Saturday, November 22, 2008

Using Customer Equity as a Dependent Variable for MarComm Optimization

I've written a lot about marketing optimization, ROMI, and mix optimization. In past posts, the dependent variables--the "things to optimize"--have been defined as time series variables of aggregated data. Some examples--"sales", "profits", "awareness." These would be entered into a model on a weekly or monthly basis, and then once a company has 70 or 100 of these data points, it can start building regression models to understand the effect of stimulus on response.

There are a lot of problems with this approach, particularly when trying to understand short- vs. long-term effects. A short term effect would be the impact of a demand gen campaign on sales. This would happen fairly quickly. A long-term effect would be attitudinal. As customers' perceptions of brand quality change, for example, this has a long-lasting effect that must be taken into account over many years. Decisions to spend money to affect perceptions of quality, though, must somehow be balanced next to decisions on how to spend money that will get customers in the door tomorrow. It's tough work.

Rust, Lemon and Zeithaml, in their 2004 Journal of Marketing Article "Return on Marketing: Using Customer Equity to Focus Marketing Strategy" have a neat solution. It's to make the dependent variable the customer. In other words, when making marketing investment decisions, companies should understand how each investment impact core "orthogonal perceptions" for each of its customer segments, and then trace the impact of these perceptions on customer behavior in the form of acquisition and retention. They suggest thinking of each customer relationship as a string of future actions--either they will or won't buy, at different amounts, through time. They do this using a mathematical technique called a Markov Chain.
So the approach that others recommend and I think can work really well for B2B marketers is to change the dependent variable from an aggregation to the atomic customers.
The nice thing about going investment-->perception-->customer behavior-->customer equity is that you have almost unlimited scenario flexibility. Because the atomic level data is at the customer level, you can change assumptions about segmentation easily and see the impact on ROMI. There are, however, a couple of key challenges:
  1. How do we understand the impact of marketing investments on perceptions? The marketer still needs to do research, in the form of recognition tracking through time, to understand this.
  2. How do we understand which core attitudes are truly important? Market research needs to be done, in the form of qualitative followed by quantitative research, to understand the constructs that truly drive acquisition and retention behavior. In the article, the authors tested questions on inertia, quality, price, convenience, ad awareness, information, corporate citizenship, community events, ethical standards, etc totally 17 questions. They used PCA (Principle Components Analysis) to whittle this list down to 11 constructs.
  3. Customer segmentation. Not all customers act the same way, and the more discrimination we are able to build into our model, the better. Ideally, we'd like a unified segmentation to drive our research here.
  4. True short-term goals. While this approach can theoretically handle all marketing activities, it doesn't describe how to handle true short-term actions. However, I think it's pretty easy to bolt this on. Because we're looking at customers as the dependent variable, we'd simply add on known short-term acquisition and retention levers to only impact the first step of the Markov Chain. If an email acquisition program drove 5% of a segment to but, we'd simply add 5% onto the first Markov chain probability.

There's a lot more detail to this, but it should give a taste to the marketer who's interested in using customer equity as a dependent variable for ROMI / mix modeling. If there's more interest, give me a shout, go to MarketBridge and register your name, or download the Journal of Marketing Article (or all three).

1 comment:

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