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.

Istobe develops predictive models that recommend which products to market to customers via email and which are the best times to market those products. But how does Istobe measure the actual ROI returned by these models? The Istobe team burns many cycles discussing measurement techniques for the lift that we are delivering to our clients. And we’re constantly updating the formulae that we use to evaluate how our predictive models actually perform in production. Ultimately, the measured lift that we generate is the result of another model where we tie in the relevant factors according to different weights. What are the relevant factors? Read on.

Our model vs. current practice or our model vs. the naive approach
This actually isn’t a debate among us but it’s the most important part of understanding what kind of monetary benefit we’re actually delivering to the customer. Oftentimes, a model’s output will simply deliver lift in contrast with the naive approach. That is, the model will assume that our client is, at worst, merely flipping a coin in terms of the next-best product for their customer. Or, at best, the model assumes that the client’s customers will likely want the most popular product. So our models self-reflexively examine their benefit against these two benchmarks. However, when it comes time to actually measure how much better our model is, we always measure against our clients’ current practices. The assumption is that our clients already have a smart strategy for targeting their customers. So we get their rules for targeting their customers and then figure out how much better our models are at generating the right type of product offering.

Our model’s email timing vs. typical email timing
Email timing is starting to get a lot of traction at Istobe these days. After all, if the email is never opened then it doesn’t matter if the product that our clients are offering is a better fit for a set of customers or not. And there are better and worse times to send emails if you want them to be opened. So we take into account the timing that we suggest vs. the normal send times of these emails. Basically, timing is just another part of our models’ output. The models take into account the whole path for purchasing a product and getting an email to the right person at the right time is the first step in that process. When we track the Istobe improvement, we build email open rate into our evaluation and track how much lift we give our clients by understanding how many more opens and click-throughs our models were responsible for.

That’s about enough for today but I’ll talk about two other evaluation factors on Thursday that are a little more arcane: Email influence zone and opt-out rate.

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.

Customer analysis is the process of determining customer segmentation, value, purchasing behavior and motivation in order to better target marketing and increase sales. Well, that sounds ok in theory but is perhaps a bit too abstract for practical use. Practically speaking, what is customer analysis, really?

The crux of customer analysis is that all customers are not created equal. Companies have some customers who are worth their weight in gold. They buy frequently and spend a lot. They are a pleasure to deal with: no returns, no complaints, no hassles. They also have customers who make life miserable. They inundate customer service centers with calls and constantly return merchandise. They buy infrequently and are happy to take their business to a competitor if it means they can save a dollar.

The top 20% of customers are the lifeblood of a business and contribute 80% of the profit. The bottom 20% at best contribute nothing to the bottom line, and at worst cost more than they contribute.

The purpose of customer analysis is to identify the top 20% of customers (gold customers), middle 60% (silver customers) and bottom 20% (lead customers). We then try to determine how to keep the gold customers happy and how to encourage silver customers to become gold customers. Then we have to figure out what to do with our lead customers.

We can do a basic customer analysis in four steps:

1. Who are my customers? Which customers are valuable? Which aren’t?

There are many ways to determine a customer’s value. One of the most accepted from is using a metric called customer lifetime value (CLV). CLV estimates how much a profit customer will contribute over his/her “lifetime”. By comparing CLVs among customers (or more often, among customer segments), we get a good idea of which customers are valuable and which are not.

Another method that can be slightly easier to use is a recency, frequency, monetary analysis. This method breaks customers into groups based up on how often they purchase, how much they spend on average, and how long it’s been since their last purchase. Customers who rank high in all three criteria are most valuable while those that rank low are least valuable.

Over the next few weeks we’ll talk more about these techniques and how to use them. The general idea, though, is that we separate our customers into gold, silver, and lead groups based on how much profit we expect them to contribute.

It is important to note that the gold, silver, and lead groups are not customer segments. Customer segments are a function of demographics and behavior, not value. Our gold customers can be made up of many different segments and the customers who comprise a particular segment may differ wildly in how much money they spend.

To make things a bit more confusing, customer segments can also have CLVs associated with them. Assigning a CLV to a customer segment is a useful technique in determining how much to spend to acquire a customer in that segment. We’ll touch on this more in step 4.

2. What do I do with my most valuable customers?

Congratulations! You’ve identified your lifeblood customers. These customers feel like they have a relationship with you and buy based on this relationship, not on price. From a marketing perspective they are already “maxed out”. You can’t spur them to spend any more with you — they are already giving you all of their business! All you can do is make sure that they are stay happy.

The single biggest mistake in marketing to these customers is to give them discount offers. These customers do not make their decision to purchase with you based on price. By giving them discount offers you simply throw money away and, worse, train these high value customers to wait until they receive a discount offer before buying.

One of the best ways to market to your gold customers is to offer perks that are not available to other customers. This is essentially what airlines do with their status levels. By treating these customers as special, they will be more likely to continue spending with you to maintain their special status.

Sometimes, however, it can be appropriate to give these customers special offers that aren’t necessarily discounts. This is something we will discuss in a future post.

3. How do I make less valuable customers more valuable?

We have to face facts. Some customers are too far gone to save. Lead customers are money losers. It is almost always best to stop spending marketing dollars on them. If you can entice them to go to your competitor it is a double win — not only do you get rid of a money losing customer, but now your competition is saddled with them.

The majority of your marketing budget should be spent trying to move your silver customers up to gold customers. This is where the real action happens. You can entice customers to step up their spending with a clever combination of incentives, promotions, and discounts. Since each segment can be expected to respond differently to your messaging, however, it is critical to really understand what drives these customers to buy. Often the best way to find the optimal marketing mix and message is through rigorous testing and analysis. No fun, maybe, but the payoff is huge if done correctly.

Again, we’ll talk about specific testing techniques in a future post.

The simple rule in customer acquisition is that you want to spend money to acquire customers who look like your current gold customers. These are the customer segments that are most represented in your gold group. It’s ok to spend to acquire silver customers, too, as long as the cost per customer is less than the expected lifetime value of the potential new customer.

This high level view of customer analysis is a good starting point for companies who are looking to use their customer data to increase sales and get better ROI from their marketing budgets. By following these 4 simple steps you’ll be well on your way to understanding your customers and better targeting your marketing spend.