I recently hosted a webcast for Smart Data Collective titled “Putting Customer Value to Work: What Predictive Analytics Can Do for Your Bottom Line”. With Korhan Yunak of Vodaphone Group, Anne Milley of SAS Institute and Mike Rote of Teradata Corporation on the panel we discussed how predictive analytics can provide the knowledge you need to make better business decisions.
When you are talking about predictive analytics it is important to understand that this is not the same as talking about business intelligence or even data mining – predictive analytics is about using mathematical techniques to turn uncertainty about the future into usable probabilities. Applying predictive analytic techniques moves you from BI’s focus on knowledge and understanding towards action and prescription – not “what happened” but “what is likely to happen and what should I do about it”. Business intelligence helps you acquire and manage data to understand past or current trends. But predictive insight takes you a step beyond BI, so you can make real-time predictions about the future that can be acted upon in real time to achieve better results.
When it comes to customer treatment decisions, picking between the various alternatives you have each time you interact with customers, predictive analytics really comes into its own. Korhan showed that analytics can be used at every stage of a customer lifecycle from acquisition right through to retention and re-acquisition. And analytics is not a one-time enhancement to your process. Anne pointed out that the use of analytics is a process – one that constantly refines models through an observe and measure, test and learn, inform and act cycle so improvements keep coming.
Having set the stage we dove right in to the trends in predictive analytics and in the use of analytics to improve results. The inclusion of text, and even web content, is really hitting the mainstream now and is used for everything from data enrichment to sentiment analysis. Social network analysis, adaptive analytic models and the integration of real-time data led us into a discussion of timely decision making. All the panelists had examples of faster, data-driven decision making and how analytics help to turn lots of data into something usable – insight.
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Returning to customer decisions there was some lively discussion of personalization and the broader use of analytics to improve customer-centricity. As companies realize the value of the data they have about their customers, this application of analytics is only going to grow. Everything from estimating propensity to buy to retention risk to cross-sell and up-sell opportunities can and should be informed with predictive analytics. And companies that are making this happen are seeing some great results.
Turning to audience questions next we discussed how to get started with predictive analytics and how to prioritize data for analytics – after all most companies have a lot of data in many different systems. The panelists advised a focus on the decisions with highest risk or greatest opportunity. With the decision in mind, the data that is required or that might be useful to inform that decision will be clear and the prioritization of data can proceed. The importance of measurable results, of decisions that can be improved in a measurable way, also came up. After all if you can’t prove the results have improved how will you show an ROI?
There’s a lot more in the recording so if this topic interests you, check out the recording here and if this topic interests you there are two white papers you might want to download – “Unlock the business value of enterprise data with in-database analytics” by SAS and Teradata and “Putting predictive analytics to work: Using Decision Management to maximize the value of predictive analytics” by yours truly. You should also seriously consider coming to Predictive Analytics World in DC next month.
Finally thanks to Eric Siegel, Ardath Albee, Vincent Granville and Brent Leary for their help in promoting the event.
