eCommerce Analytics

I just ran across this quote from Will Schnabel and it gave me pause. In Lead Score and Activity Alerts: One in the same?, Schnabel says, “In order to identify the few qualified prospects from the remainder of the inquiries, you first need to determine your ideal customer profile or in other words, what segment is your best target. From there, BANT questions (budget, authority, need, and time frame) help determine the qualification status of the leads.”

So I agree with the last part. BANT qualification is important, especially in the B2B and B2P (Business to Prosumer) markets. After all, no one will ever buy your product without budget or the authority to make decisions. It’s the first part that’s becoming increasingly interesting and more complicated for me. Is the customer profile so rigidly first in this timeline? Or is privileging this activity just another reason that the Marketing/Sales political split persists? Certainly, this is the right way to pursue new business strategically: decide the size and relative need of a target market, build or alter product to address needs in that market, create messaging that speaks to the market about its needs, and then attack the target market.

Well, what happens when that target segment trickles in while a different segment - one not entirely counted on - starts to beat down your doors? OK. If the doors are really being kicked in, your company is likely to notice. But let’s say that this secondary segment buys on par with the target. Sales is likely to notice it first, right? And this is usually what causes the rift between marketing and sales: The marketing department often continues to chase the strategically-desired and well-messaged segments instead of the empirically-best segments.

This is where a data mining approach refines the way we look at segments. There may be unforeseen criteria that ultimately make its way into the segmentation definition. Clickthrough data is the most interesting and most-discussed piece of data in this vein. Schnabel would have you believe that this data determines the relative need of the segment in the BANT qualification. I say, why can’t it be part of the segment itself? A shrewd marketing department might change the amount of information available on the website so that the “heavy researcher” psychographic segment is more fully qualified. Particularly if the heavy researcher is also a heavy buyer.

Many companies are starting to break down the barriers of B2B and B2C. The B2P audience is a great example of this. This means that traditional customer segmentation is becoming more difficult. Customers may be segmented along demographic, business history, behavioral, and psychographic variables all at once. Ultimately, a data mining-aided approach to customer segmentation lets you adapt to customers who find you by breaking with these traditional barriers to make segmentation a fluid discipline.

Most of the talk today in B2B marketing is about gathering leads. With Google AdWords going crazy on us and SEO the hot new term, it seems that everybody is intent on generating a boatload of new prospects. Nothing wrong with that. But it does create a problem of scale. Does your organization have the salesforce to handle the explosion of leads? Probably not. Why is that a problem? Because without a lead scoring system, you’re trading quantity for quality by putting some of your best prospects on the backburner. With the glut of new leads out there, it’s easy to see that lead effectiveness is more important than ever.

Sure, most companies have some form of lead scoring in place. But the two most common methods, telemarketing pre-qualification and manual qualification, are expensive in the first case and inaccurate in the second. Manual qualification has the added problem of exposing the divide between sales and marketing. (They often have diverging views on what a good prospect really is.) And scoring is also only as good as the rules that are created. If the scoring system isn’t constantly revamped based on the actual sales wins, then what good is it?

Data mining techniques are different than common scoring techniques because they constitute an impartial observer. Data mining keeps departmental biases and political loyalties out of the equation. More importantly, data mining techniques effectively lop off the worst leads - the leads that clog the sales funnel - simply by projecting a prospect’s likely behavior based on your past prospects’ behaviors. Best of all, data mining techniques can account for hidden attributes that a human can’t. Did you notice that your best prospects visit your web pages in a specific pattern? Well, a well-trained predictive model does. And it’ll mark the clickthrough pattern as more important in determining the prospect conversion probability than how many white papers they’ve downloaded.

Now that leads are on the rise, B2B marketers should focus on increasing the sales win rate by intelligently funnelling the best leads. And this means dropping some of the prospects that likely won’t produce. To weed out my worst leads, I’m betting on data mining over rule-based scoring systems any day of the week.