I saw this article in Computerworld today - Celtics Turn to Data Analytics Tool for Help Pricing Tickets. Now this real-time display of ticket sales trends stuff sounds great and I am sure it really helps experienced sales executives figure out how to price remaining tickets etc. I have two questions though:
- What about less experienced sales executives? How are they meant to interpret this given their lack of experience?
- What about the website? Their website links to Ticketmaster for tickets but the pricing there is not dynamic, not informed by the data analytics they are doing.
Now what if the Celtics had taken an EDM approach instead? They would have taken the rules of thumb of their experience staff, plus any rules the NBA has about how tickets can be priced, and combined those with predictive analytic models that predicted how likely a game was to sell out or how likely a particular area of the stadium was to have open seats etc. With these they would have built a pricing engine that sat behind the CRM systems the sales executives use and made that decision service available for Ticketmaster to provide dynamic pricing.
Now the same engine provide multiple pricing options when providing answers to a person and a single one when providing answers to a system but the insight into pricing trends would be leveraged more widely and even new sales executives would get the pricing right. This would leave the experienced ones more time to work with the callers to maximize the sales and improve customer satisfaction.
Of course you could always add models that predict likelihood of upselling a season ticket etc etc.
So, to paraphrase George Orwell, "Data mining good, EDM better".