Data analytics innovator, Manny Aparicio, (co-founder and CEO at Saffron Technology), explains how big data analytics allows for a new sort of customer segmentation. Aparicio describes the trend away from sales based on standardisation, and what’s normal or average – towards personalised, operational, decision-making based on individual customer memories.
Using big data analytics tools to learn about individual customers
Stereotyping a group of people has been the norm for marketers.
This is too superficial.
For decades the method that’s been used for market segmentation is clustering … we take all our customers and try and boil them down to a few types.
Are there five kinds of customers? Are there ten kinds of customers?
Obviously, if there’s only five or ten, we’re going to basically be stereotyping everybody.
Unsurprisingly, this ‘normal’, ‘average’, rule-based approach, has had low accuracy and low ROI.
But if you think about, you know, how a real sales person works, I don’t care in general what people buy together using basket analysis.
In general, people that buy diapers may also buy milk or some such obvious association but extracting these general associate rules are pretty superficial.
But, if you knew every single customer and could immediately at the point of transaction, understand their individual preference, their individual life, their family structure, their habits, their preferences, that is selling.
That is what a real sales person does by having repeated experience with every individual customer that comes in the store and says welcome back and oh I have something for you I think you’re going to like.
It’s by knowing at that detail,that really satisfies the customer and generates the margin of profit that is available.
But what you need then is the underlying big data analytics technology.
Not market segmentation, not clustering, but memories of every single customer and their associated buys.