Insight by Algorithm

With simple digital analytics you can quickly and easily paint a rich picture of your customer. Let’s say you’re Ford and you want to know about visitors to pages on your site featuring the S-Max MPV. A quick run might turn up the following:

Visitors to the S-Max pages also spend time on car sites such as What Car and Car magazine - maybe they are looking for some independent advice. They also over-index on sites such as AutoTrader - perhaps they want to see what prices are like for used Fords. Intriguingly they also score highly for food related sites - perhaps they are modern cosmopolitan types who love cooking as well as cars.

They usually arrived at the site from Google, but downstream they often went to social networks after leaving the Ford site - to talk to their friends about cars perhaps.

The analysis also shows that they are most likely to be ‘new homemakers’ - so maybe looking for a car to suit a young and growing family. You can also see that ‘new homemakers’ are likely to visit interior design sites such as Mydeco and Fired Earth.

So, a strategy built around young families with an interest in food, design and interiors, utilising social networks, looks like a great idea.

However, the following could also be true:

It’s true that visitors to the S-Max pages did also visit What Car and AutoTrader, but they couldn’t really give a stuff about food websites, that was their partner looking for recipes. People usually share the same PC at home and analytics can’t accurately tell different users apart. The downstream traffic to social networks wasn’t the potential S-Max buyers either, it was their 16 year old using Facebook.

And they are not what you would describe as ‘new homemakers’. They have two teenage children. Their interior design tips come from the Argos catalogue. But they do live near to a new housing estate and the geodemographics that link to the analytics assign all addresses in a postcode the same category.

In reality of course most of this noise comes out in the wash. But it does highlight the increasing amount of trust we place in algorithms to decipher the amount of media data we have to play with.