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 »

When we talk with customers about improving the success rate of cross sell and up sell opportunities on their existing products, many reference Amazon.com as the retailer they believe is doing the best job in the industry.  While I agree that Amazon does a fine job with following up on self-generated leads, I don’t agree that they do an especially good job of anticipating or prompting additional purchases.

Let me give you an example. I bought my mother a cookbook three weeks ago for her birthday. I wrote out a gift card, opted for the additional gift wrap, and shipped it to my parents’ home. Additionally, because I’m particularly anal when it comes to data organization, I tagged it within Amazon as a present that was bought for my mother. Now, knowing all of that info, any guesses as to the next product Amazon decided to pitch me on? Cookbooks. I can maybe understand the tendency for someone to purchase the same product again after purchasing it once…maybe…but in this case, really? After doing everything I could to signal to their system that the purchase wasn’t for me (different ship to name, different address, tagged as belonging to a different person), I still get an offer for cookbooks?

Now, don’t get me wrong, Amazon does do many things right. They are one of the best on following up on customer clickstream data. A recent visit to the site to research GPS systems for an upcoming trip resulted in two emails offering deals on GPS systems I actually might be interested in.  My problem with Amazon, and more specifically, the perception of their analytics is that their email marketing model is based on being reactive - the customer is responsible for coming to the site and narrowing down the type of product they want to buy - and Amazon reacts to this behavior with an appropriate offer.  But for those retailers that aren’t Amazon, who can afford to sit and wait for customers to tell you what they want?

The key to predictive analytics is being just that… predictive.  Most retailers don’t have Amazon’s budget and need to have better information about who is going to buy next month or next quarter, and more importantly, what they’re most likely to buy. And for most, the surprising fact is it’s not much of a leap to get there since the majority of companies are already sitting on all the information they need to better target email blasts by matching offers with customers who are willing to buy.

I praise Amazon for their ability to follow up with customers that express interest in certain products, but as far as predictive analytics….there are better solutions out there.

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.

In the age of Amazon, up-selling a customer has become tantamount to suggesting more products during checkout time. In fact, Amazon’s recommendation engine is a relentless upseller and its success has spawned a breed of companies dedicated to building recommendation engines that they license to online storefronts. The truth, however, is that good upselling seeks to harness a larger percentage of the customer wallet over a longer period of time.

While Amazon and other storefronts are undoubtedly suggesting some of the best items to customers when those customers are at the point of purchase, this type of selling also has the feel of the chewing-gum rack at the grocery store checkout counter, attempting to capitalize on customer impulse. Checkout-time recommendations are great if you’re a grocery store and selling a product that is quickly consumed. But what if those checkout-time items have a shelf-life? More likely than not, you’ve saturated your customers for the foreseeable future. If you’re not selling a consumable, upselling with shopping-time recommendations is tantamount to cannibalizing future revenue from that customer.

Think about it. If someone adds a third t-shirt to their purchase because they react impulsively to an upsell recommendation, are they likely to buy a t-shirt from you two months later? The risk of checkout-time up-selling, of course, is that while you do sell more in that instance, adding one more needless box to an already-tipping armload, you are not delighting your customers. Worse yet, you may give them a sense of buyer’s remorse, an association that your company most definitely does not want.

To truly delight your customers, isn’t it better to give them what they want when they likely want it, not when you feel they are in a captive position? In other words, truly satisfying your customer doesn’t mean compelling them to engage in impulse buying. Instead, your company should be seen as a trusted advisor that brings the right information at the right time. Oftentimes, this is not when they are on your website. Let’s take the case of a customer who purchases yarn. Would you rather sell them so much yarn - in colors that they may not ultimately want - at one time that they feel buyer’s remorse? Or would you like to be there with an email right around the time that our theoretical knitter runs out of yarn? Which approach do you think will turn that customer into a loyal customer who buys at your store again and again?

This is the customer service side of marketing that seems lost with checkout-time recommendation engines. I believe that the data mining behind recommendation engines is unquestionably the future of marketing. In fact, as a co-founder of Istobe, I bank on it since our specialty is helping to understand customers - and their buying patterns - based on their purchasing history and their demographic information.

Up-sell today is not necessarily what up-sell will be tomorrow. As customers become more savvy in their online purchasing, bridging the gaps between site visits will be increasingly necessary to establish a relationship with your customers and to signal to the customer that your company is a trusted advisor not a relentless upseller.