So Last Century!—What Is a Price, and Why?
We think we know what a price is, but a price has an economic function, and that function has changed.
Through most of history a price was the outcome of a personal negotiation between human buyers and sellers, depending on the needs and powers of each, in an individualized context of personal interaction and knowledge of one another. Different buyers got different prices. Prices were a very personal thing. Buyer and seller were usually part of a community, and communal norms encouraged social values of caring, and even generosity between buyers and sellers.
Over the last century or so, institutionalized mass-marketing pushed that to the margins. Consumers no longer bought from individuals, but from institutions with standardized prices—starting with department stores in the mid-1800s. The relationship became asymmetric, and consumer prices came to be set uniformly and unilaterally by the seller, based on their calculus of what single set price would yield the most total profit from all customers. This was seen to provide fairness, in that everyone was treated equally, but customers lost their individuality and came to accept these set prices as a given—take it or go elsewhere.
Some consumers still build relationships with sellers for reasons beyond price, but most consumers play the modern game of bargain hunting. Merchants generally decide between pricing for bargain-hunters or holding themselves out as premium providers, leaving it to the customer to self-select, depending on their price sensitivity. Behavior has shifted from communal norms to economic exchange norms that are focused on hard-headed quid pro quo and alienated from social values.
So, what started as a gain in transparency evolved into a cat and mouse game of mark-downs, discount codes, timed sales—a new opacity. Even new methods that are made to sound win– win, like Priceline’s “name your own price” (NYOP), were really structured as opaque zero-sum games more accurately described as “guess our price” (very different from the similar-sounding pay what you want [PWYW]).
Now we are in an age of one-to-one relationship marketing, deep tracking, and mass-customization. It is now well established that offers should be targeted and customized—but prices, still not so much. We recognize that prices might theoretically be individualized, but have no clear idea of how to do that. (Early attempts by Amazon were reportedly dropped after a backlash—perceived as unfairly imposed, and abusively manipulative—price discrimination as an evil.) Most businesses still customize price only to broad segments, such as by zip code, if at all (and those that are known to discriminate, such as airlines, are widely reviled for it).
The Paradox of Pricing “Experience Goods”
A postmodern pricing concept must deal with the paradox of experience goods. Regardless of cost or scarcity, the true value is not known until after the customer experiences the product/service, and that value varies widely from buyer to buyer. That value is unknown before the purchase. Modern pre-set prices often lead to buyer remorse (or windfall). If value is unknown until after the experience, traditional negotiation is not applicable at all (if negotiation is done after the experience, the buyer would have all the power to pay whatever they want, including nothing). How can we set a fair price after the value is known? Only by negotiating a process, not a price. Only by agreeing on a process for pricing experiences will we achieve mass-personalization of prices and value-propositions.
With the growth of digital offerings, we face a huge new kind of market inefficiency—to the point of an economic crisis in many content industries—and with significant loss of economic efficiency far more widely.
What we need is a new concept of what a price is and why. Key requirements of a price for the new economy are to accommodate and exploit:
The asymmetry between mass-market institutional sellers and individual human consumers,
The Alice in Wonderland economics of digital goods/services,
The paradox of pricing experiences that cannot be accurately valued until after they are experienced,
The power of mass-customization to find a new way to set prices that reflect the widely varying contexts of individual consumers,
A relationship view, in order to maximize the lifetime value of a customer and encourage their loyalty,
The new power of collaborative, computer-mediated dialog, to humanize institutions and re-engage communal norms of behavior, even between consumers and institutions.
FairPay shows how we can use automation (and computer-mediated communications) to solve these problems—to humanize rather than dehumanize institutional relationships with consumers.
FairPay Is a Rethinking of What a Price Should Be
The FairPay architecture seeks to find a new and better way to manage economic relationships. It throws off the tyranny of set prices, and balances the interests of both buyer and seller, while embracing the asymmetry—and efficiency—of institutional mass retail. FairPay lets us agree on a process for setting prices dynamically, after the experience is known.
For digital products—which can be costly to create, but nearly free to replicate—the old idea of a price as balancing supply and demand is no more meaningful than division by zero. Marginal cost is zero and supply is infinite. There is no inherent price derivable from classical supply-demand economics. The invisible hand can only flail about in the air.
We must go back and revisit the fundamental economics of sustainable digital business. If we frame the problem correctly, the path toward a solution emerges. How can a business make sufficient profit to be worth producing its product? How can it maximize this profit in this new kind of marketplace? How can it get customers to willingly pay a price commensurate with the value the seller provides, one that grants them a reasonable profit from each of a mass of widely varying customers? This seemingly paradoxical challenge requires a new paradigm—one that achieves a new balance of forces—a new invisible hand that can account for individual variation.
FairPay goes outside this box by shifting from a transaction view to a relationship view. Sellers need to find buyers who willingly pay fairly, in a way that generates a profit over the life of their relationship. Individual transactions can be unprofitable (even free), if that leads to a relationship that generates a fair profit from that customer over time.
The genius of freemium was to recognize “the power of free,” but freemium just pushes the can down the road a bit. The product still has a set price, in this case two of them, applying “artificial scarcity” and the power of versioning: either free (a set price of zero) for the basic version, or some nonzero set price for the artificially scarce premium version. Neither is an efficient price. We need to look beyond our ingrained assumption that prices must be controlled and set by the seller before the sale!
FairPay creates a dynamic pricing process that lets buyers and sellers explore pricing levels jointly over time, building a relationship based on finding a level of payment that both parties consider fair overall. It does this by rearchitecting how prices are set. Instead of trying to find a price, we need to find a broader kind of agreement. Instead of agreeing on price, we need to price out of agreement. Profitable transactions are just the trees—profitable relationships are the forest—and it is that forest that we need to see.
Embrace the Variation—The Long Tail of Customers
Why is personalization of prices so important? Let’s look at how value perceptions and willingness to pay vary from person to person. The Internet supports infinite variety. Just as Internet-based retailing allowed Chris Anderson’s Long Tail of Items (catering to every interest) to be uncoiled, Internet-based pricing is poised to enable the uncoiling of a similar Long Tail of Customers (catering to every individual valuation and ability to pay).
Anderson’s Long Tail is a tail of items ranked by units sold. As described in his Wired article and book (Anderson 2006a), online merchants such as Amazon and Rhapsody can stock many times more titles than brick-and-mortar stores, since they have essentially no limit to shelf-space. As a result, half of Amazon’s total sales were accounted for by books that were not even stocked by a Barnes and Noble store. The well-known long tail curve (a Pareto distribution) shows a plot of the number of items sold, ranked by popularity (Figure 3.1).
Similarly, the Long Tail of Customers is a tail of potential customers ordered by price sensitivity—the price they are willing to pay for an item at a given time (a different Pareto distribution, known as a demand curve) (Figure 3.2).
Conventional set prices lop off this long tail by refusing to make sales to those unwilling to pay the set price. This eliminates a potentially significant market, out of fear that selling to those customers will cause the other customers to demand lower prices. Conventional set prices also lop off the top of the tall head, since the seller gets only the set price, even from those who might be willing to pay more. So revenue is only the green box, even though there is a red surplus at the top of the head, and a long amber tail to the right. This shows the huge opportunity that FairPay opens up.
Figure 3.1 The long tail of items
Source: Adapted from Anderson (2006b), www.longtail.com/about.html
Figure 3.2 The long tail of customers
Source: Adapted from Anderson (2006b), www.longtail.com/about.html
With FairPay, prices are individually set, based on what customers are willing to pay (subject to sellers using feedback on FairPay reputation to weed out free-riders who don’t pay at an acceptable level). For digital products with near-zero marginal cost, the acceptable price level for large numbers of those potential customers might be relatively low. Most of these would be customers who do not buy at current prices, because they are not willing to pay that much—the Long Tail.
But because of their large number, this means that the Long Tail of Customers might turn out to contribute a very large portion of total revenue. Large numbers of customers at low prices, who would not otherwise be customers, can add up to a very large revenue increase.
Of course sellers might find that revenue from some of their current customers drops a bit (the curve may shift downward), because customers have freedom to pay less than the conventional set price. It may at first seem that sellers could not tolerate the risk that many FairPay customers might pay well under standard prices. That is the usual problem with PWYW pricing, but the FairPay control process is designed to maintain reasonable levels of pricing. And even though many current customers might pay a bit less, some can be motivated to pay more.
So the areas under the curve in red and amber potentially represent found money, money that would otherwise be left on the table. Why not sell to all who will pay more than the marginal cost of the product? Since prices are individually set, low prices to some need not imply low prices to all. If you can get customers to consider fairness, and to look at the rationale for this value exchange, most will accept that there are reasons why some deserve lower prices than others (more on that later). And even low-paying customers can add to advertising revenue, where that is a factor.
And why not try to motivate your happiest customers to pay a bit more? If you position that payment as going to a good cause (like museums and artists do), some who can afford to pay more will see reason to do so. Enlightened businesses recognize the value of likability and the inclination of people to be fair—this is a way to capitalize on being likable.
The Psychology of Price Variability
Let’s look closer at the psychology of pricing on the long tail. Consumers hate to pay, or even think about paying, so content providers need to make it simple—but simple does not work well. Consider a content subscription Web site such as a newspaper or magazine, or a music or video service (but this applies broadly).
Conventional paywalls (even soft paywalls or other freemium models) face the dilemma that they put price as a hurdle in front of sales. Price too high and many potential customers will simply turn away; price too low and much potential revenue is left on the table. Pick your poison—there is no win–win, only bad or worse.
Add refinements such as versioning tiers of premium content or tiers by usage volume or other segmentation by market (artificial scarcity), and you add complication, and still have a step function that runs well below the price sensitivity curve. Even freemium still has this set-price hurdle when the customer wants to step up to the premium version. Remember that we are talking about experience goods, where the perceived value is not stable within a market segment, but highly dynamic, varying widely not just from person to person, but from time to time, depending on needs, moods, and reactions to dynamically varying products (such as news stories or video episodes).
FairPay may seem complicated, but that complication can be largely hidden from the customer. Yes, customers are forced to think about pricing more than once, but is that such a problem? Don’t you think about pricing every time you eat in a restaurant and leave a tip?
With a simple paywall, customers must think about prices when they first hit the paywall. The hope is that they will pass through it, go onto an auto-renew subscription plan, and never think about it again (while the renewal money just rolls in). That will be the case for some, but others will balk (either up-front, or after they see they are not getting the value they want). Either way, the Procrustean paywall will cut off a huge portion of the potential revenue (whether the red head or the amber foot). One size just does not fit all. This simplicity is very costly to the seller—and a turn-off to many customers.
Is FairPay really more complicated or more of a hurdle? FairPay costs what the customer thinks fair. It is not rocket science, but gut intuition, guided by some conventions (much like tipping). The threshold for seller acceptance can be soft enough to remove all but a minimum of anxiety (as with tipping)—and once customers establish a reputation, price-setting actions can be infrequent and easy, and left on autopilot except as change is warranted. These psychological issues are important, and we will deal with them from many perspectives.
FairPay can hide a huge amount of complexity, because it is customer-driven and intuitive. Why not outsource to the expert? The customer can set a price that naturally reflects his needs, his usage, his valuation of the product and of the relationship, and his ability to pay—all with unlimited nuance and adaptation to current circumstance, and with hardly a thought. Fully evaluating the fairness of that on the seller side will take sophisticated decision rules, but the seller can begin with a simple, forgiving, fairness model to get close enough to true sensitivity, and later refine it to get even closer. This is just another aspect of customer relationship management (CRM), one that gets to the heart of the value exchange. Isn’t it just this kind of customer relationship management that modern businesses should be seeking to cultivate?
The customer can enjoy a new kind of freedom, almost as much as free or PWYW. He no longer needs to wonder how much he will use and how much he will value it. That can be determined later, after he knows exactly what he got. If he underprices, the seller will be forgiving up to a point—and the worst that can happen is he is back at the paywall. There is no fear of buyer remorse to stop him from using a product he thinks he might value. … And the buyer and seller learn how to work together to find the fullest desirable and fair value exchange.
Price It Backward!—“Pay as You Exit”—The Wisdom of Alfalfa and Our Gang
FairPay turns many conventional ideas about pricing on their head, to the extent that, on first look, many people don’t fully appreciate some of its key features.
Pay after the experience. One of the most powerful and underappreciated methods behind FairPay is stunningly simple. An amusing portrayal of this simple twist was in “Pay as you Exit,” a 1935 episode of the very popular “Our Gang Comedies” series of short films (later shown on TV as “The Little Rascals” and now on YouTube). Alfalfa, one of the lead characters, was trying to drum up interest from neighboring school kids to buy tickets to a production of Romeo and Juliet to be put on by the gang. They were asking one-penny admission (this was 1935), and he was going on earnestly about how wonderful it was, but the potential attendees were having none of it. “How do we know it is worth a penny?” Alfalfa finally had a flash of inspiration and said, “I’ll tell you what I’ll do. If you like the show … pay as you exit.” A leader of the skeptical kids responded “OK, we’ll take a chance,” and so they all went in. The play was riddled with pratfalls that pleased the audience, and they all happily paid the one cent as they exited. Very simple, but very powerful.
FairPay extends offers forward in ongoing cycles of transactions, but for each transaction cycle, the pricing decision looks backward. FairPay price-setting is retrospective.
Conventional prices are set before the buyer uses the product or service.
With FairPay, the buyer sets the price after using the product or service.
Setting the price before use exposes the buyer to risk of “buyer’s remorse”—the buyer can only guess whether the value received will be as expected.
So, setting prices forward (as is conventional) requires the buyer to take a leap of faith. As a result, the buyer must build a discount into what he is willing to pay, to compensate for the risk of a value surprise. Depending on the nature of the product, this risk (and the corresponding discount) can be significant. That is especially true for “experience goods” such as music, movies, and the like, and that risk can significantly reduce their potential market—buyers often will not take the risk at all. It also is significant when dealing with unfamiliar (untrusted) sellers for any kind of good.
Even with many basic forms of PWYW, pricing is usually forward (even if set by the buyer). So up-front price-setting depresses PWYW results, since it leads buyers to set prices lower than they might do after the value was confirmed.
By setting prices backward, after the product or service has been used, the buyer has no risk of remorse. They know what they got and what value it delivered, so they can set their price based on that full value. Think about it: after you saw a great movie or played a great album several times, wouldn’t you consider paying significantly more than the standard price for an average movie or album? Or with a poor movie or album, aren’t you sorry you paid what you did, and wishing you could get a reduction or refund?
FairPay Cycles Go on Forever
It should be emphasized that FairPay is not just a temporary negotiation stage that leads to setting a fixed price after one or a few cycles. That could be done, but generally seems undesirable:
Value considerations change over time.
The beauty of FairPay is its ongoing adaptation, so why cut that short?
The buyer’s usage or needs may change, so their pricing should be allowed to change accordingly.
The seller’s product or service may vary in quality and value, so, again, pricing should vary accordingly.
Why not let the adaptive process inherent in FairPay keep doing its work to adjust for that? In its pure form it works in repeating cycles of an ongoing relationship, and that continues indefinitely—as long as both sides view it as fair. Of course this is not an absolute—FairPay is a just a framework, an architecture that can be adapted and used in hybrid forms as the situation warrants—but in general, it seems that there is little reason not to continue it.
The power of FairPay comes from being a repeated game. What matters is not any one cycle, but how the cycles motivate and converge on cumulative fairness over the relationship. If the game works, why end it?
Fuzziness in Pricing—Seeing the Forest, Not the Trees
A related feature of FairPay is its embrace of fuzziness. Just as we think of price as set by sellers, even if it need not be, we tend to think of price as exact, even though it need not be. By shifting our perspective to value over the course of a relationship, individual prices are reduced to individual data points that combine into a larger pattern. What matters is that a business relationship should be mutually advantageous and profitable over its life—that is, the forest. Individual transaction prices are just the trees that form the forest. We need to look beyond the trees, to see the forest.
Thus, it is not important that any one cycle generate a close approximation to an optimal price, as long as we get fair prices on average over any extended period. It is OK if the process is fuzzy and imprecise. The trick is just to keep nudging toward fairness (and to change the game for that customer if that fails repeatedly). Just as long-time friends accept that “it will average out” when settling up after a shared meal—splitting a check or alternating who picks up the tab—rather than adding up every item to get an exact allocation.
FairPay Negotiates a Relationship, Not a Price
FairPay is not a price negotiation, as such. It is easy to misunderstand the basic idea of these cycles. FairPay has similarities to a price negotiation process, in that it creates a two-way dialog about price, but (in pure forms) FairPay never leads to a set price that goes forward. For each cycle, the price set is always a retrospective price, set after using the product/ service during that transaction cycle. Each new cycle is open to a new price evaluation by the buyer. The negotiation in FairPay is on the underlying agreement on a process for seeking prices, not agreement on the individual transaction prices themselves. Focus on the forest, not the trees.
There can be indirect negotiation. It is not yet apparent how widely useful it might be, but sometimes it might be desirable to add a layer with a new kind of indirect price negotiation. That negotiation is not about the price itself, but about the implications arising from a possible price—what potential buyer-set price level might lead to what further offers in the continuing relationship:
With FairPay, what the seller has to the power to negotiate over is not the price, but the offers that will follow. There might be a negotiation dialog on the current backward price set by the buyer, and how that might relate to forward offers from the seller that might follow.
The seller might react to a buyer’s tentative (retrospective) price $X, and advise that if the buyer prices at that level, the seller will next extend basic offer A, but if the buyer sets a higher price $Y (for the transaction that is ending), the seller will extend a more attractive offer, B (perhaps including premium items, or some other perk), for the following cycle.
Similarly, if the tentative retrospective price $X is considered unfair by the seller, the seller might advise that no further offers will be extended based on that price, but that if the buyer changes the price to $Y, then further offers will be made.
The idea is to give the buyer more or less complete freedom to set each retrospective price as he sees fit, but to enable the seller to provide guidance on how he will view that, as it relates to further offers in this continuing relationship.
The buyer retains full control of the price—what the seller controls is whether the relationship will be allowed to continue on that basis.
Even in this scenario, future prices remain retrospective. The buyer is still free to pay whatever he wants after the next cycle. What is negotiated is not the price for the next cycle, but only what price for the previous cycle will motivate the seller to make a particular offer for a next cycle. (The customer can ignore that and do as he sees fit.) Similarly, even in the streamlined “autopilot” scenarios mentioned earlier, the customer retains the option to adjust autopilot prices in hindsight, if they decide that the fair value was not as expected.
A Price Discovery Engine
Some interesting insights into the power of FairPay were suggested by John Blossom, a strategy and marketing consultant to content providers, on his ContentBlogger blog (Blossom 2011).
It was John who reminded me of the Our Gang “Pay As You Exit” comedy episode that I had long forgotten. “It seems strange in a way to think that such an idea might actually help to save today’s premium content sellers from their often rigid pricing regimes that seem to hold back their growth potential ….” He goes on to explain how this derives from my Long Tail of Customers and highlights the value of FairPay as a “pricing discovery” regime. “The key to all of this is the profile data, of course, which is where Reisman may have his finger on a very valuable idea. FairPay is in essence real-time market research tool, enabling media providers to get more sophisticated insights into real willingness to pay for specific content under specific circumstances.”
He adds that “It’s not just a matter of knowing when to knock down prices for whom; it’s also a matter of knowing when to mark them up, because one person’s trash may have become another person’s treasure. In such highly contextual markets, supply is perfectly matched with demand when the right content is available instantly at the right time.”
His conclusion: “While it’s very early days for the FairPay model, it could turn out to be a tool that content producers could use to experiment with pricing in new and exciting ways that could lead to higher margins and deeper market penetration for their content—two concepts that could lead to more happy endings on their bottom lines.”
Win–Win Customer Journeys—With Dialogs About Value
It should be obvious by now that FairPay goes far beyond pricing to restructure the entire process of commercial relationships with consumers. This is consistent with other modern thinking about marketing, and one useful perspective on this is the idea of “customer journeys,” a holistic view of ongoing customer relationships.
This emerging marketing paradigm provides just the context for a further step of proactively ensuring the journeys are maximally win–win. Some helpful background on this is in “Competing on Customer Journeys,” by Edelman and Singer (2015). Their subtitle is “You have to create new value at every step”—FairPay suggests a way to enrich the customer journey to do that much more explicitly. They explain that:
Rather than merely reacting to the journeys that consumers themselves devise, companies are shaping their paths, leading rather than following. Marketers are increasingly managing journeys as they would any product. Journeys are thus becoming central to the customer’s experience of a brand—and as important as the products themselves in providing competitive advantage.
They suggest how this can enable “a ‘loyalty loop,’ … a monogamous and open-ended engagement with the firm.” This is a big step forward in developing long-term profitable customer relationships, and it dovetails with the similar kind of continuing feedback loop that drives FairPay. The idea that FairPay adds is to insert “dialogs about value” into each cycle of the journey after the “enjoy” step—when the customer knows the value of the experience—and to adapt the pricing based on that. This enables participative personalization of the value proposition, as a simplified form of value-based pricing:
Buy
Enjoy
Value (the added step—dialogs about value, to personalize the value proposition)
Advocate
Bond
Without this added step, the loyalty loop does not fully realize a cen-tral driver of engagement and loyalty—a proactively personalized value proposition that is win–win for both the customer and the firm. Without this we just perpetuate the idea that the firm decides on value propositions and tries to coax customers into accept them. Adding explicit value assessment into the loop engages the customer more deeply and enables the firm to serve the customer far more effectively. This element of customer participation builds customer loyalty by demonstrating the firm’s commitment to learning exactly what each customer values in varying contexts, and seeking to deliver it by customizing the value proposition to match.
Edelman and Singer go to the threshold of this, just short of this one more step:
We’re now seeing a significant shift in strategy, from primarily reactive to aggressively proactive. … companies are designing and refining journeys to attract shoppers and keep them, creating customized experiences so finely tuned that once consumers get on the path, they are irresistibly and permanently engaged. Unlike the coercive strategies companies used a decade ago to lock in customers (think cellular service contracts), cutting-edge journeys succeed because they create new value for customers: Customers stay because they benefit from the journey itself.
The driving goal of FairPay is to make price reflect value, over time. When it comes to value propositions, firms remain coercive, effectively saying: “We give you this value package for this price. If you don’t like that, how about this other value package for this other price? If none of our options suit you, we are just not listening—you will have to settle or go elsewhere.” FairPay dialogs about value open a new dimension of adaptivity and dialog to co-design value propositions based on individual context, needs, and value perceptions. These dialogs about value become central to the journey, and a key driver of the loyalty cycle.
As the article explains, adding this focus on value does not just increase loyalty, but promises to dramatically increase profitability as well. The value step added by FairPay would bring value pricing directly into this journey—in a uniquely simple and lightweight form. This can enable a business to evaluate the value their services actually deliver to the customer, and to engage in dialog with the customer to share a portion of that value with the business. Rather than expecting the customer to take a risk that they will get a predicted value (which forces the consumer to discount the price they are willing to pay to allow for that risk), the business can design the journey to share the risk, measure the realized value, and share in that value. (More about this in the next section.)
FairPay is a very natural extension to the customer journey perspective. Edelman and Singer explain that “The move from selling products to managing a permanent customer journey has required mastering the four capabilities that all companies will need to compete: automation …; personalization …; contextual interaction …; and journey innovation ….” The same four capabilities support FairPay as well.
As the new logic of customer journeys becomes accepted in marketing, the related new logic of FairPay and its adaptively win–win value propositions should become increasingly accepted as well. Different levels of FairPay empowerment may be applicable to different consumers and different business contexts, but a more explicit focus on value can benefit almost any customer journey.
Design Thinking About Customer Relationships and Business Models
The value-centered customer journey can be understood through the lens of “design thinking”—the recognition that our customer relationships should be subject to continuous redesign. Design thinking starts with user needs, and seeks to understand their journeys based on empathy maps. From that perspective, FairPay dialogs about value become a GPS for empathy mapping. FairPay helps move us toward better design thinking at two levels:
As a form of design thinking, FairPay makes consideration of the user—and empathy and experimentation related to that—central to every customer relationship.
FairPay can help drive entire businesses and industries to recenter on design thinking—by shifting the customer journey to drive dialog with customers to co-design value propositions—and to reflect that in pricing, so that it factors directly into the bottom line. That can drive everything else.
This is not unilateral design by the firm, but truly participative co-design. It is not occasional co-design, but continuous co-design.
This also relates to recent ideas made popular in the book, Business Model Generation (Osterwalder and Pigneur 2010), aptly self-described as “a handbook for visionaries, game changers, and challengers striving to defy outmoded business models and design tomorrow’s enterprises” (and expanded on in a follow-on book, Value Proposition Design [Osterwalder et al. 2014]).
The adaptive and dynamic nature of FairPay is itself a process for generating the details of business models (because it can take on the characteristics of many different models), and thus can help bake this idea of business model generation into core business processes.
FairPay treats pricing as an ongoing process (to be designed), not a static endpoint (to be calculated). It offers an open-ended framework adaptable to almost any kind of pricing metrics and behavior desired. It is also a process for adaptive value proposition design.
More broadly, it inherently links to all other business processes in a way that makes doing business a far more flexible and generative process.
The way to generate a business model in the Internet age is for the business to dynamically and adaptively generate its own right model—a model that optimizes value creation for both seller and buyer, for each relationship as it evolves.
Cloud of Value Marketing—New Big Data for a Customer-Centric Revolution
This idea of customer journeys and how to manage them leads us to another key development in marketing that FairPay exploits and expands on, the application of Big Data of all kinds, with growing focus on the Internet of Things (IoT). (Big Data refers to the growing availability of huge amounts of data that are now increasingly applied with advanced data analysis methods, starting with things such as Web clicks, searches, postings, texts, and tweets. The IoT refers to how even more Big Data is being derived from smart things—phones with sensors [including GPS], wearable health sensors, smart TVs, other appliances, cars, smart buildings, and beyond).
A perspective on how central this is to customer relationships comes from none other than Salesforce.com (Bosworth 2015). The idea is that IoT data can be marshalled and analyzed in the cloud to be acted on in real time, to enable a new level of business transformation, and more deeply intelligent, proactive, and personal customer engagements—CRM grows into a Marketing Cloud and the IoT Cloud feeds that. FairPay adds a new aspect to the IoT that we might think of as an IoT Cloud of Value.
We know how Web browsing and online content consumption creates a wealth of Big Data that is used extensively for marketing and to customize services, and customer relationships. What is less recognized is how this can provide rich data for personalized pricing, and how smart “things” create similar data even when seemingly offline. For example, the instrumentation of e-books provides great detail about how you consume them. That enables new kinds of pricing: How much you have to pay for a book can depend on how you read it—how much, how long, how deeply, how repetitively. That data is indicative of the value you receive from the book. Why should what you pay to read it not depend on how you read it? (See Chapter 10.)
From this perspective, FairPay can be viewed as having two fundamental and interrelated Big Data components:
Implicit signals of value. These are drawn from conventional IoT data, including all of the usage tracking, activity, and clickstream data now widely used on the Web, and increasingly for any digital service.
Explicit expressions of value. These are new kinds of data generated by the FairPay dialogs about value between the customer and producer. (These customer expressions can be partially validated by testing their consistency with the implicit signals of value.)
Applied together, these drive the FairPay process as it seeks to build mutually beneficial customer journeys, and generate dynamic pricing based on customer-context-specific win–win value propositions.
That enables a new kind of value-centered marketing that can transform how we do business—call it Cloud of Value Marketing. (And that, in turn, can lead to new kinds of Ecosystems of Value–more in Chapter 13.)
Computer-Mediated Commercial Dialog
The growing attention to these ideas of the customer journey, the Marketing Cloud, and the IoT Cloud reflects that customer journeys are increasingly computer-mediated. We have long seen it in pure ecommerce, and with the growth of “bricks and clicks” broadening into “omni-channel” retailing, it is penetrating what might have been purely physical retailing (as businesses reach out to our mobile devices using location-based services in and near their stores). Similarly the IoT Cloud enables computer-mediated contacts driven by any usage of any product or service.
Increasingly, we find ourselves in a cloud environment where computer-mediated commercial dialogs can occur anytime, anywhere, for any reason. If computer-mediated commercial dialogs are the norm, why not dialogs about value? Isn’t that the heart of it all?
Testing FairPay at Low Risk
Of course any established business 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. It will also be important to focus initial uses on the low-hanging fruit of selected customer segments who will most readily take to the cooperative behaviors of FairPay relationships. We will address a variety of strategies to start in phases and in controlled market contexts in Chapter 14.
Just to plant some seeds—some of the best opportunities for such limited trials are in:
Retention “win-back” offers to customers asking to cancel their subscription. Customers who are known, and have proven interest in subscribing—who clearly value having a subscription but have doubts about the price/value proposition—can be offered a temporary trial of FairPay. Especially if the test is limited to users with low usage (for whom the regular subscription price may be inappropriately high), this makes good business sense all around.
Acquisition offers to specially selected test populations.
Premium level services, perks, and loyalty programs.