Posted June 14, 2017
In the past decade, “big data analytics” (or “big data”) has grown dramatically in business. Big data can help develop and execute strategy—as in Google and Facebook’s ad businesses, and Amazon and Netflix’s recommendation engines. Seeing these huge tech successes, many decided to just emulate how big data is used, hoping that big data analytics alone can substitute for long-term growth strategy. This belief is a delusion.
Winning strategies require that businesses discover new or improved experiences that could be most valued (though unarticulated) by customers, and redesign their businesses to profitably deliver these experiences. Big data can increase communication efficiency and short-term sales, or “clicks”, but changing the most crucial customer experiences can transform behaviors, attitudes, and loyalty, leading to major growth. Such insight is best found in many businesses by in-depth exploration and analysis of individual customers—and cannot be found in the correlations of big data. Some questions are easiest answered with big data, but availability of data should not drive what questions to ask. Data-driven priorities can obscure fundamental strategic questions, e.g. what could customers gain by doing business with us—what value proposition should we deliver, and how?
Discovering such insights requires deeply understanding customers’ usage of relevant products or services. In some businesses, such as online retailers, customers’ buying-experiences constitute usage of the service, so these businesses do have usage data, and can use big data in developing strategy. For most, such as product producers, however, usage happens only after purchase, so they have purchase but not usage data, and cannot properly use big data to develop strategy. Feeling compelled to use big data, such businesses may use it anyway, on the data they have, which can help achieve short-term sales, but not to develop long-term growth strategy. However, these businesses still can— and must—develop insights into what usage experiences to focus on changing, and how.
Digital marketing now plays a major role in developing business strategy, and heavily uses big data. Big data predictive algorithms analyze customers’ past transactions and purchase or shopping behaviors, to increase the efficiency of matching customers with marketing offers, and strengthen short-term sales. Sustained major growth requires more than ratcheting reach-efficiency and tweaking the week-end promotional tally. Sustained growth requires creative exploration of customers’ current experiences, to discover breakthrough value propositions, and design ways to profitably provide and communicate them. This post and follow-ups discuss these concerns and suggest solutions.
Predicting transactions is not strategy
As illustration, a Customer Experience Management (CEM) system by Sitecore helps fictional apparel maker “Azure” (Sitecore’s name) use big data to customize marketing to individual customers. Here, Azure intervenes with consumer “Kim” on her decision journey. When she visits their site anonymously, the data shows her matching their active-mother profile. Clicking on a shoes ad, she signs up for email alerts, providing her name and email. Azure begins building her profile. They email a belts promotion to customers predicted by the data as potentially interested—Kim buys one. Later, real-time location data shows Kim near an Azure store, so CEM texts an in-store discount on a new boots line; Azure is confident she’ll like it based on her past actions. Scanning the coupon on Kim’s phone, the CEM enables the clerk to offer Kim another product, a child’s backpack, based on Kim’s profile. Kim is impressed—Azure understands her interests, tracking her every action. She joins Azure’s loyalty program, giving her sneak peeks at coming products. With data showing that Kim most often accesses the site by smart phone, Azure offers her their new mobile app. Via big data, Azure has improved the shopping and buying experiences, and efficiently stimulated short-term sales.
In applications of big data for marketing and growth-strategy, data scientists search for previously unknown correlations among customer transactional and behavioral data. For growth strategy, however, more understanding and creative thought is needed about why customers do what they do, what the consequential experiences have been, what is imperfect in these experiences, and how the business might cause these new or different experiences. These are typically unarticulated opportunities for improved customer experiences. Identifying them requires skilled observation and creative interpretation of current experiences—not replicable in most businesses by data collection and analytics. Such analysis, including customers’ product-usage behaviors, not just purchase, is crucial to developing value propositions that can generate major new growth.
Urging us to “Use big data to create value for customers, not just target them,” Niraj Dawar said in HBR that big data holds out big promises for marketers, including answers to “who buys what, when?” Marketers “trained their big data telescopes at a single point: predicting each customer’s next transaction,” in detailed portraits of consumers’ media preferences, shopping habits, and interests, revealing her next move.
In the Azure narrative, Azure is “pretty confident” of what Kim will want, where and when, based on understanding her interests and interactions. In addition to targeting, big data allows “personalizing”—using our knowledge and references to customers’ past purchases and interests, to make our marketing more relevant and thus more effective in winning that next short-term promotional sale. This saga, of Kim’s “well-guided shopping journey” with Azure, leaves Kim happy (though not entirely of her own free will). In this way, it is reminiscent of Minority Report’s mall scene. The novel and 2002 film focused on prediction (“precognition”) of crimes not yet committed (supernaturally foreseen by “PreCogs”). We can hope this premonition is only a dystopic nightmare, but marketers may find the film’s futuristic marketing a utopian dream. The marketing is precisely targeted and highly personalized—ads and holographic greeter automatically recognize and call out the character’s name, reminding him of a recent purchase.
Fifteen years ago, the sci-fi film’s marketing technology was showing us the future—ever increasingly accurate predictions of each customer’s next purchase. Big data is thus a kind of commercial precognition. Data scientists are PreCogs using big data, not supernatural powers. Both narratives are fictional, but illustrate the big data logic for marketing and growth-strategy. Able to predict the customer’s next transaction, the CEM produces targeted marketing, more efficient in customer-reach. Personalized marketing is more relevant, helping it stimulate short-term sales. A fundamental problem with this paradigm is that growth strategy needs more than accurate predictions of transactions. Such strategy must transform behaviors, attitudes and loyalty of customers and other players in the chain, based on insights about the causality underlying correlations.
Summary: Strategy is More than Prediction
Marketers are right to have yearned for much more factual data on what customers do, i.e. their behaviors. However, with big data it has been easy and commonplace to overemphasize customers’ behavior, especially as related to their buying process, without adequately understanding and analyzing the rest of their relevant experience. Businesses must understand customers’ usage experience, not just buying. They must also explore what’s imperfect about this experience, how it could be improved for the customer, what value proposition the business should deliver to them, and how. Such exploration must discover the most powerful, unarticulated customer-opportunities for greater value delivery, and redesign the business to profitably realize such opportunities. These traits are essential to how strategy is different from prediction—strategy must focus on what we want to make happen and how, not just what we might bet will happen.
Kim’s past transactional behavior is analyzed to predict what she’ll likely want next, but needs to be pushed further, to discover experiences and value propositions that could influence her, and yield long-term growth. (See a similar complaint about limitations of data, from Clayton Christensen et al.) Actions—including product and service improvements, and intense focus of marketing communications on customer benefits—must then be designed to optimally deliver these value propositions.
Growth of big data in tandem with digital marketing
IDC estimates global revenue for business data analytics will exceed $200B by 2020. As a recent review said, this expansion was enabled by several trends: continued rapid expansion of data, doubling every three years, from online digital platforms, mobile devices, and wireless sensors; huge capacity increases and cost reductions in data storage; and major advances in analytic capabilities including computing power and the evolution of sophisticated algorithms. Previously, “industry poured billions into factories and equipment; now the new leaders invest…in digital platforms, data, and analytical talent.” This investment expands the ability to predict the next short-term transaction, increase marketing-communications efficiency and promotional impact. It also drains resources needed for the more difficult but, as argued here, more strategically crucial exploration of customers’ usage experiences, and discovery of breakthrough-growth value propositions.
Using digital technology to market products and services, the digital marketing function has risen rapidly. Last year for the first time, US digital ad-spending surpassed TV, the traditional dominant giant. And digital marketing, both the source of most big data and the easiest place to apply it, increasingly leads development of business strategy.
Efficiency and relevance: important but usually much less so than effectiveness
More efficient marketing is desirable, but only if it’s effective, which is often taken for granted in the focus on efficiency. Much digital marketing faith is put in the four-part promise of “the right ad or offer, to the right person, at the right time, via the right place” (see here, and here). Most big data focus on the last three, which mostly enhance efficiency, instead of the “right ad” which determines effectiveness.
Hunger for efficiency also drives the focus on targeting. Personalizing, when possible and affordable, can also make customers more willing to hear the message, increasing efficiency—and possibly effectiveness—by its greater relevance.
However, effectiveness is the far more crucial issue. If a message does not credibly persuade customers, it is still of little use to the business, even if “efficient.” But targeting and personalizing marketing typically do not identify what behavioral attitudes to change, or how to change them. This more fundamental strategic goal requires deeper understanding of the unarticulated value to customers of improved experiences, and detailed creative exploration of the business’ potential to profitably cause these improvements.
Reinforcing the predominant near-term and efficiency focus of big data in digital marketing is the nature of online sources typically available for big data. McKinsey estimated that, “so much data comes from short-term behavior, such as signing up for brand-related news and promotions on a smartphone or buying a product on sale. That short-term effect typically comprises 10 to 20 percent of total sales, while the brand…accounts for the rest.” This short-term nature of the readily available data biases marketers to focus on short-term transactional results.
Location-based, real-time big data—another advance in short-term promotion
It seems worth noting here that location-based marketing excites the digital marketing world, seeing the “next big thing.” Below are examples, from Skyy Vodka and Starbucks:
As location data gets more accurate (problematic today) this approach will again improve promotional efficiency. In one illustrative test recounted in Ad Age, Brown-Forman, suppliers of Herradura tequila, teamed with Foursquare (a search-and-discovery mobile app that helps find “perfect places [food, entertainment, etc.]”). Foresquare used Brown-Forman’s list of accounts where Herradura is sold, to target mobile and other Herradura ads to consumers whose mobile was close (or had been in) shops, bars, or restaurants in the account list. They saw 23% increased visits to accounts, a positive signal.
Since big data was applied early by direct marketing companies, big data today (further illustrated above by advances in location-based marketing) works more like direct-response marketing than demand-generation. The problem, as noted earlier, is that businesses more than ever also need the latter—demand-generating activity, creating loyalty, behavioral changes long-term growth actions. Some businesses don’t need these luxuries, when cheap, automated big-data options—digital PreCogs—proliferate.
But most businesses do need to make these serious strategic investments, in addition to and complementary with big data analytics. Having digitally captured countless petabytes of data describing Kim’s every action of shopping and buying, the business managers now need to spend time with Kim learning about her usage of that apparel. What were her experiences before and during usage of those shoes, the belt, and other items? And what of her daughter’s experiences with the backpack? What was imperfect, what could some better experiences be, what would be an improved superior value proposition, and what would it take to provide and communicate that proposition effectively and profitably? These intensively customer-focused actions can enable the discovery and activation of powerful insights for profitably influencing customers’ (and others’) behavior, a key basis for generating profitable major growth over time.
* * *
As mentioned above, this blog series will expand on these concerns about the way that big data analytics has evolved for use in growth strategy, including digital marketing; and will expand on the above recommended solutions for marketers and businesses, including how these solutions apply to most businesses.
Make Value Propositions the Customer-Focused Linchpin to Business Strategy
We suggest that Businesses should be understood and managed as integrated systems, focused single-mindedly on one thing – profitably delivering superior Value Propositions, what we call delivering profitable value (DPV). But most are not. Some readers may assume this discussion can only be a rehash of the obvious – surely everyone ‘knows’ what Value Propositions (VPs) are, and why they matter. But we suggest that most applications of the VP concept actually reflect fundamentally poor understanding of it, and fail to get the most out of it for developing winning strategies. In this post I’ll summarize 4 ways to improve on delivering profitable value, using the VP concept far more effectively – as the customer-focused linchpin to your strategy.
Delivering Profitable Value – Let’s first recap the key components of this approach:
Real & Complete Value Proposition – A key element of strategy; internal doc (not given to customers); as quantified as possible; makes 5 choices (discussed in depth here):
Deliver the chosen VP – A real VP identifies what experiences to deliver, not how; so manage each business as a Value Delivery System with 3 integrated high-level functions:
Profitable Value? – If customers conclude that a VP is superior to the alternatives, it generates revenues; if the cost of delivering it is less than those revenues, then the business creates profit (or shareholder wealth) – thus, it is delivering profitable value.
* * * * *
4 areas where many businesses can improve on delivering profitable value:
Now let’s consider each of these 4 areas in more detail:
The table below summarizes some common misperceptions about Value Propositions, followed by some discussion of the first two.
Of these misperceptions, the first two are perhaps most fundamental, being either:
It’s not your Elevator Speech – a VP is strategy, not execution – For much greater strategic clarity and cross-functional alignment, avoid the common misunderstanding that confuses and equates a VP with messaging. Execution, including messaging is of course important. A VP, as part of your strategy, should obviously drive execution, including messaging; but strategy and execution are not the same thing. If you only articulate a message, without the guiding framework of a strategy, you may get a (partial) execution – the communication element – of some unidentified strategy. But you forgot to develop and articulate the strategy! The execution might still work, but based more on luck than an insightful, fact-based understanding of customers and your market.
This common reduction in the meaning of a VP, to just messaging, not only confuses execution with strategy, but also only addresses one of the two fundamental elements of execution. That is, a VP must be not only Communicated, but also Provided – made to actually happen, such as via your products/services, etc. If customers buy into your messaging – the communication of your VP – but your business does not actually Provide that VP, customers might notice (at least eventually). Though some businesses actually attempt this approach – promising great things, but not making good – and may even get away with it for a limited time, a sustainable business obviously requires not only promising (Communicating) but actually Providing what’s promised.
And it’s not about us – focus VPs on customers, not our products, services, etc. – The other common misuse of the VP concept starts by treating it (rightly) as a strategic decision and internal document. But then (again missing the point) such a so-called VP is focused primarily on us, our internal functions and assets, rather than on the customer and resulting experiences we will deliver to them.
Here it’s helpful to recall the aphorism quoted by the great Marketing professor Ted Levitt, that people “don’t want quarter-inch drill bits, they want quarter-inch holes.” A real VP is focused on detailed description of the ‘hole’ – the resulting experiences due to using the drill bit – not on description of the drill bit. Of course, the drill bit is very important to delivering the VP, since the customer must use the drill bit, which must have the optimal features and performance attributes, to get the desired quarter-inch hole. But first, define and characterize the VP, in adequately detailed, measurable terms; then separately determine the drill-bit characteristics that will enable the desired hole.
It’s impossible to link Providing and Communicating value without a defined VP. However, even with a chosen VP, it is vital to explicitly link its resulting experiences, to the requirements for Providing it (e.g., product and service) and for Communicating it. Companies can improve market share and profitability by rigorously defining the VP(s) they aspire to deliver and then rigorously linking to the Providing and Communicating processes. Failure to make the right links leads to a good idea, not well implemented. (See more discussion of this Value Delivery framework here.)
Most businesses are in a value delivery chain – simple, or long and complex, e.g.:
Many companies survey their immediate, direct customer, asking them what they most value. Less often, they may ask what that customer thinks is wanted by players down the chain. They may rely for insight on that next customer, who may or may not be knowledgeable about others in the chain. There is often great value in exploring entities further down, to understand how each link in the chain can provide value to other links, including final customers, often with important implications for the manufacturer, among others. (See more discussion of Value Delivery Chains here.)
Businesses often conduct research, essentially asking customers, in various forms, to specify needs. A limitation of such research is that customers often filter their answers by what they believe are the supplier’s capabilities. We believe a better way is to deeply study what entities (at several links in the chain) actually do. First capture a virtual “Video One” documenting current experiences, including how an entity uses relevant products/services. Then create “Video Two,” an improved scenario enabled by potential changes by the business in benefits provided and/or price, but which somehow deliver more value to the entity than in Video One. Then construct a third virtual video capturing competing alternatives. Finally, extract a new, superior VP implied by this exploration. Results come much closer to a winning VP than asking customers what they want. (See here for more discussion of creatively exploring the market using this methodology.)
In his recent interview of Michael Lanning (shared in the previous post of this Blog), Brian Carroll asked Mike about the role of Value Props in B2B strategy; Brian wrote about this conversation in the B2B Lead Blog.
Recently Michael Lanning was interviewed by Brian Carroll, Executive Director, Revenue Optimization, MECLABS Institute, for the MECLABS MarketingExperiments Blog. Brian asked Mike, as the original creator of the Value Proposition concept in 1983 while a consultant for McKinsey & Company, about the evolution of this concept over the past three decades. Their discussion, on “Real Value Props, Direct from the Source,’ is here.
This Blog aims to help illuminate what makes for winning and losing business strategies, discussing what I hope are interesting examples, and suggested explanatory principles that readers in many businesses may find persuasive and actionable. In this Blog I will especially view these topics through the lens we (the DPV Group, LLC ) call ‘Delivering Profitable Value (DPV)’ which contends that strategy should focus single-mindedly on profitable, superior ‘Value Delivery.’ That is, sustainable business success, while elusive and subject to good and ill fortune, can most realistically be achieved by a concentrated, deliberate effort to creatively discover and then profitably Deliver (i.e. Provide and Communicate) one or more superior Value Propositions to customers.
While many senior managers would say this idea is ‘obvious,’ we suggest that most do not actually develop or execute their business strategies based on this understanding of the central role of the Value Proposition. Thus in our view, a Value Proposition should not be a slogan or marketing device for positioning products/services, but rather the driving, central choice of strategy, fundamental to success or failure of the business. This Blog will try to bring that idea further to life for a variety of industries and markets.
Although unintentionally a rather closely held secret, I actually first created and articulated/coined some of these concepts, including the now widely used (also misused and abused) ‘Value Proposition,’ plus the ‘Value Delivery System’ and the related concept itself of ‘Value Delivery.’ I did so (after my first career, in Brand Management for Procter & Gamble) while a consultant for McKinsey & Company, working closely with Partner Ed Michaels in Atlanta in the early 1980s.
In life after McKinsey, I built on those seminal concepts in my work, first with then-professor at Stanford and Berkeley Business Schools, Prof Lynn Phillips, and later with my DPV Group colleagues such as long-time P&G-managers Helmut Meixner and Tim Fealey, and others. Those expanded concepts included the Customer’s Resulting Experience (pre-dating the also widely-adopted idea of customer-experience), and the notion of organizations that are, in contrast to Value-Delivery-Focused, best understood as ‘Internally-Driven’ and/or ‘Customer-Compelled.’
Today, the DPV Group and I try to help clients appreciate, internalize and apply these Value Delivery strategy concepts, to pursue profitable, breakthrough growth in a wide range of B2B and B2C businesses. We hope that this Blog will also contribute to that goal.