Four Ps of Marketing and Strategic Analytics

Price, Promotion, Product and Place have been among the traditional marketing instruments for decades. However, “four Ps” marketing has been criticized by professionals and academics in the direct marketing/database marketing and CRM space. These experts correctly point out that 4P conspicuously lacks any meaningful consideration of customers. In fact, many in the CRM industry dismiss the usefulness of these old concepts and argue that customer-centric analysis and marketing is the only way to go. Some even call such change a paradigm shift both from marketing strategy and
execution standpoints.

I share some of these viewpoints and agree wholeheartedly that a strong customer-centric approach should be the over-arching strategy framework. I will also point out that the 4P way of thinking has not adjusted itself to reflect the Internet and today’s multi-channel world, where there is a noticeable change in the way customers consume information. These changes have had a dramatic effect on marketing landscapes.

However, I do not agree with the assertion that 4P has become completely irrelevant. Quite the contrary: I argue that they remain highly relevant and, if anything, marketers in the CRM space should use them more.

Despite of all the changes in media, marketing culture, and practice, these four instruments remain important. No one would argue that Price, for instance, no longer matters; indeed, the Internet has given consumers considerably more information about pricing and tools to compare competitive prices. The real change is coming from the technologies we use to execute marketing. In the old days, 4P strategies were designed at a mass level. Today, we have the data and technology to tailor them to smaller segments or even to individual customers. Such individualization of the 4Ps is not only an improvement: it has become a necessity for companies to stay competitive in the long-term.

Let’s use Price Setting to illustrate the point. The traditional approach of determining price is based on some sort of econometric marketing mix model or a conjoint analysis across the entire population. In other words, one would determine some “average” price that we could promote, something that made sense when the predominant marketing channels were mass “push” channels. As technology has enabled targeting of increasingly small segments, marketing researchers are increasingly using segment-based conjoint analysis to get a more complete picture of the differences in price preference among segments. Even for industries where different pricing is not realistic or desirable in practice, such more customized information allows more diversified product/feature/service packaging, as well as bundling tailored to different types of consumers.

One retail executive Fulcrum works with half-jokingly stated that if one wants to know the optimal price of a product, the answer can be found on eBay. Dan Ariely, the author of the book Predictably Irrational, illustrated how Apple uses behavioral economics and social media to set the reference price for its new products . In any event, what has changed is how pricing decisions are derived and how pricing is promoted. What has not changed is that pricing remains a paramount decision variable in marketing.

CRM professionals have been right in their criticism of the old style of 4P analysis and marketing. However, at the same time they have ignored the importance of these elements. For example, back to pricing: I do not see many models in CRM sector, especially on the practitioner side, use pricing variables at all. This is extraordinary. How can we build marketing models without using the basic marketing mix variables? Is there really a belief that pricing does not matter, that the price a customer was presented was unimportant in whether they responded to an offer or not?

Part of the reason is due to the direct marketing heritage of CRM. Most of the models in direct marketing are tactical models supporting a particular event or campaign where the price is already fixed or totally controlled. We do not have to include pricing variables in models (testing two pricing in a campaign is not the kind of marketing mix modeling I am discussing here.). Another major factor is that most “CRM models” are designed to be more predictive than normative. For example, most of the attrition models I have seen in various industries do not have pricing variables, or indeed any important marketing mix variables. Again, common sense would indicate that at least some customers defect due to pricing effects.

Discerning marketers have started to express concern with such practices. While such models are often predictive, they do not provide much insight on combating attrition behavior in an actionable way. In other words, it is good to know with relative precision which customers are at risk for leaving, but it is much more important to understand what the drivers are among various segments. By not including such factors as price, models provide no strategy for addressing attrition.

One disturbing aspect I have observed is the lack of skill sets in using such variables in model building. I once met a business analyst working for a major retailer. When I asked him how he could have developed a customer segmentation without understanding his customers’ price and promotional elasticity, he argued for simplicity and ease of communication, points I agree with. But then he argued that all this “math stuff” was not important, as long as we send the people the right offer and mail them enough times. Clearly, price matters to many customers and will strongly influence response. Moreover, much of our marketing today does not trigger a sale so much as it triggers research by customers. So such blind marketing may end up driving sales, but potentially to competitors who have a better understanding of fundamental drivers.

At Fulcrum, we believe that a good customer-level model is in essence a marketing-mix model developed at a more granular level. We are not suggesting that we should turn every predictive model into a marketing-mix model; they certainly require very different data and technical approaches. But conceptually, they have more similarity than differences. In fact, we sometimes encounter surprise among potential clients that we approach problems in this way, since they expect a CRM expert to have a sole focus on “predictive” modeling.

Forrester research in a recent study advocated astutely and correctly that we need to elevate CRM to go beyond database marketing. It is a process, enabled by technology solutions that help companies uncover customer insights, design customized strategies, solve business problems, and promote sustainable and profitable long-term relationships. In essence, we need to elevate CRM to a broader and strategic level. That is why this blog’s theme is strategic analytics. The appropriate adaptation of 4Ps in analysis and modeling is a pre-requisite to that goal.

Comments are closed.