In this series, we've looked a lot into predictive analytics and the goal of obtaining a sneak preview of the future. In this installment, I'd like to do the opposite and look back in time. Once a company has climbed the added value ladder of Data → Predictions → Decisions → Actions, making better decisions in all customer channels is not the only benefit. A centralized (customer) decision hub will not just improve the quality and consistency of decisions, it will also record every single one of them. This doesn't only impact the decision itself, but also the data used to make the decision; the rules and predictive models that represent the actual decision strategy; and last but not least, the business outcome that resulted from that decision (not a trivial process in itself, and a worthy topic for a future discussion). This detailed record of all previous decisions has major business benefits, as we will see.
Recording all this data adds up. Companies that climb this added value ladder will routinely make multiple decisions during every interaction. With interactions in all sort of channels - from online, mobile, and social, to more traditional ones like branch, call center, mail, email, text, etc. - we're easily looking at many hundreds of millions of recorded decisions, which pretty soon adds up to Big Data.
The value of interaction data
So, is it worth keeping all that detailed history? You bet it is - and for a multitude of reasons. The obvious one, that I will largely ignore here, is for the purpose of (retroactive) reporting. It's crucial, for instance, to understand what customers bought what and in which channel. What are the demographics of the most valuable customers? This kind of business intelligence (BI) yields insight where previously intuition may have reigned - and evidence always beats assumptions.
But nowadays, keeping a very detailed decision history has other benefits as well. First, as a basis for learning, adaptive - or self-learning - predictive models need detailed data to calibrate their understanding of customer behavior. Modern predictive analytics technology will sieve through big data unsupervised, only reporting on the quality of its conclusions. If the models become reliable enough (once the discovered patterns in customer behavior prove stable), these models can start contributing to better decisions. This process of automated, continuous learning requires detailed recording of all decisions and the result of those decisions.
Big data rules
The third benefit is one that needs to be addressed with a little more detail. Recording not just every single decision, but also the rules and models that were used to make it, creates the opportunity for simulation. This is not the high-level spreadsheet variant but a full rewind of all the detailed customer interactions followed by a fast-forward action of using a modified version of the customer strategy. The result is a detailed "what-if" analysis: what if we had prioritized this higher/lower, priced this higher/lower, accepted a higher/lower risk? Having recorded the full detail of all customer interactions we can use the exact same data, but then apply a modified version of the rules and predictive models (i.e. a revised customer strategy) and look at the delta. This leads to a more empirical basis for prospective changes to the customer strategy.
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