boretos, and is subject to the following conditions:
(a) The content provided by George Boretos may only be used for the purpose of inclusion within the aforementioned article and for related promotional activities.
(b) No Intellectual Property, including but not limited to copyrights, trademarks, patents, and any other rights associated with creative works, is transferred from George Boretos, his company FutureUP, or any third parties. The author and his associated parties retain full ownership of all such rights and, for the avoidance of doubt, do not require separate permission to reproduce, in part or in full, or create derivative works based on any materials contributed and included in this article.

In this paper, we will explain how pricers and product managers at large corporations could benefit from the techniques adopted by fast-growing SMBs, avoiding common pricing mistakes and driving faster market adoption and higher value capture.  

Getting to and maintaining great pricing outcomes in large companies is hard!  As shown in Figure 1, the organisation needs to have the following:  

1. Visibility
   
   It is essential to have visibility and insights into customer value across multiple product and service offerings, channels and geographies in near-real-time to support effective pricing decisions. In practice, this involves tracking end-customer trends and competitors.
   
   
2. Capabilities
   
   Distributed commercial teams must have the capabilities to make well-informed pricing decisions at the speed of the market. With specialised teams (sales, marketing, product, etc.) and pricing decisions delocalised across the organisation, needed pricing skills vary by function.
   
   
3. Controls
   
   Controls must be in place to minimise unnecessary price leakage, drive price discipline amongst decision-makers, and ensure that the implications of key decisions are appropriately evaluated (e.g. low prices that could ‘leak’ into a neighbouring market). Given the distributed pricing decisions, balancing fine control with agility is often challenging.
   
   
4. Ownership
   
   This must all be supported by leadership that takes ownership of pricing: good pricing often requires tough decisions and risk-taking. If you win every deal, pricing is not optimal. Strong commercial leadership is demonstrated by investing in a team of dedicated pricing professionals who are well respected in the organisation and have appropriate tools and systems to support pricing decisions.  

Figure 1: Effective pricing at large companies requires these four elements 

However, for high-growth startups and scale-ups, pricing challenges are, in practice, quite different. Hard facts are scarce, as are money and time. The startup emphasis is on moving fast, with broad team skills that are strong where it counts and good enough elsewhere. Small teams mean control is easy, and pricing ownership by leaders is clear, but how to decide on the price model and price points is often challenging.   

From our work with innovative startups and scale-ups across all major industries, we have seen patterns that have implications for mature, slower-growth companies, particularly when they attempt to launch innovative and disruptive solutions and business models.   

What can mature companies learn from pricing used by startups? We break down the insights into five areas: mindset, process, tools, customer value, and price model design. Note that the recommendations here are far from exhaustive, but rather focus on those areas where mature companies can learn from their younger and nimble competitors.  

Have an experimental mindset 

Mature companies have the means to access extensive market and price data (even if they sometimes choose not to invest!). However, for companies getting started, and teams driving disruptive solutions, hard data is in short supply, leading them to act very differently. The right mindset means: 

• Be clear about goals and context. Where do you want to be in 5 years’ time? What is the value proposition that will get you there? Why is it compelling? What is the ‘secret sauce’ for your new offering, and how defensible is it? Getting alignment on these questions is key!
  
  
• Take a hypothesis-driven approach. It is so much faster and more efficient to think-through pricing based on assumptions and then validate these key assumptions rather than “boiling the ocean” to see what the data says. Write down the assumptions: this gives your colleagues a chance to challenge them openly, improving alignment and understanding. And, of course, make sure they validate the key hypotheses.  
  
  
• Learn by doing. Work iteratively and quickly, trying new things and ‘fail fast’.  Embrace any failures as an opportunity to learn.  
  
  
• Take more calculated risks.While mature companies need to be careful with their brand image in ways that do not constrain smaller companies, taking carefully-considered pricing risks in controlled situations (like smaller markets or customer segments) yields so much information and faster progress.  
  
  
• Start focused, expanding the vision over time. Your innovation’s business plan may be talking about millions of dollars of revenue from thousands of customers in 5 years’ time, but you will get there by selling to one customer first, then the next 10, the next 100, and so on. It is vitally important to understand why those early customers are going to buy your solution.  
  
  
• Focus on the end customer. All the value created will start with them. Work backwards from end-customer value.  

Process  

Designing new pricing is different from maintaining existing pricing: there is a much greater level of creativity and risk-taking required, which does not fit well with mature company pricing processes.   

With our startups and scale-ups, we use a four-step ABCD approach (Figure 2) that focuses on working on addressing the right questions in the right order and iteratively as new insights emerge.    

• Ambition and context. As mentioned previously, this is about ensuring you know what the goals are, what context you are in (both internally and externally), and where you are going.   
  
  
• Blueprint the pricing design. Coming up with a concrete proposal for pricing will focus the team on the highest-priority assumptions on which the design rests.  
  
  
• Check key hypotheses. Using a hypothesis-driven approach is efficient, but it is vital that key assumptions are validated before launch.   
  
  
• Deploy your new pricing. This includes how value and prices will be communicated and how prices to channel partners will be decided.  

Figure 2: ABCD process for pricing innovative & disruptive solutions 

Tools 

A couple of go-to tools are particularly helpful for startups before they get to pricing. The first is the Where to Play framework, and it is Market Opportunity Navigator (Figure 3). This helps organisations identify, evaluate, and prioritise opportunities. Using a hypothesis-driven approach is a powerful way to make sure you are going after the opportunities with the greatest potential payback and pricing power.  

• Starting with the Market Opportunity Set, based on your technologies and capabilities, list out the possible market opportunities you could address 
  
  
• For the most interesting opportunities, evaluate them for their potential to deliver value to your organisation 
  
  
• After prioritising the top market opportunity, identify backup and expansion opportunities that will be kept open, and relegate other opportunities to storage 

Figure 3: Where to Play’s Market Opportunity Navigator and supporting worksheets 

While a simple approach, taking a systematic and documented approach brings a level of focus that eliminates distractions.  

Once the opportunity is identified, the Business Model Canvas is an excellent, 1-page summary of how you see the business working. The canvas helps brings clarity to complex business models.  

Customer Value 

Understanding and playing to customer value should be the key element of all pricing decisions. This sounds very simple, but in practice, there are multiple elements in play. To break this down, walk through the CON-VERT steps to understand how early customers will choose to buy your offering.   

CON stands for Customer, Offer, and Next Best Alternative.   

Start by describing the Ideal Customer: These are the customers who should be most excited about your offer and whom you should be most excited to sell to. What urgent and important problem are you solving for them? Which stakeholders will be involved? What industry and geography are they in? Are they small or large players in their market?  Amongst the best or struggling? Are there any particular technologies they use that make them a strong fit? What is their cultural orientation? How are they going to evaluate and buy your offer? As you can see, initially, taking a very narrow focus ensures you can identify and prioritise pricing for these very early adopters.  

Next, we need to outline the Offer for these customers. Again, be specific and detailed. In addition to the core elements of the product, what are the included services wrapped around the offer? Are there any optional products, features, or services they could also choose? Do not wait until the actual offer is fully defined: use your best guess of the early MVP (Minimum Viable Product) and use that. When things change, it is usually much easier and faster to adjust from a specific starting point. 

One last step before we get to the customer’s evaluation criteria: what will they be comparing our offer to? What is their Next Best Alternative (NBA)? Obvious NBAs include directly competing offers, but for innovative solutions, this might be indirect competitors, self-build solutions, or (most commonly), carrying on as today.   

Having defined the initial CON, we move on to considering the VERT. We start with Value but also need to assess the negatives a customer is likely to face with a new solution: Effort, Risk and Time-to-Value. 

What is the Value that the ideal customer will get from your offer versus their NBA? Value is a slippery topic that even your customers are likely to struggle to define. In a B2B setting, your new solution can add value in 5 ways:  

1. Reducing the customer’s short-term costs
   
   Perhaps your solution is just less expensive or requires less setup and configuration or training.   
   
   
2. Reducing customer’s long-term costs
   
   Electric vehicles fall into this category, requiring much less expensive energy, but savings can also come from reduced operating costs (people) or maintenance. 
   
   
3. Increasing the customer’s revenue and profit
   
   Perhaps you help them sell more? Or sell more at a higher price? Alternatively, sell a more profitable mix of their products. Maybe you help them increase customer satisfaction, increasing their customer’s lifetime value.
   
   
4. Increasing the customer’s indirect value 
   
   Now, things get a bit more difficult to put a price on; these include reduced risk or helping them meet their environmental goals.
   
   
5. Assess the customer’s intangible value 
   
   The emotional value of buying something new and cutting-edge fits here, as does the opportunity to improve their personal reputation (and get their next job). 

Note that your offer will not be positive in all these areas, and this is not a marketing exercise. Place yourself in the customer’s shoes, and document both your advantages and disadvantages. Repeat for other customer segments, offers and NBAs as needed. A clear view of all aspects of customer value is critically important.  

After value, we move to Effort. Almost all innovative solutions require upfront costs, changed procedures, etc., that loom larger in the customer’s assessment compared to more everyday purchase decisions.  

Risk is the second big factor that buyers consider. While a startup’s risks of failure will not be a factor for larger companies, customers have significant fears of change. As a result, they are likely to overweight these compared to the expected benefits. In addition, user adoption, reliability, fitness for purchase, and other factors need to be considered.  

The effort and risk can all drive a longer customer Time-to-Value. This tends to increase as the size of the change increases. Unfortunately, many innovators, large and small, fail to assess just how long customers are likely to take before they can see the benefits.   

These four factors together create the “checkmark” curve, as shown in Figure 4. Understanding the shape of this curve will allow. 

Figure 4: Mapping out the four drivers of customer buying decisions for innovative solutions 

Price Model Design 

Now it is time to design the pricing model and set prices. Having understood the customer side of the equation, we use pricing to capture a fair share of the value created (Figure 5).  

Figure 5: Mapping out the four drivers of customer buying decisions for innovative solutions 

The more innovative the solution, the more likely an innovative business model is to make sense. The key element here for startups is deciding what you will charge for - the price carriers. Consider price carriers in 4 groups:  

• Upfront, one-time - capital equipment purchases and initial configuration services and training fall into this category. 
  
  
• Subscriptionsare set in advance but paid over time based on the planned number of users, or modules, or other metrics.   
  
  
• Pay-per-Use is similar to subscriptions in that they are paid over time, but the amount of the payments depends on some metric around usage, like the # of transactions, users, etc.  
  
  
• Outcome-based pricing is also set after the fact (sometimes long after the fact!) and is most closely aligned with customer value.  

Each of these four types of price carriers has its own pros and cons (generally, value capture potential increases as you go down the list, but so does the effort required, the risk transfer to the seller, and the seller’s time-to-value). So, while there is no one ‘best’ price model, having followed the steps above, you are in great shape to design a price model that balances value capture, effort, risk and time between you and your customers.  

Conclusion 

Large enterprises can significantly benefit from adopting the pricing strategies and techniques used by fast-growing SMBs for pricing high-growth solutions. Embracing an experimental mindset, implementing a structured process, utilising effective tools, focusing on customer value, and designing innovative pricing models can together lead to greater market adoption and value capture for large corporations.  

By learning from startups, mature companies can avoid common pricing mistakes and better position themselves in the market when launching innovative and disruptive solutions. In today's rapidly evolving business landscape, taking calculated risks and remaining agile is essential for staying competitive and capturing value. 

As pricing is becoming a key topic for the industry due to multiple factors eg competitive pressure, supply chain issues etc, the focus and investments would continue to rise in the time to come. Pricing as a function has matured over past years in many companies and in the process these companies have also embarked upon the digitization journey which includes implementation of pricing management software or systems. This carries forward the larger intent of bringing in efficiency, transparency and business benefits (not to forget cost savings). While the intent has been right along this maturity journey, industry has also seen some unexplored challenges and bottlenecks which were unanticipated. In the process of implementation of digital systems (mostly off the shelf), companies have often been found grappling with some common challenges. Some of these common challenges are: 

• Cost and effort overruns
• Business requirements lost in translation leading to less realized business benefits and 
• High operational costs downstream.  

Key business priorities therefore take a back seat during this critical juncture once the system is implemented. Also post implementation the system is meant to derive business value, but on the contrary what we have seen is that the major effort goes either in operational issues or in solving day to day technical challenges with the system. This leads to a never-ending loop of system enhancements and hence a cost overrun. This is a challenge many companies have faced and are facing off late. While the initial end goal was to focus on business value and drive strategic pricing topics, the actual result is just the opposite. 

Because of this never-ending loop, some companies have taken a tough step of replacing these off the shelf solutions even after investing millions in budget and approx. 2-3 years of intensive resource effort on an average. The one’s which do not take that tough call continue to live with the challenges as the investment is already consumed. These companies then scale down the initiative and keep it to a bare minimum. This is the classic Sisyphean challenge in the past few years that many companies in Pharma and Manufacturing continue to live with. This is not a good scenario either for the company or for the solution provider which has implemented the solution. Neither the company who is implementing the solution nor the solution provider are able to move to more important strategic business topics and this is detrimental to the larger objectives of the industry. 

Based on a detailed evaluation of some companies (Big, Medium and Small), this paper illustrates factual challenges and also talks about a potential way of improving the entire process of system implementation through a streamlined approach. The paper also suggests some key points which should be taken care of which can possibly help both solution providers and companies implementing the solution to make the journey an enriching one. The answer to this Sisyphean predicament can be achieved through setting up some ground rules of implementation and also through a robust business framework-based approach as part of the pricing maturity journey. This paper can benefit both Pharma & Manufacturing companies and service providers. 

Pricing systems are here to stay: 

A recent study by ‘EbelHofer Consultants’1 has demonstrated that pricing system implementation is a strong need in the industry and is here to stay. The survey was done across companies in different sectors including Pharma and Manufacturing. Out of the 75 companies assessed, almost 30 % of them have already implemented a pricing system. Another 25 % have a plan to implement a pricing system in the next 24 months. This clearly demonstrates that companies are progressing well in their pricing maturity journey and would like to optimize it further to derive key business benefits out of the digitalization process. But companies where the work is in progress have also acknowledged that the pricing system implementation is facing challenges.  

The classic Sisyphean challenge: 

Based on some real-life examples of such projects, let us look into the classic Sisyphean challenges some companies have faced. This can provide a cue or two on the journey and others can learn from it and avoid repeating the same mistake. What’s important to know is that there are some common reasons which lead to such a challenge, and these should be avoided to reap the benefits of such a digitization approach and tool.  

The below table2 shows four such examples from the industry where companies invested lot of time and effort to build a pricing system, but they had to reinvent the wheel again after a few years. Below might not demonstrate the situation in all companies but it’s a good enough sample size to take conclusions home. All these companies were at a similar stage of pricing maturity journey with similar processes.  

Is implementing an ideal pricing management system an unintelligible chimera? 

The above examples clearly demonstrate that Companies can often land in such an unwanted situation. Looking into a larger pattern based on a detailed study of these situations, it has been realized that companies fall into this classic trap when with less mature process and data landscape, they try to move to an ideal state of art integrated solution. While the motive is right, such a move does not turn out to be practical. ‘One step at a time’ is probably the mantra to go. The below diagram demonstrates the classic mistakes that is being made during the pricing system implementation journey. Waiting to correct the processes and optimize the data landscape should be the ideal first step before taking the plunge into a fully fledged pricing system. Having the right model is key for success in this case. 

Summary and recommendations-prevention is better than cure 

Companies and service providers both expect to derive financial and operational benefits from such implementation. The larger motive is to drive business value through integration, data analytics not to forget cost and operational efficiency. To overcome the implementation challenges, four key points which should be considered are as below: 

1. Put the Cart before the Horse: Do not go into a full-fledged implementation unless you have figured out the maturity of your data and process function in pricing.
   
   
2. Stick to the 80-20 rule: Do not overengineer the pricing system. Stick to core processes which can fit to most of the business requirements. Avoid over customization.
   
   
3. Business ownership and model: Keep the project ownership on the business side and develop a business centric delivery model else the system and product will end up becoming a technical tool only.
   
   
4. In-house solution can be a better match than off the shelf: While off the shelf solutions bring value in some cases, do a fit gap analysis if they really meet your process needs. In most of the cases, building a new solution grounds up is better in all aspects. 

• When is it appropriate to build in-house or to buy, and why?
• Success story to building a customised Pricing solution in-house - how to do this by close collaboration with various functions such as data engineering, data science, business translation and business owners
• How to move from fully customised to easily scalable solutions?

With inflation at record-breaking highs across the globe, manufacturers have never been under greater pressure to protect their margins and stop revenue slipping through the cracks. Rebates have the power to boost sales and strengthen customer relationships, but when not managed properly can also be a cause of added friction and lost income. Join this webinar with Flintfox's Steve Peppler, Chief Product Officer and Cath Brands, Chief Marketing & Innovation Officer to discover how intelligent automation can streamline the rebate process from start to finish - improving relationships and eliminating revenue leakage.

• Change your perspective: Learn key insights on the intersection of pricing and customer lifetime value, and how to use the former to increase the latter  
• We are on the same page: Let us kick off with a refresher on CLV in Retail 
• Understand a new mental model: Complement pricing for transactions with pricing for (product & customer) relationships 
• Get ideas to get started: Let ideas sink in on how to get started and create a first pilot project 

Join us for an insightful webinar as we delve into the future of retail pricing and explore how AI will shape the industry by 2030. In this session, we will discuss the challenges faced by retailers today, the technical enablers that are driving change, and how AI is poised to revolutionize the way pricing strategies are formulated and implemented. Don't miss out on this opportunity to gain valuable insights and actionable strategies to stay ahead in the evolving retail landscape.

• Organisational turn around to really understand the volume challenges and value focus
• The latest pricing strategies to deal with deflation and implement agile repricing
• Pricing strategies in an economic recession
• How to adapt prices, stay competitive and implement an agile price policy within the current situation

Are you tired of spending countless hours grappling with manual tasks and drowning in a sea of frustrating Excel sheets while managing pricing strategies for a vast array of product combinations? If this is you, look no further! Join our transformative webinar, "Simplifying Price Management: Ideas to Manage Your Pricing Strategies for Thousands of Price-Product Combinations,". In this webinar, we will explore the challenges of price management and unveil powerful solutions that pricing software can offer. Discover innovative ideas and strategies to streamline and optimize your pricing processes, ultimately enhancing your profitability and competitive edge.

Our seasoned pricing experts will guide you through a comprehensive journey aimed at simplifying and streamlining your price management processes. We will explore innovative techniques and cutting-edge tools designed to revolutionize your approach to managing complex price-product combinations. Say goodbye to manual calculations, data inconsistencies, and spreadsheet nightmares, and embrace an efficient, automated pricing system that maximizes profitability and minimizes effort.

Key takeaways from the webinar:
1. Understanding the complexities of managing pricing strategies for diverse product portfolios
2. Overcoming common pain points associated with manual tasks and Excel sheet overload
3. Exploring advanced pricing strategies tailored to your unique business needs
4. Leveraging technology to automate pricing calculations, data analysis, and decision-making
5. Implementing best practices for accurate pricing, revenue optimization, and competitive advantage
6. Real-life case studies showcasing successful price management transformations
7. Q&A session with our experts to address your specific challenges and concerns

Don't miss this opportunity to gain valuable insights and strategies to elevate your price management approach.

The largest European economy, i.e. Germany, is in a recession: the GDP decreased in the first quarter of 2023 by 0,3%. In the previous quarter the decrease was by 0,5%. Several countries in Europe and globally will be hit by the current recession: this is the conclusion of the International Monetary Fund after a series of studies.

In addition, global inflation, with a 40-year-highs in major economies, declining real wages and growing interest rates have driven consumers into a cost-of-living crisis. And this situation will not change in the short term.

All this impacts heavily consumer behavior and willingness to pay. Discretionary categories like fashion, furniture, cosmetics, leisure or alcohol are affected. Promotions become a key driver for shoppers, who also seek more often for deals online to get lower prices and reduce physical point of sale visits. This is also linked to the mentioned decline of wages with the European Commission projecting further wage loss to occur in 2023, and disposable income at the end of 2023 projected to be 6.5 percent below 2020 levels. 

In this context, consumer packaged goods (CPG) companies need to calibrate RGM levers to balance growth and profits. Based on our project experience with economic crises, we have seen many companies focus too heavily on obtaining short-term results, putting insufficient emphasis on establishing medium-term RGM capabilities to capture future growth.  

However, CPG companies investing in developing medium-term advantage, e.g. based on AI, data and technologies, demonstrated above average results. In the  current inflationary and wage-constrained context we recommend to focus on 5 key RGM levers to win. These 6 key RGM levers will provide both short-term benefits, but also lay the ground to consolidate the competitive advantage.

Six RGM Levers to Win in the Current Crisis 

To tackle the current crisis CPG companies that follow an RGM approach that balances short-term wins with a medium-term vision are on the winning side.

The six levers will be exposed one by one.

• Lever 1: Optimize Promotions, with advanced technologies. 

  Market volatility requires a review of the promotional strategy. AI helps enhancing decisions around which promotions to run and what the uplift will be, improving the quality of forecasting, and enabling faster responses to unexpected changes. In different Valcon project promotions were optimized with AI: the sophistication, accuracy and speed of the developed AI-based promo tools boosted EBITDA by over 350 basis points as CPG companies could avoid unprofitable promotions and focus only on the ones with the strongest outcomes.  
  
  

• Lever 2: Review Terms & Conditions. 

  In a crisis and volatility environment legacy terms & conditions quickly become obsolete. In the relationship between a CPG retailer and producer such legacy terms that are not creating any more value to either party can be spotted. When promotional activity changes drastically, multiple price increases follow each other triggered by ongoing inflation or pack prices and size are affected by the so called shrinkflation, the legacy terms and conditions have no reason to exist any longer. By replacing obsolete and out-of-date trade terms, both parties find a more productive way to cooperate.  
  
  

• Lever 3: Optimize prices focusing on the future.

  In the current volatile context, extrapolating the future from past data can even become worthless, as in many cases this is not a meaningful proxy of the future. However, we see that a high amount of CPG companies to spend three quarters of their analytical time on efforts to understand the past, e.g. elasticities, baseline, competitive moves etc. This diagnostic exercise shall be limited and a future-oriented perspective shall replace a past-oriented perspective. Valcon supported a leading CPG company using AI and data to generate forward-oriented recommendations to optimize price points, which turned to be a much more accurate way of setting and optimizing prices.  
  
  

• Lever 4: Differentiate price increases.

  Before Covid, planning and forecasting at CPG plyers was easy: a relatively constant past was projected into a relatively foreseeable future. The challenge is that nowadays lie beyond historic price levels and household budgets. This means they lie outside historical data. CPG companies who want to shed light on how shoppers would choose at new price points not previously observed in the market, apply AI approaches combining forward-oriented consumer research with granular analyses of historical data. Pairing forward-oriented with historic data is the basis to pass from uniform to differentiated price increases.  
  The advantage is to be able to apply de-averaged price increases across channels, SKUs or brands depending on e.g. competitive landscape, fluctuating raw material costs, brand health, price elasticities etc. Avoiding undifferentiated raw-material driven price increases across the board will yield superior results as often proven by the project work at Valcon.  
  
  

• Lever 5: Orchestrate mix management with portfolio complexity reduction.

  You can seldomly observe CPG players optimizing revenue management and simplification end-to-end. However, the current crisis provides the ideal context to choose a holistic approach. The risk is namely that fixating only on inflation compensation will push income-limited shoppers to value brands or private labels or switching to discount channels. On the other hand, CPG players intend to optimize their margins simplifying portfolios.  
  To achieve both objectives key SKUs shall be reengineered at the formulation and packaging level and enhanced in terms of value, based on what shoppers perceive and are willing to pay for. Such an exercise minimizes waste reduction in the assortment of on average 33% as proven by a series of engagements.
  
  

• Lever 6: Change the perspective on RGM.

  We observed CPG companies following two separate routes in their strategic planning for 2024. A number of players are pushing significant price increases that are given back via a stronger use of promotional activities. Other players are strongly reducing the promotional funding. Both approaches can work. To determine the best one, it is key for CPG companies to shed light on shopper behavior holistically with respect to promotions, packs, price architecture, rather than looking at each separately. Some shoppers will accept higher shelf prices supported by promotional activities to generate incremental volume. Other shoppers can accept only limited price increase with less frequent promotions. Segmentation plays a key role. 

The above described 6 levers represent a concrete help for CPG companies to overcome the current market challenges. After having concentrated narrowly on short term reactions in the last 1,5 years, the time has come to prepare for a future rebound.

RGM approaches that integrate future-oriented perspectives without relying too heavily on historic data with an assessment of the benefits for customers and shoppers, and employ state of the art technologies have the highest chances to win in the current crisis. 
 
 
 

Executive Summary

The Life Sciences industry is constantly evolving, driven by innovations, market dynamics, and regulatory changes. One critical aspect that often goes overlooked is the management of Loss of Exclusivity (LoE). LoE represents a pivotal point in a product's lifecycle when market exclusivity ends, opening the door to generic competition. Unfortunately, many companies fail to proactively plan and manage their LoE transitions, leaving them vulnerable to immense revenue erosion and market share loss. This does not only account for big pharma, which have a wide-range of products in their portfolio, and many have already faced the patent cliff, but may be even more critical for Emerging and Biotech companies, who have not yet reached LoE for one of their products and hence, only limited to no experience.

This article explains in the beginning what “Loss of Exclusivity” (LoE) means and then explores the pressing need for Life Sciences companies to properly prepare and conduct their LoE management. After, it introduces a comprehensive suite of activities designed to empower Life Sciences companies to navigate this challenging phase strategically. By building insights to derive strategies, capabilities, and tools, companies can capitalize on their advantages while mitigating risks during the LoE period to maximize the value retention.

The emphasis of this article is mainly for pharmaceutical manufacturs with a focus on markets and regulations outside the US.

The Patent Cliff – the end of the Product Lifecycle given its Loss of Exclusvity (LoE)

"Loss of Exclusivity" (LoE) in the context of life sciences, particularly pharmaceuticals, refers to the point in time when a drug loses its patent protection or other forms of market exclusivity. Pharmaceutical companies typically receive patents for their new drugs, granting them exclusive rights to manufacture and sell the drug for a certain period, usually around 20 years from the filing date of the patent. Once the patent protection expires, other companies can produce and market generic or biosimilar versions of the drug.

In Europe, the regulation of the Generic and Biosimilar market access pathway is stated within the “Directive 2001/83/EC” and “Regulation (EC) No 726/2004” which aims to facilitate the approval and market access of these types of medicinal products, while ensuring their quality, safety, and efficacy. Both generic and biosimilar products play a crucial role in leading to increased access to more affordbale medications for patients, as well as in promoting competition in the pharmaceutical market, to achieve cost savings for healthcare systems while maintaining rigorous standards for quality and safety. The regulatory framework aims to strike a balance between encouraging the development of these products and ensuring that they meet the necessary standards for approval and post-marketing surveillance. Additionally, many governments have implemented national legislations or guidelines to promote the adoption of generics and biosimilar, such as the “Act for the Financial Stabilization of the German Statutory Health Insurance System” in Germany with increased mandatory price cuts, or the “Social Security Financing Act” in France with intensified substitution rules.

Therefore, loss of exclusivity (LoE) is a critical event for pharmaceutical companies because it results in increased competition, often leading to a significant reduction in the price of the medication. The entry of generic versions can impact the market share and revenue of the original drug's manufacturer. Understanding the timeline of LoE is essential for both the original drug manufacturer and competitors interested in entering the market with generic alternatives. To counteract this, companies may not only employ various activities, such as developing new formulations, seeking additional indications, or introducing follow-up drugs with improved features, but also pro-actively plan their Commercial & Pricing Strategy, as well as develop organizational readiness by establishing robust capabilities, tools & governance.

The Imperative of Proper LoE Management

The Pitfall of Neglecting Proactive Planning

It is not uncommon for Life Sciences companies to focus their attention and resources primarily on the development of new products and the growth of existing in-market brands. Unfortunately, the vital process of LoE planning often takes a backseat. This neglect can have significant consequences as it leaves companies ill-prepared to address the challenges that come with LoE, including patent expirations and generic competition, which can create a blind spot that jeopardizes the company's long-term stability, profitability and hence, sustainability.

Therefore, one of the key reasons why proactive planning is crucial stems from the highly competitive and regulated nature of the Life Sciences industry. With patents expiring and generic competitors entering the market, the landscape becomes increasingly volatile, making it essential for companies to anticipate and prepare for potential challenges well in advance. If not, the company risks to not only jeopardize revenue streams and market shares but also compromises the company's ability to maintain a competitive edge in an ever-evolving industry. Moreover, the implications of overlooking proactive planning extend beyond financial setbacks. Without a robust strategy in place, Life Sciences companies may struggle to effectively communicate changes to stakeholders, including investors, employees, and regulatory authorities. This lack of preparation can erode trust and credibility, damaging the company's reputation and potentially impacting future partnerships and collaborations. Additionally, a delayed response to LoE challenges can lead to over-rushed decision-making, resulting in suboptimal solutions that may further diminish the company's market position and inhibit its ability to recover effectively.

Given the multifaceted nature of the Life Sciences industry, proactive planning should ideally commence at the early stages of product launch and more importantly, continue throughout the entire lifecycle of a product and become integral at a minimum two to three years prior to LoE. By integrating strategic planning into the initial launch phase, and seeing it through the product lifecycle, companies can not only anticipate potential obstacles but also incorporate contingencies into their business models. Furthermore, by fostering a culture of foresight and adaptability within the organization, companies can effectively stay ahead of market shifts and regulatory changes, ensuring a sustainable and resilient position in the industry, throughout peri-LoE and even long post-LoE.

The Challenge of Resource Allocation

Resource constraints and the urgency of new product launches often lead to underestimating the importance of LoE management. While the allure of new products is undeniable, insufficient resource allocation for LoE can create significant challenges. Companies must recognize the complex nature of post-patent expiry transitions and implement well-defined resource allocation strategies. Adequate allocation of financial, human, and technological resources ensures thorough market research, effective pricing strategies, and robust marketing campaigns, mitigating the impact of generic competition.

Navigating the Regulatory Landscape

The Life Sciences industry is heavily controlled, and regulatory changes can significantly impact LoE management. These regulatory changes can have a direct consequence on product exclusivity, market entry, and competitive positioning, and necessitate a thorough understanding. Also, the regulatory landscape has various nuances across different regions, and country-by-country, with specific requirements and timelines in each target market. Moreover, establishing robust monitoring mechanisms, engaging in proactive dialogue with regulatory authorities, and participating in industry forums and discussions can provide valuable insights into upcoming regulatory changes, enabling companies to adapt their LoE strategies in a timely and informed manner, especially about their competitons behaviour to anticipate their Go-to-Market strategy. As a result, companies must have a comprehensive understanding of the regulatory environment for developing a robust and adaptable LoE strategy that aligns with the evolving regulatory framework.

Leveraging Brand Advantages

The advantages inherent to branded manufacturers present valuable opportunities for these companies to navigate the complexities of the Loss of Exclusivity (LoE) phase and maintain their competitive edge within the market. These include established brand loyalty, efficient supply chains and logistics, robust customer relationships, and extensive networks. Especially, cultivating strong relationships with healthcare providers, key opinion leaders, and patient advocacy groups, as well as procurement bodies and stakeholders, not only fosters brand backing but also facilitates access to valuable market insights and feedback. Leveraging these relationships enables companies to tailor their product offerings, develop targeted promotional campaigns, and establish collaborative partnerships that support continued brand visibility and market relevance throughout the LoE phase. Therefore, to harness these advantages, companies need a well-structured strategy tailored to the unique dynamics of LoE, enabling companies to maximize their market presence, mitigate competitive threats and sustain their brand value beyond the patent expiry stage.

Empowering with Capabilities and Tools

Inadequate capabilities and tools further hinder effective LoE management. Many Life Sciences companies lack the necessary expertise, data analytics capabilities, and resources to navigate LoE successfully. The absence of a well-defined LoE strategy for capabailities and tools can lead to missed opportunities, unfavorable contracts, and the inability to maintain competitive pricing, given the struggle to accurately assess market trends, forecast demand shifts, and identify emerging opportunities for product differentiation and market expansion. By implementing specialized capabilities and tools, companies can empower their ability to make informed strategic decisions, anticipating opportunities for product diversification and innovative portfolio management. Additional benefits of leveraging critical insights from market data, patient behavior, and competitive intelligence are to develop tailored marketing strategies, optimize pricing structures, or establish effective reimbursement models and securing favorable contracts with key stakeholders – including healthcare providers, insurers, and distributors.

Quantifying Impact for Strategic Decision-Making

Only with the right capabiltiies and tools in place, the company is able to underscore the urgency of LoE management by quantifying the risk and potential losses. For example, inadequate LoE planning can lead to revenue drops of up to 90% for certain products. Therefore, a comprehensive analysis of market size, revenue projections, and case studies illustrating revenue drops reinforces the critical need for proactive LoE management strategies. Additionally, quantifying the potential losses associated with patent expiry serves as a powerful motivator for companies to allocate resources and prioritize investments in developing robust LoE strategies. By contextualizing the potential revenue losses within the broader context of the company's financial performance and market competitiveness, organizations can effectively communicate the urgency and significance of implementing proactive measures to mitigate the adverse effects of LoE and sustain long-term value retention and profitability.

In conclusion, a holistic approach to LoE management, encompassing proactive planning, strategic resource allocation, capabilities and tools, brand leverage, regulatory insights, and quantifying impact, is paramount for Life Sciences companies to ensure sustained competitiveness and success in the dynamic post-patent expiration landscape.

Key Activities for an effective and successful LoE Management

Navigating the intricate landscape of Loss of Exclusivity (LoE) in the Life Sciences industry demands a nuanced approach. Tailoring strategies to individual company needs and their respective asset/product facing LoE, ensures a comprehensive and effective response to the dynamic nature of this phase. The following key 10 activities outlined below cover a spectrum of critical stages, including analysis, strategy development, implementation, and ongoing execution:

1. LoE Landscape Analysis and Analog Assessment
   
   The foundation of a robust LoE strategy lies in a comprehensive LoE Landscape Analysis. Delving deeply into regulatory pathways, competitive landscapes, and market trends provides invaluable insights to proactively anticipate regulatory hurdles and potential Commercial strategies for oneself, as well as Go-to-Market strategies of the competition. In addition, an Analog Assessment serves as a vital tool by leveraging data-driven insights from past LoE transitions and comparing the current LoE situation to similar cases, gaining valuable guidance and insights that inform the development of comprehensive and adaptive LoE management strategy. Thereby, increasing the organizations chances of minimizing unforeseen risks and maximizing value retention. Moreover, companies should leverage a combination of qualitative and quantitative research methods, including market surveys, data analytics, competitive intelligence, and regulatory trend analysis. By integrating these diverse research methodologies, companies can gain a holistic understanding to draw meaningful comparisons between their current LoE situation and analogous cases in the industry. This strategic approach of LoE Landscape Analysis and Analog Assessment empowers companies to identify niche market segments, potential partnerships, and competitive differentiators, as well as learn from the experiences of others, avoid common pitfalls, and replicate successful approaches to maximize the efficacy of their own LoE management strategies. As a result, this enables companies to position their products effectively to mitigate the impact of generic competition and sustain their market share and value retention during and after the LoE phase.
   
   
2. Risks & Opportunities Assessment
   
   Conducting a wide-ranging Risks & Opportunities Assessment is integral to the successful management of Loss of Exclusivity (LoE). By systematically identifying potential risks and opportunities associated throughout the LoE phase, companies can develop proactive mitigation strategies that enable them to navigate uncertainties with confidence, minimize operational disruptions, and capitalize on emerging opportunities that sustain their market position and retain value and maximize profitability.
   
   
3. Dual Brand Strategy & Debranding
   
   A Dual Brand Strategy can serve as a proactive approach for Life Sciences companies to sustain their market share and competitiveness during the Loss of Exclusivity (LoE) phase. By effectively engaging with internal and external stakeholders, companies can develop tailored strategies that leverage dual branding initiatives, allowing them to strategically position their products in the market and maintain a strong market presence despite the challenges posed by generic competition and patent expirations. Therefore, the implementation of a Dual Brand Strategy involves the simultaneous promotion of both the original branded product and its generic counterpart. By highlighting the unique value propositions, superior quality, and distinct attributes of the original brand, companies can continue to cater to customers who prioritize brand loyalty and perceive added value in the branded product. At the same time, effectively positioning the generic alternative as a cost-effective and accessible option enables companies to capture a broader market segment, catering to price-conscious consumers and maintaining market share even in the face of generic competition.
   
   In instances where the debranding of products becomes necessary, ensuring a seamless transition is paramount to minimize customer confusion and maintain trust. Clear communication strategies, educational campaigns, and transparent messaging are essential for guiding customers through the debranding process, explaining the rationale behind the transition, and emphasizing the continued availability of quality healthcare solutions despite the change in branding. By prioritizing customer education and transparency, companies can navigate the debranding process effectively, mitigate any potential negative impacts on customer perception, and maintain long-term trust and loyalty within their customer base.
   
   However, bear in mind that this strategy may not be applicable nor feasible for all markets and in contrary, based on our experience, only makes commercial sense on a country-by-country level decision which needs to be assessed based on the product, its brand, and market environment.
   
   
4. Competitive Intelligence, LoE Scenario Planning & Simulations
   
   The proactive monitoring of competitors' actions and market trends is foundational for maintaining a competitive edge during patent expiry. The process of capturing Competitive Intelligence entails employing sophisticated research methodologies, including in-depth market analysis, comprehensive competitor strategies & profiling, product pipelines, pricing models, market positioning and vigilant trend monitoring. All these valuable insights should be leveraged for LoE Scenario Planning & Simuations. Therefore, by developing a comprehensive framework that encompasses best-case, worst-case, and moderate-case scenarios, companies can effectively anticipate the potential challenges, opportunities, and risks associated with LoE, allowing them to devise proactive strategies and contingency plans tailored to each specific scenario. As a result, conducting such LoE simulations facilitates the the proactive identification of potential risk factors and allows to develop mitigation strategies, to optimize resource allocation, streamline operational efficiencies, and implement agile decision-making processes that enable them to adapt to changing market conditions and sustain their competitive edge amidst industry fluctuations and competitive pressures. Additionally, it fosters a culture of strategic foresight which is a critical factor in terms of change management within the organization and helps to maintain trust and confidence.
   
   
5. Capabilities, Tools & Trainings
   
   Establishing a comprehensive toolkit equipped with the necessary capabilities and tools is essential for Life Sciences companies to streamline the planning and execution of Loss of Exclusivity (LoE) strategies across global and local teams. Therefore, building capabilities within the organization includes providing specialized training programs, workshops, and educational resources that enhance the understanding of LoE dynamics, regulatory compliance, market analysis, and competitive intelligence. By nurturing a workforce with the requisite capabilities, companies can empower their teams to make informed decisions, drive innovation, and adapt to evolving market conditions, ensuring the effective execution of LoE strategies across various regions and markets, whilst building confidence in the organization. Furthermore, implementing advanced tools tailored to LoE planning and execution is integral to optimizing operational efficiency and facilitating seamless collaboration among global and local teams. These tools may include sophisticated data analytics platforms, project management software, and communication tools that enable real-time information sharing, data-driven decision-making, and cross-functional collaboration. By integrating these tools into the organizational workflow, companies can streamline data management, enhance communication channels, and facilitate efficient coordination among teams, thereby ensuring a synchronized approach to LoE planning and execution across diverse markets.
   
   
6. Tenders and Contracts Planner & Calendar
   
   Optimize your tendering and contracting processes to secure favorable agreements during the LoE period and beyond. Ideally, companies can leverage existing contracting and tendering systems which they have implemented previously and simply need to enhance specific functionalities. Otherwise, companies should explore existing solutions and consider an implementation to support these processes and to develop a comprehensive Tender and Contract Calendar. By organizing and categorizing tender deadlines, contract renewals, and submission timelines, companies can proactively plan their tender submissions, allocate resources effectively, and prioritize strategic segmentation and targeting that maximize their chances of securing positive agreements during the LoE period. This proactive planning approach minimizes the risk of missing critical opportunities, enhances the company's competitive positioning, and ensures that tender submissions are well-prepared, strategic, and tailored to the specific requirements of each client and market segment.
   
   
7. Pricing Guidance & Governance
   
   Establishing a comprehensive Pricing Guidance and Governance framework is essential for Life Sciences companies to maintain profitability and competitiveness while navigating the complexities of the patent expriy phase. By developing a structured pricing strategy and governance process, companies can anticipate market dynamics, ensure flexible adjustments, and strike the right balance between competitive pricing and revenue preservation, while enabling swift and efficient decision-making with the appropriate approval flow. Therefore, building a pricing guidance framework involves leveraging market research, competitive analysis, and customer insights to develop a well-defined pricing strategy that aligns with the company's overall business objectives and market positioning. By considering factors such as product differentiation, value propositions, and customer affordability, companies can establish pricing guidelines that optimize profitability, sustain market competitiveness, and mitigate the impact of generic competition and market fluctuations during the LoE phase. Furthermore, by establishing clear decision-making protocols, approval workflows, and cross-functional communication channels, companies can streamline the pricing governance process and enable efficient collaboration among key stakeholders. Ultimately, facilitating fast and flexible decision-making within the pricing governance framework is crucial for responding to market changes, customer demands, and competitive pressures during the LoE phase. This can be achieved by enterprise Price Management & Approvals tools, as well as the LoE Scenario Planning & Simulations, to anticipate pricing adjustments, capitalize on emerging market opportunities, and adapting pricing strategies in real time to maintain a competitive edge and maximize revenue potential throughout the LoE phase and beyond.
   
   
8. Account Planning
   
   Account Planning plays a pivotal role in retaining and growing customer relationships during the critical phase of Loss of Exclusivity (LoE). Conducting in-depth account analyses, customer segmentation, and personalized engagement initiatives, allows companies to proactively address customer concerns, provide transparent communication, and offer value-added services that go beyond product offerings. As such, companies can develop tailored and account-specific strategies, to  ensure that their valued customers remain loyal, engaged, and supportive throughout the entire LoE phase, fostering long-term relationships that extend beyond the transitional period and sustain the company's market position and revenue streams.
   
   
9. Cross-Functional Engagement Plan
   
   Establishing a comprehensive Cross-Functional Engagement Plan is essential for fostering collaboration, communication, and alignment across various functions within the organization. Therefore, by promoting such culture of transparency and open dialogue, companies can break down silos, encourage interdisciplinary collaboration, and leverage the diverse expertise and perspectives of each team to develop wide-ranging and effective solutions. However, this requires the establishment of clear communication channels, defined roles and responsibilities, and structured cross-functional meetings and workshops that promote active participation and input from all teams involved in the LoE management process. By encouraging a culture of inclusivity and mutual respect, companies can ensure that every team is empowered to contribute their unique insights, expertise, and perspectives, raising a sense of ownership and shared commitment to achieving the organization's LoE goals.
   
   
10. LoE Community & Action Plan
    
    Setting up an inclusive LoE Community and implementing a comprehensive Action Plan is essential for fostering collaboration, knowledge sharing, and collective problem-solving within the organization. By connecting with country peers, sharing insights, and collaborating on collective actions, companies can address common LoE challenges, promote cross-market collaboration, and leverage the collective expertise and experiences of the internal community to develop effective and tailored solutions that drive sustained success and growth within the dynamic LoE landscape. Furthermore, archetyping similar markets within the LoE Community enables companies to tailor and accelerate the learning and collaboration for specific LoE activities based on market-specific nuances, regulatory landscapes, and customer preferences. By identifying commonalities and differences across similar markets, companies can develop tailored strategies and targeted interventions that address the unique challenges and opportunities presented by each market, promoting a culture of adaptive learning, strategic agility, and cross-market collaboration.

Conclusion

In conclusion, pro-active and effective LoE management is crucial for Life Sciences companies to navigate the complex landscape of transitioning from exclusivity to competition. Proactive planning serves as the cornerstone of effective LoE management, allowing companies to anticipate potential challenges, mitigate risks, and capitalize on emerging opportunities that arise during the transition phase. By implementing comprehensive strategies that encompass thorough scenario analyses, cross-functional engagement, and adaptive resource allocation, companies can optimize their operational efficiency and maintain their competitive edge, thereby safeguarding their market share and sustaining their revenue streams amidst the dynamic market shifts and competitive pressures associated with LoE. As such, companies can not only survive LoE transitions but also thrive and unlock opportunities for value retention in a highly competitive market.

To learn more about our experience and how our LoE management services can benefit your organization to enhance your competitive advantage, please contact us to schedule a consultation. Together, we can transform the challenges of LoE into opportunities for value retention and success in the Life Sciences industry.

Inflation increases directly promotional costs. How to manage each promotional folder with a clear ROI lense, combining brand strategy, market share increase and profitability at the same time? Artificial Intelligence leveraged by Intelligent Assistants step change your promo strategy through alerting, simulations and recommendations.
But Retail is detail. Indeed, once your promo strategy is designed, what would be the impact on your mix management? Do we promote the right pack with the right size in the right channel for the right retailer with the right profitability?

WHO IS THIS WEBINAR FOR?
• Revenue management teams
• Sales & Category management teams
• Finance teams
• Anyone interested in leveraging Intelligent Assistant for Promotion & Mix strategy

WHAT YOU WILL LEARN?
• What AI is bringing in the Revenue Management Strategy ie. Promo & mix?
• How Intelligent Assistants can leverage your Promo & Mix strategy?
• How to delegate alerts vs competition to Intelligent Assistant?
• How Intelligent Assistants will accelerate adoption for all teams?

Digitalisation over the past years has laid the base for the strong growth of new business models in B2B and B2C around software, data and analytics, as well as other digital offerings. Many of those business models are based on „Software as a Service“, or just SaaS, which is an enormously growing market globally with an expected CAGR of 8 % until 2027, according to Statista. However, the key to the sustainable success of those business models is how much of the promised and generated value by these innovative services can actually be extracted. And this is where the problem starts – value extraction or, in other terms, the monetisation of the SaaS offering, provides a substantial challenge to many firms and is unfortunately associated with many misconceptions, as we will illustrate subsequently: 

1. “We need to highly differentiate our offering”
   
   In the ambition to cater to every tiny target group, firms overcomplicate their SaaS offering. Too often, we see a jungle of packages, bundles with exceptions and additional elements and upgrade options, which are hard to grasp. Instead, simplicity shall be king, particularly in a B2C environment. In contrast, in B2B offerings, support services are too often overlooked – these are typically services that make customers move to SaaS offerings and consequently can be easily monetised and boost value extraction. For instance, integration services such as setting up the solution in brownfield ERP and CRM landscapes, maintenance services, and customer or technical support shall be considered in pricing schemes.
   
   
2. “We need to determine the right price point”
   
   We always notice an overemphasis with many SaaS offerings on accurately assessing the actual price point. While “price point engineering” is vital, it is only one facet of the overall offering and value extraction. Typically, there are many discussions around competitive and value-based pricing (and, to some degree, even cost-plus pricing), which should not fill the pricing manager’s agenda too much. In the end, price levels shall oscillate between competitive and value-based depending on the overall strategy of the offering (penetrating vs skimming). If the price point is not hit right the first time, this should not worry too much as the actual price level can typically be easily changed - other than, for instance, the price model or metrics.
   
   
3. “Price metrics are not so critical for us”
   
   Surprisingly, other than the price point, the pricing metrics do not get the attention they deserve. Nevertheless, this is typically the “secret sauce” for successful SaaS pricing and value extraction –look at the many creative metrics digital native players such as Amazon Web Services apply: Pay by gigabyte, by click, by data load, or by API calls. Applying the right price metric is vital as, especially in the pay-per-use price model, fixed costs are “variabilised” for the customer. This means that customer costs match the value created for the customer, making the offering more attractive and, consequently, granting more pricing power.
   
   
4. “We determine pricing with market research methodologies”
   
   The good thing is that “SaaSletees” typically want to prove their assumptions and hypotheses with data. Still, our experience with traditional price research techniques is mixed: Often, a quick online survey with random samples leveraging Gabor Granger or Van Westendorp analyses is conducted to get a feeling for the prospects’ willingness to pay. Again, typically, this ignores the equally important components of the price model and price metric. On top, especially for very innovative offerings, the survey sample owns only limited competency to truly judge the value of the service provided. This is also true for conjoint surveys despite their higher validity by taking trade-offs into account. Hence, we recommend two alternatives: First, a small sample focus group approach with experts tends to yield better results, especially in an early stage. On top, A/B testing or introduction to pilot markets and a phased roll-out of a pricing concept is a much more robust approach, especially for highly innovative offerings.
   
   
5. “We apply an agile approach and emphasise fast go live over too long testing”
   
   One note of caution:   Do not run too fast. We have also seen very agile start-ups burning their price positioning by moving too fast. Testing features, the price model and its metrics (other than the price point!) is crucial and takes time.  This requires analytical groundwork and a deep understanding of customer segments, use cases, applications, and the benefits of the respective offering. Particularly in B2B, large-scale quantitative approaches might not be necessary, but deep insights from interviews and focus groups might be sufficient. In B2C, approaching potential prospects with pricing scheme mock-ups and conducting interviews (e.g., based on “thinking aloud” methodology) typically results in profound insights and significant input for further validation rounds. Take the time to sharpen the offering with its features, the price models and the metrics, as this is much harder to change and the core of value extraction!
   
   
6. “We urge sales teams to sell the software with the hardware”
   
   In many traditional firms now moving into the SaaS world, sales have been used to sell tangible assets like industrial equipment. In many cases, sales now need to sell the machine and software contracts that earlier might have been given away for free on top. This requires a ton of change management initiatives starting, for instance, with adapting the sales force’s incentivisation but also offering training. Large organisations might even consider adjusting their organisations as sales might have been split up into hardware and software sales.
   
   
7. “We do not need a KPI structure for our pricing”
   
   We all know the famous saying by Peter Drucker, ”What gets measured, gets done” – so with SaaS pricing. Too often, however, we see that either “classic” pricing KPIs like average margin, discount, or enforced price level are measured or are neglected altogether. For SaaS, we recommend diving deeply into the digital world and building a performance indicator framework fitting your business. Typical KPIs cover customer acquisition costs, average recurring revenues, and average revenue per user (or ARPU) and always come with a “growth” twist. These KPIs shall be tracked ideally daily and compared over time to learn and quickly take action in case of deviations from the plan. Hence, a robust KPI structure and concept shall not be set up at the end but at the beginning of the SaaS price model design to measure already your pilots against those KPIs and continuously track your SaaS performance. 

Of course, with all the different price models out there, there might be much more flaws but also much good practice. Based on our experience, not everything can be anticipated in pricing innovative SaaS offerings. Still, the discussed misconceptions might help avoid common mistakes and focus on the right components of your pricing model. 

• Four key mandates of the pricing function
• Strategic pricing - pricing for sustainability
• Pricing as a process - digitisation
• Pricing compliance – competition law compliance framework
• Pricing governance – Pricing Organization Blueprint
• Food for thoughts: 3 questions to reflect on for the future

• The use of dynamic pricing based on the supply-demand mechanics to optimize the top line and the margins 
• How pricing investments such as coupons/vouchers and subscriptions are personalized to focus on the goals on top line and optimal profit levels. 
• Learn how to improve your overall profitability and understand how to use map pricing and dynamic pricing to create a more personalized offer and segmentation