But all of this presupposes that we know our customers. That we are able to read patterns into their behaviour and can use those patters to break the overall meta group into smaller and thinner slices, targeting each slice with customized offers and communications to trigger individual and nuanced behaviour. It is this that has come to form the basis of concepts like custom targeting, moment marketing and programmatic led re-marketing.
However, to make any of this possible, and effectively so, the marker must have the ability to both store and mine customer data, being able to arrive at sharp understanding and insights which will in turn fuel the marketing and campaign engines. This is easier said than done especially if it needs to be done in the rigour that it deserves.
Collecting “what” data is relatively simpler. Often from your POS, this data will help you tag a customer in your database often using his unique mobile number or email id. This data will also tell you what does this customer normally buy, how often does he buy it and what is the average value he purchases every time he makes a transaction with the brand. All of this and some more depending on what your system is configured to collect and analyse.
However, it is the “why” that is much more difficult to glean and collect. This data or information forms the fundamental basis of understanding of the “what” data. This should lead a marketer to understand why does a customer buy what he does and not another but similar product. Why does he shop at the frequency that he does it and what is it that can be done to nudge a higher frequency of visit to the store? Unfortunately, this “why” data is not what the system can capture automatically while its stores purchase details and history but needs to actually be fed by customer interactions through formal and informal surveys, researches and focussed discussions.
While marketers often follow the individual twin tracks of the “what” and the “why” data individually, where they fail usually is to be able to find a structure that can join both – almost superimpose one set over the other. It is here where the opportunity to meaningfully use data and information actually slips between the two legs of the stool leaving the marketer none the wiser. I believe that it is actually this aspect that needs most urgent attention and remedy. In the absence of requisite technology and human systems, the process will not deliver a single and comprehensive understanding of the customer, making his targeting a less than optimal exercise.
Technology can come to the rescue if you know exactly what you are looking at. Let me share some thoughts for starters. A data management system will have to be evolved – one that is capable to receive customer data – not just purchase details but all interactions that he may have ever had with the brand in any manner. That would mean that if a mobile number may be that unique customer tag, it should be linked to every time the customer has been shopped, written on your feedback book, engaged with you online and perhaps even interacted with you on social media. Some of this information would have been left by the customer voluntarily and there is other that you can seek by reaching out to him. Store-based surveys (if you are in a business with physical foot-print) and web and mobile based surveys in other cases would help you collect information on your individual customers, in your attempt to paint a more detailed and vivid picture of each one of them. And it is these pictures that will give birth to creation of customer personas – something that becomes the starting point for any marketing and targeting exercise.
Outside of technology, you will need experience human capital that can work with data points, layering one over the other. This is important because I believe that irrespective of all analysis that is processed by any Data Management Platform (DMP), it will also come down to the human ingenuity to see and identify patterns which can be used to reclassify data (and its owners) into smaller and more distinguished sub-groups. Therefore, a team of data scientists and over and above technology platform wizards is an equal requirement for a powerful system this system to come alive.
Without any doubt, this is a painstakingly laborious process. One that you need to continuously improvise up on as you go along the journey. But having been the architect of one, I will vouch for what it can do if it is done right. From targeting individual customer with more of the same product that they have bought in the past, you begin targeting with products which although at first glance look completely unconnected with their original purchase, ultimately reveal how they are relevant to that consumer’s overall life stage at any given current context.
The lady who just bought green tea from you doesn’t need to be sold another bag of the same green tea. Especially once you have come to know that she is consuming it in preparation of her upcoming marriage. Then instead of trying to sell her another bag or variant of the same tea, what in fact you have an opportunity to sell is home furnishings and furniture because she could possibly also be doing up her new home she shifts into once married.
That is the opportunity to drive business and excellence by connecting the various data dots. It is not easy. But had it been, then it wouldn’t have been half as rewarding as it can be if and when it is done right!
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