Maximize Marketing Impact with Net Lift Models: Interview with Kim Larsen, Charles Schwab & Co (1 of 3)

Kim Larsen is the Director of Advanced Analytics at Charles Schwab & Co. with headquarters in San Francisco, CA.

In this first of a series of interviews, (1-3) Kim shares his thoughts regarding Maximizing 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.


Bill Cullifer: I am on the phone with Kim Larsen, Director of Advanced Analytics, Charles Schwab & Co. with headquarters in San Francisco. Good morning Kim and thanks for agreeing to the interview.

Kim Larsen: Thank you.

Bill Cullifer: Kim, you recently presented a case study regarding maximizing marketing impact with net lift model at Charles Schwab & Co.

Kim Larsen: Yeah.

Bill Cullifer: Could you summarize that session for the subscribers of this podcast with a walk away?

Kim Larsen: Yeah. Sure.

Bill Cullifer: Thank you

Kim Larsen: We call it the net lift model and net lift model is really motivated around when whether it is [Inaudible] or a resale or when you launch marketing, you measure the impact of the campaign by what we call increments [Inaudible] a test group of people that get a offer that’s related to your product. Then you will have core[Inaudible] groups for that book[Phonetic] like those people that did not [Inaudible]. The ultimate test comes of how well your program worked, is what’s the difference in performance in the[Phonetic] test group, i.e., for that [Inaudible] offer versus those that did not offer. Problem is that many times marketing says targeting strategies are not time[Phonetic] optimized increment impact. So, people might not [Inaudible] and make it a great response rate, but your core group[Phonetic] does this as well that simply [Inaudible] bottom line. That’s the motivation. The purpose of this talk was basically to talk about [Inaudible]. We basically think of your client base as consisting of three base segments.

You have a segment of clients that are not interested in whatever you are trying to sell. So, those, just leave alone and in fact if you market to them, it might have an adverse effect. Then, you have some clients that we call self selectors. These are clients that will mostly likely purchase your products on their own and marketing does not give motivation and actually leave them alone so to speak and they will buy the products regardless. The third segment is called the swing [Inaudible] and you know this sort of… this name is sort of derived from a parallel that I have been drawing with presidential candidates. Think about it. When you look at for example the latest [Inaudible] Obama, they spent most of their money on TV time in the swing[Phonetic] State of Ohio for that [Inaudible]. If you live in California like me or in Bay Area of California like I hadn’t seen a single McCain ad or Obama ad. However, when I went to Las Vegas for a conference, turned on CNN and I saw four ads for an hour. So, the presidential candidates have already figured out that spend your marketing money on the swing voter, the undecided and leave the decided alone and optimize market spend. So, that’s… we had the technical swing [Inaudible] they certainly have a tendency to buy the products by themselves, however, they need motivation. So, the purpose of net lift model is to find swing [Inaudible]. Two options when people do targeting, they end up finding the self selector. So, they build typical targeting models where really identifying self selectors, those models are those that… those models are typically done by looking at the clients that you offer the products to and then became converted, but what net models do… net lift models do that they don’t just look at that population, i.e., the population you mail to or contacted.

They also look at the core[Phonetic] group. Those are the people that you did not contact. Many of them also converted. Understand what’s different in the profile of self selectors versus the average converter and if you have those dimensions, you can focus in and find the swing [Inaudible] that will… it’s not always easy to do, but when you do it, it can be your impact of your marketing direct[Phonetic]. I have seen… we can talk typically about Charles Schwab itself, but I have seen campaigns where a typical targeting model to get to a incremental impact of about say five basis point that could essentially be that the people who you contacted converted at say 1%, so 1% of them bought products, but the people who you didn’t contact, they converted at say 98[Phonetic] basis points, so the difference was just two basis points which would not cover your cost. I think cases like this where a net modeling gains your net… your net impact into something very possible.

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