Now, we have access to future trends based on historical and
real-time data. We can now predict at least something about our customers’ future
actions. However, we are not sure that our predictions are 100% sure; there is
always a clause. Why?
It is because that quantitative data alone cannot give the
100% answer to our future queries. We need context related data which we most
often won’t get. For example, if a customer of a retail chain suddenly triples
his purchases for 2 months; then what do you predict based on the data that you
have? Is he going to buy at the range in the third consecutive month also? Or
is it just a fluke purchase spike which you can ignore? Or is it something like
that the customer’s family has been extended recently and due to this his
spending pattern has changed drastically? Which one of the above questions correctly
predicts the customer’s behavior?
Quantitative data only provide the future trend based on the
purchases that the customer has made so far; it won’t collect the reason behind
this sudden spike in purchases. In this scenario, to get a comprehensive
understanding, you need to have context related data along with the regular
quantitative data; only then your predictions about the future purchases of the
customer holds true up to a majority of the extent.
What do you say?
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