Setting the Stage for the Next Best Offer
By John Lucker
Once the job of a knowledgeable, friendly salesclerk, the retail strategy of framing the customer’s “next best offer” (NBO) is now an analytical, strategic solution that introduces customers to what they need next, and, just as important, when and where they need it.
As introduced in an article I co-authored for the Harvard Business Review (”Know What Your Customers Want before They Do,” by Thomas H. Davenport, Leandro Dalle Mulle and John Lucker, Dec. 2011), the NBO requires careful planning and staged implementation. After all, the helpful salesperson did not acquire customer knowledge overnight. In fact, it could take months, if not years, to create a close-knit relationship between salesclerk and shopper.
I believe the science—and art—of an NBO is a surprisingly immature area, but one with great potential in the retail space. As customers, few of us believe that the companies we do business with appeal to us often enough with timely, compelling and insightful NBOs.
Fortunately for retailers, rapid technological innovations and related analytics are expediting the getting-to-know-you timeframe. Now, within milliseconds, retailers can use internal company information, external data resources and synthetic data insights—coupled with a variety of advanced analytic innovations—to move consumers toward what they are searching for, even before the consumers know they need or want it.
However, retailers should stage the process of steering consumers to their next best offers via a deliberate and planned process, rather than spontaneously making the offer in a potentially misunderstood context.
First, retailers need to carefully define the objective of the NBO. What is it that you want to achieve? Increased revenue? Improved customer loyalty? Identify new customers? Many organizations flounder in their NBO efforts, not because they lack analytics capability, but because they lack clear objectives and strategic direction.
Equally critical to the well-crafted NBO objective is gathering critical data about your customers, the offerings your company has available and customer purchase patterns. Customer demographics and psychographics, as well as product or service profitability and availability and a host of other potential factors, should be carefully considered. In addition, the NBO should be framed around customer purchase histories and the context in which products or services are acquired, as well as predicted macro future trends and purchase appetites.
With data in hand, the next step is to use advanced analytics, predictive modeling and other quantitative tools to match customers to offers. Business rules often serve as a guide to what offers are made and under what conditions and circumstances. It is essential to remember that offers are best made sparingly, and they should be well-timed and monitored frequently for effectiveness.
Think of each offer as a test, an experiment. Fast failure or, better yet, fast success is essential to motivate NBO activities with favorable economics. Ideally, a road map of short-, medium- and long-term activities—with planned and articulated values that feed subsequent costs—should be derived to gain maximum company and executive buy-in for a sequence of NBOs.
Learning from mistakes or successes is critical to defining and redefining the NBO. Also keep in mind the need to be flexible and to modify an NBO when defining objectives.
Advanced information technology, rapid data gathering and analytics are about staying not just on top, but ahead of the marketplace. Companies that lag behind in modifying their NBOs can quickly lose the race to their more agile competitors.
With an excellent NBO strategy, it’s now the computer asking, “How can I help?” or “I know this will delight you.” Welcome back to the age of personalized shopping—with a new technological, data-savvy twist!
John Lucker is Deloitte’s Global Advanced Analytics & Modeling market leader and a leader for Deloitte Analytics. He is also a leader of Deloitte’s Advanced Analytics & Modeling practice. John provides clients with strategy, business, operational and technical consulting services in the areas of advanced business analytics, predictive modeling, data mining, scoring and rules engines, and other analytic business solution approaches.
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