eCommerce Analytics

Over the past two weeks, I’ve been watching the fallout from two important conferences taking place on the West Coast (where I would much rather be right now given the latest round of snow in Boston.)  The first annual Predicitive Analytics World ’09 was staged in San Francisco on Feb 18th and 19th and eTail West 2009 took place last week and drew in a host of e-Marketers from around the country. One common theme that I seem to be hearing from the blogs and articles I’ve read from both: make sure you’re using your customer data effectively and, most importantly, keep it simple.
Read Simplicity is Key for Marketing Analytics »

I am a bit discouraged today. I am surprised that we have not had more takers on our free customer scorecard application. Granted we have not unleashed the Social Media channel on the tool but I figured that after a week of availability we would have 10% more customers. The customers that have used it are overwhelmingly surprised of the results but I still wonder if the tool’s purpose is clear.

One of the leading, and might I say insightful, retail marketing professionals, Kelly Mooney, recently posted a blog titled: “Myths about online Retail Marketing”. In the blog she does a great job of providing an analysis of several marketing myths and there are a couple of takeaways that directly support how we perceive our tools to add value.

Read How Predictive Analytics Optimizes Frugal Environments »

The core of Istobe’s technology is an engine that predicts the next product or product category that a customer wants, a la Amazon or Netflix. However, we try to make this technology more applicable to other processes within the retail realm, such as direct marketing and merchandising, by looking at actual sales data. The typical recommendation engine (like richrelevance) only lends itself to realities in the online world, since they take advantage of website data exclusively. The Istobe engine is a bit more universal, using actual sales transactions to build models and predictions. In either case, the goal of either approach is to predict the next best product for your customers. I’ve compiled a good starter kit containing three links for retailers and others who may be interested in learning about Next Best Product approaches.

Read Predicting the Next Best Product for Your Customers: A Hyperlinkset »

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 »

I’m a big fan of Seth Godin, but his latest blog post, Ads are the new online tip jar, seems a bit shortsighted to me.  Seth argues that to support their favorite blogs readers should click on an ad to provide some small financial support to the blogger.  Taken to its logical conclusion, however, this strategy will only harm bloggers and other content providers in the long term.

Read Ads the New Online Tip Jar? Maybe in Small Quantities »

What’s not to like about fantasy football? It raises the importance of the unimportant football games (i.e., those games in which your team is not participating). It allows for some of the finest trash talking on the planet - with few repercussions for losing the Sunday match-up as long as you win the weekly war of words. And, most importantly, fantasy football gives Istobe yet one more opportunity to crunch data in an effort to predict outcomes. So in the next two weeks, we plan on crunching the last seven years of NFL player data in an attempt to predict this year’s fantasy workhorses.

Read Get Ready for the Ignite Fantasy Football Draft Predictions »

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.

Something that we constantly talk about around Istobe is why more small companies aren’t interested in implementing customer analytics. Especially given that these companies are pressed now more than ever to keep up with big companies that run customer analytics algorithms as part of their hourly routine. And in an era when a tome like Supercrunchers proclaims a new dawn for predictive models in business. Even with a value proposition that demonstrates a solid return per thousand customers, we often have problems convincing customers that our mojo is good and that data mining is more than just voodoo. Well, today I prepared a really simple sample revenue calculation to quantify the value of Istobe’s predictive models. Take a look at how much more an Istobe-based email campaign makes on a yearly basis.

The rule of any business venture is it must either make money or cut costs for clients. If MIT Sloan didn’t drill this into us, we got a refresher into this the other day when Doug, Chris, and I met with Chris Merrill, founder of Thrive Networks, The Orchid List, and Owner of Ass Industries. He broke that down pretty plainly for us when talking about our need to educate our customers. Well, here is as plain a case as I can make for the revenue increase that Istobe brings to the table when we mine customer data to determine what product they want to buy next.

The Revenue Increase

Assuming a company that emails 500,000 customers weekly, 50 emails a year, and $1.90 per email open, the Istobe predictive models allow a company to make $412,300 more than just basic email blasts and nearly $80,000 more than a baseline targeting approach. There is a full calculation below but let’s talk about what some of the numbers are before you examine them.

The Setup

First, let’s take a very conservative number of 4% as the increase in predictive power of our models over a general baseline. In this case, we assume the baseline to be the company offering the same product that a customer already bought. As creatures of habit, consumers are typically likely to buy the same thing from a company that they just bought. Let’s take dog food at a grocery store for example. Whoever buys dog food probably has a dog and there is a good chance that they’ll keep buying dog food. It’s a safe bet. The same goes for internet retail. If I buy a shirt from an internet retailer, it’s likely I’ll go back there to at least look for another shirt - assuming I didn’t hate the first shirt and the customer service was adequate. Now, the pitfalls of offering the same product is something I won’t go into in detail here; suffice it to say that offering the same thing that a customer is likely to buy again and again from your company is not the best business move. Really, your company should be looking to expand the share of your customers’ wallets. And this means offering them new products that they currently buy elsewhere. Like I said, a topic for another day. So Istobe does about 4% better than offering the dumb alternative, which is offering the same product again. 4% doesn’t sound like a lot but let’s play this through.

Now, let’s assume that a client uses our predictions to send out targeted email. It’s been assumed that normal behavioral targeting enhances your open rate by 40%. This means that just offering your customers the product that they bought last time increases your open rates by up to 40%. Well, I’m not so sure that I trust those studies so let’s just play it conservative and say that targeting spurs a 20% open rate increase. What that means ultimately is that Istobe predictions improve the targeting - the 20% increase - by 4%.

As a base open rate, we’ll use 3.5%. That’s a pretty conventional rate when sampled from our customers. When you apply the Istobe predictive power (4% increase) and the lift from general 1:1 targeting (20%), Istobe’s open rate is 4.4% and the baseline targeting rate is 4.2%. The normal open rate, remember, is 3.5%. This means that, if we take 1000 customers, Istobe will get 44 opens, baseline targeting will get 42, and the traditional email blast will get 35.

Here, we’ll use $1.90 per open as the amount of money that our fictitious company earns per open. Ultimately, not all opens are sales and this figure essentially backs out the complications associated with the website, ordering mechanisms, sizing problems, etc. In other words, it’s a way to understand how much each open is worth on average.

When you take into account that each email open is worth $1.90, and that Istobe has 44 opens per 1000 customers, Istobe’s predictions make our fictitious company $82.99 per 1000 customers per week. Baseline targeting makes $79.80 and regular email blasts make $66.50. Calculated for 500,000 emails per week for the course of a year, we get the figures that I began with at the top.

The Calculations

Trigger marketing is a good idea. Customers are not always ready to buy when you want to sell so it’s important to nuture leads through periodic communication triggered by events such as website interaction, inquiries, birthdays, etc. The more relevant and well timed that communication, the more likely you can keep the customer engaged until he or she is ready to pull the trigger, so to speak.

Michael Thompson of ClickTactics/ClickSquared makes good case for trigger marketing in his recent DMNews article Keys to trigger-based e-mail marketing. What caught my attention most was:

Timing and relevancy are the foundation on which successful marketing programs are built.Missing on either front can often spell disaster for a campaign.

Much as a dancer needs both rhythm and knowledge of the right steps to dance, marketers must hit both “right message” and “right time” to move the customer to action. By aligning the content and timing of e-mail messages with customer needs, you’re increasing the relevance, response and, ultimately, revenue from your direct marketing programs.

There is no question that communicating the right message at the right time is critical in trigger marketing. Often, however, more resources are devoted to implementing the data collection systems required to track customer behavior than to determining the marketing rules that will make trigger marketing a success. This is not surprising since vendors make their money selling analytics systems, leaving analysis to hapless marketers awash in a hurricane of raw data.

Faced with the task of developing trigger rules and content, many marketers go with intuition. Did the customer just abandon a shopping cart? Send an email asking them to finish the purchase! Did he click a PPC link for one product but then look at another before leaving? Send an email pushing the first product! Or should it be the second? Or both? And do it immediately! Or maybe give them a day to think it over!

The fact is that using human intuition, while better than nothing, rarely produces optimal trigger marketing rules. The optimal rules are there, though, hidden in gigabytes of customer data collected in analytics systems waiting to be uncovered. Few marketers, however, have access to the statisticans and data miners needed to unlock the secrets.

We’re looking to change that and I suspect that others are too. Trigger marketing will play an increasing role in marketing and it will always require elements of human intuition. Our bet, however, is that it will require data crunchers even more.

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.