eCommerce Analytics

With the holiday season upon us, and with the specter of economic downturn no longer looming but now in full unfurling, the hue and cry for free shipping to draw customers has never been greater. Almost everyone insists that drastic price cuts and coupon-like free shipping are necessary evils to keep your customers from the competition. But I would urge smaller retailers to resist giving up margin and making deal-seekers of their entire housefile. Instead they should get more personal.

Read Retail Marketing Strategy Should Rely More on Personalization, Less on Free Shipping »

One question that we constantly come up against is why online retailers shouldn’t blast their entire customer base with every email promotion they create. Granted, most companies use some form of segmentation to track email responses (the usual 0-12 month buyers vs. 12-24 buyers is a common example) , but besides being defined by arbitrary recency and monetary spend cut-offs, these groups have no real bearing how the customers contained in those groups will respond to a given ad. My colleague Matt wrote a nice entry about this back in July (see Does your email response rate depend on how many emails you send?), but I recently came across some new metrics that I think help drive the point home.

Read Timing and targeting: why you shouldn’t blast all your customers with every offer »

I was going through my usual news download this morning when I happened across the following blog article (here) from the washingtonpost.com that talks about the recent disclosure by major search providers that they are tracking ever increasing information on their users without their users’ explicit consent. The reason why their collecting this information? Better ad targeting.

For many online retailers, this may not seem like the worst idea - better information from search providers means a better understanding of who’s buying their products, when they’re buying them, and what they’re most likely to buy next.  For many consumers though, there is an increasingly fuzzy line between companies collecting information about their buying habits to better meet their needs and companies invading their privacy.  Rep. Ed Markey (D - Mass) is actually proposing new legislation that would limit what businesses could collect and ultimately do with their data, whether it be analyzing clickstream data to track customer behavior on their own site or trading information with data providers and other retailers to get better qualified lead information.

As someone who is entrenched in the analytics business, I fear the backlash from overzealous companies that trade on customer trust in return for increased revenue streams. There is much discussion online over NebuAd’s deep packet inspection technology being deployed by many ISPs to track individual user’s web activity for micro-advertising. While they provide an opt-out service for consumers, it’s still unclear whether you are opting out of receiving micro-ads or opting out of being tracked by the service. There was a similar outcry with the launch of Facebook’s Beacon technology which sent data from external websites back to Facebook for better ad targeting. Although they too now provide an opt-out for users, many bloggers and websites have resorted to publishing step by step instructions for completely blocking the Beacon technology through your browser.

Clearly, there is a middle ground where both consumers and businesses will feel comfortable with the amount of data being tracked and analyzed - my hope would be that this is can be found through best practices and not legislated through Congress. In the meantime, companies should focus on the following to maintain a level of trust with their customers:

• Transparency - Most important, let your customers know what information you intend to collect and how you intend to use it - and more importantly make this statement prominent and easily accessible on your website. Many customers don’t mind being targeted as long as they know up front how the company aims to target them.
• Opt-Out and Opt-Down - All companies should provide an opt-out of targeted advertising, but most should also provide an opt-down - the ability to decrease (or in the rare case, increase) how often or which types of products get advertised to the individual user.
• Anonymization and Obfuscation - While email targeting usually comes down to the individual level, most data analysis is done in aggregate and doesn’t necessarily need personally identifiable information to come up with important rules and formulas. If you’re working with a data analysis provider, they should be able to outline what information they need and what information can by anonymized or obfuscated to hide the identity of the underlying customer.

If all companies could follow these simple guidelines, I think we could find a happy medium between information collection and consumer privacy that wouldn’t necessitate legislative intervention.

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.

These days even the most technophobic consumers have inboxes full of marketing from companies they have interacted with. As responsible marketers, we have ensured that these customers have opted in to our communications and we know that we must promptly remove them from our house file when they no longer want to hear from us. However, according to Marketing Sherpa’s Email Marketing Benchmark Guide 2008 (summary here), ensuring opt-in may no longer be enough to keep our company’s image clean.

In a survey of over 4000 consumers, half consider email to be spam if it arrives too frequently, even if it comes from a known sender. This has serious consequences for email marketers using “carpet-bombing” strategies to spur customers to purchase. Even if consumers have opted in and know a company well, they may come to think it as a spammer if they are receiving marketing emails every day or every week.

The sentiment that, regardless of permission, frequent email marketing is spam will only grow as inboxes become even more flooded. Marketers will be forced to migrate to a “surgical-strike” strategy where customers are targeted with highly personalized messages only at the most likely time to buy, and probably no more than once a month.

In an environment where consumer trust is hard to gain and can vanish with one misstep, nobody wants to be seen as a spammer. Unfortunately, the risk of marketing too frequently is now beginning to outweigh the benefit. If email marketers do not adapt through better targeting, they may find themselves relegated to the junk folder for good.

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

On Monday, I began a discussion about how Istobe evaluates the ROI from email marketing campaigns based on our predictive models. At the end of my post, I promised a discussion about other factors that we take into account when evaluating the lift. And…voila. Today we unveil those factors: the email influence zone and opt-outs, and we discuss how Istobe accounts for them in our lift calculations.

Email influence zone
Sometimes referred to as decay rate in the catalog industry, the email influence zone (EIZ) - not unlike the catalog influence zone (CIZ) - is essentially the time period after an email is sent. And we assume that each succeeding day after the email is received has less effect than the day before. Thus, the moniker decay rate. Catalogers have believed for years that their catalogs have a carry-over influence: the catalog accounts for many web purchases. In fact, this is very reason that catalogers are loathe to cut the number of catalogs that they ship. Even to those customers who have never purchased from the catalog itself. We believe this is also true of email marketing.

Basically, the idea behind the EIZ is that an email offer has an effect on online purchases that have no other obvious origin and which relate to the product that we predicted. For example, if our models predict that shoes are the likely next product for a particular customer and that customer purchases shoes online five days after receiving an email that advertises shoes, then we can assume that the email - and our product recommendation - influenced the customer’s purchase. Our model gets credit for a small percentage of this purchase even though the purchase didn’t come directly from an email click-through. The EIZ period that we calculate differs per client depending on the frequency with which our clients send emails.

Opt-outs on the Istobe watch
If we’re going to give ourselves some of the credit for purchases that occur in non-email channels, we also have to take a hit for bad events that occur during our watch. The bad event that Istobe tracks carefully is email opt-out. We track whether the opt-out rate goes up during our watch. If it does, we have to assume that next-best offer has somehow turned customers off. If the opt-out rate does go up, we deduct a portion of our lift because we believe that we were responsible for that incline in opt-out rate. We’re responsible for that small piece of customer attrition.

Taken together with the variables I spoke about last time, these are just four factors that we constantly adjust in determining how successful we are on behalf of clients. And we’re always looking for new ways to perceive actual lift. If you have new ideas for evaluating predictive-model efficacy, please email me. I’d love to talk about them.

Collaborative filters, the heart of the recommendation engines used by companies such as Amazon and Netflix, are quite good at predicting items that might be of interest to you. Essentially, these filters work by trying to group you with people that have expressed similar preferences — whether it’s by CDs you rated, the movies you chose, or the items you bought — and then finding the items that the other people in the group like that you have not yet seen.

When the dimension of time is introduced into the environment, however, collaborative filters can quickly lose their predictive power. This is particularly true when filters are used in retail for regularly purchased goods. What may interest a customer at checkout time is not likely to be what interests them six months down the road.

For example, my cat has a chronic eye condition that requires semi-regular treatment. On average, we purchase eye drops about once a year though it can vary anywhere from three to 18 months. Due to the quirks of cat physiology, after application the medication drips into the cat’s mouth and, if her reaction is any indication, tastes horribly. To mitigate the yuckiness we usually give her some treats. I imagine we’re not the only ones who do this.

A good collaborative filter might find that cat treats are a good cross-sell for eye drops. I would, in fact, be likely to add cat treats to my shopping cart at check out if they were offered. A retailer using a recommendation engine would get extra business from me. A win for everybody.

Three months later, however, that same retailer is now sending me promotional emails for cat treats because their collaborative filter has no concept of time. It should be sending me offers for eye drops — that’s something I would probably be interested in buying again. Instead, after a few weeks of receiving irrelevant offers, I find myself not opening these emails or, even worse, consider opting out of receiving them. Not only has retailer lost an opportunity to make an additional sale, but they’ve come close to losing a valuable way to communicate with me.

The key is to use recommendation engines that are specifically designed to handle the time varying nature of email. In work we’ve been doing with customers, we’ve seen than a recommendation engine that considers when a person is most likely to purchase coupled with the purchasing sequence of groups with similar preferences yields results that are three times better than traditional collaborative filters. I have little doubt that we are the only group pioneering this concept and suspect that we’ll see a lot more email specific recommendation engines in the future.

In the months and years to come, smart retailers will look past off-the-shelf recommendation engines that are optimized for cross-sell at check out. They will see that the opportunities are tremendous for those using recommendation engines that are specifically designed to understand time.

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.