Category Archives: DataBase Marketing

Marketing into a Downturn

Analytical Culture, DataBase Marketing, Driller Q & A, Newsletters

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Q: I have been asked to create a whitepaper on marketing strategy and tactics for a down or recessionary market. In your studies and travels have you come across any literature or have thoughts of your own that I may quote?

A: Well, I suppose someone has written something about it somewhere. The trades write about it for every downturn! But I don’t know of any primary work on the topic – case studies, research, etc.

I do know that when we get into a down / recessionary market my phone rings more and I work a lot harder. The “new client” customer retention business is counter-cyclical; people always wake up during the soft times and say, “Hey, if we can’t drive new customer volume, maybe we can sell more to existing customers!”. You know, the CEO or somebody read that somewhere…

The problem with this kind of thinking is, in most cases, it’s already too late to do anything about customer retention.  That’s not something people generally want to hear. I then say, “The economy is cyclical.  Do you want to be prepared for the next downturn?”

The people who answer yes to that question will often become clients; those looking for the “quick fix” generally won’t become clients – but they call again into the next downturn…

It’s a strategy thing, you know? Long term thinking? But I digress…

The insidious thing about customer defection is that it’s always there, eroding the asset base, wasting away the hard work. But people don’t see it until the flow of new customers shrinks, and then all of a sudden, the defection issue is laid bare.

This is why the retention business is so counter-cyclical; why “discovery” comes in the downturns.

What you normally find is whatever business change / policy / product is causing customer defection, it takes as long to build up the customer asset again as it did to destroy it. Here is a real-world example.

A retailer makes a significant change in the types of products it sells, because it wants to “attract more new customers”. For existing customers, revenue per customer starts to fall. This fact is masked on the revenue side by the attraction of new customers to the new products – for a while. But it ends up these new customers, in terms of revenue per customer, have a value about 30% less than the old customers? So even though new customer adds remain consistent, sales start to drop, and over time drop by 30% as old customers defect and are replaced by the new customers worth 30% less.

Two years into this process, a downturn in the economy causes more attention and analysis of the customer base, and this issue is exposed. Surprise! The newer kind of customers defect at a higher rate and in a shorter time than the old type of customers.

New management is brought in, and they decide to go back to selling more of the “older” product to attract the higher value customer. Once they make the switch, it takes just as long for sales to get back to where they were as it did to create this problem in the first place – 2 (very long) years.

And that’s why it is so tough to deliver a “quick fix” to these kinds of problems. They are systemic in nature and because you are talking about the value of a customer over time, take time to fix.

So, it may well be that your advice should ultimately be “use this downturn to prepare for the next one”, if you know what I mean. Investigate, learn, and understand what happens this time, so you know what to do next time. In terms of action items, a few:

1. Analyze the customer base, to understand the source of customer value. Who are the best customers, where do they come from Which media, sales persons, product lines, services, geographies, etc. create the “best customers” for the business?

2. Analyze these best customers, and understand their behavior. What would be a warning sign that these best customers – who are probably responsible for the lion’s share of your profits – are cracking into the downturn? Slowdown in orders per month, average order size, number of contracts, whatever the relevant metrics are.

3. Track a handful of these customer metrics and see how they change as the economy slows. These metrics will be a map for predicting actual trouble the next time – predicting trouble even before everyone is already talking about “a downturn”. This gives you the extraordinary advantage of lead time over your competition in reacting to the downturn in business.

4. Complete the same 3 steps above for medium value customers and low value customers, if you have the resources.

5. Now, fully understanding what you have to work with (perhaps for the 1st time?), what is the strategy for a downturn?  Generally, it would consist of a reallocation of resources away from lower productivity to higher productivity activity, in order of importance:

a. For best customers, how do we keep them?
b. For mid value customers, how do we grow them?
c. For low value customers, how do we reduce costs to acquire or service them? Note I do not advocate “firing” customers, but you certainly can cut back on acquiring as many low value ones.

For each group, you should have a specific (and probably different) strategy and set of tactics. What a lot of folks don’t understand is there is almost always a truly remarkable difference between these customer groups, and any “one size fits all” edict or direction is bound to screw up the business, just like the example of the “new customer” effort from the retailer above.

For example, we know that marketing spend generally softens in a downturn. Companies cut back on marketing because they feel like they are “pushing on a string”. They cancel or don’t buy advertising, they fire salespeople. This is the wrong move. The old saw about buying more marketing into a downturn to “grab share” can also be the wrong move, though has some “accidental” positive effects.

The company should invest in more marketing, but not across the board. They should buy the right marketing, the marketing that generates the best quality customers.

They should reallocate marketing resources away from generating “c” customers towards generating “a” customers. If you know trade shows generate leads which turn into “a ” customers and online ads generate leads that turn into “c” customers, you take the money you spend online and book more trade shows. You let go of salespeople that generate “c” customers and use that salary to bonus salespeople generating “a” customers.

Of course, this analysis and planning is an exercise that should be done all the time, not just into a downturn. A business should always be trying to understand where customer value comes from and how it is created. But unfortunately, this issue most often comes up going into a downturn.

You’ll have to excuse me now, the phone is ringing again…

Jim

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Control Group Series

Analytical Culture, DataBase Marketing, Web Analytics

This post is an index for the Control Group series.  The following posts were written sequentially but appear on the blog in reverse chronological order which makes a hell of a mess of trying to understand a somewhat complicated topic.  So instead, try reading them sequentially using this index:

Why Use Control Groups?

Control Group Benefits

Culture of Control (Groups)

Are You in Control?

Poison Control

Poison Control

Analytical Culture, DataBase Marketing, Web Analytics

This post is part of a series on control groups.  The first post is here, a list of all posts in the series here. 

Using control groups standardizes success tracking across:

Platforms
Sources
Channels

so that you begin to really understand what types of marketing create the most value.  There’s only a couple of things left you need to know to start using this gold standard of customer campaign measurement.

I would be remiss if I didn’t at least warn you once to make sure you use a true random sample of the campaign population for the control group.  The direct marketing road is littered with the bodies of those who failed to create a truly random control group for one reason or another, usually accidently, sometimes intentionally. 

For example, they sort by customer number lowest to highest then truncate sample selection before the whole population has been sampled, not realizing the lower the customer number, the longer the person has been a customer.  This creates a bias in control towards “older” customers and screws up the result.  Another common mistake is while trying to make sure the sample is random from a demographic perspective, they end up with a behavioral bias like a higher percentage of Recent buyers in Control than in Test.  There’s nothing that will make your campaign look like it sucked more than stacking Control with customers more likely to respond than those in Test!

The final issue I’d like to bring up is the “organizational stamina” required to execute a controlled testing program. 

In large organizations, a challenge you may encounter is having other people’s campaigns “poison” your control or test groups.  The whole idea of the control is to have this group different in only one way from the test group – they don’t receive your campaign. 

What can happen is someone working with a different segmentation scheme can end up targeting portions of your test or control group, and now you don’t have a controlled test anymore – the control or test has been “poisoned”.

Just to be clear, if the test and control groups are targeted equally, then your test should still be valid, though the overall outcome might be different.  For example, let’s say you have your test and control groups and the company decides to drop a newsletter or announcement to all customers.  Since both test and control will be exposed equally to this newsletter, the incremental effects of your campaign should be preserved. 

Likewise if a national TV campaign is launched.  Your campaign might perform better overall because of the TV, but the lift you get in test versus control should be the same because the TV should affect both test and control equally.

In large organizations where many different groups access the same customer or prospect database, you can see how this poisoning of controlled tests would get to be a mess in quick order.  Without coordination, people would be stomping all over the tests by targeting a piece of a control here and a piece of test there. 

In orgs that are serious about Marketing Productivity, you do typically see a gatekeeper of some kind at the database, making sure that new list pulls do not interfere with any controlled tests that are running.  And yes, sometimes you have to wait to execute your test because there simply are not enough names to go around for the segment you want.  But this is a small price to pay compared to the total chaos of not ever knowing which marketing really works and which does not.

Clearly, there are some Marketing folks who don’t care to know how a campaign really works; “response” is just fine.  In fact, marketing chaos in the database is good for these folks.  Chaos is a fantastic barrier to accountability and the Marketers can just claim ignorance of this control group issue.  That is, until someone with a background in Business Intelligence asks why controls are not being used – and that will not be a pretty day for the Marketer.

But for the analysts out there, I really think it is your duty to start looking at the use of control groups.  Try it a few times and see what you get.  I guarantee you’ll be surprised, and the data you see will open the door to new kinds of thinking and more effective marketing programs for your customer base.