Music is inherently and intrinsically personal, I have avery eclectic taste in music, I’m a butterfly floating across genres dependingon my mood, the weather, music in a film I just watched etc.

The other day I listened to some classic country music (yesI know, my bad) on Amazon Music. The next day I listened to some alternativerock but Amazon was still recommending country. My county music fad had passed,that was yesterday and if I’m honest I was a little bit disappointed thatAmazon was out of kilter with what I wanted.

No doubt Amazon were using some form of machine learning affinityanalytics to work out what music I liked and then predict what else I mightlike. The thing is that affinity analytics doesn’t always get it rightespecially for people with varied tastes. Hyper personalisation will not alwaysdeliver relevance. No matter how clever the data algorithms get one of theproblems with affinity analytics is that it is often using a rear-view mirror. Lookinginto the past is not always a good prediction of the future.

Big data analysis like this will get it right most of thetime but the occasions that it gets it wrong can create a really big disappointment.Customers now are used to having recommendations and are almost not noticingwhen a brand gets it right but they will definitely notice when it gets itwrong.

Using a combination of data sources will help you understand your customers better. You need to develop a full, rich picture based not just on your brand behavioural and transaction data but also what your customers do when they are not interacting with your brand, what other brands they like, what interests them, and what type of person they are.

If Amazon had worked out that when it comes to music I’m a butterfly, they may have looked at the data differently.

Further Reading

If you would like to know more about how to develop a segmented approach to creating enduring customer relevance then read this article which is a guide to enhancing customer data for better personalisation.