eCommerce Analytics

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.