It has been a marketer’s dream since “Marketing” got invented: Having all the data you need to predict customer behavior and engage with each customer one on one. Remember Don Pepper’s book “The 1:1 Future”? This was 20 years ago. But even today, reality is often different:
As I’m checking out at my local Safeway, I’m handed a coupon with my receipt. The coupon is for diapers – despite the fact that my youngest child hasn’t needed them for five years – and I’m getting it when I am walking out of the store! I get home; a hundred page book of coupons is waiting for me included in the Sunday paper. If I want to find a coupon that is relevant to me, I have to dig through the book for ten minutes. I’m not going to.
Big Data has captured all the attention, because, for the first time, companies have been able to collect data about millions of customers, down to the individual level. And it’s leading to a paradigm shift in marketing and customer interaction – as Philip Kotler said: “mass marketing is dead”. But many companies are struggling with the challenge it presents and many have not been able to make effective use out of it. The explosion of data – transactional data, purchasing history, customer profile and preferences, social media conversations etc. – has been at high volume and is increasing with extreme velocity – it can be very difficult to filter out the signals in a lot of noise. Even if you are able to capture it all, how do you make sense of all the data?
The real action is in Small Data – the individualization of that large data trends to a single person and the automation of actionables at that level. That big data set is near useless without connecting it all: store inventory, global shopping trends, the weather, and all the data about you, the individual – your shopping history, your location, your recent website history. You need to be able to break it down to the individual level so companies can create automated, personalized offers in the moment when it really matters to you and – by using learning algorithms – offers that get more relevant to you each time.
Small Data is all about generating these actionable nuggets. To go back to that coupon example; getting a coupon as I leave the store is a terrible use of data, as it’s unlikely I’ll ever use it – even if it’s for something I want. A newspaper coupon is even worse, as its targeting is about the newspaper demographic.
However by receiving a location and preference-triggered coupon for marinara sauce on my smart phone as I reach for the linguine – that’s Small Data (derived from Big Data) in action – producing actionable results, and creating a still-profitable sale out of nothing. If I don’t buy the sauce, then the algorithm uses that feedback and will learn for the next person and the next time I’m in that spot.
Of course, it’s not just about coupons – other small data models we’re working on automatically identify the top five customers each of a firm’s sales reps should be visiting. The power lies in the predictive capabilities of the self-learning engines that sift the big data for nuggets and create the small data, which is actionable and can drive actual results.
So Big Data is all around us and here to stay (it will grow even more and faster), but it is irrelevant unless you can turn it into Small Data to produce the actionable results for your business
In other words: Big data is a challenge, but it can become an opportunity if you leverage the right technology (such as in-memory and predictive analytics) to turn data into insight into action and to engage with your customers like never before.
To learn more about how we can help you with your business challenges please have a look at SAP’s Solution Explorer for Marketing Solutions.