SAS customers and optimization

Next up in my SAS day was a panel on optimization. Bobby Hull of BGF Industries and Bill Nowicki of the Carolina Hurricanes were joined by Larry Mosiman of SAS on a panel hosted by Tammi Kay George. BGF is a leader in high-end composites and textiles. The Carolina Hurricanes, of course, are an NHL team. Optimization is, of course, the 8^th of the levels of analytics that Jim Davis was discussing. Optimization is used in lots of ways and Larry gave a quick overview. Optimization is a about finding the best of all possible solutions. In the context of marketing or collections it is about taking limited resources (offers that cannot be made to everyone) and finding the optimal pattern (where optimal means most profitable, resulting in most retention or whatever). Larry walked through how various approaches to assigning campaigns to customers produce sub-optimal approach. Just allocating first-come-first-served is inefficient. An explicit, rules-based decision is better but does not manage the trade-offs across large numbers of customers. Optimization focused on the whole problem and maximizes across the customer portfolio.

Bill, from the Hurricanes, discussed how they got started with optimization in ticket pricing. They had always done a lot of sales analysis to price tickets but needed to do more. Obviously tickets are a perishable item (with no value once the event passes) so maximizing revenue up to the moment of the game is important. Bobby, from BGF, was focused on optimizing the production of the various products which require different quality standards, have expensive raw materials and which are produced in high volume so mistakes escalate quickly. Besides problems with perishable items (like tickets or flights) and complex supply-side problems, Larry pointed out that optimization is increasingly used in areas like collections.

Tammi asked about the best outcomes from adopting optimization. Bobby said that the ability to relieve experts of the need to manipulate data allowed them to take information about the process and make it available to everyone. He also pointed out that this new project finally used all the data they had been warehousing – a classic example of getting more value from data once you focus on a decision. Bill agreed, emphasizing that a lot of what it did was show them the data behind the things they had previously suspected. The organizational change was really significant – starting to use data-driven decisions was a challenge. But the new approach allows the Hurricane’s to put their resources behind the games that will show the best return on that investment – optimizing the use of their resources.

Interesting that the optimization of ticket sales pushed discounts and deals earlier in the process. Obviously this avoids conditioning people to wait for last minute discounts. This is an interesting side effect of a more formal model of decision making. The optimized prices are used everywhere too – in promotions, at the ticket window and in the sales department. Both organizations reiterated that they had a lot of data that had been accumulated over the years but never really been used. Without a focus on the decisions they were trying to make the data just ended up being stored and accumulated.