In the age of convergence, customer churn is a concern for service providers, challenging most retention techniques...
With a number of digital and traditional channels now available to interact with your customers, businesses often find themselves asking some very intricate questions. One of the most important ones being, what is the best way for me reach out to my customer X vs. customer Y, does he like to see my promotion at 11pm before he sleeps or is he an early riser and I want him to see my ad when he reads the news on his iPad at 5am?
Let’s face it – everyone is competing for the customer’s attention, mindshare et al. The key for marketers is to know at what time in my customer engagement cycle I get him/her to focus on me – the marketer! The window to grab the attention, sell the deal and get a buy decision is shrinking and very fast and by now you’ve guessed I am talking mainly about online sales and marketing. The engagement cycle with your customer is getting short, the window to get him to buy even shorter and now because of all these return policies you’re not even sure, whether the sale is final or not, the buyer might just return your item and all that effort to get him in that “buying moment” window went to naught!! Ok let me state here, what I just described above is not for all marketers, scenarios definitely vary for every industry and market.
The fact is predicting customer buying behavior is not about whether he/she will buy something; it’s about knowing what is the “customer lifecycle value” to me as a marketer. Wouldn’t you love it if you had a crystal ball and you could say that John in the next 10 years after he finishes in the top10 of his class, he is likely to buy these 20 things and I want to be there in his mindshare at those precise points when he takes those buying decisions?
This is where the power of advanced analytics comes into play. Classifying my potential buyers and then using recommendation engines to propose the right slew of products is something that has undergone multiple iterations and tested over time, but on a large population, the results thus far being average and with the highest “returns” thus negating any sale from the “recommendations” aisle.
More than ever, companies are twisting, turning and tweaking and extracting the last drop of information and insight from their data to see how and where they can make the right sellers pitch. But the key still remains whether I am targeting my customer at the right time – in the lifetime value of my customer- with the right product or service.
That is where the radical shift has to occur – only those that can figure out how to use internal data and external behavior will transcend this barrier, I mean those that can use both past historical data to predict behavior and those that can incorporate past behavior to predict data.
In summary, advanced analytics in isolation by applying just data principles is not enough, combining it with the right approach and philosophy is what makes it truly useful for marketers.