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simmas
Calcite | Level 5
was hoping someone had any ideas on the following.

let's say we have monthly mailings plus a holdout cell over a period of 12 months.

we measure response of mailed vs control at aggregate level, but how would i go about building something to rank sites based on how likely they are to respond. In essence to identify the key characteristics (firmagraphics, behaviours etc) that are proving the most responsive and to rank a site based on the impact of DM on their response.

having trouble formulating the model i want to capture the impact of the mailings as opposed to those that spent vs those that didn't

cheers
4 REPLIES 4
statsplank
Calcite | Level 5
Hi simmas,

Did think about fitting a logistic regression model with responded/not responded as an outcome and the characteristics you mention (firmagraphics, behaviours etc) as predictors?

In fact, logistic regression models the probability of event, so in your case it's the model for probability of site responding, and you can rank the sites based on their predicted probabilities. Also you can put the sites into categories according to the predicted probabilities of responding, for example low:<10%, mid:10-30%, high:>30%.
simmas
Calcite | Level 5
thanks for your response,

this approach however doesn't deal with whether the mailing is impacting on the response rate or not. i.e. there will be sites on the mailed and control side who will have responded (by spending).

so the part i can't figure out is how to incorporate the mailing impact.
statsplank
Calcite | Level 5
you can include a categorical variable "mailed" (yes/no) in the model. The regression coefficient for "mailed" will show if mailing has an impact on the probability of the site responding.
simmas
Calcite | Level 5
unfortunately that model is still looking at who is most likely to start spending, with an overall mailing effect.

What i want is for the model to measure how that 'mailing' coefficient varies across the firmagraphics etc

As i see it, these are 2 separate approaches,
- the 1st measures how likely a site is to start spending, which is the natural rate if you like.
- the 2nd measures how much the mailing impacts whether they start spending (if it does at all!)

I could just measure the differences in uplift at some pre-determined level (e.g. industry by size), however i need to take into account the repeated mailings and a model would also allow me to throw more variables into the mix.

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