Why data helps you better understand your customers
Some time back, I was invited to speak in our partner's m-brains marketing intelligent conference about analytics in IoT in London. Naturally, having been granted such an opportunity, I also attended the discussions around measuring the success of analytics, or preferably in this context, measuring the business value of marketing intelligence.
The connection between analytics and more accurate (and successful) business decisions is hard to define for many reasons. One obvious factor is that we are missing the reference point for the comparison, having make the same business decisions in the past without the help of analytics.
Due to this and other reasons, a cleverly accurate phrase came up during the discussions that I took a liking to: "The best ROI-discussion is not having one at all". Or as I like to think of it, when one is doing the analysis correctly and has invested into the right tools, the benefit of that is self-evident and understood. I believe we are well beyond the point of needing to prove the benefits of the analytics investments in general to business decision makers.
Understanding the value
Does this mean that there should be no measuring of the performance of the analytics? Of course not. For instance in marketing, the accuracy of the customer segmentation and profiling should be constantly measured through key indicators like strong leads or the number of sales created. These figures aren't just important for business calculations, but also for improving the accuracy our predictions.
The value proposition of a customer analysis is the same as it ever was, but new technologies and sources of data are emerging all the time to make it easier and more effective. This allows us to create much more accurate descriptions of the customer segments, bring new viewpoints and make our view on the matter more dynamic and real-time. The business need is acknowledged already, but the fulfillment of that need is technology driven.
How it works?
To elaborate on this, let's take a look at customer segmentation in brick and mortar store. While it is already quite common for shops to have some segmentation done to their customer base, this is usually a static profile that in the worst cases does not even update information such as the address of the customer.
But imagine bringing a new data set to complement this customer profile, which can describe much more accurately the hour and route that the customer has spent in the brick and mortar store. This would allow few extra degrees of freedom in branding and improving the customer service, even on hourly basis in the stores. This is not a new approach and much used with a great success in e-commerce. To drive this in use in brick and mortar stores we need to employ new technology, such as the one used in Tieto's Intelligent Buildings.
Our needs and so do our need for different types of services vary, and not only in a monthly or yearly scale. Why wouldn't we as service providers prepare ourselves to meet these needs more accurately?