Given all these possibilities, it's perhaps not surprising that the first task of a retailer looking to enhance predictive analytics is to decide exactly what it's supposed to do.
"Whoever is doing this on the analytical side [needs] to really have a very good knowledge of what are the business issues most important for retailers," says Eugene Roytburg, managing partner of 4i, a growth and foresight analytics firm. "Existing and new business issues that haven't been addressed by predictive analytics yet....People have been buying different tools and processes, developing those functions, without clearly understanding the value that analytics can create."
"People have been buying different tools and processes, developing those functions, without clearly understanding the value that analytics can create."
THE HUMAN FACTOR
Once it's up and running in the real world, of course, further adjustments often must be made. That usually requires the human touch.
"It is common practice to monitor the level of error (or success) that a model has generated and then adjust as needed," says Alpert of 1010data. "Automating the detection of the error is rather simple, where adjusting the error is not so simple and will require an analyst (statistician)."
As with any software, predictive analytics applications are only as good as the data they receive. Retailers often focus on the accuracy of the predictive model and not the accuracy or completeness of the data fed into it, Mani says.
"Human business acumen, plus the model coming together, is where you get the maximum results," Mani says. He recalled a client who asked PwC with help with chronic stockouts that its analytical software kept failing to predict. Once PwC looked into the situation, it became clear that the software had no way of addressing key business events or tracking certain vital SKUs. PwC gathered information from store personnel and revised the model to take these events into account, accuracy greatly improved.
One of the challenges with predictive analytics is for retailers to think of it as a function of a specific part of the organization, like supply chain or marketing, rather than a bunch of results that the IT people generate and then hand off to other departments.
"More often than not, [predictive analytics is] discussed in terms of a technology capability, with the technology teams driving it," Mani says. "But as you walk through the continuum, more and more businesses are realizing that there's value to keep them as a function that drives analytics services across all parts of the organization."