eCommerce Analytics

I was reading up on analytics technology today and ran across an interesting article at TDWI (The Data Warehousing Institute) which surprised me.  It was surprising due to the fact that it was a year old but was reporting the same results as today:  predicitve analytics solutions are still novel to many companies and unknown to even more.  Even after dozens, if not hundreds, of successful case studies show how predictive analytics are a low-effort, high ROI solution to help a company achieve strategic goals:

[P]redictive analytics can yield a substantial ROI. Predictive analytics can help companies optimize existing processes, better understand customer behavior, identify unexpected opportunities, and anticipate problems before they happen,” Eckerson writes. For six years running, he points out, a majority of TDWI’s annual Leadership Award winners have used predictive analytic solutions to achieve noteworthy business results.

Before we created our predictive analytics solution for email marketing we knew the benefits of predictive analytics solutions and we realized that marketing has many metrics and data points as well as a very strong set of historical data which we can and do use to build solid, accurate models of customer behavior and desire.   Why are users of predictive analytics still considered Early Adopters?

Read Predictive Analytics Provide Big Payouts For Early Adopters »

Just finished getting through the backlog of my Don Dodge RSS feed today and I’m happy to report that venture capitalists seems to think that businesses like Istobe are about to break out. Let me qualify that. Venture capitalists seem to think that using data to improve e-commerce is an industry that clearly needs some maturing and that maturing time is nigh. Istobe represents that maturing, combining hundreds of models and data integration routines into a package that lets you target the right customer with the right product at the right time.

Investors believe that the maturation in this industry will occur in the next five years. Well, so does Istobe. We believe that it’s time to put your data to use. If you don’t use it, data is no more than the new shelfware: that software you just had to have before you realized you lacked the in-house talent to unlock its value. Istobe is your outsourced in-house data analysis talent that lets you ask simple questions and get answers without analysis, questions like: I need to sell this overstock of shirts, to whom should I market them? In reply, Istobe gives you a list of your customers that are most likely to buy your shirts and the probability that they will buy. This is the new paradigm in predictive modeling that Gartner calls the data mining packaged application. Let’s just take a quick look at the moment in time at which we are poised.

Data collection methods are clearly refined

Nowadays, everyone is sitting on a pile of customer data that they don’t know what to do with. As Rob Hayes, partner at First Round Capital, says in the Stefanie Olsen article from which Dodge draws his inspiration, “Everyone talks about all the data that’s being created and how valuable it is, but the way you make it available is by doing something actionable with it.” The glut of data is due, in part, to years of CRM implementations and the current CRM zeitgeist. You can’t turn around without being inundated by a flood of marketing for “next generation” CRM systems. In fact, I used to be one of those marketers at a not-too-small company that builds Dynamics CRM.

Of course, online everything has made data more prevalent as well. Returning some kind - any kind - of functionality in exchange for your profile is no longer new hat. Every widget and social network known to man requires you to divulge information before you start using it. And then it collects your clickstream as you use the app. Heck, I’ve got at least 50 different login/pass pairs that I need to remember now.

Purchases, as well, fall into this category. With online shopping increasing at a terrifying clip, all of your purchases are more seamlessly collected and tied to your profile and your clickstream, meaning that now, more than ever; pre-purchase behavior and purchaser characteristics - on a large scale - are at a marketer’s fingertips.

Data analysis tools have matured but haven’t turned the corner

So there are various types of data out there right now that need tying together and, ultimately, analysis. But have the tools to merge and make sense of that data improved? Not appreciably. Really, when you get right down to it, the tools used to perform data analysis are still catch-all tools that can build any model you want or merge any type of data you want. But they can’t help you with specific business problems. In other words, the tools exist for database experts (data integration) and PhD statisticians (statistical modeling tools).

For years, these tools have gotten easier for experts to use but haven’t gotten any easier for business users. This means that your $300K worth of in-house data integrators and PhD statisticians have become slightly more productive over time but translating their language into the language of business is as difficult as ever. And turning the data they spit out into a meaningful business strategy is just as tough.

We talk a lot about the nuts and bolts of predictive analytics on this blog - how we build customer analytics models, how we interpret them, and how we establish whether or not they’re working in production. However, one item we have yet to tackle - and a very important item at that - is how to integrate data mining and/or predictive models into your organization and your company’s routines. Indeed, the black box in the middle should be the necessary data integration (getting the data into the right format for the models) and running a bevy of models against the data to see which one projects as the most effective. But it’s the before and after this black box that really make or break any attempt to incorporate predictive models into a company’s processes: data collection and incorporating data mining results into your processes.

Do you need to collect more data than you already have?
The main problem that companies run into is the need to collect more data in order to build the necessary predictive models. For example, if a company wants a model that tells them which of their products a specific customer might buy next, does it already collect the data necessary to support a model that does that? Ultimately, the question is: Is it worth the time and cost needed to invest in collecting more data that may not yield any better results when supercrunched? New collection methods take months to set up and sour clients on predictive models before the fun has even begun.

We believe that it’s important to try to work first with the data that our clients have and add new sources of data over time. This is a handy tip for business users who really want data mining to work in their organization: start small by using the data you have and get more sophisticated as you go. I know it may be tempting to put in that clickstream collection database right now so that you can use online behavior to segment and market to your customers. But trust me, get the buy-in from your organization first by proving that predictive customer analytics work. Then ask for the stars.

Are you ready to completely change the way that you do direct marketing?
Some companies would have you subscribe to a whole new way of doing business in order to use their models. Customer-centricity is my favorite new business philosophy. Though I firmly agree with the need for firms to be customer-centric, is it realistic to expect companies that use offers, coupons, and holidays to draw new and existing customers to their websites - and have for years - to change their direct marketing approach to accommodate a new set of tools? Not really. Company culture and routines are slow-developing and even slower-changing.

So the question at the end is really: What are you going to do with all this newfound predictive power? How are you going to fit in customer-centric model results - We’re 99% sure that Brad Pitt will buy two Baby Bjorns with lumbar support - to your next email blast that has a summer theme and features hats and sunscreen? And what about the extra creative necessary to relay that customer-centric message? You’re going to have to make a new email blast featuring the Baby Bjorn with lumbar support, in addition to the summer-themed email.

Well, I’m pretty sure that the summer themed email blast was probably going to draw some business but I’m also positive that luring customers with what they want when they want it is a lucrative way to market. The best practice is to initially skim the cream off of the predictions, to take those that have the highest probabilities of succeeding, and run with them. Collect a group of customers that has the highest probability of buying the Baby Bjord - Mr. Pitt among them - and send an intermittent email to just that group. Now watch your open rates and clickthroughs soar.

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.