eCommerce Analytics

As a follow-up to Doug’s post about customer lifetime value yesterday, and the advent of our online customer lifetime value calculator, I wanted to revisit the most famous use of customer lifetime value in recent business strategy and practice: Gary Loveman and Harrah’s. Harrah’s developed sophisticated customer lifetime value models to predict the ultimate value Harrah’s could aspire to for each individual customer. Then Harrah’s used its well-known customer loyalty program to try to reach that value.

Read The Strategic Impact of Customer Lifetime Value: The Harrah’s Story »

Recently, I’ve noticed that the vast majority of articles and blogs out there on marketing and customer analytics primarily focus on how to use data-mining to increase revenue and cut costs in B2C oriented companies. Today, I wanted to take 5 minutes to outline some ways B2B companies should be using analytics to increase their top line while minimizing costs.

Knowing Your Leads

Many sales people will tell you that they already know which companies and decision makers they should be selling into. The marketing folks will tell you they already know who matches their ideal lead profile. Funny part is that in many cases these two ideals don’t match up.  A recent marketing research report concluded that in nearly 50% of companies out there, marketing and sales do not have a direct view into what the other side is doing. Data analytics can clearly help bridge this gap by following a customer through - from lead to purchase to maintenance - showing which leads result in the most profitable customers - not just in terms of initial sales, but also in terms of potential lifetime sales. It’s the best way to make sure both departments stay on the same page and the entire company increases its overall hit rate.

Better Cross Sell and Up Sell

It shouldn’t surprise anyone that better targeted email results in better cross-sell and up-sell rates. Personalization is a hot topic in the B2C space, but there is no reason why B2B companies shouldn’t have the ability to tailor their emails to match the company profiles of their customers.  A CTO shouldn’t be receiving the same sales pitch as the marketing folks and someone in the energy business shouldn’t get an email that is designed for online retail. These seem like easy rules to follow, but the trick comes in knowing how to segment these customers by industry, contact, and previous purchase history to minimize the work needed to pitch the right offer at the right time.  No one has time to draft up 100 emails to each type of customer - which is why predictive analytics is critical for optimizing the segmentation process to forecast which customers are going to want similar products in the future.

The True Cost of Support

Many times support personnel get relegated to the back office and we tend to forget about them when factoring in the true costs of sustaining a relationship with a customer. If a customer spends $1,000 on one of my products, but then uses up 100 hrs of one of my support staff, are they still a profitable customer? Factoring in and then forecasting support costs can be an extremely valuable way to judge the lifetime value of a customer and help management decide whether they should renew contracts or continue maintenance agreements.  Working this back into lead generation can also help marketing make sure they focus on customers that will not only buy product, but also won’t cost the company an arm and a leg once that customer comes onboard.

These are really just a few ways B2B customers can make use of data analytics. There are many more, and next time I’ll try and focus on some leading edge cases out there that show how B2B companies are using analytics to increase revenue and cut costs.

Someone asked me the other day, in response to my assertion that one-to-one marketing on a massive scale was the wave of the future, how a company could possibly send out so many personally-tailored emails. Being in the local Irish Pub, The Burren, I almost laughed Guinness out of my nostrils. But I couldn’t avoid the underlying message. One-to-one marketing never really has been embraced because no one really thinks that they do customer segmentation very well, that there are too many obstacles to customer segmentation for it to be entirely useful. Ultimately, this means that few believe they have homogenous enough segments to deliver the personalized goods.

What this also means is that one-to-one marketing is complex due to the fallacies of profiling. I once worked at a company that had such in-depth profiles for each segment that the profiles read like a Faulknerian novels. At this company, I learned that our target female customer in the 35-40 range probably once wanted to visit France but was now stuck with two kids in middle America and made meatloaf once a month for a husband she rarely saw. She obviously consoled herself by buying our software.

What’s my point with all this? This kind of profiling is for low-transaction sales, nothing more. Direct marketing units with high transaction rates should never take the tack of email blasting a segment based on their demographics. Nevermind writing fanciful biographies for said segment. Instead, direct marketing should ignore demographic profiles and concentrate on profiles that accomplish an immediate business goal (see below). Given the immediate needs that direct marketing normally serves, it needs to have a shortsighted, tactical approach, not the strategic approach that profiling represents. Below I look at the goal of getting rid of an overstock of shorts via the email channel. In doing so, I explore two, important dimensions of personalization: what the segment is willing to buy cross-referenced by when that segment most likely opens email.

The Group that Will Likely Buy Shorts Next

The truth is, the customer is not out there to buy from your company. They’re out there to purchase the product they want next and you’re merely there as a direct marketer to insinuate yourself into the buying equation. So which segment of your customers is likely to buy shorts next because that’s the group you want to reach when your shorts have been sitting in inventory for way too long and the leaves are already falling from the trees. So is this a profiling problem? In other words, is it time to blast every demographic who might wear shorts. I suppose you could. But then you’re likely to turn some people off. If you ran your customers past transactions through a classification data mining task, what you’d come up with is a list of people who are likely to buy discounted shorts at that time of the year. In fact, you’d probably come up with a few segments that demonstrate such a propensity. And they would definitely cut across your demographic profiles. You’ll have some moms buying shorts for their sons and some dads buying shorts for next summer’s Hawaii trip.

When Is the Best Time to Reach My Shorts Group?

Almost everyone out there sends me email blasts on Tuesdays and Thursdays. Why? Well, the general belief is that it adheres to the customers’ work/open schedule. I have seen elsewhere that most emails are opened on Sundays. That’s a compelling argument. But I tend to believe that each of your potential shorts purchasers has a more personalized schedule as to when they open and read emails. And that leads to the answer to the question in the subtitle. There are many times to best reach your customers who will buy your clearance shorts. The best web article I’ve read on this is by Bill Nussey of silverPOP who argues for tuning your send times per customer based on their last-recorded response. Couldn’t agree more. In fact, I believe that timing is the hidden axis of personalization. I would actually alter Nussey’s belief just slightly. And that’s simply to say that I would average their responses - and give the most recent responses just a bit more weight - to triangulate on the time your shorts buyers are most likely to open your email. For ease of use, you can bucket this into days or half-days so you don’t have to schedule an email every hour. If you record response data to your email blasts (opens), then this really shouldn’t be a problem.

So what do you ultimately have? You have customers that are most likely to want discount shorts and you have the best time to contact each of them. Now that’s personalized marketing.