eCommerce Analytics

Tim Ferriss makes some excellent points in his post The Margin Manifesto: 11 Tenets for Reaching (or Doubling) Profitability in 3 Months which it got me thinking about how margins are changing in the software business and why enterprise software companies must start “firing” their high maintenance customers.

The software industry for some time has been forgiving of poor fiscal discipline. With 90% margins, it is possible to blow lots of cash on unprofitable sales and marketing campaigns and still make a mint. Furthermore, Wall Street has always rewarded new license revenue growth over cost control. In this kind of environment any new revenue is good revenue, regardless of its ultimate price.

Sadly, the days of inflated margins are nearing an end. The price of software is crashing, and SaaS along and the consumerization of IT is turning software into a commodity. Enterprise software companies doing $500k deals on six month sales cycles will have to reduce their cost structures quickly to survive this disruption to their model.

With these changes afoot, plenty of blog space has been devoted to exploring how software companies can cut sales and marketing costs through search engine optimization, pay-per-click advertising, and viral marketing. Comparatively little has been written about Tim’s #10 point, however: firing high maintenance customers.

Despite the huge improvement in margins that result from firing poor customers, there are three reasons that the idea rarely gains traction in an organization:
1) Cultural resistance due to short-sighted metrics

Most companies are reluctant to fire customers. After all, no sales or marketing organization can be convinced that it’s a good idea to forgo revenue in pursuit of improved profitability down the line. This is especially true when they are measured on how much they drive top line growth, as they almost always are.
2) Data integration challenges

Even if sales and marketing can be convinced of the value in firing poor customers, there are still huge technical barriers to integrating the data required to for analysis. Challenges abound in getting CRM data to merge neatly with support and billing databases. Unless IT has a lot of spare capacity (an occurence as common as a Bigfoot sighting), significant budget will have to be allocated for data integration.
3) Inability to make sense of the results

Finally, once all the data is integrated, a healthy dose of marketing analytics know-how is required to make sense of it all. Without highly trained business analysts on staff, it is very difficult understand which customers are profitable and which aren’t. Furthermore, unless you want to keep spending money acquiring bad customers, statisticians and data miners will need to be called in to help build attribute profiles of unprofitable segments.

While firing unprofitable customers is a powerful way to improve margins and profitability, these three barriers ensure that it rarely gets done.  Unfortunately for most enterprise software companies, it will be too late by the time they realize how criticial it is to shed themselves of poor customers.   Those with the foresight and fortitude to make it happen sooner than later, however, can expect great rewards.

The knock on B2B data mining has always been that there isn’t B2C-like data available. Instead of multiple transactions that give us customer behavior patterns, we have company demographic information (industry, company size, revenue), some information about the person from the company who we’ll deal with (position/title), and where that person came from (lead source). It’s not behavioral data, which we know to be inherently better as a predictor than demographic data. But some data is better than none, right?

And we can certainly create transactional data that gives us some behavior pattern. If we throw in the contact schedule - the touches - from your company’s representatives, don’t you have a transactional pattern of both buying and non-buying customers? Coupled with the demographic data, you can drum up a model that predicts how many touches a lead might need to become a client and maybe a best guess at the path that should be pursued with a new lead.

More to the point of this post, this is the great thing about click-through data: it has a transactional quality. In fact, it just might be the transactional data for B2B companies. (Aside: This is also one of the reasons why companies like Omniture are becoming so notable: they provide some behavioral patterns, however small.) If we can combine click-through patterns from the person representing the prospect company with the company’s demographic information, then we might have a real interesting model that determines just how serious a lead is about buying from you and their company’s relative experience level with your product area.

Let me close out this post by refuting two of the main complaints about B2B data and its unsuitability for data mining-based models.

There’s Not Enough Data

Everybody loves data mining when it comes to consumer-focused companies. The vast amounts of transactional data are transfixing. The thinking goes something like this: “I’ve got hundreds of thousands of transactions here so whatever our predictive model spits out must be right.” Well, this may be true. And it mayn’t. But that doesn’t make a model built with less data any less compelling. It just means that one model has more data points. Don’t feel inadequate for the difference. Just make sure that you have data that’s important to the business problem you’re trying to solve. For example, if you want to know the next-best product for newly-minted customers, then you’d better have a solid set of second-time customers who bought a bunch of different products. Do you need thousands of these second-time customers? C’mon.

Missing and Bad Data

Isn’t this a reality everywhere? Even consumer-focused companies (with hundreds of thousands of transactions) have this issue. Oh, and I have a suggestion on what to do with that missing and bad data. Throw it out. Chances are, it will have absolutely no effect on the predictive models, unless of course all of the missing or bad data has a common characteristic that isn’t found in the rest of the data. For example, let’s say you’re building a model that predicts the next software product that a first-time customer might want from your company. Well, if everybody that bought a specific product as their first purchase is missing a zip code, then you can’t very well throw all of those records out. It would skew the model irreparably. But as long as the missing data is evenly distributed throughout the records, don’t be afraid to trash ‘em.

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.