In recent years, globally significant events have dramatically impacted supply chain related costs obliging many businesses to substantially increase their prices. The magnitude of price adjustments was so unprecedented that many organizations lacked the necessary experience to manage their offerings effectively in rapidly changing market conditions. This resulted in some companies failing to increase prices sufficiently, foregoing profit due to fear of lost sales, whilst others hit an inappropriately high price-point, to the detriment of sales. Many businesses ended up in a bedlam of distorted value propositions, incomprehensible price structures across markets and channels, and hence sub-optimal market competitiveness. Now, as the economy and markets start to recalibrate, there is urgency for businesses to rationally revisit pricing. The current article discusses some of the price analytics that could help on the way. They could form, as the author explains, a formidable basis for the application of AI – based pricing. Author Slobodan Farago is a pricing expert with more than 25 years of experience. He can be reached at farago@bluewin.ch.

The Journal of Professional Pricing, March 2024

In an earlier article (“Pricing Through High Inflation”; Journal of Professional Pricing Q1 2023), I elaborated on the difficulties that organizations were facing due to inflationary challenges. Indeed, to most organizations that have not maintained a professional pricing department with solid pricing know-how, analytics, established pricing processes, tools and systems, the new and unprecedented situation might have led to some torpor, stopgaps, and many wrong decisions and irrational pricing actions. Retrospectively, price adjustments often appear a bit like the result of tossing a coin. The conundrum here is how do you adjust prices professionally or, now even more, how do you re-price to correct the blunder?

Although there is no unassailable and all-encompassing response to appropriate pricing strategy – the complexity and the specific needs of different industries, markets, even specific situations which organizations bring along preclude this – there are some pricing basics and core methodologies which have a broad validity across all repricing projects. The goal must be to apply such core principles nuanced with customized analytics to serve the purpose. The three core elements discussed here encompass a.) past pricing performance, b.) the possible price implications for the future, and c.) the price sensitivities of the market.

A. How did price impact your business?

The first step is to extract and analyze price impact information from the most recent past. One of the key reports to employ in such an analysis is the price-volume-mix report (PVM). This analysis is sometimes also called variance analysis or causality report. In detail, the report compares two periods in terms of their revenues or profits, and assesses the contribution of price, volume, and product mix towards outcomes. If revenues have declined over that time, was it because of a pricing action, or did losses in volume or change in product mix cause it? Starting at the top level and drilling into more detailed analyses will also reveal if and where in the portfolio price or volume contribution have fallen, where customers might have switched to other products in the portfolio (changed the mix), and also where there has been no impact from pricing on volume uptake at all.

Adding exchange rates (if applicable) will also provide an understanding to what degree currency developments may have impacted a business in an international trading context. Thereafter, adding profit to the analyses will, in addition, reveal the impact of costs (which may also have changed over the period). Thus, even under the aspect of a positive revenue development, the impact of a surge in costs may lead to a negative development in profits. Such impact can be quantified and separated from other positive or negative impacts.

In short, the PVM report will identify and quantify problem zones – by market, geography, portfolio, and product – and will separate and quantify those areas which have been of no concern from those that experienced impacts (and quantify those). Thus, the PVM greatly increases transparency and helps to focus repricing to the areas of greatest concern.

Figure 1: A typical display of a Price-Volume-Mix analysis and how this analysis assists in understanding where the biggest impacts have been (past business actions/developments).

PVM can be an enormously helpful analysis when it comes to understanding the impact of previous business actions, economic developments, market sensitivities towards product areas, etc. Where price change might be only one of several actions, it is important to understand how other underlying effects may have contributed to a change in revenue and profits, and to what extent. Further detailed analytics (including the involvement of the marketing, finance, and sales functions in the discussions) will be necessary to fully interpret the data to eliminate underlying effects in order to define the impact of price alone.

Organizations that have a dedicated pricing department would generally allow this function to take the lead in generating the PVM report (often in co-operation with finance/controlling, however, for the sake of best data quality possible). Further, pricing would thereby mediate and guide marketing, finance, and sales departments through the interpretation of the data, and align the organization appropriately for setting of responsive pricing actions.

Depending on business and organization, a PVM analysis can be provided on a monthly or a quarterly basis or, in a particularly sluggish business dynamic, even an annual provision and review of the report could be enough. The frequency is dependent upon how long prices and other components (such as costs, FEX budget rates, etc.) stay valid in a specific business (for example due to contracts, fixed budget rates, etc.). The provision of the report should be aligned to any applicability of price change.

Experience has demonstrated that once the PVM report has found regular usage with an organization, it becomes an excellent means for organizational learning and development: for sharpening the commercial intuition around a business, for capturing changes in market dynamics, and for comprehending the positive or negative impacts of different business elements, including pricing, towards the company goals. Moreover, it becomes an excellent means to align an organization behind fact-based actions and decisions that are solidly backed by data and thorough discussion. Once established, retrospective analysis of the PVM will help to monitor how actions undertaken have actually impacted business. As such, although pricing impact is the core element of the report, it additionally provides necessary connectivity to other pieces of the revenue and profit calculation as well as any feedback information in relation to the impact of commercial decisions.

B. How may price impact your business?

Once an organization has obtained a view of the results of past actions, the next question to address is “what will be the future impact of defined pricing actions?” That is, of course, not an easy question given the many unknown parameters, such as the actions and responses of competitors, the often-unforeseeable changes in the market environment, the occasional unpredictability of the economy, etc. Without a crystal ball, predicting the future will always remain difficult and uncertain. Nevertheless, an organization can and must employ their best knowledge of today to model appropriate pricing. An excellent way to do so is with the Price Impact Calculator (PIC), which every organization should always have on hand when it comes to budgeting, forecasting, or any modelling/planning of future business.

As the name suggests, the PIC is a calculator. It contains the most recent information on parameters, such as costs, FEX, volumes, prices, targets (revenues, growth, or margins, etc.). Ideally, the information is provided granularly at the line-item level and by market and is then aggregated up to higher product hierarchy levels.

Often, pricing will receive objections from sales that a lower price would help a business to grow, boost revenues, and even deliver more profits when a higher price will actually lower sales. Such discussions often take place in a vacuum of data. This is where the PIC comes into play by assisting with modelling and in quantifying the impact of pricing decisions. Understanding the level of increase in volume required to reach a defined revenue or profit target (e.g., as according to budget) – for example if prices are to be reduced – is one of the benefits of the PIC. Presented with the answer, sales can immediately see that lowering price may require substantially more volume to be sold to reach the required budget. Is this realistic? It may be or it may not be. However, confronted with the consequences of the equation, and the most complete impact calculation, sales as well as other functions will have the data required to rationally discuss and decide. For example, if a 10% decrease in price will not deliver a 25% hike in product uptake (because of switching costs, brand loyalty, or other reasons), then it would not make sense to pursue a price decrease of that magnitude; the organization would only give profit away, and in some cases also lose future revenues (which tends to be the first consideration for the sales function).

Organizations that employ a PIC will have an excellent tool at hand on which to base their future pricing decisions. Clearly, what the PIC provides us with is “just” a projection. However, starting with a few parameters (the simplest is price and volume trade-offs), the calculator can be enhanced to include other components which are known or are somewhat predictable. In some more sophisticated cases, probabilities can be added to model those into the different scenarios.

Figure 2: An example of a simple Price Impact Calculator: % of volume increase necessary to keep margin stable at a certain price reduction. A price decrease of 15% requires a 33% unit sales increase in volumes to maintain the same 45% gross profit margin.

In summary, the PIC will provide an organization with a tool to model pricing in a more fact-based manner. It will help with transparency and context, and it will uncover early on the uncertainties which an organization needs to closely monitor and quickly react to (if needed). Over time, enhanced versions of a PIC will integrate business knowledge and experience into the mix, thereby enabling an organization to refine its business intuition and react more swiftly and appropriately to a changing business environment.

C. How may your customers be impacted by your pricing

Once it is understood how price changes have impacted a business in the past, and a conclusion has been reached as to how future price changes would affect future objectives, it becomes important to validate this reasoning by closely examining key market parameters such as customer price sensitivity.

Price sensitivity is often assessed by a price elasticity analysis. Understanding price elasticities is a useful indication of how customers may respond to price changes. That said, price elasticity assessment and its relevance will very much depend on the industry, but also, and here more importantly, on the past stability of an economy. If several economic parameters change in parallel, superpose each other suddenly and in short sequences, the deployment of price elasticities might frizzle at the sales counter and lead to wrong results and misleading interpretations (see also “Price Elasticities Demystified”, Journal of Professional Pricing Q1 2021).

There are several concurrent causes which may hamper price elasticity-based price adjustments:

1. Price elasticities look mostly at a company’s own historic data, heavily disregarding competition, and hence the overall market response.
2. Quantification is complex and outcomes are not only related to price. Changes in factors such as, for example, advertisement spending, GTM, etc., will also alter price elasticities, but their contribution is often not captured, segregated, or quantified.
3. Price elasticities experience heavy swings during extreme economic situations (for example, as seen during the impact of Covid-related government policies and the subsequent period of high inflation), and hence, become unreliable for predicting responses to price changes during such times.

Thus, the most recent years would exactly be also those years most inappropriate for assessing price elasticities and most perilous for deriving price changes from them. Still, there are possibilities to employ price elasticities when also in a more simplified trending environment which could provide the necessary input to finally define the scale of repricing.

1. Moving trend of revenue and profit maximums

Demand curves will usually display a price where revenue is maximal. By adding costs to the calculation, you will also get a profit maximum. Looking at historic data, you can derive a trend on these two outcomes that is dependent on the price. The trend can provide a useful additional view on how a company’s revenue and profit maximums have trended around prices over previous years and up to the most recent period.

Figure 3: Assessing revenue and profit maximum price points and derivation of a historic trend can help to understand which of the two to prioritize and how repricing could impact each.

1. Qualified price elasticity

Qualifying price elasticity is an alternative (and often more reliable) approach to simply “quantifying” it. Here, in a first step, some key factors that impact a price elasticity are defined and weighted in a scorecard manner. Typical key drivers of price elasticities are:

• Substitutability: Elasticity depends on the availability of product or service substitutes in terms of their quality, performance, availability, etc.
• Essentiality: Elasticity depends on how necessary and unavoidable the product is (if purchases can be reduced, delayed, or even completely omitted).
• Branding: Elasticity depends on the branding and marketing of a product/offering. Customer loyalty and resistance to switch to other brands can significantly impact their response to a price change.
• Current price positioning: Elasticity depends on the current pricing. Lower priced offerings will occasionally respond more strongly to price changes than higher priced ones.

Behind each factor, a weighting has to be defined. It is recommended that weighting is assessed professionally by superimposing a multiple regression analysis across all possible price and business drivers. In fact, a multiple regression analysis will not only assess the contribution of the drivers but even identify and shortlist the most relevant ones. Every business should know which factors matter most and define to what extent they will impact price elasticity! Usually, a multiple regression analysis will deliver a maximum of four to six relevant drivers.

Figure 4: Example of a coding scheme for scoring of price elasticity drivers.

Once the drivers and their weighting have been identified, one will need to score the offerings accordingly. This is best done in a workshop with functions that understand the market and products (sales, marketing, etc., and occasionally external consultants or industry experts). There should be at least two scoring teams engaged in parallel, which will allow for a quality control/cross-comparison of the scoring results. The difference in scores, views and results should be discussed and sorted out in a panel discussion between the scoring teams.

Scoring should take place at the level of an offering or group of offerings with similar behavior (e.g. a product family) where a score is to be given to each driver. The scores of all drivers are then added together and are translated into price elasticities according to predefined coding.

Bringing the pieces together

The last step of repricing analytics is to coalesce the three analyses and to derive an appropriate price increase. From the Price-Volume-Mix report (PVM), the past impacts regarding any price changes will emerge, providing a view on what could happen in the different buckets of the product and the business portfolio if a price is changed, and what range of price changes could apply for each offering in order to not overly hurt the other contributing elements of the revenue and profit calculation.

The Price Impact Calculator (PIC) will help to answer the question of what a specific price change would mean regarding the targets set by a company, and what this price change will mean for the other elements of the equation in order to reach goals, and/or assess whether or not expectations are realistic or if they may need to be re-calibrated and re-balanced. Last, but not least, price elasticity analysis gives a view on how sensitively a market could react to a change in prices in a particular context. Simply put, if a market is not overly sensitive to a product price, the likelihood that price increases will be accepted is relatively high. If price sensitivity is high, then the risk of switching to alternatives will also be high. The analyses described will add meaningful data to allow understanding of these factors.

PVM and PIC analysis is a relatively straightforward data crunching exercise and can be provided on a regular basis by controlling or pricing teams. The same is true for the revenue and profit maximum trend analysis as a function of price.

The qualitative assessment of price elasticities is, on the other hand, best done in a workshop with stakeholders and experts. This is a once-in-five-years exercise because the underlying rationales and drivers behind price elasticities will usually not change too much over time.

In summary, whereas the analytics would be relatively easy to obtain, the more challenging aspect is the coordination of the tasks, interpretation of the results, and facilitation in the alignment/bringing-all-together workshops. The lead of repricing should, therefore, be with a pricing person privy to the methodology and approach and ideally also familiar with relevant offerings and markets. Hence in house expertise is preferred but, if not available, external consultants should be retained.

Going together through the data and information on major products, product families and/or markets in a workshop-like manner will provide needed transparency and will help senior management and other functions to align behind repricing. At the end of the project/cycle, every stakeholder should have the answer to what, where, how much, and why, when it comes to changing prices.

In sum, the three analytical elements discussed in this article will provide a reasonable and reliable basis for pricing decisions and will often suffice for most repricing purposes. More specifically, specialist analyses can be added depending on business and industry situations.

Inevitably, the advance of artificial intelligence (AI) has enabled the development of ever more sophisticated and data-intense approaches to pricing, often allowing the development of industry-specific software and associated consulting services. It is to be expected that such approaches, based upon sound and established business and pricing principles as outlined above, will increasingly become the norm. However, for the foreseeable future, human input to interpret and guide will be an integral part of commercial decision-making, requiring a sound understanding of the guiding principles and models which allow rational decision-making. An overview of the development and potential use of AI in the pharmaceutical, life sciences, and MedTech markets is available from the author upon request: farago@bluewin.ch.

References

1. Pricing Through High Inflation; Slobodan Farago; Journal or Professional Pricing Q1 2023
2. Price Elasticities Demystified; Omar Ahmad and Slobodan Farago; Journal of Professional Pricing Q4 2021