White Paper: B2B Pricing
Pricing has the power to rapidly increase or decrease margins. However, for many executives today, pricing remains a paradox. There is often uncertainty about how to set the correct price proactively based on the customers’ willingness to pay (WTP). Furthermore, the vast amount of accessible data, the enormous range of analytical possibilities, and increased purchasing power make the decision increasingly more complex. It is therefore important to highlight what it takes to enable AI-assisted pricing and what pricing options exist. In this White Paper, Porsche Consulting presents seven challenges in pricing and shows how AI can successfully help to overcome these. It provides pragmatic guidance on how to elevate pricing along three main building blocks from the overall pricing strategy and setting the right prices through to sustaining them – for top decision makers as well as for operational staff. In addition, case studies illustrate the achieved business benefits and demonstrate the cross-transfer potential of the methodology.
- 01 | Changing market conditions and tense, competitive environments increase global price pressure
- 02 | Vast amount of data availability and complexity require technological support through AI
- 03 | Due to the rising complexity long-standing practices of many B2B pricing organizations are being challenged and need to be adjusted now
Pricing has the power to rapidly increase or decrease margins. However, for many executives today, pricing remains a paradox. It is often uncertain how to set the correct price proac- tively following customers’ willingness to pay (WTP). What is more is that the vast amount of accessible data, an enormous range of analytical capabilities, and increased purchasing powers when negotiating make the entire phenomenon more complex. In a recent Porsche Consulting survey, nearly half of the participants (46 percent) do not have a clear and reli- able pricing strategy to tackle the pricing challenges lastingly1. Pricing, however, can be a substantial source of exceeding market growth, especially when using artificial intelligence (AI). Organizations that get the most from pricing are excellent in the basics (e.g., price set- ting, discounting, and monitoring) and build expertise in AI- and data-driven pricing solutions. Therefore, it is of importance to highlight what it takes to enable AI-assisted pricing and what pricing options exist.
"What do you associate with artificial intelligence (AI) in pricing?” – There are many different expectations and correlations. Some consider AI to be overestimated in recent years and not yet implemented into business-to-business (B2B) pricing practice. Some B2B companies are already success-fully using this application with a significant impact; it is safe to say it is a game-changer. With the help of AI-based pricing and subsequent machine learning algorithms, companies can optimize the pricing structure by aligning supply and demand in an age where market conditions and competitions are intensifying. Moreover, it helps with respect to price variants—when, for example, many products that are close to commodities such as dairy products (e.g., container shipments of milk powder) can be sourced globally. This requires suppliers to set their prices in real time considering global competitor prices and supplies together with their specific advantages and disadvantages, like closeness to a specific customer or quality advantages. Another example is the procurement process of many large corporates that requires supplies to make fast price quotes in automated bidding processes. All this can best be managed using an AI-based pricing system.
The application in B2B commerce is still relatively weak. Pricing is often carried out using Excel sheets with a basic price, various quantity scales, standard discount lists, and many customer-specific prices. In a globalized world, prices are no longer negotiated regularly or remain fixed until the next negotiation. A considerable part of the margin is left, thanks to rigid price cycles. In general, B2B companies often struggle to determine a price in line with the market for a product or service, not knowing how customers will react to price fluctuations and changes in the portfolio. External events and their effect on customers’ buying behavior and WTP are difficult to predict. The main issue with accurately setting prices is the complexity of the available internal and external volume of data. Here, AI and machine learning have numerous benefits. The foundation is a solid database. Aside from analyzing large amounts of data in a brief period, AI recognizes patterns and derives pricing insights and a customer’s precise WTP hard to achieve through manual simulations. That is how, despite all the obstacles, companies can use AI to stay ahead of the competition in pricing and achieve a decisive competitive advantage.
The key advantage of AI systems lies in their ability to consistently evaluate large amounts of pricing data in a short period of time and draw inferences on how to best set situation-specific prices and to quickly adjust to changes in the market. However, here also lies the key disadvantage of AI systems, as they can only be as good as the data they are fed and how their insights are implemented. Therefore, for an AI system to function properly, it needs to be well integrated into the pricing organization.
Porsche Consulting’s AI-based pricing definition: AI in pricing supports the continuous adjustment of portfolio and prices to demand, market, or competitive changes using machine learning algorithms.
We have identified these seven pitfalls:
//PRICING IN CHANGING MARKETS
- CHALLENGE 01 | International trading makes profitable price management more complex
- CHALLENGE 02 | Product life cycles have shortened and more frequent price adaptions are needed
- CHALLENGE 03 | There is greater market transparency and easy access to any type of price information
//PRICING IN CHANGING BUSINESS BUYING
- CHALLENGE 04 | There is a higher demand for flexible pricing models, shifting from selling products to selling as a service
- CHALLENGE 05 | There is a higher usage of multiple channels, which requires holistic pricemanagement across all channels
//PRICING IN CHANGING DATA CAPABILITIES AND NEEDS
- CHALLENGE 06 | There is a greater need for profound pricing fitness due to the vast amount of data
- CHALLENGE 07 | There is a need for solid pricing organizations and suitable procedures
Using AI to overcome these challenges, Porsche Consulting presents its transformed framework to precisely predict future prices and portfolio adjustments with confidence, despite complex and extensive alterations. To do so, a common ground for pricing in B2B is set.
Essentially, in B2B pricing, customer specifics matter more than in business-to-customer (B2C). This is because organizations tend to have far fewer B2B customers and these have specific needs that need to be addressed when configuring offers and setting prices. On the other hand, the value of a product or service provided to a business customer, be it in the form of savings or an increase in sales, is often easier to assess than the WTP of consumers. What is more, relevant pricing data is often unavailable. Customer insights (e.g., con-joint studies), competitor price information, and valuable sales data are often unavailable in B2B companies. For example, many companies do not systematically collect data on their won or lost deals. A sales representative with a high average selling price might be losing too many deals and is not a good reference point for others. Lastly, the impact of the company’s sales team on pricing is often challenging to assess. Suppose different sales managers achieve different prices for the same products. In that case, this can result from differences in their customer bases, pricing and negotiation capabilities, or the value they offer to their customers.
Addressing challenges in B2B pricing
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International trading makes profitable price management more complex
Today’s world economy is ever more open and integrated—international trade has grown and continues to proliferate. In terms of pricing, this means the differentiation of factors and preconditions from country to country and effects on price setting in an international context. These changes are caused, among other things, by the purchasing power, tax burden, currency exchange rates, or condition system of countries. According to the Porsche Consulting Pricing Study, 19 percent of all B2B companies see these market changes as one of the biggest challenges for B2B companies in terms of pricing.2 How can a company adapt its international pricing approach individually and how can AI help?
Among other aspects, it is essential to focus on parallel im-ports. This means setting aligned price points for each country so that unwanted flows of goods are no longer profitable. B2B companies should define an overall price corridor to balance differing price levels and avoid profit losses as well as parallel imports (see Figure 2). International price harmonization must be the aim at all times, taking into account the different characteristics of the respective national markets. Currently, such price corridors are often defined too narrowly and are too undifferentiated amongst different product groups. Here, AI can support setting more tailored price targets by critical account or customer segment based on specific needs. For example, by analyzing a critical account’s global purchasing patterns, AI can identify thresholds for the emergence of grey market activity and suggest appropriate price corridors.
In this respect, a focus must be set on the (international) pricing strategy. This means developing a structured pricing approach to pave the way for achieving the longterm business objectives. From that, an international pricing strategy must be derived across all markets and sales companies to ensure a consistent price positioning and minimize the risk against re-importers and grey market dealers who conduct a cross-border arbitrage business. International pricing must proactively counteract this development. Local sales potential is set against the long-term grey market risks and ensures the ability to act locally. To keep the grey market risk acceptable, two solutions for intragroup price setting are standard: centralization of pricing authorities and decentralization. When centralizing, specific target prices are set for each product (group) and monitored compliance with the target price. Usually, prices are reported by the national pricing/sales manager to the headquarters, e.g., via Enterprise Resource Planning (ERP), which directly shows deviation. When decentralizing pricing authority, general guidelines are defined and established. Here, AI can support the organization in establishing consistent global pricing across the organization—not in price levels, but in the method that determines the prices. This can allow an organization to benefit from the best of two worlds: the use of specific (decentralized) insights that are assessed with a (centralized) standard machine-learning method. This typically allows prices to be set that are 4 to 7 percent more profitable than prices set manually via Excel.3
A grey market refers to all distribution channels used outside those intended by the manufacturer. On unplanned distribution channels, such markets are created by lower purchase prices. The price difference is made possi-ble, among other things, by the global arbitrage opportunities offered by different countries and the subsequent re-import of the products.
Product life cycles have shortened and more frequent price adaptions are needed
A product’s life cycle portrays the length of time a product is in the market, from the beginning of its introduction until it is removed and phased out. This cycle is often divided into three phases: launch, midlife, and late-life. Due to re-cent developments, product life cycles have been becoming ever shorter, especially for technology-based products. De-pending on the relevant stage, companies must adjust their pricing strategy to achieve their desired revenue and margin (Figure 3). Yet, according to the Porsche Consulting Pricing Survey, one-fifth of the B2B companies do not adapt their prices to the stage of the life cycle at all (19 percent).4 Un-fortunately, great potential is unused.
When done right, the focus must be set on intensive market analysis when introducing a product or service (launch phase). A long-term pricing strategy before entering that life cycle stage must be defined. In the introduction phase, companies usually set the price to attract the most revenue.
Within the midlife phase, a decision must be made whether to adjust and fine-tune the price or not. Often, the price strategy transits to take market share away from main competitors. Potential threats by internal and external triggers must be identified, such as market trends or new pricing models expected by the customers. When a product or ser-vice is mature (late-life phase), the focus should be set on adjusting price points to suit more customers in the market. Here, AI can better deal with the different challenges in each stage and suggest product and price changes accordingly. For example, the preferences of early adopters can differ significantly from those of late adopters. In the launch phase, pricing AI helps predict how customers react to different offers and prices and suggest the best growth trajectory. Then at later stages, the AI determines the best pricing response if competition intensifies, considering customer preferences and observed competitor tactics.
There is greater market transparency and easy access to any type of price information
Traditionally, B2B sellers have a list price and implement pricing per individual customer segment for companies with the most significant price sensitivities. So, while there might be a public price listed, negotiated prices are different from buyer to buyer and prioritize for frequent, high-volume purchasers. Ultimately, this is how B2B sellers have historically driven their profits. Nevertheless, platforms such as Amazon Business offer a new kind of price transparency, which has become a severe threat to traditional sellers. Customers to-day are increasingly self-informed. Amazon Business offers a single, competitive public price for all buyers. In a 2018 study conducted by WBR Insights, researchers found that 87 percent5 of B2B buyers already buy regularly through online mar-ketplaces (see Figure 4).
This is not a problem for core products where big companies have negotiating pricing with their suppliers. However, it is a problem for sellers of long-tail products whose buyers are less price-sensitive; buyers have less market awareness of these products due to price opacity, which creates incentives for competitive, transparent pricing. So how can B2B sales organizations provide the necessary and expected price in-formation? At this crucial moment, accessible price points of competitors such as list price, ship price, buy-box price, out of stocks, geography, and product reviews and ratings are increasingly becoming available. However, with more available data comes the responsibility for more capable analysis. Here, AI pricing tools help B2B sales organizations better structure and interpret relevant data and provide deal-specific offer recommendations in line with their overall strategic objective.
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There is a higher demand for flexible pricing models, shifting from selling products to selling as a service
For several years now, the buzzword recurring revenue management, i.e., converting one-off payments into regular payments, has been circulating as an idea. However, while this has often been a rough idea so far, many B2B industries are starting to work with different price models. According to the Porsche Consulting Pricing Study, already 26 percent of the surveyed B2B companies are charging their customers based on usage.6
These new as-a-service-models have been around for a while: Rolls-Royce introduced its Power by the Hour pro-gram, pricing their Viper aircraft engines based on flight hours, back in 1962.7 More recently, Hilti, a leading maker of portable power tools, introduced a time-based, subscription model. The company offers a tool fleet program that provides contractors with access to a varied assortment of tools for a fixed monthly fee. The model eliminates up-front investment and covers repairs, loaner tools, and even theft.8
As a rule, the customer pays a monthly price that exceeds the one-time payment after three to four years. The bottom line is that these “new” pricing models must increase customer benefit because they correspond to the way customers calculate for themselves and better distribute risk. While several alternatives exist, B2B company executives are too often not informed of all the options or are reluctant for change regardless of how it is working. So how can B2B pricing leaders do better? Based on historical data, current pricing models can be evaluated and trends predicted for the future. AI can detect the optimal pricing model by examining customers’ WTP for a product or service and their instant reactions in terms of profit, revenue, and market share to different pricing models.
Assessing different pricing models is difficult and is currently most often done using an Excel model specifically built for each business case. Such models work with simplifying assumptions (e.g., constant price elasticities) and can only provide a rough ballpark comparison of different business models. Using AI technology, different business models can be assessed with realistic customer reactions. This allows for a higher precision in the prediction of customer reactions and the selection of the most profitable business model—with high reliability.
A price model refers to the manner (metrics, frequency, amount) in which the customer is charged for the product or service.
There is a higher usage of multiple channels, which requires holistic pricemanagement across all channels
Across all industries in B2B, the customer journey is becoming increasingly digital9 and the online business is recording strong growth. Conventional methods cannot keep up with the speed and multitude of factors impacting the market. In B2C, Amazon, for instance, changes prices around 2.5 million times a day.10 What began in the B2C segment has recently started to have an enormous impact on many B2B prices. The enormous price dynamics and the increased price transparency quickly brings the entire sales and pricing system to its limits for many B2B companies. It inevitably becomes more crucial to know what customers would be willing to pay through which channel. Yet, according to the Porsche Consulting study, 66 percent of B2B companies do not use systematic methods to determine their customers’ willingness to pay across different channels.11
The consequences: cannibalization of different sales channels, a slipping of the price level, the delisting of one’s own products from long-term retail partners, and damage to the manufacturer’s brand. Here, naive AI-based price elasticity measurement built on regression models per sales channel often lacks the necessary precision for good price decisions. This is particularly the case in a compound situation involving many products and competitor offers, as cross-price elasticities are challenging to measure.
However, how can businesses integrate their multichannel strategy effectively into their pricing strategy? The decisive factor is that the entire product portfolio with all its price differentiation along every individual sales channel and their conditions must be coordinated at one central hub. This hub must be efficiently managed using AI, with prices constantly differentiated according to the sales channel.
If such a price differentiation is implemented and segment- or even customer-specific prices are set, the profit can be maximized, as the highest possible revenue is skimmed off. As a prerequisite for adjusting the price and conditions system, historically grown discounts, bonuses, or special prices must be ended at the customer level.
Price differentiation refers to charging different prices for (nearly) the same product or service depending on certain differentiation criteria (e.g., sales channel).
To increase revenue through pricing (Figure 5), it can be generally concluded that sales channels should differentiate prices. Segmentation is nothing new in sales, but AI can correlate customer information in a well-structured database. Here, AI can be applied to differentiate prices based on sales channels (e.g., online, offline, third party), customer properties (e.g., industry, size, negotiation behavior), or deal situation (e.g., expected competitors, urgency).
To implement price differentiation correctly, B2B companies need to estimate the individual WTP of their customers. AI helps to differentiate prices along with specific criteria such as demographics, respectively in B2B firmographics, purchase location or time, quantity purchased, or used sales channels. Particularly in the introduction and test phase of new sales channels, AI in pricing can help define the number of products and observe the reactions of customers’ WTP; only then should the next expansion stage be ignited.
The WTP of different virtual customers can be derived from historical data. It is not only necessary to analyze price elasticity for individual products in isolation; rather, the relationship of the products in a product line or the entire product portfolio must be understood, and cross-price elasticity evaluated. Here, AI-based technologies are necessary to measure the effects of portfolio changes on revenues, profit, and market share. Not only the effect can be measured, but also specific business recommendations and implications are derived based on the pre-defined objectives.
The better sales leaders know their customers’ needs and use automated pricing, the easier it is to offer personalized products and services that attract, inspire, and retain B2B customers.
Virtual customers are models of real customers that make purchasing decisions just like their real-life counterparts. Their preferences are estimated based on a range of different data including sales, transaction, or survey data.
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There is a greater need for profound pricing fitness due to the vast amount of data
Digital tools and platforms are disrupting B2B sales and forcing players to rethink their pricing strategies fundamentally. And there is a need for action. According to Porsche Consulting’s recent B2B Pricing Study, 33 percent mentioned that weak digital tools and systems drove their failure of pricing initiative (see Figure 6). On another note, 56 percent do not consistently make decisions based on valid and innovative pricing data tools.12 Although interest in AI-based pricing continues to grow, leaders tend to have a vague understanding of what it is and what its actual benefits are.
So how can such B2B leaders take advantage of the tremendous amount of data and be future pricing-ready? As this paper states, AI-based pricing allows B2B companies to understand better and predict when to push prices higher or lower or extend or shrink the portfolio to maximize profit, revenue, or market share. AI helps to improve and speed up decision-making while providing more granular insights by analyzing a large amount of data and offering rich, fact-based, and consistent guidance on product- and customer-specific pricing. All this helps to take maximum advantage of the vast amount of data and efficiently and reliably take AI-based pricing recommendations into profound decision-making. AI-based pricing tools offer pricing suggestions rather than for-mal directions and provide the rationale behind them. Organizations then can stay on top of what are often very complex product portfolios. This helps teams prepare for negotiations while giving salespeople control over the ultimate negotiated price. Incentive structures need to be readjusted so that salespeople are rewarded for following the recommendations. This means compensating representatives based on the results of recommendations generated by the pricing tool.
There is a need for solid pricing organizations and suitable procedures
According to the Porsche Consulting Pricing Study, only 48 percent of the surveyed companies have established their pricing organization as a central business unit. In contrast, the others organize a decentralized business unit in each region/division (26 percent), or a hybrid pricing organization (21 percent).13 That is why a central decision-making authority for pricing along all products or services is often missing. So how can a durable pricing organization be established and interdepartmental conflicts minimized? Through the establishment of a suitable pricing organization taking into account the corporate and environmental conditions (structure of the sales organization, level of heterogeneity and complexity of the products, part of a corporate group, international pricing structure, level of competition), the right skills can be dedicated towards a sophisticated pricing approach. Regardless of the geographical setup, the organization needs to include the following core elements: a local, regional, global pricing office that maintains the pricing machines, constantly monitors price and sales performance, tracks nonconformity from pricing models, develops suggestions, and advises management on price evolution (see Figure 7). Such an office needs to combine traditional roles (industrial) with new roles (data scientists), who bring relevant analytic skills.
Standardized pricing processes are needed that start with market intelligence and other pricing inputs and end with granular, AI-based price recommendations, actions, and execution monitoring. A transparent pricing calendar with milestones helps ensure flawless execution and that pricing happens proactively based on the forecast. Pricing performance can be managed using AI tools by defining targets on pricing, margins, and profitable growth, not only on volume acquisition and developing a simple but effective dashboard. Market dynamics can be continuously monitored and adjusted to exploit optimal prices, observe new entrants and offerings.
Delivering on these challenges will prove to be a tricky balancing act for pricing organizations. Not every B2B company will become the next prominent pricing pioneer. However, every company must identify its sweet spot and craft a future-proof pricing strategy, making maximum use of current technological possibilities and translating this into a new target operating model.
A dedicated AI-based pricing excellence program can have a significant impact on some of the aspects stated above. While the challenges vary across sectors, several activities can be observed that are required for all pricing organizations. This framework presents three main blocks that provide an individual path towards pricing excellence (see Figure 8).
Build a winning pricing strategy. Derive a pricing strategy from the overall corporate strategy or sales and marketing strategy. It’s about building a consistent storyline around the price, the value you deliver, and the premium you deserve for your products and services. The Porsche Consulting Pricing Study clearly indicates need for action: nearly half of the surveyed B2B companies do not have a clearly defined pricing strategy (46 percent).14 Think of an investment in pricing as an investment in the future. Set clear pricing objectives. Real pricing pioneers are true to their top line. Consider overall pricing architecture along with all sales levels and markets. Have a precise market positioning. Again, this positioning should reflect your corporate strategy. Design your pricing method accordingly. Find ways to directly monetize your products and services (price model design) and do not forget to constantly communicate this value. If you act in an in-ternational context, adjust pricing individually and especially keep the international pricing strategy, parallel imports, and exchange rates in mind. To conclude, it is crucial to develop a structured pricing approach to pave the way for achieving your long-term business objectives.
Analyze the market. Specifically, look at cost-, company-, customer- and, competition-related information before setting prices. Gaining an in-depth understanding of the importance of pricing is what makes you best equipped to guide your sales teams toward healthy price levels, prepare thoroughly for price increases, and ultimately lead your company to high-er revenues and profits. More than half of the companies (51 percent) surveyed in the Porsche Consulting Pricing Study do not know the willingness to pay of their customers to its fullest extent.15 That is why our profound AI-based pricing approach (which will be explained in the next chapter) provides an unique AI to set optimal prices within an optimal portfolio. To differ prices amongst segments (such as products, sales channels, customer geo data, etc.) offer maximum capture value. Evaluate your current conditioning system and, if applicable, adjust. Think about methods to tactically enforce your prices on the market; give your customers reasons why they should pay the desired amount of money. Analyze grant-ed discounts and rebates following the performance. Assess your current portfolio pricing and take measures with regard to the product mix. Understand your customers and monetize the value of your products to fully exploit the profit potential.
Monitor prices regularly. Create an empowering environment to enable a long-term and successful implementation of the planned pricing approach. Structure and optimize your existing pricing processes to create perfect conditions for AI-based technologies. According to our Porsche Consulting Pricing Study, there are still B2B companies that do not use any defined KPIs to steer their pricing processes (16 percent).16 Focus on data management. One thing is certain: AI-based technologies will not reveal their full potential if the pricing basics are not fulfilled. If the price targets are not de-fined, or the data are of low quality, AI cannot compensate for this deficiency. The best pricing strategy and determination are useless if the necessary conditions for successful implementation are not in place. Develop a long-term and straight-forward approach for life cycle and model hierarchy pricing. The appropriate conditions must be created to be able to use AI in a target-oriented manner. This also includes a skillful, competent, and dedicated pricing team. Define the organizational integration of a competent pricing team in line with your general organizational structure (divisional, functional, matrix). Show a high commitment and recognize the strategic importance of pricing, and inspire others. In summary, build a competent pricing team to first optimize all pricing processes and then use suitable pricing tools.
Pricing is straightforward—if the costs, competitor, and customer’s WTP are known. Most companies know their costs, and in some B2B industries, competitor prices are accessible.
Measuring customers’ preferences and WTPs with the necessary precision is complicated.
To better help clients advance their pricing capabilities, the pricing approach is transformed from traditional to AI-based (see Figure 9).
Porsche Consulting conducted a survey with 57 B2B companies, which revealed that 66 percent of companies don't even consistently use systematic methods to determine their customers' willingness to pay—not to men-tion artificial intelligence.
At its core, the Porsche Consulting approach identifies the optimal portfolio to the optimal price point. It not only identifies how each factor drives revenue, profit, or market share individually but how they interact. Inferences about the distribution of preferences among customers are measured (see Figure 10). This information is then used to create virtual customers that behave like their real-life counterparts in a specific purchase situation.
The virtual customer technology is an AI-native pricing and product development tool. Using different data sources including sales, transaction, and survey data, the system determines real customers’ preferences and willingness to pay for different products and product features. Using these insights, the platform then creates virtual customers that make purchasing decisions just like real customers. The AI system can then test large numbers of different offers and price combinations by looking at what the virtual customers buy. This provides precise predictions of how actual customers will react and allows the user to select the offer and prices that maximize profit, revenue, or whatever the user is aiming for.
By observing what these virtual customers would purchase when shown products at different price points, price managers gain a much more precise understanding of what quantities real customers would buy at different price combinations. Depending on the initial target, profit, revenue, or market share for the entire organization can be optimized. An improvement of three to five percentage points in return on sales (ROS) can be achieved.17
To illustrate the virtual customer technology, consider the following example. An industrial machinery manufacturer offers two different drilling machine variants, A1 and B2, to a customer from the automotive supplier industry. The virtual customer model is trained using the past offer data of the manufacturer across industries and customers. This includes information on the machine specifications and sup-porting services offered, the prices asked, the sales situation including details on competition, and the procurement process. The model quantifies customers’ WTP for specific product features or supporting services. It also determines how preferences differ across industries, customer sizes, and other relevant dimensions by assessing previously won and lost deals. For example, if customers from one industry value a higher service level more than customers from another industry, they will—if all else remains constant—be more likely to buy the product with the higher service level. The machine learning algorithm can extract such insights given the complexities of real-life offers, and in cases where data for specific market segments are scarce, insights from other sources such as surveys or expert insights can be added.
The trained virtual customer model then assigns purchasing probabilities to individual products in a portfolio from which a customer can choose. These purchasing probabilities de-pend on the specific customer, the sales situation (e.g., competitor offers if known), and the prices at which products are offered. Figure 11 shows the purchasing probabilities for the example products A and B. The purchasing probabilities are interdependent. If the price of product A is changed, it also affects the purchasing probability of product B.
Figure 12 shows the expected profit for products A and B derived from the purchasing probabilities from Figure 11. It is worth noting that if the portfolio of the two products is in the overall profit optimum, then both individual products are typically above their individual profit optimum.
The challenges for B2B pricing will continue to grow—as will expectations to overcome them. There are no simple solutions. Nevertheless, organizations that do not adjust their B2B pricing organization to the new requirements and use the AI possibilities outlined in this paper will fall behind their competitors and may never achieve pricing excellence.
Today’s market environment and the rapid digitalization are the biggest challenges for B2B companies in terms of pricing. Markets are transforming and new global price pressure and competition put tremendous pressure on traditional B2B pricing organizations. And action needs to be taken now: The Porsche Consulting Pricing Study shows that the pricing maturity of B2B companies is rather weak. There are even a few companies that do not use any defined metrics to steer their pricing processes. And more than half of the surveyed companies do not use dedicated pricing software throughout their entire pricing process.20
Yet, the boundaries between B2C (business to customer) and B2B (business to business) are already blurred and are likely to disappear in the years to come. This will inevitably influence pricing organizations. Potential revenue is not generated due to the rather traditional pricing approaches in the B2B industry. The vast potential for technological advancements in pricing through artificial intelligence (AI) is unused. That is why B2B pricing must now alter beyond traditional limits to safe long-term profit, revenue, and market share.
Porsche Consulting proposes three building blocks to master these challenges, benefit from the opportunities, and achieve pricing excellence. The framework is a practical blueprint to guide sales organizations to pricing excellence in today’s world.
- 01 | The boundaries between B2C and B2B are already hazy and are likely to disappear in the years to come; this will inevitably influence pricing organizations.
- 02 | Markets are transforming and new global price pressure and competition put tremendous pressure on traditional B2B pricing organizations.
- 03 | There is a vast potential for technological advancements in pricing through artificial intelligence (AI).
- 04 | B2B pricing must now alter beyond traditional limits to safe long-term profit, revenue, and market share
- 05 | Porsche Consulting proposes three building blocks to master these challenges, benefit from the opportunities, and achieve pricing excellence.
(1) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(2) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(3) Buynomics (2021): Key performance indicators
(4) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(5) Mirakl: The next generation of B2B Purchasing. Across the ages: B2B sellers aren't meeting modern buyer needs. https://info.mirakl.com/the-next-generation-of-b2b-buying
(6) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(9) See Porsche Consulting’s paper on “The Future of B2B Sales”
(11) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(12) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(13) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(14) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(15) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(16) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries
(17) buynomics Pricing Tool
(18) buynomics Pricing Tool
(19) buynomics Pricing Tool
(20) Porsche Consulting Pricing Survey (11/2020): 57 B2B companies operating in 8 selected industries