eCommerce Analytics

Recently, I’ve noticed that the vast majority of articles and blogs out there on marketing and customer analytics primarily focus on how to use data-mining to increase revenue and cut costs in B2C oriented companies. Today, I wanted to take 5 minutes to outline some ways B2B companies should be using analytics to increase their top line while minimizing costs.

Knowing Your Leads

Many sales people will tell you that they already know which companies and decision makers they should be selling into. The marketing folks will tell you they already know who matches their ideal lead profile. Funny part is that in many cases these two ideals don’t match up.  A recent marketing research report concluded that in nearly 50% of companies out there, marketing and sales do not have a direct view into what the other side is doing. Data analytics can clearly help bridge this gap by following a customer through - from lead to purchase to maintenance - showing which leads result in the most profitable customers - not just in terms of initial sales, but also in terms of potential lifetime sales. It’s the best way to make sure both departments stay on the same page and the entire company increases its overall hit rate.

Better Cross Sell and Up Sell

It shouldn’t surprise anyone that better targeted email results in better cross-sell and up-sell rates. Personalization is a hot topic in the B2C space, but there is no reason why B2B companies shouldn’t have the ability to tailor their emails to match the company profiles of their customers.  A CTO shouldn’t be receiving the same sales pitch as the marketing folks and someone in the energy business shouldn’t get an email that is designed for online retail. These seem like easy rules to follow, but the trick comes in knowing how to segment these customers by industry, contact, and previous purchase history to minimize the work needed to pitch the right offer at the right time.  No one has time to draft up 100 emails to each type of customer - which is why predictive analytics is critical for optimizing the segmentation process to forecast which customers are going to want similar products in the future.

The True Cost of Support

Many times support personnel get relegated to the back office and we tend to forget about them when factoring in the true costs of sustaining a relationship with a customer. If a customer spends $1,000 on one of my products, but then uses up 100 hrs of one of my support staff, are they still a profitable customer? Factoring in and then forecasting support costs can be an extremely valuable way to judge the lifetime value of a customer and help management decide whether they should renew contracts or continue maintenance agreements.  Working this back into lead generation can also help marketing make sure they focus on customers that will not only buy product, but also won’t cost the company an arm and a leg once that customer comes onboard.

These are really just a few ways B2B customers can make use of data analytics. There are many more, and next time I’ll try and focus on some leading edge cases out there that show how B2B companies are using analytics to increase revenue and cut costs.

Maybe. But I can guarantee your revenue per customer does. And not in the way that you might believe.  There is strong evidence that reducing email in an intelligent way actually increases your revenue per customer.

Just yesterday one of my colleagues asked me whether, in addition to the weekly timing of an email send, the quantity of emails sent to one person mattered. In other words, is there a limit to the email offers that a marketer should send? The intuitive answer is: of course. If we look at catalogs alone, consumer dissatisfaction with this method of direct marketing is at an all-time high. After all, no less than six websites have sprung up that allow consumers to opt out of catalogs. You’d have to have a powerful argument for me to believe that overzealous emailers are perceived any differently than overzealous catalogers.

My partner Doug Bright has already spent some time fleshing out this hidden cost of excessive email. So I’ll just add some more beef to his already meaty argument. In March, 2006, noted marketing researcher Dr. V Kumar, along with Rajkumar Venkatesan and Werner Reinartz came out with an article entitled “Knowing What to Sell, When, and to Whom.” You can see the abstract here at the Harvard Business Review. The article is utterly fantastic; you should get a hold of it.

What does this have to do with overemailing? Well, at the end of the article, the authors reveal an interesting, yet tangential, finding about email in their research. They found that purchase increases were tied to marketing communication in a strange way. It was not linear. In other words, more communication did not continually yield more purchasing. Instead, the authors found that above a certain threshold of communication, customers were put off. To quote the authors, “Clearly, many companies may be actively damaging their customer revenues in attempts to make sure that no opportunity for a sale is missed.”

The upshot is that they found that a data-driven approach to reducing marketing communication leads to “not only lower costs but to a revenue increase per customer.” When then tested this hypothesis using data-driven models and A/B testing at two client sites, the reduced communication strategy outperformed the traditional “blast ‘em” approach on both occasions. How much did it outperform the “blast ‘em” approach? I’m glad you asked, because these are the truly staggering numbers. For the B2B firm they worked with, the potential profit based on $1600 of additional revenue per customer, came to $320 million in additional profit. Now the cynical might say that this was mostly a reduction in cost. And I would have to admit that’s true. However, what the authors found was that the revenues for all product groups still increase, meaning that customers were spending, on average, $365 more with the reduced communication schedule. Similarly, at the financial services firm they worked with, the authors found an increase of $400 per customer using this data-based communication schedule.

To me, these results are unequivocal: sending too many emails not only is a waste of time and labor, it also hampers your sales. We all know it’s tempting to equate activity with results. But it may be better to turn your attention toward an intelligent use of your data to figure out who you really need to email and how many times you should email them.

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.

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.