As we discussed in Part 1, The Ends Game argues that today’s organizations are addressing “only half the battle” concerning the central question of “What are we asking customers to pay for?” The other half, which has only recently been made practical by new ways of collecting real-time information, is to evaluate how you’re helping a customer achieve desired ends.
There is another important aspect to answering that question still to address. That is to directly engage the customer’s unique ability to help you be even more efficient and adaptive in understanding what you are asking them to pay for.
FairPay shows how we can more fully enlist the customer as ally in understanding impact, outcomes, and ends, and in modeling value in terms of satisfaction of their needs and wants. FairPay is a rich framework for increased cooperation with the customer in playing the Ends Game. The proven principles underlying that framework make a strong argument for enlisting each willing customer in helping to determine what you should be asking them to pay for.
I do not argue here for the specific methods of FairPay. My point here is simply directional — that the strategies of FairPay point to how the unique wisdom of each customer can help cut through the most knotty challenges of the Ends Game.
The breakthrough in the FairPay framework is to restructure the price-setting process using the continuity and context of an ongoing relationship to get customers to cooperate with you in determining what is a fair price. Why is that vitally important? Because, as Marco and Oded say, “[t]he ultimate outcome, of course, is value…Actual satisfactions.” The customer is the final arbiter of which of their outcomes matter and how much value and satisfaction they deliver. They decide to become and remain your customer on the basis of their perception of value, and of the fairness of your price. You can use all the modeling and impact data you can find, but until you are able to know what is in your customer’s mind, you may not get to the answer that counts.
FairPay begins as a price-setting matter (and so may seem of relatively narrow interest). But price is just the monetary balancing of net value exchange. FairPay works for two reasons:
- Each customer has insights into the value they obtain that you can only understand if they share those insights.
- You can draw those insights out because most customers (especially your best customers) want to be fair about what they pay you — if you gain their trust and motivate their cooperation.
FairPay centers on the point of price-setting, by asking each participating customer to have a say in what the fair price is. How much of a say is determined in the context of the relationship, recognizing that the game of commerce is usually a “repeated game” that involves repetition of transactions over time. A repeated game works best when both parties benefit from cooperation, and so can be motivated to build on that in a virtuous cycle.
FairPay makes that motivation to cooperate central and explicit: “You, the customer, can have a say in what the price is for each transaction, but we, the business, will continue to play that kind of FairPay game with you only as long as we agree that your pricing is reasonably fair. We agree to have ongoing dialogs about value — so we can agree (or not) whether the price for any interval of service is fair.” But wait, there is more…
Price-setting is just the start of how FairPay changes the Ends Game
Price-setting can only be fair if the revenue model is fair. The FairPay dialog is not just about the price, but also about “What are we asking customers to pay for?” The customer has an intuitive, but richly multidimensional, model of what value they want, what value they are getting, and what they think is a fair price for that value. Dialog can surface whatever outcomes or other value metrics the customer thinks are relevant to justify their sense of what is fair for them to pay at any given stage in the game. The business may suggest and counter with any factors that it thinks relevant. This creates a new dynamic that opens up the kind of inter-party negotiating range and nuance that is familiar in traditional bargaining, but with a key difference: the ends of the negotiation are explicitly on lifetime value over the relationship rather than on one-time transactions.
That ongoing cooperation simplifies the challenge of finding proper metrics of value. That effort becomes more nuanced and forgiving, because it is just a stage in an adaptive process that emerges as this dialog unfolds. Different value metrics may be posited by either party. Any working agreement on fairness can be reopened as the context changes and other metrics emerge as more relevant. The exact choice of metrics (and of price) at any point in the game becomes just a working approximation in an ongoing process of continuous learning. The process of identifying and eliminating barriers to access, consumption, and performance is no longer just a process of one-sided inference by the business, but also of asking the customers what barriers they see.
The process becomes more fuzzy — but that is its benefit. Modern business abhors fuzziness as unpredictable and hard to manage, but value to humans is inherently fuzzy. The “proof” of the value is in the customer’s agreement to that value as being fair, not in some abstract mathematical construct of value metrics. Those constructs are only a tool for reaching human agreement.
As Marco and Oded say, “The challenge lies in accountability, which means cultivating the relationship between organization and individual in a manner that is sustainable and mutually beneficial. The right revenue model is what sustains that relationship.” FairPay is a method for enlisting the customer in a process for converging on accountability and agreement on the right revenue model (and adjusting it when needed) — even if that model is a fuzzy one.
Managing an emergent and cooperative process of value discovery
None of this is counter to the lessons of The Ends Game. To manage this process at scale, a business must be able to reduce decisions to algorithms that can be automated in a way that requires more nuanced human judgment only on an exception basis. We need to study the barriers and be creative about finding the right metrics of value and combining them with the right weights.
That is how we evaluate whether the customer’s assessment fair value is one that we should consider fair enough for us to be able to benefit from doing business with them. We work with all the impact data we can glean, and use it the best way we know how.
FairPay dialogs provide a way to work heuristically around the limitations that Marco and Oded describe in how our impact data inform us about the outcomes and their perceived value — when they are not meaningful, measurable, robust, and reliable enough, or lacking in breadth and depth. We build a tentative valuation model for each customer, and use that model to suggest a price that seems fair based on what we know about the value they received. But then, if the customer disagrees with our assessment of value, that is where we work to build in a new level of learning. We can use multiple choice dialogs to ask the customer why they disagree.
The power of FairPay to draw out the customer’s perspective in a trustworthy way can be better understood by considering the three building blocks that drive this process (a formulation Marco contributed to in our journal paper on FairPay):
- Empowerment to participate in pricing. (Asking customers to participate in pricing decisions is empowering, and empowerment is known to foster engagement and satisfaction.)
- Dialog that is open to considering the price in terms of all aspects of value, including needs, wants, features, services, pain points, barriers, and price levels.
- Reputation, as the way to build trust that the customer’s use of that empowerment will be acceptably fair. (Develop a fairness rating for each customer. continuously update it, and use it to decide how to reward generosity and when to warn or restrict customers who are repeatedly unfair.)
This drives the new form of repeated game structure of FairPay, and informs it to serve as a cooperative value discovery engine that iterates to be adaptively win-win (as explained in detail in my book and the many works listed on my blog).
Algorithms can become increasingly effective in understanding how the value metrics of the customer differ from our models for that customer, determining if that is fair, and if so, adjusting our model for that customer, to build a new and better model for them. We can apply heuristic thresholds (simply, or with machine learning) to determine what price is fair enough to continue the game profitably and what is not. We can also draw on human intervention to deal on an exception basis with an ordinarily fair customer that surprises our algorithms by seeming unfair in a given context.
FairPay might appear to each customer as a “bot” that acts as a customer contact who knows them as an individual — representing the business, understanding that customer’s needs and values, interacting with them in whatever way works best for them, and managing the relationship. This FairPay bot serves as an approximation of my value demon, to learn how the customer thinks about value — and to nudge them to see the value that the business would like them to pay for. It manages a 360 degree relationship of cooperation (a much expanded level of CRM), to co-create value in whatever way is desirable to both parties, and to divide the value surplus fairly. (Again, for exceptional cases where the bot hits its limits, human managers can intervene.) This adaptive learning process can drive service improvements, bundling, up-selling, and development.
What the customer knows and thinks
This dialog with the customer about value should be central to all business. How can you expect to understand the value and satisfaction your customers perceive if you don’t ask them? How can you make it easy and natural for them to tell you when you don’t seem to get it?
Sure, even without these formalized dialogs, some subset of customers alert you with complaints about the most egregious outcome problems, but how many don’t bother — because they don’t have an easy mechanism, and they don’t think you really want to hear from them (or that you will not really listen if they do tell you). Instead, it seems that, outside of small, carefully managed focus groups, businesses are afraid to talk with customers, to ask what they think about value, and seek only to talk at them about the wonderfulness of their offerings.
As Marco and Oded say, “There may be factors that contribute to an outcome that the organization cannot observe, measure, or control. To the extent that there are significant differences in the value customers derive from a product or service, then the chosen outcome measure must be ‘personal’ enough to reflect this.” Making that personal enough will be a tall order for the foreseeable future if we only look through a one-way glass, and don’t find a good way to ask the customer for help.
This FairPay process helps to more fully address “…the trillion dollar question…the extent to which customers are willing to share their information with firms and fuel the Ends Game …companies must be able to communicate that sharing one’s data has never been a more valuable investment.” The FairPay repeated game structure seeks to constantly drive that communication and generate the proof to the customer that it beneficially results in value at a fair price. In parallel, you can use whatever impact data you can glean to validate what the customer reports, and determine if they are being honest with you or trying to game the system.
Marco and Oded argue that “when customers know firsthand that an organization can use these [impact] data to deliver the outcomes they desire, it puts both parties in the exchange in an enviable position.” The deeper cooperation of FairPay gives businesses a way to play the Ends Game in a way that is seen to be win-win — to deepen their relationship with those who want to play fairly, and to cull out those customers who choose not to deal fairly. Some businesses, and some customers, may be slow to recognize how powerful this is, but those who do will find new power to co-create and share in value that more one-sided approaches cannot equal.
Opening this level of dialog with customers will take learning and experimentation. But finding the right impact data and using that to build the right revenue model without asking the customer will also be very challenging — especially wherever customer needs and wants are diverse and subject to change with context and time. FairPay points to ways to harness what the customer can tell you, combine it with what you can figure out for yourself, and continuously adapt your revenue models accordingly.
This kind of deeply cooperative relationship can enable you to play the Ends Game more effectively — to attract and retain more customers, make them better customers, and increase the lifetime value that you and they share in.