Predictive Analytics – that is extracting information from data in order to predict future outcomes and trends – has become a hot topic for marketers in their mission to get to know their customers better.
Of course, this sort of data mining has been around for years, but it probably first came to public prominence in 2012 when President Obama’s campaign team used it to good effect when identifying the so-called ‘persuadables’ – or ‘undecideds’ – potential voters who hadn’t yet made up their minds who to vote for and were open to being persuaded one way or the other.
Using a predictive analytical technique called uplift modelling, which measures small, incremental changes in a person’s behaviour after being targeted by messages, the Democrats’ political analysts were able to focus their messages only at those people who were open to persuasion, rather than wasting their resources on people who would never vote for Obama or those who had already been convinced.
Put simply, predictive analytics in marketing seeks to answer two basic questions: who is going to buy my product (or service) and which of my customers is thinking about cancelling and going to a competitor?
It’s not about predicting the future with 100% accuracy, but it is about predicting outcomes significantly better than using guesswork and not replying on a “needle in a haystack” approach.
The reasons for this are obvious. You may have created a special retention package to offer to wavering customers - a discount or an ‘added value’ sweetener - but you won’t be able to afford to offer it to everyone.
So, you’ll be saving yourself a lot of time and money, if you can just offer it to those customers who your analytics are indicating are actively thinking of going to a competitor.
But maybe this sounds as if it’s all about selling as much as possible and just stopping customers from leaving? Well yes and no. The benefits of predictive analytics cut both ways.
There are clear benefits to customers as well. Everyone knows about the 2002 Tom Cruise film ‘Minority Report’ which examines whether free will can exist if the future is known in advance.
Well, in the future, predictive analytics may help to take a lot of the legwork out of your daily shopping routines.
Your ‘smart’ car, which can drive itself, will take you automatically to your favourite shops based on your previous preferences; when you arrive, the shop ‘recognises’ you – well, at least it connects with your wearable technology - and tells you that your usual brand of coffee is out of stock, but helpfully suggests an alternative.
We see hints of this world today everywhere, in such developments such as the Internet of Things where ‘smart’ devices - household items for example – can talk to each other and keep your household running smoothly with the minimum of human intervention.
We'll be able to buy what we want, when we want it, and we won't have to put up with adverts for products that we don't want – in the future adverts will all be personalised to our preferences and aspirations.
Intelligent Virtual Agents
And of course, predictive analytics are behind Intelligent Virtual Agents (IVAs) – those human-like chatbots that are available 24/7 on business websites to answer your queries helping customers to help themselves with the minimum of fuss.
Predictive analytics track customer data from whatever channel the customer has previously used and identifies patterns to alert Customer Service Representatives (CSRs) about potential reasons why the customer might be calling. These predictive services are also linked into smart IVR (Interactive Voice Response) systems to provide customers with a tailored support experience right from their smart phone.
So, you can see that predictive analytics don’t just benefit one side. Customers regularly gain from their wishes being understood, even before they articulate them; just as much as the companies they interact with gain from knowing that predictive analytics drive better business performance and provide greater insight into their business dynamics.