In this third and final interview of Kim Larsen, Director of Advanced Analytics at Charles Schwab & Co. Kim shares his thoughts regarding strategies tp Maximize Marketing Impact with Net Lift Models.
Net Lift Models are designed to maximize the incremental impact by targeting the undecided clients that can be motivated by marketing.
Check out the three minute interview on the Predictive Analyticswebsite.
Kim Larson: That approach is my own sort of favorite because it’s very easy to do and it’s very intuitive. It’s using a k-NN, it’s like a nearest neighborhood classifier. So, it’s very simple essentially. First you identify say 10 or 20 variables that you think are great net lift variables meaning that they can differentiate conversion rates between test and control group and you can think of many ways to do that, but that’s actually quite easy for a single variable to see is this variable really a variable that can differentiate the self selectors from the… just the three other converters. Then, once you have… let’s say you got about 20 variables or so; based on those 20 variables, you find… you would go to a client, you will find say the 100 clients that look most like that client. So, you will basically find the 100 closest neighbors to that client like the “friends” of that client and then we are in that neighborhood of a 100 people, you basically just do a voting so to speak. You calculate the net lift weight for those 100 people and that net lift weight is the estimated net conversion rate for that client and then so forth. You can use SAS to do this or any other software that provides a k-NN classifier. In SAS, you can use [Inaudible] and I am sure about [Inaudible] and all those other packages have similar… [Voice Cross Over] it’s very easy to do, that’s the last one.
Bill Cullifer: Great.
Kim Larsen: Tends to give good results as well.
Bill Cullifer: Question for you. Is this a function of a business unit or information technology or of combination of both?
Kim Larsen: The LIFT model?
Bill Cullifer: Yeah, I mean, LIFT Model.
Kim Larsen: Yeah. I think typically you would… these models would be undertaken by some analytical team within your company like the team I worked in. I think typically most companies will have a modeling team and they would be the ones doing that. They would need to work with the marketing people to implement this into a campaign and the biggest challenge there is when you have got… when you have got a net model and you are telling people, “Well, these are the clients that I intend to target.” People will notice that these are not the typical people that you target. What I always tell people is that if you forget about all the fancy modeling and all the fancy stuff, there is a general rule of thumb that I have found. Your most engaged, wealthy, happy clients that love you the most, they are not the ones you need to hit with direct marketing. They are going to buy your stuff regardless, because they already like you, they already think you provide great service. Where you need to put your money is on the fence sitters, the people that are right below your best clients. Those are the people that are sleeping a little bit. They have all the propensity and the wealth and all that’s needed to really… to drive your bottom line, but they are not doing it fully because something… maybe they are not engaged with you. They don’t know what you offer, etc. So, that’s a general rule of thumb. Now, you as the analytical manager or the analytical analyst, you need to convince your marketing colleagues that that’s the way to go and that sometimes… I mean at Schwab it’s never really been an issue for me, but I know some other places that that can be an issue.
Bill Cullifer: Yeah, fair enough, I bet it can be.
Kim Larsen: Because you think…
Bill Cullifer: Then it’s an huge issue, right?
Kim Larsen: Yeah.
Bill Cullifer: And part of it is an educational process.
Kim Larsen: Exactly.
Bill Cullifer: Yeah, fair enough. Well, that’s an excellent perspective and I appreciate you sharing that as well and for your time today.
Kim Larsen: You are welcome.