Wednesday, October 15, 2008

Lead Qualification in the New (Bad) Economy

I was talking today to Marketing Sherpa. They're wondering if any B2B companies are using analytics to qualify leads on the basis of their "health" or "likelihood to close" given contracting budgets. I wasn't aware of anything specific, but it's certainly SOP in B2C. Car Insurance, for example, are qualifying "real value" all the time just on the basis of the numbers people fill in on display ads.

Businesses do it with BANT--budget, authority, need, timing. This is obviously still in play in today's environment, maybe even more so. However, is there another way to do it that keeps track of industry dynamics... the micro dynamics in an economy? For a company like Microsoft or Cisco that is getting literally 1000s of leads a day through search, display, partners, inbound call centers--what if they could prioritize these leads on the basis of "real economic activity?"

It's an embryonic idea, so pardon if it's a bit crude. The idea is that you take a look at trailing 3 month data on "proposal to close" discrete events. So you're looking for proposals that closed and proposals that actually were lost. Then you'd put a data mining algorithm on it against all the industry and firmographic data you had on the close. So you'd be looking at city, SIC code, company size, etc... all of the variables that are relevant from an economic perspective. This could give you a predictive model that changed daily on which companies are more likely to close in a tough economic environment, and would facilitate reprioritization of closing efforts on these types of companies.

You'd have to be careful on bias here, obviously. But, I think it's an interesting idea. You could even enrich the data with region-specific industry insights. E.g. if the beige book shows a bright spot for small manufacturers around Philadelphia, you could manually crank up that part of the model.

Descriptive statistics would be interesting, too. A daily report could be built showing changes in close rates by company type (SIC code), geography, etc. This could be compared to economic data and would provide a good macroeconomic headlights tool for a B2B company.

These same ideas could work at all stages of the pipeline. I guess this is my first post about specifically "marketing in the sucky economy" and I know everyone's going there now. Time to rev up the contra funds and jump on the bandwagon.

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