I along with two of my colleagues (Anand Rao & Dick Findlay), recently conducted a workshop at the World Research Group’s Predictive Modeling conference at Orlando.
Many of our clients have been asking about predictive analytics, and it is a timely example of how companies are capturing value from information. As part of the workshop, I spoke about our perspective on eight things that our clients should keep in mind as they consider using predictive analytics in their organizations.
In this post, I will list the eight points and discuss the first one. My subsequent posts will explore the rest of the themes.
- Understanding the cost of a wrong decision helps target investments
- Strategic and operational decisions need different predictive modeling tools and analysis approaches
- Integration of multiple data sources, especially third-party data, provides better predictions
- Since statistical techniques and tools are mature, by themselves they are not likely to provide significant competitive advantage
- Good data visualization leads to smarter decisions
- Delivering the prediction at the point of decision is critical
- Prototype, Pilot, Scale
- Create a predictive modeling process & architecture
Theme 1: Understand the Cost of a Wrong Decision
Is it even worth investing in developing a predictive analytics solution for a particular problem? That is often the first question which should be answered. The best way to answer it is to understand the cost of the wrong decision. We define a decision as ‘wrong’ if the outcome is not a desired event. For example, if the direct mail sent to a customer does not lead to the desired response call, then it was a ‘wrong’ decision to send the mail to that customer.
A few months ago my colleague Bill told a story which illustrates the point.
Each year Bill takes his family to Cleveland to visit his mom. They stay in an old Cleveland hotel downtown. The hotel is comfortable, with all the trappings that you would expect of an old and reputable establishment. During the last trip they decided to have breakfast at the Ritz-Carlton hotel across the street. After the breakfast, when Bill and his family were in the lobby, the property manager spotted him and the kids and walked over to talk. After chatting for a few minutes the manager surmised that Bill was a seasoned traveler and an attractive prospect. The manager asked Bill’s children to wait for him and in less than a minute he returned with a wagon full of toys. Imagine the children’s delight when they were invited to pick a toy out of the wagon.
Think about it. They were not even guests at the Ritz, but the interaction provided a memorable experience. The kids loved the manager and Bill remembered the gesture. Fast forward to this holiday season, and sure enough Bill and his family booked a suite at the Ritz for six days. For the price of a few nice toys, the manager converted a stay that generated a few thousand dollars in room charges, meals, and parking.
Now suppose Bill did not go back to the hotel. What would have been the cost of the manager’s ‘wrong’ decision? The cost of a few toys! The cost compared to the potential upside is negligible. Does it make sense for the hotel to build a predictive model to determine which restaurant diners to offer toys to so that they come back and stay? I don’t think so.
Don’t get me wrong. We’re big believers in the power of information analytics. But understanding upfront the cost of making a ‘wrong decision’ can save a company from making low value investments in predictive analytics.
Next week, I will discuss how strategic and operational decisions need different tools and analysis approaches.
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Very good, Amaresh – Nice way to tell the story. Keep ‘em coming.
I learned this first point from my boss at a college job. We served all-you-can-eat spaghetti to college students. The extra spaghetti we served cost just pennies, but the second and third round of beer and wine made good money. Whenever a fun-loving drinking couple came in for the first time, the owner would go over to their table near the end of the meal and present them with a beautifully printed invitation to be our guest for a “two for the price of one” dish. It was a clear success, very cheap to do. The waitresses often got big tips from these guests.
But here is a key factor. The owner who delivered the wedding-quality engraved invitation was very well dressed and gave it his full charisma. He acted as if this couple had done him an enormous favor by coming by for their first visit. And, he always started off by asking them questions about their meal, what they were studying at college, who their teachers were. He made “acquaintances” of them first and let them know he liked them and was interested in what they had to say. Then he would give them the surprise gift. Wonderful.
Thank you for helping me remember this technique, Amaresh.
Jon Byous
Jon,
Thank you very much for the comment and for sharing your wonderful story to illustrate the point.
Good story and post, interesting.
Josesph,
Thank you for reading and leaving a comment.
Dear Amaresh
Nicely you have put your thoughts!
Also the way you have presented!
Prabin
Prabin, Thanks for following and the comment.
I view the cost and the decision itself somewhat differently I suppose. The cost of the wrong decision is not entirely the cost of toys. It is also the lost revenue should the prospect (Bill and his family) opt to not stay at The Ritz. The decision regarding making an offer or not is not a yes or no decision. Some offer is invariably going to be made even if its just going up and greeting the family. The cost/benefit of various other offers, i.e. room discounts, complimentary event tickets, meals, free days for extended stays, etc., could be measured as to their relative effectiveness. In those cases, the analytic could evaluate the results of various offers and use that data to predict success.