David Shepard Associates Blog

Without question there is an urgent need among direct marketers to prove that their investments in technology (databases, websites, social media, email, SEO, kiosks, call centers, catalogs and mailings, …) are more than paying for themselves.  How, then, should companies that transact and communicate with their customers through multiple channels evaluate the cost-effectiveness of their multi-channel marketing strategy?

In this article we suggest two metrics that managers should use to better understand how well their multichannel efforts are paying off:

1. Cost to serve – The customer specific marketing and servicing costs typically incurred by multichannel marketers to initiate and maintain a business relationship with individual customers.  Examples are: freebies and promotions (shipping and handling costs, two for ones, cents or dollars off), fees and commissions (to affiliates, retailers, etc.), customer service and support (returns, call center support usage), loyalty costs (miles redeemed, gifts), etc., etc.

In a previous post I suggested that modelers could improve their results by splitting
their datasets according to some critically important variable, such as Tenure (the length of time a customer has been on the file) and then build separate models for each major segment.

The argument being that it is intuitive that the usual set of modeling suspects (Recency, Frequency, Monetary Value, Products Purchased, Source and the whole set of Demographic Variables) will display different relationships with Response or Sales, depending upon the Tenure Segment, and that just adding Tenure as a variable, without taking interactions into account, isn’t sufficient to capture the full effect of this variable.

As if this isn’t complicated enough, I came across an article that questioned fundamental direct marketing beliefs, including the belief that there is a strong positive relationship between customer lifetime and profitability in a non-contractual relationship. In other words, they think that direct marketers think that customers that kind of hang around a long time, buying every once in a while, are profitable and every effort should be made to enhance the relationship between buyer and seller.

Of course, direct marketers who have looked closely at the data know that the costs of servicing infrequent buyers may indeed exceed the margins they yield; and the authors discovered for themselves that the simple relationship between lifetime months on file and lifetime profits is relatively weak (r = about .2 for the two groups studied).

What I did find interesting and potentially actionable was that they could divide a
significant number of customers into four meaningful groups:(Some 9000 households were studied over a three-year period. The households were correctly split into two cohort groups, January and February starters.)

Segment 1. Those that had relatively Long Active Lives and High Lifetime Revenue

Segment 2. Those that had relatively Long Active Lives and Low Lifetime Revenue

Segment 3. Those that had relatively Short Active Lives and High Lifetime Revenue.

Segment 4. Those that had relatively Short Active Lives and Low Lifetime Revenue.

The Graph below indicates that customers in Segments 1 and 3 kind of look alike, behave in a similar fashion, over their first 12 months and then begin to separate over time. No doubt that this is true, the operable question is can this disparity be predicted, and predicted early enough in customer’s life so that corrective action taken be taken.

The argument is that simple RFM analyses will miss this phenomena, and that database marketers, as a consequence of their not understanding that their database consists of these segments, will overspend on the Short Life-High Revenue segment, before traditional RFM analysis will depress mailings to this segment.

So, the key question for marketers is, if this effect is widespread — if there really are customers that come in for a short while, buy a lot and then leave — can they be detected? Will modeling Tenure Segments, as suggest above, and in last month’s article capture this effect.  Probably not, at least not by itself. What might work is a Principal Component Analysis of the available purchase behavior data over the last six months.

This approach might discern either a trend in dollars spent, or a trend in the particular products purchased that would indicate that the customer was displaying a pattern associated with customers that buy heavily for a short while and then switch to someone else – for reasons we can only speculate about.

In our “The New Direct Marketing” course, there’s a slide with the following text:
 
The Database Marketing Business Model 

“Acquire new customers, almost always at a loss, in the expectation that the present value of their future contributions will exceed their acquisition costs, and, in addition, manage new customer acquisition expenditures consistent with fiscal sales and net income objectives.”
 
And, until the internet bubble burst, we would joke that the eCommerce Business Model looked like this.
 
The eCommerce Business Model 

“Acquire new customers at a loss, always, in the exception that no one cares, provided you spend enough on new customer acquisition, so that the possibility of making a profit, by accident, is minimized.”
 
A long time ago, this business model made sense, at least from a stock valuation perceptive.  The more you spent on customer acquisition, the more customers you would have — no argument there — so that when the big shakeout came, your company would be left standing – while the competition slipped away into bankruptcy.

Remember when they said that if you were showing a profit, you were not investing enough in the business? 

Well, those days are long gone. The question every dot-com company President is asked on CNN these days is when will your company turn a profit, and an answer of more than another four quarters is frowned upon.
 
So now all dot.com e-tailors are direct marketers or retailers without a store, which is another way to define a database marketer.  The implication for these new players, let’s call them edirect marketers is that if they are to survive they need to become fairly sophisticated direct marketers, and they need to do so fairly quickly. Which means exactly what? 

For starters, it means that they must learn to measure (or at least estimate) new customer acquisition costs by acquisition source, just like any other direct marketer must do in order to grow their business.
 
Now, admittedly, in a multi-media environment, when a company is running Web advertising, TV, radio, print and direct mail simultaneously in multiple markets, measuring the effectiveness of each is easier said then done. But, there’s no choice. Unless you understand the effectiveness of alternative acquisition sources, or acquisition strategies, you don’t have the ability to intelligently manage the allocation of your acquisition budget. 

What’s more in addition to measuring cost per new customer, it’s equally important to be able to measure the value of the customers you are acquiring. And, as suggested above unless the average present value of the average customer exceeds the cost of acquiring the average customer, the business is inherently unprofitable.
 
And here’ the rub, for new edirect marketers it’s exceptionally hard to measure and project the value of customers by acquisition source, in fact it’s exceptionally hard just to measure and project the value of the average customer –period—even without regard to source. 

Why is it so hard for e-direct marketers to measure customer value, or stated more accurately why is it especially hard for edirect marketers to measure and project customer value? Or, stated still another way, why is it harder for them, then for a traditional direct marketer? 

If you’ve been reading carefully you may have noticed that I’ve always linked measurement with prediction. Why?

Because in order to use the notion of lifetime value you need to be able to predict future behavior based on measurable behavior to date. In truth, it should be no harder for an edirect marketer to measure customer behavior, measured at any level, average, by source code, by campaign, or by enrollment group, then it is for a traditional direct marketer.

They just don’t know, as far as I can tell from talking to the internet marketers that attend our seminars, that unless you’re tracking customer behavior, multiple ways, you have no way of knowing the real profitability of the business you’re managing.  Nor, do you have any way testing and measuring the effects of alternative marketing strategies. 

What’s even harder and truly different for edirect marketers is the inherent lack of loyalty of the e-customer. Amazon.com aside, customer loyalty among Internet users is an oxymoron. Part of the fun of being an Internet shopper is finding that new site, it’s about surfing the Web and just because somebody stops by and buys something from you once, or even every once in while, does not make that buyer a customer, or a customer with a significant predictable lifetime value. 

It’s the traditional catalog one-time buyer problem, but magnified multiple times. 

Because of the inherent disloyalty of the Internet shopper it’s particularly important that the edirect marketer go out of his or her way to develop the beginnings of customer loyalty, or in even more traditional dm terms repeat purchase. 

What does this mean, in concrete actionable steps?

It means that a nice welcome back on your site, is mandatory but certainly not sufficient. It’s expected, therefore like an expected increase in interest rates, the market has probably discounted it. It means that routine e-mail messages, Facebook updates and Tweets probably won’t be sufficient to maintain customer loyalty, because everyone does it, and does it about the same way.

It means that customer cultivation will require using a combination of new and old direct marketing tools including direct mail, contests, events, reward programs, and customized Web sites for repeat customers.  It certainly, most emphatically means, that you just can’t sit back and wait for that “one time visitor/buyer” to come back on their own.
 

If you’ve been following our running commentary over the last year or so you know that I’ve become somewhat obsessed with the issue multiple models. For those that haven’t been paying close attention, meaning just about anyone with a life, here’s the problem. You want to build a model to predict some outcome, a response to a cross-sell mailing, attrition, lifetime value…whatever.

You’d like to come away with one simple to use equation that can be applied to your entire customer file. But you intuitively know that this might not be possible or at least easy. For example, Tenure, how long someone has been your customer, is certainly an important variable, but how does it relate to the other variables in you model?  Consider this, demographics may be important predictors, for customers that have been on your file for just a few months, but will they be important predictors, or as important predictors, for customers that have been on the file for years, and about whom you have lots of transaction information/variables?

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Let’s talk about Lifetime Value, why not, everyone else does.  Lifetime Value even made it into a recent publication where someone replayed the “old saw” about the Manager who looked at his customer leaving the store and “envisioned him or her (I don’t know why he couldn’t tell the difference) walking out with $50,000 worth of groceries, which is what [he] expected the customer to buy during the life of the relationship”. The author used the above example to help describe “what the term CRM really means.”

Nice idea, great concept, now what? Was that customer really worth $50,000, and if so, what should the store manager be doing about it…perhaps he should build a database so that the surly teenager at the checkout counter doesn’t completely ignore him after he passes his loyalty card through a reader that has kept track of his lifetime-purchases-to-date and has predicted that unless he comes down with mad cow disease, from the meat he just purchased, he will come back on Saturday for a six pack of beer and some toilet paper…which helps explains why the beer and the toilet paper are kept in close proximity. (Readers not familiar with the groundbreaking beer & toilet paper study (or urban myth), from the 1990’s by market researches using three-dimensional cubes, should look it up.) (more…)

Want to make your head hurt?  Think about this.  You have X million customers, N number of products and Y dollars to spend over some promotion period, say three to six months, and at least three contact channels: direct mail, telemarketing and e-mail.

The smart reply is that why would you want to make your head hurt. So, skip to another article or go for coffee.  That’s kind of like what a lot of direct marketers do. They try to keep it simple. This month, they say, I’m going to promote product A, next month I’ll promote product B and so on; I’m going to use Direct Mail or telemarketing; my fiscal plan calls for so many pieces; I’ll sort my customer file on the basis of RFM or some regression or artificial intelligence model, and I’ll mail/call as deeply as my budget calls for.  That’s it. Next month I’ll do it all over again.

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I recently saw a pie chart that indicated companies were spending 50% of their direct marketing dollars on Customer Retention and the 50% on Customer Acquisition. Last year,  Customer Acquisition accounted for 60% of spending and only 40% on Retention.

Assuming that these numbers were true, in the sense that they were an accurate barometer of what’s happening in the direct marketing world (whatever that means) the implication would be very disturbing. While there is nothing wrong per se about spending on customer retention, you can’t grow a business that way – certainly not in terms of the number of customers, and almost certainly not in terms of revenues and eventually not in terms of bottom line profits.

A related finding was that 60% of the respondents reported that they would be spending more (across all channels) on direct marketing activities next year while the balance would be spending about the same.

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