Aqumena Review

A survey conducted by Duke University’s Fuqua School of Business reports that the percentage of US marketing budgets that companies plan to allocate to analytics over the next three years will increase from 5.8% to 17.3% – a whopping increase (trends in US marketing are indicative of what is likely to happen, with a lag in other parts of the world. In the world of marketing it is still more or less the American century!).

At the same time the survey found that top marketers report that the effect of analytics on company wide performance is fairly average – a performance score of 4.1 on a seven point scale where 1 = not at all effective to 7 = highly effective.

The performance score has not changed much over the years. The score was 3.8 on the same scale in a survey conducted five years earlier.

Clearly performance is not matching expectations. So what gives? Why is marketing analytics not delivering on expectations?

Carl F. Mela and Christine Moorman, professors at Fuqua, posit several challenges.

Among them are:

The Data Integration Challenge : While data is becoming ubiquitous, it comes from disparate systems. Data Integration strategies need to match data and integrate across different coding schemes and these strategies need to be in place before data collection systems are put in place .

The Information vs Insight Challenge: Very often data analytics provides reams of information but with holes in them that prevent using the information to drive marketing action (in some cases the holes even subvert marketing action). For example information derived from data analytics might club a set of customers as heavy purchasers of a company’s products. But hidden in  this information is the fact that with a sub-set of this customers the heavy purchase represents a high share of the wallet and with others there is still some way to go to saturation levels of share of wallet. The absence of  this extra layer of information could lead to inefficient marketing action. But who is to decide that this extra level of information is required? This brings us to the the third and most important challenge

The Talent Challenge: Most analytics departments are driven by data scientists. While a crack team of statistics PhDs might produce mind-blowing inferences from a complex deluge of data, it is not in their bailiwick to dig for actionable consumer insights. The marketing team – the brand managers, the consumer researchers, the marketing communication planner – need to be part of the analytics team – driving the analytics instead of just being users who are fed it.

To my mind deeper than the inter-related challenges of data integration, information vs. insight and talent identified by Mela and Moorman, is the need for a basic shift in marketing paradigm that needs to happen before marketing analytics can fully deliver on it’s tremendous potential.

Most marketers today would tell you that they believe that the digital age has fundamentally changed how they think about marketing and sales. However scratch below the surface and you will find that most marketers are, at a sub-conscious level, still stuck in the era of mass marketing (why is is to so? One reason is that many marketing decision makers are of the older generation who learned their ropes in the mass marketing era. Even with many Millennials, the problem arises because they had these older generation mass-marketers as tutors and mentors. I am sure this will change as the next generation of marketers move to positions of power and influence).

A lurking belief in mass marketing subverts the use of marketing data analytics.

It demotes marketing data analytics to being just a new fangled way of doing consumer research that is to think of marketing data analytics as a service that delivers inferences and insights that then can be projected on to the market to drive mass designed and produced products into mass distribution channels and promoted through mass media campaigns – “Ah yes don’t forget to allocate a substantial share of the budget to digital and social media. See I am a digital age marketer!”.

I believe marketing data analytics is stuck, on average, at mediocre levels of performance delivery because on average it is used as a “projective” tool and not as a “focus” tool.

I believe the right use of marketing data analytics is to go from the general to the particular and not from the particular to the general. The right use of marketing data analytics is to gain better and better insight into a particular consumer and then marry this insight with the power of digital platforms to communicate one-on-one, build a relationship and sell to the individual (not covertly but overtly and with permission. The gentle pull strategy instead of the obnoxious push). I would like to call this “focus marketing” in contrast to “mass marketing” which I would like to re-term as “projective marketing”

The era of “focus marketing” has already dawned and is being practiced in some pioneering quarters. Take the example of StitchFix – a fashion subscription service that uses data analytics to better understand a given individual’s tastes, proclivities and lifestyle. The analytics is driven by both questionnaire directed at the individual and relevant sampled and Big Data sources. As Stitch Fix’s CEO Katrina Lake explains she sees data analytics as being at the core of her business. Not to project inferences and insights to a bunch of consumers but to use inferences and insights to focus better on one customer.