Learning the New Logic—Low-Risk Testing, Sweet Spots, and Continuous Adaptation
How to start? Find the sweet spots. Established businesses will want to do controlled testing in a restricted environment to learn how these methods work and tune the process before putting mainstream revenues at risk. Even start-ups will want to move in managed stages. This chapter explores some strategies for doing that, and growing from there.
Be sure to keep an open mind. The starting points proposed here need to be tested. The paths to increasing sophistication and variations in the next chapter should be kept in mind as well. Such enhancements might turn out to be important to achieving success early on.
Perhaps the most promising areas to consider for proof of concept testing in small, controlled trials—that do not put profit or reputation at much risk—are the following three:
Retention—win-back offers to subscribers who are asking to cancel (but had previously seen value).
Acquisition—offers to potential new customers in selected segments that might see new value.
Premium “club” or “patron” tiers—offers to existing customers known to recognize the value.
Other possibilities include selection of test populations in any way that limits risk and targets segments that may appreciate the value of the offering and the personalized value proposition, such as:
Usage or style segments
Content segments (such as long-tail items, or by genre)
Device segments
Family plans
Segments that can highlight “deserving” sellers
Trials, specials, coupons
Distinct branding or white label offers.
Find the Sweet Spots
A key factor is that FairPay involves behavior change by consumers. It is a shift back to behavior norms that are natural and were common through much of history, but still a change, and some customer segments will adapt more readily than others. Some will be slow to shift from zero-sum thinking, while others will jump at the opportunity. The trick will be to find low-hanging fruit—lines of business with customer segments who are most disposed to welcome this new logic.
The best customers to start with will be those who are disposed to generosity. That may include “superfans” who are loyal and perceive high value, and loyal customers of providers who demonstrably deserve generosity for delivering high quality, service, and social value.
The best prospects will also be disposed to strong cooperation—receptive customers who are thrilled to share pricing responsibility—and are willing to bear some modest burden to do that right.
That will serve as the thin end of the wedge of behavior change. The initial strategy should be to find these sweet spots and do low-risk controlled tests there. Businesses and customers will begin to see good results and learn how to apply this kind of adaptively win–win strategy. That will generate a viral effect to attract more customers to the game, and lead a growing range of firms to create FairPay zones. (Background on the behavioral economics of this is in Chapter 20.)
Retention: Winning Back Lost Customers Before They Get Lost
For existing subscription businesses, retention is so important and such an ideal place to do limited testing of FairPay that it may border on irresponsible not to seriously consider testing that. It is widely accepted that retention is the key to profitability.
Keeping good customers is increasingly central to maximizing profit and customer lifetime value (CLV), as highlighted in the March 2016 HBR Idea Watch “Winning Back Lost Customers” (Greig 2016). Companies are realizing that customer acquisition is very costly, and saps profits if churn is high. Win-back offers must be personalized more smartly, to present the right value proposition to the right customer.
Retention win-back offers are already made on a one-to-one basis to selected customers who are already well known to the business, and have a record of valuing the product. The problem is not their failure to recognize value, but a misalignment of perceived value with the set price—that is exactly the problem FairPay is designed to solve.
Because win-back offers are often made one-to-one, typically using costly human agents, testing of FairPay can be limited, controlled, and done with varying levels of automation. This enables careful selection of test subjects, tight control of offer policies, and use of semiautomated, simple decision rules prior to investing in more complex automation.
Win-back offers are naturally viewed by both parties as trials, so tests can be framed as limited-time special programs that can be ended at the business’s discretion.
So, why not try enhancing win-back efforts:
Automatically create personalized win-back offers that are adaptively win–win.
Shift from reactively trying to win back customers who are almost lost—to proactively enhancing the customer journey to retain them before they get lost at all.
FairPay can initially be trialed as adding a new, more automated, and more efficiently win–win tool. But FairPay leads to a deeper breakthrough—to put proactive retention directly into every customer journey loyalty loop.
We know that an ounce of prevention is worth a pound of cure, but any retention strategy is just a belated attempt to cure a customer’s dissatisfaction with the value proposition after it has become a serious problem.
When proactively integrated into the customer journey loyalty loop, FairPay creates ongoing dialogs about value, so that potentially good customers are routinely engaged to jointly craft personalized value propositions, long before they approach the point of being lost.
Just as smart marketers are building loyalty loops into every cycle of the customer journey, they can do the same for value and retention.
Think loyalty loop = retention loop.
Key retention tasks are selecting the right customers to try to retain (using propensity models), and finding the right value propositions to present successful win-back offers tailored to those individual customers. Key success parameters for a retention strategy are both cost and return on investment (ROI). Typical options include discounts, service upgrades, and combinations of the two, and results can be enhanced by tailoring the nature of the offer—and whether to make an offer—to the reasons a customer seeks to cancel. The HBR article gives the example of how Cox Communications is getting smarter about customizing its retention offers to individual customers of its cable TV and Internet services, and triggering such offers at the right points in their customer journeys. A big step in the right direction—of customizing the pricing and features of their offers—but still doing it in a costly manner, relying on human intervention, on a reactive exception basis.
Consider two stages of development: first at doing this better and more automatically, and then at doing it more widely and proactively.
Retention Level 1: Adaptively Customizing Retention Offers—Try More Effective Pricing for Those Who Are Demanding It
First consider how FairPay can enhance conventional win-back offers. FairPay generates an ongoing multivariate pricing experiment with each customer.
FairPay can be offered to limited numbers of customers seeking to cancel, framed as a special trial offer that will be continued only if they price fairly, and only if enough other customers show that they, too, will set prices fairly.
FairPay retention options can be offered selectively to known customers, based on data that suggests which ones are worth keeping and which ones seem most likely to demonstrate the positive social value orientation that will lead them to set prices fairly—using strategies along the lines outlined in the HBR article.
Selection criteria might further isolate testing of FairPay to those customers with a history of relatively light usage—for which discounted prices would be fair—a strong incentive, and still very profitable.
FairPay can be tested in controlled populations, and framed as a special experimental offer, to minimize risk—a privilege that will be continued only if enough customers cooperate, and revocable for any customer who is not reasonable.
It is essential to be smart about what offers to make to which customers. FairPay offers an automatically adaptive way to do that, with offers that are flexible and constantly tuned to individual customer value perceptions.
Customers seeking to cancel are sending the message that the conventional value proposition does not work for them. The way to retain them is to find a profitable value proposition that does work for their particular situation and point in time. FairPay offers an automatically adaptive process for doing just that.
This first stage effectively provides a low-risk test—to try more effective pricing, but only for those customers who are about to leave if they do not get something better. Subscription businesses face not only simple questions of price, but also the dilemma of whether to apply usage-based pricing (which consumers find unfriendly and subject to nasty surprises), or all-you-can-eat pricing (a one-size-fits-all price that is too high for light users and too low for heavy users). FairPay’s adaptive pricing can work better than either option, adding its new kind of value focus to blend the best aspects of both plans in a dynamically adaptive way. Those customers who reject the conventional pricing are a natural place to try this new and more flexible alternative on a limited basis.
The beauty of FairPay retention offers is that they are self-adjusting. Once you learn how to set key business rules for what to offer, to whom, and when, it then takes a minimum of live intervention by costly and hard-to-manage support staff. Customized FairPay offers can be made automatically, quickly, and at low cost, to every customer worth keeping.
Based on what you learn in such a limited trial, you may find it desirable to expand it, refine it, and extend it more widely—to apply FairPay to customer acquisition, special premium/loyalty programs, and perhaps even to your core subscription pricing.
Retention Level 2: Proactive Retention—Seeking Win–Win All the Time
Managing retention on an exception basis is counterproductive. Do you let your spouse feel neglected or abused and ignore that until they ask for a divorce? If customizing value propositions makes sense when customers ask to cancel, wouldn’t that be much more effective before they get so far gone? Why wait until then to begin dialog on the issues? Think about what listening to your customers really means. Real listening may seem impractical, but FairPay provides a process for doing that efficiently and profitably.
Why are we content with poor value propositions for those customers who don’t take the trouble to complain? Why do we wait for them to cancel? It may seem nice to have quietly dissatisfied customers on autorenew—and hope they will never think about what they are paying and what they are getting—but is that really the way to grow loyalty?
Maybe we would have more and better customers if we tried to proactively find better value propositions for all of them. That is what FairPay is designed to do. Once you have learned the basics of managing dynamically adaptive FairPay retention offers, why not extend that to all of your regular customers?—at least for all of those who seem to value your product, and who demonstrate their fairness during an initial learning period. (Those who do not prove to be fair can be returned to the conventional set-price plans.)
The whole point of FairPay is to continuously seek win–win relationships that customers are satisfied with. It gives early warning of dissatisfaction, and provides processes for resolving issues—as customers become aware of them, and long before they ask to cancel. The idea is to build consideration of value—and how that drives retention—directly into the loyalty loop, for every cycle of each customer journey.
Premium “Club”/“Patron” Segments and Loyalty Programs
Premium tiers and loyalty programs have particularly strong promise of good results with FairPay because they target your best, most engaged, and loyal customers. Tests can be framed as trials, and loyal customers can be enlisted much as for focus groups. The New York Times provides an excellent example, having introduced Times Premier in 2014 (without FairPay, at $10 per four weeks), at which time I did a blog post proposing FairPay as a better alternative.
“For those with a curiosity that matches our own,” the Times’ pitch read, but what I was most curious about is whether and how I would value it. ... And whether the value to me would be consistent, or highly variable and hard to predict. (Apparently Times Premier did not meet objectives and was reintroduced in 2015 as Times Insider. I did try Times Premier, and canceled after the four-week trial. I have since subscribed to Insider, and don’t see much difference, but, as an ongoing experiment, have acquiesced to this unsatisfying value proposition.)
The Times’ metered paywall has been working better than many feared, but is obviously leaving money on the table from loyal, engaged readers who can and would pay more for Times’ journalism and extras. Premier/ Insider attempt to capture that value, but, like the old “ Godfather”-inspired joke, they have made me an offer I cannot understand. As I said at the time:
It offers me a combination of new features, some in specific quantities.
I don’t know what these are, have never seen many of them, have no idea if I will like them. And, even if I do like some of them, how many will I want in any given four-week cycle?
Some sound interesting, and some not at all.
Even after I have tried them, I expect my desire and opportunity to enjoy them will vary and might decrease over time.
I may want more than the included number of some features, while having no interest in others.
I can afford the extra $10 per four weeks cost, but have no confidence that I will value the service.
Maybe I might try it and, if not too disappointed, just continue to pay the $10 without much thought (as the Times might hope)—but profiting from my inattention leaves me feeling exploited.
Alternatively I might try it for a while, then cancel—even if I would be willing to pay something for the occasional feature—leaving both me and the Times losers.
In any case, I feel little temptation to even bother—again leaving both me and the Times losers.
The core problem is a rigid, one-size-fits-all pricing scheme—for a time- and quantity-varying experience good that is offered to diverse customers with different and time-varying needs.
I had previously suggested to the Times that FairPay offer an adaptive FairPay premium-level strategy that is far more promising. Much as explained before:
The Times identifies me as a current digital and print subscriber, and offers to let me try Premier on a FairPay basis, as a “patron” of their quality journalism.
They “bill” me in arrears on a pay what you want (PWYW) basis, telling me for the past four weeks how much of each Premier feature I used, and advising me of a suggested price based on that usage and my history. The suggested price may reflect volume discounts and a maximum for “unlimited” use, and may have adjustments for students, disadvantaged, or affluent patrons.
They try to nudge me to pay well by reminding me of their quality journalism, telling me that others are paying much as they suggest, and offering added incentives.
I decide whether I think it is fair to pay as suggested, higher, or lower, and check off possible reasons for that.
The Times weighs the reasons, considers my history, demographics, and usage, and decides how fair my price seems—on an individualized basis.
After a period of learning, the Times decides whether to continue as is, bump me up to more privileged offers, or drop me from FairPay pricing and require that I pay the standard $10 per cycle if I want to continue Premier/Insider.
This dynamic adaptation continues indefinitely, as the product and the relationship change and evolve.
This has the usual benefits of FairPay to both me and the Times:
It lets me try Premier at no risk (the Times could suggest that I am expected to pay, even for the first four weeks, but only if I do find value in it).
Each cycle, I can pay as suggested, based on my usage—or more or less—as I think fair.
If I have an occasional heavy usage cycle, I can apply whatever “volume discount” I think fair to avoid an unduly high charge, as long as I don’t abuse that privilege.
If I thought that the features were especially good that cycle, I can pay a bit more, which would increase my fairness rating to show my “patronship”—and earn more privileges.
If I thought that the features were less good that cycle, I can pay less, and only harm my reputation and privileges if I make a habit of devaluing the product.
Some of the key benefits to The Times
They can get far more people to try Premier/Insider, and retain far more, for wider market reach and greater profit. Many may pay less than the standard $10, but some may pay more, generally in line with the value they receive. The net profit can be higher—with a lower average price, but from more patrons (and with more advertising views).
When Premier was a new offering, they would not risk cannibalizing existing revenues. Even now, an added FairPay option could extend its reach down-market, and add more premium revenue up-market.
They can build a deeper relationship with their patrons, based on this deeper empowerment, dialog, and experience.
That can shift the relationship with the Times from quid-pro-quo business exchange norms to cooperative, communal norms, and foster social values of fairness and reciprocity, both of which increase willingness to pay.
They can learn far more about what their patrons value and why.
They can justify different prices to patrons with different value propositions and abilities to pay.
They can start with fairly simple decision rules and liberal continuation criteria, and gradually add more nuance and discrimination as they and their patrons gain experience with the process.
Trying a radically new approach like FairPay has risks, and takes some effort, but I suggest that a FairPay version of Times Premier/Insider offers far more profit potential and far better relationships with the Times’ patrons than the conventional version.
More generally, FairPay promises to be a particularly effective way to set pricing for rewards in loyalty programs, such as those based on points or miles (as noted in Chapter 7). Instead of set prices in points, why not FairPay dialogs? What better way to more deeply engage customers who already show loyalty and appreciation of your services? To limit risk, initial trials can begin with selected customers, and selected classes of rewards that have low marginal cost.
Testing FairPay for New Customer Acquisition
Why not start FairPay trials at the beginning, with new customers? This can be done quietly, on a very selective basis—such as to prospects thought to have strong social values of fairness, and likely to value the service, even if their willingness to pay is unclear. This can be positioned as a special trial that will end if not enough customers play the game in a way that works.
Instead of a free trial period, as is usual with many subscription services, with FairPay it can be framed that payment is expected as soon as you see value. Maybe with a trial discount to compensate for your forebearance for a while, but why should it be free if you do see value? Some population of new customers could be onboarded to this special trial, and given 6 to 12 months to see how this works. How much can be learned?—and what harm if results are not good?
Transitioning from Free or Ad-Supported to FairPay
If paid subscriptions are not yet in place, why not try this segue? While many businesses have bit the bullet and transitioned from free or ad-supported to paid subscriptions, it is often the case that many consumers will balk. One way to ease this transition is to use FairPay as a way to offer a kinder, gentler paywall—and combine that with the bonus of going ad-free (or at least reducing the ad load). This benefits both the business, and customers:
Customers feel more respected and empowered by the added trust and flexibility.
Customers might get ad-free basic content, at some modest cost, presumably less than the likely paywall rate.
Some will pay less for the premium content, but some will pay more.
Relating pricing to usage can get heavy viewers to pay more, compensating for those who pay (and use) less.
Many who might refuse a conventional set-price premium service might be willing to pay something reasonable for a FairPay basic service—added revenue.
The details of the offers and the process can be individually and dynamically tuned to encourage good payment levels, and to send free-riders back into the hard paywall.
Bottom line: more happy customers; more revenue.
FairPay “Free Trial” or “Survey” Mode
Easing into FairPay, and Understanding Your Customers
Looking at the details of phasing in the FairPay process itself, there are ways to do that in steps that maximize joint learning with your customers and defer much of the software development until you gain some confidence.
The idea is to get some basic dialogs about value started at low cost—and to learn more about your customers, so you can better manage your investment and your risk (and improve your business overall). Whether for an entirely new business, or for a new pricing approach in an established business:
Key questions about FairPay are how hard is it to integrate with your pricing systems, and to what extent might it put revenue at risk.
Key benefits of FairPay are as a learning process, centered on dialog with your customers, about what they want from you, and how they value it.
This can not only be applied in existing businesses, but is also especially suited to new lines of business where the value proposition may be uncertain or temporarily limited (such as beta tests, pilots, etc.). Limitations may be due to lack of system function or lack of critical mass network effects, such as those affecting content richness, community participation, and so on. This can also apply to a shift from ad-support to customer-supported subscriptions, as described earlier.
FairPay Free Trial Mode: Initially, FairPay Free Trial Mode should stay largely out of the way, behaving generally as a conventional “free trial,” with the addition of some key features:
The initial objective is to serve as a learning platform, apart from any revenue generation, to obtain customer “survey” data related to the potential value of the service. This role can integrate with other customer feedback to focus on the perceived value exchange, to better understand the benefits and problems in using the service by the consumer.
From this perspective, FairPay Free Trial Mode can be thought of equally as a FairPay Survey Mode, in which data on willingness to pay is collected, but is not used to limit continuing use, even if users do not pay. Depending on the situation—or in successive phases—users might be asked:
No-Payment Survey Mode: to simply say what they “would” be willing to pay without actually making any payment.
Real-Payment Survey Mode: to actually pay what they think fair, with the understanding that all payments are entirely voluntary (pure PWYW, in arrears, “pay as you exit”)—there are no adverse consequences (no reputational harm) for nonpayment.
A secondary objective is to set the stage for Full FairPay (Fairness-Gated) Mode use to follow, by facilitating learning by both customers and the service provider on how FairPay is best applied to the particular services. This would educate customers on basic concepts of the FairPay process, and help the service provider learn where to apply FairPay, how to frame offers, how to suggest prices and assess fairness, and the like, in the context of their particular services and customer base.
To aid in customer understanding, it might be clearly stated that FairPay is currently in Free Trial Mode for some or all services, and that a future transition to Full FairPay Mode was planned.
These modes can be enabled before a full FairPay system infrastructure is built, since they require none of the real-time feedback analysis, buyer fairness reputation rating, and reputation-based offer gating of a full FairPay service. Thus these partial steps toward FairPay require very minimal development investment.
Full FairPay (Fairness-Gated) Mode: As the service matures, the Full FairPay (Fairness-Gated) Mode can be implemented and gradually turned on—with any desired phasing as to customer segments and service categories. Full FairPay Mode would enforce FairPay “fairness-gating”—limiting access by buyers who develop a reputation for failing to pay fairly. This can overlap with continuing use of FairPay Trial Mode for other customers and other service categories.
The shift to FairPay Fairness-Gated Mode might be related to achieving a level of maturity as to service robustness, critical mass scale (in content and community), initial education of customers on FairPay concepts—and to scale-driven needs for revenue.
This shift can be phased in (sector-by-sector, if desired), using an appropriate change management process. Customer communications and dialog can be applied to prepare customers for this change (to include customer reputation rating and gating of offers), to identify any issues or concerns and ensure that they are recognized and addressed. It would be made clear to users that gating was being activated, why the time for that was right, and what changes to expect.
The result of this staging approach is that FairPay can begin quickly to create a lightweight dialog with customers about value, and to generate valuable data long before it is integrated into actual payment processes. That imposes minimal burden on customers, and also allows the development work of building the full FairPay infrastructure to be phased with careful controls.