About a month ago I moved.
I closed after lunch on a Friday afternoon. The only reason that is relevant to this story is the timing: my cable provider called me the next day – Saturday morning around 9 a.m.
I knew it was my provider, thanks to caller ID. Granted I’m not that old, but not too long ago you had to actually answer the phone to know who it was. In fact, I now have a phone that will announce out loud who is calling me. Ah, technology.
Being a Wisenheimer, I answered the phone not with a “hello,” but with, “I bet you are calling about the sale of this house.”
Without missing a beat, the customer representative answered, “Yes I am, and I’d like to get you the best possible package for your new house.” Note the use of the word “best.”
Thus began my willingness to be retained. And, at the time I wondered to what extent predictive analytics were being used to “retain” me during the conversation.
Because “best” was enough to get my attention, I let him ask me the location of my new house. He was quick to pull it up and confirm the deal he had in mind could actually be pitched.
“Yep, looking at your location, I can get you set up with the following package at [about half of what I was paying before!].”
Here is the critical fact in this story: the “package” he pitched included internet connectivity speeds at 2X-3X what I had before the move AND a television package that was two upgrades above what I was leaving. All for half the price I was paying before the move. Too good to be true?
Efficient retention. Impressive.
As someone who has held sales positions, works in predictive analytics, and has a technical background, I could really appreciate the efficiency of this win-win transaction. My provider retained me as a customer on a Saturday morning with a single 10-minute phone call AND my new house will have quadruple the package of the previous house for half the price.
Hold on. It gets more impressive from the telco’s standpoint.
Then I had a revelation. After only two weeks in the new house enjoying my new services (key word “my,” read “personalized”), I figured out that if I paid more than I am paying now – but not much more than I used to pay in the old house – then I could have the top-of-the-line package: super-duper connectivity, high definition, DVR, and on and on.
That is to say, I just up-sold myself as a result of a 10-minute phone call on Saturday morning four weeks prior!
Needless to say, my telco provider must be leveraging elements of a robust Decision Management solution. In particular, I’m sure they used my high predictive score for up-sell, coupled with the business rules that governed the initial offer, such as…
· IF (provider_jump = false) and,
· IF (previous_package = XYZ ) and,
· IF (number_complaints < 2) and…
…to produce an outcome that demonstrates the importance of predictive analytics and rules to guide optimized and automated decisions.
Said another way, my telco provider not only retained me, but got more monthly subscription revenue out of me in a very efficient manner.
And this is just one personal example from telco. Think of how predictive analytics and rules can (and are!) being used in tandem to optimize and automate recommendations in retail (e.g., customer analytics), manufacturing (e.g., preventative maintenance), insurance (e.g., claims processing), and beyond.
Speaking of optimization, stay tuned for Part III of my Decision Management series.
And, if you missed my “Ode to Rules” in Part I, you can read it here.
For more information:
· Attend our IBM Innovations in Smarter Analytics Virtual Summit (June 19, 2012 at 10:30 am ET) and learn all about our new Decision Management solution.
· Watch a demo of IBM Decision Management in action.