Does your best store associate do customer segmentation faster and better than your Marketing Automation system?
A billboard on Sheik Zayed Road in Dubai at the moment reads “Data is the new oil”. (I could write a whole article discussing whether or not this phrase is useful or not, but there are some similarities).
Unlike oil, customer data is more plentiful than ever, but data, like oil, is just the raw ingredient. There is a reason that one of the most sought after skill sets is the ‘Data Analyst’, a person who can refine the base commodity using insight – from data into information.
Data without context or analysis is useless.
But while good data analysts are hard to find, chances are there are other people in your organisation who know your customers. While the algorithms that power Marketing Automation programs are getting better, chances are that your store personnel, if experienced and properly trained, can ‘segment’ your customer on sight and create real-time, personalised recommendations.
Blink is a book about how we think without thinking, about choices that seem to be made in an instant, in the blink of an eye, that actually aren’t as simple as they seem. Some people who work in retail are able to make these decisions about customer segmentation based on years of experience personally interacting with real customers, at the point of sale.
What do we know about this customer? What data points can we collect just by looking at him?
He is male. He is 20-30. He is fit and in shape. He is not wearing a watch or jewellery. There are no visible brands, so perhaps he likes things simple or is not showy. Though not visible, there is a better than 85% chance he is carrying a smartphone with a data connection.
Based on this, and based on the kind of store he walked into, we can already be personalising his journey.
The first example was purposefully generic. Let’s imagine what our experienced and well-trained store person, working in a sports store can infer from another example.
So. Not just a male, but a brand advocate, and based on the brand (Quiksilver) perhaps a surfer or someone who identifies with a beach lifestyle.
In the blink of an eye, our store staff can do the customer segmentation for this individual. This could be the very first visit to the store, without even walking towards a specific section or item. Digital, rule-based customer segmentation models may require several visits to an online shop to gather this level of information.
There is an assumption that underlies this example. Without experienced and well-trained store personnel, this insight into the customer does not happen or does not happen with the accuracy that can inform the business. This is especially the case when you are selling complex products where the customer is well informed about brands, prices and function.
It is an example where an omnichannel customer journey and the existence of offline retail stores can offer an advantage over an internet-only or digital process.
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