Syndicated from BeyeNetwork
Last week I posted a couple of times about my impressions from the SAS Global Forum. In one post I said that “SAS customers talk about the great results they get when they put their predictive analytics to work in operational systems” so I thought I should expand on that a little, using the customers I heard during the one day I attended.
One panel I saw had two customers – Stephan Chase, VP Customer Knowledge at Marriott and Eric Webster, VP Marketing at State Farm – talking about putting predictive analytics to work in their businesses. Stephan, for instance, pointed out that 1% of Marriott’s customers generate 20% of their revenue so putting customer knowledge to work really makes a huge difference. He also made the very valid points that more data can sometimes obscure not inform and that analytics must support both the timeless core of a business and the more innovative edges. Marriott’s use of analytics drives their pricing, loyalty and marketing with models embedded in all sorts of operational systems.
Eric pointed out that Insurance is an information business – there’s no physical product. He made a great point that while State Farm is a data-driven business it is also a relationship-focused business. He saw the power of analytics in its ability to help the “faces” of State Farm (the agents, claims processors etc) build a relationship. This use of predictive analytics, again deployed into front-line systems, enables people who have never met a customer or prospect interact with them as though they have known them for years. These predictive models recreate the corner store, as Richard Hackathorn likes to say, and keep loyalty where you want it – with the company. They do so by being embedded in operational systems and by delivering predictions about individual customers.
Another session had Scott Overby of Discover talking about their Teradata/SAS data infrastructure. I like Discover as a Decision Management example (see this blog post about Michele Edelman’s presentation at the Decision Management Summit for example) and it was fascinating to hear Scott talk about the challenges of building the data infrastructure needed by a decision management business like Discover. The need to focus on scale, on real-time analytics and operational data, and on avoiding brittle systems caused by over-optimized analytic data design all came up. The Enterprise Data Warehouse that Discover has implemented is designed to support both BI/business analytics and Decision Management/predictive analytics and this is the way to go for those who want to maximize the value of their data. Personally I just love the way Discover talks about making hundreds of new data elements available to decision services as a minor change 🙂
A final customer session was also a speaker from Marriott (whose name I failed to recordNell Williams, Vice President of Revenue Management Deployment and Systems Strategy) and she spoke about revenue management at Marriott. Not only is this intensely analytic, it too drives these analytics into the transactional pricing and booking systems that everyone uses. The use of adaptive control techniques to test and learn is crucial to revenue management and she had a great phrase Marriott uses “Success is never final”. She also made the point that when measuring performance, of decision making say, you should consider both performance and opportunity. Just how good could the decision be?
Some great customers with great results. All done using SAS software and then putting the predictive analytics developed to work. Decision management, to use my favorite phrase.