by Mimi Nicklin (@MimiNicklin) How actionable is shopper data really? A ‘blockbuster’ debate between Minority Report and Zero Dark Thirty.
I don’t know about you but I find it very irritating every time I read a newspaper article or report that quotes the Tom Cruise film, Minority Report, as a reference to how ‘psychic’ we are becoming when it comes to shopper data analysis.
[pullquote]According to a recent survey by the Path to Purchase Institute in the USA 84% of organisations will be increasing their spend into data and digital shopper marketing this year to try and make inroads into this complex and vast field of expertise.[/pullquote]
It paints a picture that, as per the film, we can track down and target any consumer’s needs in a matter of seconds using the Internet, electronic databases and augmented reality connections. Not only does it entirely undermine recent developments in research, predominantly in the neuromarketing field, but it leaves every marketer reading the article dreaming of a day when it may actually come true with magic data and little analytical requirements.
The hard reality for us is that it is a lot easier to collect than it is to analyze, make sense of, and actually use data. The more recent, Zero Dark Thirty blockbuster film tells this story well – just because we have the data, doesn’t mean we can use it to effectively action the solution!
Many brands and retailers are collecting but how many are actually connecting and driving sales? With 2.5 quintillion bytes of data being created globally daily, do we stand a chance of ever getting to the bottom of it?
Many believe the answer lies in starting small. According to a recent survey by the Path to Purchase Institute in the USA 84% of organisations will be increasing their spend into data and digital shopper marketing this year to try and make inroads into this complex and vast field of expertise. More than 33% of these organisations went on to say that they will do so by 25-50% So, things are changing fast and budgets are moving even faster, but the question still remains, can all this investment really make shoppers buy things?
I believe the answer is yes, when it’s done brilliantly. There are multiple cases whereby a retailer, in most cases, is using data to form the basis of tailored and effective shopper solutions that really do help shoppers with their decisions to buy. However I also believe there are three levels from which these steps to success begin, and we need to get the basics right before we expect great results. We too might want to start small and ensure the foundations are there for ‘blockbuster’ results in the future.
Firstly, and at its most simple, is POS data directly from the retailers. This granular, direct data is a good starting point for many brands to use as a basis for shopper campaign development. Certainly if you can convince a retailer to share their transactional data over an extended period of time one can cross reference this against your own category, knowing it is very real data that can predict and segment shopper’s basket needs. This allows your promotional programmes to be more responsive to existing shopper habits and allows shopper marketing partnerships with retailers to accurately ‘talk to’ the shopper mindset in store.
Secondarily there is, of course, the wide reaching and ever expanding networks of retail store, and occasionally brand specific, card data. A 
much deeper analysis and a more accurate reflection of shopper habits and usage over time, and of course with the benefits of developing a two way conversation, direct incentives, and shopper rewards.
Thirdly, and where I believe the most value sits, is by taking the two above data sets and building this into a much richer and deeper understanding of shopper behaviour by layering on lifestyle data. The in-store loyalty card alone can define shopper habits, what she buys, when and with what other products in her basket. The development of lifestyle based understanding however creates a layer that defines personal and family needs beyond the fact that we know she buys ‘pasta in bulk’ and long life milk every month.
With this lifestyle analysis, the brands ability to incentivize and reward becomes infinitely more powerful and effective. A great example might be the Walgreens Balance Rewards programme in the USA, whereby monthly they gather lifestyle data by consumer questionnaires to overlay on their syndicated loyalty card data. With this they develop tailored in store and digital services that help shoppers shop, with everything from prescription reminders to photographic solutions for the family. Their use of data ensures that shopping at Walgreens really is the most efficient, rewarding and convenient choice for your individual needs.
Recently Tim McCauley, Senior Director for Mobile Commerce at Walgreens, indicated that customers who engage with Walgreens in person as well as via these online and mobile apps spend six times more than those who only visit stores. And with over 12 million visits to their online and mobile services a week, it doesn’t take much convincing that the data analysis model is working for them.
To create some additional context for us here in SA, I will paint a basic picture. Your data collection from the storecard database has helped you understand that Lebo (Shopper A) and Xolise (Shopper B) are both heavy users of pasta every month and that they are effected by price promotions in the category. You know that Lebo is a mother, based on her nappy and babyfood purchases, but by understanding the lifestyle data of the two women you can now also understand that Lebo prefers classical music, enjoys playing Monopoly with her family and is looking to move house. Lebo is under constant pressure with her bills and is therefore buying pasta as a cheap tummy filler each month.
Xoliseby comparison listens to house music, is an avid runner and is preparing for a marathon, so she is buying pasta in preparation for the big race. Her reasons for pasta shopping are entirely different and her mindset when she is shopping is of a very different direction.
At stage one of the data analysis one could assume a similar ‘buy pasta in bulk’ message would appeal to the two shoppers, and indeed if it is a simple price discount it probably would. However, with a deeper level of data analytics your shopper programmes and promotional messaging can take on a far more effective form, helping marketer’s define everything from the images you use, to the rewards you offer, to the types of ‘free recipe cards’ that the stores create.
So the debate continues, but in my opinion, the truth is that for most of us, the reality is far less Minority Report than one might wish! The data is being collected but our effectiveness at using it to predict and effect behaviour is still in its infancy.In fact, when you really look at it, we are far closer to Zero Dark Thirty, where it takes ten years to find the worlds most wanted man because of the web of information gaps, organizational disconnects and data misunderstandings.
The data is pouring in, but only the most organized, streamlined organisations are using it to really convert sales directly. As marketing departments start to specialize their departments with Digital Shopper Marketing teams, the quest to unravel habits and chase the shopper is closer to Minority Reports vision of the future, than the reality of our skill setsyet allows us to be.
Mimi Nicklin (@MimiNicklin) followed her passion and experience in the consumer, retail and shopper space from regional roles in Europe and Asia, to South African shores in 2010. Having led global brands through the line for Procter & Gamble, and two of London and Hong Kong’s top agencies, her background gives her an international perspective to add to her depth of SA understanding. She serves as strategic director and a partner at 34 Group. Mimi contributes the monthly “The Sell” column concerning shopper marketing to MarkLives.