I've been working on a process that features the following steps:
- Spatial match a customer list against all block groups to assign the relevant group for each
- Use the same customer list to choose appropriate larger geos, like DMAs, CBSAs, 'Places', etc.
- Append behavioral/preference data from Experian and Simmons to both sets
- Compare the values for each vs. the general population to get an Index value. For instance, if a certain factor, like 'Luxury Merchandise Buyer' appears in 0.1% of the general population and 0.2% of the customer population, the index would be 200(%), because the factor appears twice as often.
This part has been pretty straightforward, but when I start switching up my geographic units, my results sometimes change dramatically. If I do the most basic comparison of DMA (a large area) vs block groups, I see what seems to be a profile that lines up with my expectations, and as I shrink the comparison area down to CBSA and then census tract, the profile stays similar, though the index values shrink. This makes sense, as there will likely be more similarity within smaller units.
Is there a best practice in terms of what kind of comparison scale is most useful/likely to provide usable results? I've also experimented with using Custom Geographies to locate the representative customer locations at an even finer grain than block groups, but that seems to be the point where my 'Vegetarian' and 'Will pay more for organic' groups turn into 'Drink to get drunk' and 'Fast food fits my schedule and budget' groups, and I realize I'm really just fumbling around in the dark.
Thanks in advance for any insight/suggestions/witty comments.
