I’ll dive straight into what is customer analytics using an example. Consider that Pixar calls me and says about a Disney film, that they want to for instance, predict how many people are going to watch Inside Out in every state of United States. This is a really interesting problem, but this isn’t customer analytics. Customer analytics predict what each person’s going to do, how many of them are going to see Inside Out, how many are going to see the previous Disney films, how many are going to see the future Disney films. This is a customer analytics. And this is a problem that is faced in many industries, google and Facebook, etc.
Of course, we aren’t just going to simply predict. we want to be able to fuse everything we know, then optimise the future. So we will be looking at this step by step. Of course, this will not be too technical, very qualitative series of posts. You don’t have to be a stats-man.
Conclusion: Customer analytics refers to the collection, Management, analysis and strategic leverage of an organization’s granular data about the behaviour of its customers. The essence of Customer Analytics is that we want to make profits one customer at a time. So customer can be characterised as:
- Inherently granular: focus on individual-level behaviour
- Behavioural: focus on observed behavioural patterns
- Forward-looking: orientation towards prediction
- Multi-platorm: combine behaviours from multiple measurement systems
- Broadly applicable: anyone is a customer.
- Multidisciplinary: relevant in many fields.
I hope all this convince you that this is quite important to know. Next we will look at how to do descriptive analytics, aka, getting Data.
