As the technology edges closer to maturity in digital, here's an introduction to predictive analytics.
A user's profile and behaviours should allow companies to predict how that user will behave, by looking at the historic behaviour of other users.
This means a company can change how it communicates with that user or discount them altogether, based on the user's propensity to undertake a particular action.
Machine learning continually refines a predictive analytics model as more data (such as the success of its outputs) is fed into it.
It's a relatively simple concept but one that has many implementations. I've listed a few below.
Much of this information is taken from our new Predictive Analytics Report, published in association with Redeye as part of Data Month.
Okay, this isn't a marketing implementation but it is one of the early uses of predictive analytics.
Assigning a customer a credit score is a prediction of the likelihood of repayment.
Increasing the accuracy of the prediction allows for more revenue to be made as fewer customers default.
Predictive analytics can help to gauge the likelihood of repeat purchase or even CLV.
Whilst a simple RFM matrix (recency, frequency, monetary value) is the basis of prioritising customers, so much more data is now collected by businesses (and available from third parties), that CLV can be based on much wider metrics.
This is the other side of the CLV coin. Companies such as telcos want to know if customers are set to cancel their contracts or jump to another provider.
Knowing this allows these companies to incentivise such customers to stay, and to forecast more accurately.
This may be the most infamous example of predictive analytics in marketing and retail, thanks to the apocryphal (?) tale of a teenage girl being sent maternity offers by Target before her own father even suspected she was pregnant.
Supermarkets that use loyalty cards have so much transactional data on their customers that they can create incremental revenue with targeted offers, encouraging more frequent shopping.