The data being used by managers is becoming increasing messy. Unstructured data is named as such because it lacks the nice organization of traditional data. Of course, the profusion of such unstructured data (text, videos, music) makes analysis complex but also brings considerable opportunities. Big data brings big headaches and big possibilities.
Alina Nastasoiu, a former Ivey PhD student now at Booking.com, Mark Vandenbosch, Ivey’s Acting Dean, and I looked at some data from Sirius the internet radio company. The key thing that we wanted to find out was what predicted churn, i.e. a consumer ending their radio subscription.
Perhaps not too surprisingly, if you listen to the service a lot you are less likely to churn — we had data on how long people listened so we could check this. (It is simply a better value for some consumers so they keep up with the service.)
Variety has also been shown to be associated with churn. This is a bit trickier to operationalize (turn into a usable measure). A simple measure is just number of stations listened to.
We thought that we could do better than that. There are vast amounts of data on musical genres in the Million Song Dataset (available at http://static.echonest.com/MusicMatrix/matrix.html). This shows how different genres of music relate to each other by tracking how often descriptions are used together by listeners tagging the music. We can see how often Heavy Metal is used at the same time as Rock which gives the genre’s similarity. We can see how often Heavy metal and Classical co-occur to see how similar they are seen to be etc…. Using this data we can then see how much variety a consumer experiences in their listening. Such a model of consumer variety seeking can be constructed much more cheaply (given the data is freely available), quickly (it can be scraped from the internet) and with less bias (no one is telling us what they think we want to hear) than asking paid subjects to assess the similarity of musical styles.
The model we constructed, using the variety of music that a consumer listened to, led to better predictions of who would drop the service than models without. Armed with this knowledge a manager can test taking actions before consumers churn. They can test ways to re-engage the consumers who seem to be gaining less utility from the service.
The bigger point is that Big Data isn’t just something that can be studied itself. It can be a useful tool in studying the traditional questions marketers have always asked. “The positive point is that the same qualities that make Big Data hard to use (the sheer size and the way the data reflect numerous divergent opinions), make it an ideal tool to help analyse other data.” (Nastasoiu, Bendle and Vandenbosch, 2018)
Read: Alina Nastasoiu, Neil Bendle and Mark Vandenbosch (2018) Improving measurement with Big Data: Variety-seeking and survival, Applied Marketing Analytics, 4(3)