eCommerce Analytics

The blogosphere is awash in tips for doing email targeting.  Some are good (almost anything advocating testing email performance) but some are just flat out wrong.  Here are three commonly held tenets of email targeting that you should ignore.

Read 3 Email Targeting Myths »

With today’s news that the retail sector is experiencing a slowdown, now is a better time than ever for multi-channel retailers to do two things: turn to cheaper forms of advertising (email) and use quick-return customer analytics to compete with gargantuan discounters like Wal-Mart that threaten to swallow retail whole. The truth is that Wal-Mart will continue to invest in analytics during the tough economy because they will see immediate ROI from understanding which customers are poised to buy, which items they want, and how much those customers are willing to spend. I can think of two, good reasons for smaller multi-channel retailers to follow suit.

Harvest your current customers
Most would say that the thick of a poor economy is a poor time to invest in new marketing projects. If these projects are tied to new customer acquisition, I might agree. It’s damned expensive to acquire customers and you tend to forget what you already have while you’re out prospecting, buying lists, etc. Sometimes, the answer is in front of you. In a poor economy, isn’t it imperative that you retreat to your base? Multi-channel retailers need to figure out ways to:

A. Not lose your current customers to competition (like Wal-Mart)
B. Harvest your existing customers by making them feel as though you understand them

Really, achieving B is the answer to question A. A redoubling of your customer service effort will always make your customers more loyal and less likely to jump ship. But we have to remember that larger players can always offer deeper discounts in an effort to combat your superior customer understanding. One way around this is to deepen your customer understanding on the marketing front with timely, personalized emails to your customer base. Ultimately, if you can address your customers’ needs first - make your customers offers at the cusp of when they need those products - then you are likely to win their business. This is the advantage that predictive models based on your customers behaviors provide you: the ability to beat your larger competition on timing as opposed to discounting.

Quick ROI
Customer analytics like those that Istobe proposes are great because the analysis takes advantage of data that you, as a multi-channel retailer, already possess. You’ve already got a record of your cusotmers’ purchases. In other words, there is no up-front infrastructure or talent investment. What this ultimately means is that your ROI emerges quickly. How quick? Well, let’s just say that you’re in the black (or, green) around month two. This is especially true if you’re already used to sending your customer data to a co-op database (like Abacus or NextAction); you’ve already made your data collection and transfer investment. Now it’s simply about turning those investments to a different use - customer development not acquisition - by focusing how that data helps you pull in the monetary margins in your current customer base.

I noticed that the RRW Consulting blog alluded to an article on Friday that I have been promoting to my peers: a research report by the Aberdeen Group (abstract here) that discusses the importance of email personalization. The one-to-one marketing emphasis in the article is precisely the kind of email targeting that we espouse here at Istobe. Today, I want to expand on one aspect of the Aberdeen report that we spend extra time on at Istobe: the importance of the buying cycle in determining what kind of email message to send your customers.

In the Aberdeen article, Ian Michiels mentions that web analytics provide great clues to assessing where customers are in the buying cycle. For example, if a customer invests a vast amount of time clicking about a product group, that customer is likely doing research and is in the market to buy a product in that area. A discount offer, Michiels says, would likely get this customer - who is now highly qualified and advanced in the buying cycle - to act on their desire and make a purchase.

I totally agree with this sentiment. But as Chris mentioned in detailing his experience with GPS systems at Amazon, there is another way to do this. Customers can clue you into what they want via their clickstream. But even if you don’t have clickstream data, transaction histories, once supercrunched, can give you a leg up on finding customers who will likely buy next. In other words, this supercrunching can help you locate the customers that will likely buy before they locate you.

How does this work? Well, other customers have come before them and laid out patterns that aren’t perceptible to you and I but are very perceptible to Istobe’s predictive models. Istobe’s models throw out those customers that are not likely to buy again and then work with those who are. From there, Istobe’s models assign the products that are likely to be purchased by these likely buyers.

I won’t argue that this method is more statistically powerful than clickstream data, which is a solid indicator of future behavior. But I will argue that clickstream data takes vast amounts of resources to capture and use, a difficult proposition for online retailers who are just dipping their toes into analytics. And using transactional data to predict who will buy next is a more proactive approach. So what do you get from that proactivity? Probably a two- to three-month head start on your competition. You can focus on targeting your “most likely” customers with act-now offers while your competition waits for these customers to visit their web site.

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.