In previous articles on pricing (see below), we gave an overview of three generally accepted approaches: “cost plus”, “competitive” and “value based” pricing (VBP) and briefly described a methodology for the latter. In management literature, VBP is often expounded as the apex of pricing strategies, the most advanced as well as the most fair, bringing in-line what customers should pay with the (subjective) net benefits they receive. Nevertheless, while value based pricing can undoubtedly be an improvement on many existing B2B pricing approaches, it is far from a panacea and the concept is often abused (as also indicated in the last post on spare parts pricing) and over-simplified. In this article we’ll delve deeper into why VBP is a difficult and sometimes even unrealistic concept to implement, though exercises in “value mapping” and value-based market and customer segmentation are often very important in their own right, regardless of the pricing strategy finally adopted.
Links to previous articles:
Value Based Pricing (includes brief description of methodology for VBP)
A look at Spare Parts (Approaches to spare parts pricing)
Working definition: Value Based Pricing (VBP) is a pricing approach that tries to set prices according to value created and perceived by the customer. In effect this implies a price range between the supplier’s cost (assuming that suppliers will not sell below cost, not counting the special case of loss leaders) and customer perceived value, where value is defined as benefits minus cost (including what is paid to the supplier and any costs incurred beyond that). Therefore, the price that ends up being paid effectively determines how value is shared between the customer and the supplier.
First of all, the concept of VBP is not new, even though it was (re)-discovered among management academics in B2B contexts in the last 20 years or so. Suppose you are driving down the motorway to claim a $1 million lottery ticket that will expire in two hours. The lottery office is still some distance away and you are unsure about traffic. Suddenly you have a flat tire, but no spare and must call the automobile service. What would you be willing to pay for that service provided it arrived quickly and could improve your chances of getting to your destination in time? The link between willingness to pay and value received is, of course, well-known at least since King Richard cried: “A horse, a horse! My Kingdom for a horse!” (Shakespeare’s Richard III) and probably much earlier. Given the prospect of winning a large sum -a gain- most people would happily pay a surcharge related to that gain, considering it somehow “fair”. Suppose however, that instead of claiming a prize, you are rushing to the tax office to pay a fine -a loss- which will double if you don’t get there in time. How would you feel about a surcharge to avoid a loss? In fact you are much more likely to consider such a surcharge as price gouging, even if it is relatively small (other circumstances being equal). Incidentally, this is one reason why vendors have problems implementing what they consider to be VBP in after-sales services, spare parts and MRO: When equipment is down and the clock is ticking, customers don’t like suppliers who try to “excessively” profit from the problem, “taking advantage” as it were. This asymmetric view of fairness (and value) by customers (people) has been experimentally well documented in psychology and presents a first example of basic difficulties in defining, selling or pricing on “value”: Value norms are fluid, highly dependent on context and vary significantly.
Defining value is complex
Value is usually defined as net benefits (to avoid obvious circular logic), but that does little to help pin it down, because the term “benefits” shares most characteristics of the term “value”. Apart from fluid, variable, contextual and subjective, value has also been described as dynamic (changes in time), multifaceted and multidimensional with “operational, strategic, social and symbolic dimensions”. Furthermore, it is often not possible to know whether those perceiving value (e.g. purchasing managers) are doing so as individuals or on behalf of their organization (a typical agency problem) and, of course, different decision makers and influencers have different value perceptions for very legitimate reasons. In addition, value is rarely realized instantly, but over time, adding an element of risk. In the case of services, risk perception is higher, as the service “product” is intangible and is created as it is consumed.
Complexity and difficulty (i.e. cost) in defining value is therefore a main reason why most suppliers refrain from VBP. If value is difficult to pinpoint, it is difficult to operationalize, quantify and measure in a meaningful way, for example by way of indicators. When suppliers do use some form of VBP they define benefit proxies for value, using some form of “value mapping” and methodologies such as conjoint analysis or, lately, data analytics. It should however be clear, that what really is being determined is an indication of willingness to buy and pay for particular product or service attributes which confer subjective benefits to a buyer in specific contexts not inherent value of the product or service (if that even exists). For example, a customer whose neighbor owns an expensive automobile might be more willing to also purchase an expensive automobile and pay more for it, because she derives incremental value from equalizing her social position with the neighbor’s. Or a company with a broad product line, limited available physical space for inventory and rapid response times will probably incrementally prefer and pay more for just in time delivery than a company with only one product line and ample space for inventory. The exercise to determine value can be very valuable (pun intended) in and of itself. Among other things, it can help understand customer requirements better, “hot buttons” of decision makers and influencers, identify ways to meaningfully segment the market and improve product or service design and positioning. It also helps identify mis-priced products or services relative to their attributes and the preferences of customers buying them. Nevertheless it is expensive, runs the risk of becoming an end in itself, producing “actionism” rather than decisions and turning the organization inwards, or producing results that are too granular, time or context dependent to be actually useful. It must therefore be carefully managed and expected costs and benefits of such exercises must be realistically considered.
The complexity of defining value in different environments and contexts in B2B industrial markets, has led to value (benefits) being narrowed down or simplified as profitability impact for the customer -in many guises: productivity gains, performance improvement, avoidance of downtime, lower life-cycle costs etc and the attempt to put forward more strongly those attributes of the product or service that (should) more directly help a customer achieve a specific objective given that customer’s particular context and expressed (or even hidden) preferences. These may include attributes of the service (product or solution) as well as of the supplier, e.g. the supplier’s social proof and brand, references, reliability, ability to handle problems, longevity, strength of balance sheet, innovation capacity or green credentials. However these are seldom sufficiently quantifiable to be used as pricing indicators and usually become sales arguments given the price. Nevertheless, since the overriding goal of businesses is to make a profit and through that accrue benefits to stakeholders, it is rational for suppliers to simplify VBP by focusing primarily on profitability impact (performance and/or cost drivers) and mainly ignore the more subtle aspects of value for pricing purposes*.
It is therefore understandable, though erroneous, that some academics, consultants and management practitioners often conflate VBP with life-cycle-cost- based pricing approaches or so-called performance-based pricing, i.e. pricing linked to performance of the product/service or the vendor itself. It is assumed that as performance (measured by some KPI formula) reaches or exceeds some predefined standard (e.g. Service Level), the value to the customer also increases -either through the impact on cost or performance or both, that is through the impact on profitability. In straightforward cases these approaches include bonuses paid or, less frequently, penalties levied depending on performance against a standard**. The problem with this approach is that it effectively negates the real need for broader value mapping and better segmentation of customers, though customers do try to capture broader aspects of value by widening the set of KPIs to be monitored.
By revealing customer preferences, Service Level Agreements (SLAs) and performance-based pricing often help to change the rules of the game, bend cost curves, and ensure that suppliers allocate resources where it really matters. For example, already in the mid ’90s, ABB changed its approach to pricing after-sales service, maintenance and industrial process outsourcing, by trying to sell “uptime” of equipment rather than “fixing downtime”. This had the added advantage of enabling the company to sell more service contracts, which allowed better forecasting and better allocation of resources. It also allowed a new type of customer-supplier relationship to emerge, built not on a zero-sum (one’s gain is the other’s loss) but a “win-win” basis, where the supplier increases profitability by partaking in the customer’s improved performance. Of course this is easier said than done and numerous problems need to be overcome, including establishing valid base lines, isolating effects of the particular service on overall performance and, most importantly, what to do if the promised improvement is not achieved. Furthermore, it should be noted that the customer ex-ante (i.e. before a contract is purchased) cannot be certain that the supplier will fulfill the promise and it is not easy to quantify the risk. In effect therefore, not only the outcome, but also the price the customer is paying becomes uncertain and most customers require a reduction in that uncertainty. Suppliers usually provide it through some form of guarantee, effecting a risk transfer from the customer to the supplier.
For example, in a much publicized case study from 2009, the service division of SKF, a major premium bearings manufacturer, was asked to bid in a price-based reverse auction for replacement bearings for a major US account. As SKF had taken a decision not to compete on price, but on the basis of total life-cycle costs*** , it faced the dilemma on whether to participate in the auction, effectively reversing its pricing strategy or not to participate and risk severely damaging its relationship with the customer. In the event, SKF won the business by persuading the customer to forego the auction. It was obliged however to provide in return a hard savings guarantee on life-cycle costs, thus assuming customer and product operational risk. While this may have been smart tactics (essentially it shifted the short-term basis of competition to services and quality of the product where SKF had competitive advantage), in reality however, it was an exercise in competitive not value-based pricing: The guarantee took the place of a price rebate, the difference being that SKF calculated it would not have to pay it (based on its superior capability and better (asymmetrical) information relative to the client). Nevertheless such advantages are usually not sustainable. What would have happened if the customer had not cancelled the auction, but had asked other suppliers to match or improve on SKF’s offer by either providing price rebates or guarantees of their own?
The US energy services industry has a sad story to tell in this context, from the late 1990’s and early 2000s. “Energy services” describes a particular business model (so called ESCO model), where energy efficiency investments are paid for via energy savings, an ultimate form of performance based pricing approaches. This might be a good approach for one company, when many take it however, the results may turn out not that well: In the US ESCO case, it was cutthroat competition, ever increasing guarantees, promises that couldn’t be kept and, ultimately, the bankruptcy of many operators. Customers (through the savings) still paid for performance as a proxy for value. However they effectively paid less and less until suppliers could no longer amortize investment costs or afford to stay in the business.
A good plaidoyer for performance-based pricing was provided by Ben Shapiro of Harvard Business School in 2002. In his article he focuses on three benefits of performance based pricing: i) it forces customers and suppliers to cooperate (win/win); ii) it acts like a form of insurance -makes sure suppliers don’t underprice, while simultaneously ensuring that customers don’t overpay and iii) it forces much better communication and understanding between customers and suppliers and therefore contributes to better resource allocation. Nevertheless, such analyses assume fairly static, i.e. non-strategic (in a game-theoretical sense) behavior on the part of customers and suppliers (see below) or differences in bargaining power, which is seldom the case. Furthermore it assumes absence of moral hazard, i.e. a customer changing its operating behavior after outcome risk has been transferred to a supplier. We have dealt with some of these issues in the article Outcome Based Services and Contracting – A Briefing.
Competitive behavior undermines VBP
A fundamental problem of VBP is that, at first instance, it does not take competition or, in particular, competitive behavior into account. If the automobile service provider in the example above used VBP, but faced competition from an alternative provider with a comparable offering who either chose to underbid or didn’t use VBP at all, then the VBP approach would become unusable, if in order to win the business it is necessary to match or improve on a competitive bid . It is possible to modify the provided VBP definition, so that one compares net benefits to a next best alternative (a competitive offering, including internal provision of the service by the customer). This pre-supposes however that the next best alternative and its cost are known (oftentimes not possible in many B2B markets where pricing is confidential) and, more importantly, that the pricing behavior of suppliers is non-strategic in a game-theoretical sense, i.e. is not influenced by the behavior of competitors and will not change in response to competitive maneuvering, something which is almost always not the case and which, as in the case of the US ESCO industry, can lead to extremes (this is because suppliers misjudge and assume too much risk, for example when they link all payment to achievement of a certain performance threshold and then fail to reach it. The value at risk becomes too large and there is an imbalance with expected profits; A systemic problem, e.g. a design flaw or an external change in supplier costs can then prove devastating).
Revealing value and willingness to pay (or willingness to sell at a given price) is not trivial in environments characterized by competitive behavior: Hence the emergence of auctions (from game-theoretical considerations) as a powerful tool to force companies bidding for “rights” (from telecoms spectrum to broadcasting rights) reveal the value of the rights to them. The design of auctions is highly complicated and results are as yet far from perfect, both forward and reverse auctions. Nevertheless they are increasingly used in industry, for instance to acquire rights to drill for oil, rights to supply spinning reserves and backup power to the power grid or rights to operate and manage an outsourced infrastructure service, e.g. a train system or to build and operate solar pv farms and other renewable energy systems. They are also used in large “solutions” procurement contracts in defense and other major ticket industries, or for procurement of commodity items by corporations. The Ariba/SAP electronic market place evolved partly from the idea of a reverse auction service for global industrial buyers. Conversely suppliers of standard products and services (mainly, but not only B2C retailers) use tools such as revenue and yield management systems and other methods, making heavy use of data analytics, to induce customers to reveal their willingness to pay for a product or service in any context, at any time (producing a kind of auction effect). Important here, however, is that the framework (and rules) within which these exercises and transactions take place is or becomes de-facto the same for all players. This convergence is forced either by individual market participants (governments, large buyers or sellers) or the broader market itself.
Note: Data analytics will have a significant role to play in determining willingness to buy and pay, not only because insights can be based on correlations derived from massive data volumes, but also because in contrast to conventional analyses (hard) data provide insights into what customers do not what they say, privacy and confidentiality issues notwithstanding. In addition data analytics can play a role in establishing baselines and isolating cause and effect, i.e. giving participants greater insights on how a particular service contributes to a change in performance.
Problems with price differentiation
VPB is an attempt to tailor pricing to an individual customer’s expressed or derived value perceptions and implied willingness to pay, taking into account competitive alternatives. As indicated, airlines, hotels, telecommunications providers, retailers and others do this in the context of revenue and yield management, taking also into account their own utilization or turn-over factors of (often perishable) capital stock (airline seats, hotel rooms, bandwidth, inventories). Amazon.com is legendary for differentiated pricing strategies based on multiple factors, including analytics derived willingness to pay, which have since been emulated by most online (and even bricks and mortar) retailers at different levels of sophistication. Nevertheless, while price differentiation may have enabled competitive advantage at some point, it is doubtful whether this is still the case or that it contributes to incremental profitability of the retail industry as a whole. As Nicholas Carr said about IT in an article in the Harvard Business Review from 2003, the characteristics and economics of infrastructural technologies (which analytics and supporting technologies are rapidly becoming), make it inevitable that they will be broadly shared—that they will become part of the general business infrastructure, conferring advantage to no one. But toothpaste cannot be pushed back into the tube. Without sophisticated revenue and yield management based pricing systems it is no longer possible to survive in many modern markets.
Interesting reading on Amazon and other vendors’ pricing approaches:
And the story does not end here. Following Newton’s third law of motion that any action produces an equal and opposite reaction, customers are starting to fight back using similar tools. Already many analytics based software agents are crawling the web to find the best deals for customers and others predict price changes on airline tickets or hotel rooms and recommend best times to buy. Digital assistants like Apple’s Siri should be able to accomplish such tasks automatically within a few iPhone versions. As a result, many retailers now simply give best price guarantees, which in turn points to price convergence rather than differentiation in competitive markets
While in B2C markets price transparency and technology creates a convergence in pricing (essentially through arbitrage), B2B markets are, of course, different. Pricing is opaque and usually confidential, which should, in principle, allow more differentiation, e.g. based on value. However customers do act as information hubs and brokers and have access to alternative offerings and so negate, at least partly, the opacity. The main difference, however in B2B markets are transaction structures: Rather than selling to many thousands of individual buyers with negligible individual bargaining power, B2B suppliers sell to far less customers with varying, but possibly very substantial bargaining power. So the role that data plays in B2C, relative bargaining power and buying sophistication, including negotiation strategies, play in the case of B2B. This is based on the fact that (large) customers can make very visible dents on cost structures and profitability of suppliers and, equivalently, suppliers can play a significant role in the profitability of customers. This leads to convergence of behaviors and procurement methods and, ultimately, convergence of prices, or better said, bargaining power and negotiation tactics are better predictors of price than product or service attributes and, hence, value.
VBP is no panacea, nor can it stand alone in pricing strategies. For example, it would be irrational for suppliers to price irrespective of costs. The supplier who has underutilized fixed capacity (particularly perishable capacity in the form of airline seats or service personnel) and does not cut prices to increase utilization, if necessary down to the level of marginal costs, is heading for trouble (irrespective of the fact that capacity might need to be cut to match levels of demand). In a similar vein it would also be irrational for suppliers not to try to tie their compensation to the benefits their product or service provides to customers, particularly when those benefits are the result of superior performance. However neither cost-based nor value-based pricing are done in a vacuum, but in markets with many competitors, exhibiting varying degrees of aggressive behavior. Pricing therefore must fulfill three conditions (in addition to accomodating strategic objectives):
i) must be as low as necessary to drive sufficient demand and revenue to ensure coverage of fixed costs, while also covering full unit costs
ii) must be as high as possible, given the value created, to maximize customer yields (and revenue)
iii) must be flexible, so as to beat competitors
It follows therefore that companies must blend approaches to arrive at best results. The main value of VBP (again pun intended) is to force companies to better understand product or service attributes relative to customer preferences, enabling them to both better design and position products and/or services and make them more appealing to customers, increasing their willingness to both buy and pay and therefore improving their chances of making a sale.
Pricing remains challenging and requires a multi-diemensional approach.
*Widening the list and varying the weighting of indicators in Service Level Agreements is an effort to capture more aspects of value by customers
** The improvement in performance by the vendor is however usually not free of charge and the investment to achieve it must somehow be paid for by the customer. The question then is whether the supplier is better placed to make the investment -in the sense that a better outcome for the customer-supplier system as a whole is achieved more efficiently.
*** SKF promised cost reduction over time through reduced failure rates and associated maintenance and opportunity costs from lost production -net of investment in price differential for premium product and predictive services. Important here, of course, is the time frame, i.e. how soon the net cost reduction will materialize and the associated pay-back period and return on investment for the customer. In fact, life-cycle costing requires the customer to perform a (simplified) investment analysis and evaluate risks.
Titos Anastassacos is Managing Partner at Si2 Partners, a consultancy helping clients leverage services to win in industrial markets
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Categories: Services Pricing