Monthly Archives: May 2007

*** Company-Wide BI

…Inches Closer to Reality, according to this article in Optimize Magazine.  See also how Microsoft wants to make BI “Ubiquitous” in this article from Information Week.

 

(graph above from Optimize Magazine article)

Now, I generally think giving people access to data is a good thing, but I’ve seen it go horribly wrong in many cases.  You can’t just open up data access and let people construct analysis without ground rules.  You’ll have complete chaos, with people torturing the data in whichever direction best makes their case.

Rather, follow a couple of simple rules to prevent analytical chaos:

1.  Please set up a “best practices” team or “center of excellence” concept so people who want to do honest analysis on their own can get help with the simple stuff.  If you have experienced endless cycles of re-analyzing the same problem over and over with the business side, you know what I am talking about here.  People need help really tightly defining the questions they are trying to answer and guidance on what the best (most accurate?) way to generate answers is. 

Do these new analyzers know how seasonality affects the business?  Do they understand how time-frames can distort an analysis?  How cycle billing or lag times in fulfillment can affect sales recognition?  Are they aware of any data quality issues? 

2.  If you can’t set up a best practices team, at least try to create a best practices manual / library of some kind.

3.  Please, please define all the data people will be using.  Some of this definition problem you can control with the drop-downs you provide, but make sure everybody knows specifically what the drop-down items mean.  For example, if someone is doing a “customer count” of some kind, what is a customer, who is included in that count?  If someone ordered and then cancelled – no net positive financial transaction ever took place – will that person be included in a “customer count”?  If not, what is that person called, what is their “status”?  Is that status available as a drop-down selection?

If they select “Sales” as a variable, are we talking about Gross Demand?  Net of cancels / returns?  Net of discounts?  Net of bundling, packaging, volume pricing?

4.  If an analysis will be used for strategic decision making, it must be “vetted” first by the best practices group.  If people want to make “local” or silo decisions based on their own analysis, well, they are making their own bed.  But when it comes down to major shifts in business practices, you simply cannot rely on a local analysis, there is too much risk involved.

You really have to think some of this stuff through first so you don’t end up in a meeting with the CEO where 3 different people present data that should be consistent but end up divergent.  That’s the fastest way to induce a full-on beating CEO forehead vein I know of. 

I am all for people being “exploratory” and going through their own “what if?” kinds of exercises as they try to discover more about their products, services, or customers.  This is a good thing.  But confusion and chaos due to lack of standards and a central authority on the validity of an analysis is a great way to doom this kind of effort.

Anybody else been through one of these “everybody in the data pool” rollouts?  Do you have any other “rules” you would like to add to help people get through one of these implementations successfully?

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**** Great Brands Never Rest

From Target Marketing Magazine, this article embodies what I (and many customer-centric Marketers, I believe) think about when the subject of Brand and authentic Brand Advertising comes up.  Here’s the six disciplines of a Great Brand, according to the article:

#1: Know Your Position
#2: Know Your Customers’ Position
#3: Share Your Position
#4: Stay Focused on Your Position
#5: Leverage Your Position
#6: Delight and Reward Those Customers Who Support Your Position

Hmmm.  Sounds like Marketing to me.  This as opposed to the “awareness, recall, intent” mantra continually put forth by the Brand-ing folks.  You know who they are,  They’re the ones that believe when people “interact with your Brand” though a game on your web site that something of value occurs.  Perhaps so, but it’s not Brand, at least not unless you are an online game developer.  Awareness is not Brand, folks.  Recall is not Brand.  Intent is not Brand.

Trial is Brand.

I’ll have more on the history of this problem and how it applies to many of the current issues in Marketing shortly (I hope, promised a while ago).  Meanwhile, read the above short article and let me know what you think, here’s another link to it.

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Customer Value: Who Cares?

With all the focus on Engagement in the Marketing Community (I’ve had 3 interview requests this week!), one has to wonder why Marketing Productivity concepts like LifeTime Value tend to be last on the list of concerns.  All the focus seems to be on Engagement as a short term idea, as opposed to over a longer run.  Why?  Isn’t a customer who sticks with you over time a truly Engaged customer?

A lot of times people blame the “complexity” of database marketing concepts on their lack of adoption.  This may be true at some level, but I think plenty of people understand the basics of these ideas.  I think some companies simply lack the incentive (for now) to implement marketing programs with a longer-term view, and there are some fundamental reasons why:

Business Culture

In large companies, where the rewards for performance are all tied to short-term goals – your sales today, beating the quarterly estimates, etc. – then why should anybody care about Productivity over the longer term?  This is the periodic versus customer accounting problem I detailed back in 2001.  There is no incentive to act with longer term customer profitability goals in mind in most companies, save those who were “born” with this culture.  If you think about it, this is one reason the smart money is chasing retail buyouts right now.  Many companies are undervalued based on these old “sales now” rules, and without the pressure of the public markets, they can focus on profitability, including long-term customer metrics.

Nobody in Charge

In large organizations, the rapid turnover in the CMO position (average 23 months tenure?) clearly speaks to why folks at the top of the Marketing Chain are not particularly interested in the longer-term consequences of their marketing actions.  But what’s the cause of this effect?  Is CMO tenure so short because they’re not interested in long-term results, or is it just job churn?  The results of the CMO Council survey seem to confirm the former, while placing some blame on hiring practices at the C-Level.  In other words, the CEO wants a CMO with a long-term view that can back it up with numbers – but can’t find one.  Too many RockStars, apparently.

Below the CMO, how many companies have a person in charge of Customer Productivity or Retention?  If nobody is in charge of it as a full-time responsibility, then certainly it is not in anybody’s best interest to chase the goal.  A Customer Experience Officer is not what I’m talking about here; being “Nice to Customers” doesn’t always pay out.  You need a full-time person in charge of any analytically-oriented area (including web analytics) to reap optimal benefits.

Not Desperate Yet

True for both small and large companies.  The fellow quoted in the first link above simply is not feeling the pain yet.  The longer you have been around, the more meaningful the pain becomes.  Symptoms: year over year sales go flat and then trend down, even though ad spend is increasing.  The company has hit the wall.

Some of the oldest web companies are starting to feel this now.  You can hide it for a while with acquisitions or going international.  But at some point, somebody asks, “How many best customers do we have now?”  And the answer is something like “20% fewer than we used to have”.  Then people go nuts trying to “fix” a problem by looking at what they did last month or last quarter, when what matters is what they were doing last year or the year before

If you have spent the last year or 2 generating customers with low Future Value, you’re stuck.  As Future Value becomes Current Value, there is hell to pay, because you just can’t change it in the Present, no matter what you do.  The only alternative you have is to start creating higher value customers now, so that sometime in the Future, growth will return.

Now, you can argue this slowdown effect is simply the natural cycle of a business.  And it is.  But I ask you, if you can slow the cycle or even minimize it, why wouldn’t you?  I suppose it’s much easier to blame the “competition” or the “economy” or whatever your favorite excuse is for a sales slowdown other than your own execution.

Interesting Fact: The demand for Customer Engagement / Productivity / Retention / Loyalty programs and services is counter-cyclical; demand always falls off when the economy is good and when the economy slumps, all of a sudden “everybody” wants a customer retention program of some kind.  This behavior is of course suboptimal, since the time to implement such a program is during the “good times” so you are prepared for the “not so good”.

Thoughts?  Does your company care about customer value?  Or do they just measure performance against “sales”?

If you’re not doing any serious Customer Analysis, is it because you have a challenge with getting the data / analysis?  Or is it a lack of interest / sponsorship / resources that is holding you back?

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Waiting for Pareto

A Warped View of Visitor / Customer Analysis?

Many folks look at the world through the lens of the Gaussian Bell Curve when the real model they should be looking at is Pareto Power Law, as elegantly explained by John Hagel here (you really should read that post when you have a chance, another link provided at the end of this post).  For the math inclined, as quoted from another source in John’s post:

Gaussian and Paretian distributions differ radically.  The main feature of the Gaussian distribution . . . can be entirely characterized by its mean and variance . . . A Paretian distribution does not show a well-behaved mean or variance.  A power law, therefore, has no average that can be assumed to represent the typical features of the distribution and no finite standard deviations upon which to base confidence intervals . . . 

Yea, like he said.  In English, that means when you are “optimizing”, you could be driving towards a suboptimal result if you’re not paying attention to what you should be paying attention to.  Witness all the discussion of sample size and standard deviation in web analytics lately – are we talking Bell Curve here, folks?  If you walk the Long Tail walk, you should talk the Pareto talk.  Here’s a pic from the Hagel blog if you’re not following this idea:

Gauss vs Pareto

(Image originally by Albert-Laszlo Barabasi, “Linked: The New Science of Networks”)

The bell curve average can kill you.  The real results of your tests are masked because you are looking at the wrong outcome variables.  Sure, go ahead and segment source; that’s where the idea of “there is no such thing as an average visitor” came from back in 2001, right?  So fine; you have the initial segmentation correct, and if you are just optimizing for conversion, you have it right.  If you are optimizing for profit, you’re not even close yet.  I’ve referred to this situation as the “Reporting versus Analysis” problem; here’s a math-ier view from John’s post, quoting McKelvey and Boisot:

Processing dots is appropriate to what we label the routinizing strategy.  Processing patterns, on the other hand, better serves what we call the Pareto-adaptive strategy.  Processing dots means processing data, a low-level cognitive activity.  By contrast, processing patterns – pattern recognition – is a high-level cognitive activity, one that involves selecting relevant patterns from among myriad possibilities. . .

John offers his own “In other words” on the paragraph above:

In a Paretian world, surface events can become a distraction, diverting attention from the deep structures molding these surface events.  Surfaces are extraordinarily complex and rapidly evolving while the deep structures display more simplicity and stability. These deep structures are profoundly historical in nature – they evolve through positive feedback loops and path dependence.  Snapshots become misleading and understanding requires a dynamic view of the landscape.

I first saw the effect John describes above at HSN, where the surface (the TV show itself) appeared to be complete chaos but the deep inner structure (the customer) was smooth and completely predictable.  Another way to say this is any one “snapshot” of time seems quite chaotic, but if you watch the whole “movie” over time, you see the stable inner core.

The reverse is also true: as you try to artificially “stabilize” the surface – in HSN’s case, by moving to a rigid or supply-driven programming format rather than a demand or customer-driven format – the core starts to destabilize.  Marketing programs stop working as well as they did and the LifeTime Value of the customer erodes.  Darwinian, sort of.

What all this means to you: Optimizing for conversion is fine, as far as it goes.  But how do you know “the converted” are the best customers you can get?  How do you know you’re not wasting resources optimizing the conversion of worst customers, for example?  How do you know that buried in your conversion optimization – no matter how much segmentation you do on the front end – is a Pareto distribution that is skewing the actual results?

Example:

One of my earliest clients was a vintage 1998 dot-com that had a massive business in lead generation.  They basically generated e-mail names through various niche interest content sites and then rented those names.  Over time, they also started creating their own products and selling to the names they generated.  Great business, killer margins.  They were a massive buyer of all types of online media.

But then that media started to get much more expensive, and they had to start looking at Campaign ROMI as opposed to just sales, and that is why I was brought in.  We were in the middle of a system integration that would provide a “ROMI Dashboard” to the media buyers when the word came down to kill off about 1/3 of the campaigns they were running.  I begged them to wait for the data (we’re almost there!) but the “shoot from the hip” culture prevailed.

Pareto had his day.  The campaigns killed were the ones with the lowest initial response rate – and as it turns out, these same campaigns also happened to generate about 95% of their most profitable customers.  You just had to wait about 3 months after initial conversion to see the profit. 

The customer value death spiral (Kevin uses the term file momentum to describe the same idea) ensued and they were never able to recover.  Once this kind of best customer value erosion has completed, it is very difficult to survive long enough to rebuild the power of the database.  Media costs rose as customer productivity fell and when these two lines crossed, the company collapsed.

This death spiral / file momentum idea is incredibly important in the Marketing Productivity area and somewhat difficult to get your arms around, so it’s worth helping you to visualize it (if you’d like audio with your visualization, try this). 

Here we go:

Say that 20% of the customers you acquire are “best customers” and generate 80% of your profits.  In profit terms, say this means $200 in net profit per customer over 6 months.  Now, let’s say you change your customer acquisition methods and your new “best customers” generate $100 in net profits over 6 months.

The day before you initiate this change in campaigns, you still are generating best customers with a value of $200 over 6 months.  So you have about 6 months before the profit effect of these customers disappears.  And remember, these folks are responsible for the majority of your profits.  Few in number, powerful impact.

So, you start generating customers only worth $100 over 6 months.  You don’t even notice anything wrong, since the value plays out over time.  Conversions are fine, and sales are fine – because you are still living off the $200 customers while you are bringing in the $100 customers.  And the rest of your operations remain the same, so unit sales and so forth seem fine.  We’re only talking about a change to a small group of customers, right?

About month 3, sales start to slip, out of nowhere.  You’ve entered the wide mouth of the death spiral (cue the audio).  You can’t explain it, you haven’t “changed” anything recently.  Conversions of those new $100 customers are still coming in just fine, thank you.  But after month 6, you start living only on the $100 customers and sales are now 3/4 what they used to be.  You’re in the throat of the death spiral now.

Let’s say you do some analysis and “discover” your mistake.  You kill the campaigns that generate the $100 best customers and start generating $200 best customers again.  How long will it take to get back to where you were?

Right.  6 months – at a minimum.  Problem is, the campaigns that generate $200 customers are twice as expensive as the campaigns that generate $100 customers, and you’re now in the vortex of the death spiral.  You spend like crazy but can’t make it up in time and at month 4, media costs outstrip cash flow and the company goes belly up.

Pareto plays a very tough game, eh?  While you’re thinking about it, here again is a link to the Hagel post.

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eMetrics Summit

Man, that was one marathon of a conference.

Not the conference itself really, but all the associated WAA business that gets conducted there makes it a double-overload kind of situation.  When I’m not is a session, I’m in a meeting of some kind, all day, every day.  Gruesome.  I’m still in San Fran reviewing some related analytical education opportunities…

I’m sure the other WA blogs have covered anything of significance that happened at the Summit – don’t know for sure though, have not had time to read any of them.

Anyway, the most significant thing going on there in my mind was the hard core  interest IT folks now have in the business side.  It was stunning – a pure geekfest just a few years ago, you now have a lot of these same folks attending and digging sessions on Marketing Strategy.  A real win on the analytical culture issue, methinks.

More later – another damn dinner to go to (just kidding Eric!)

 

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*** RockStar CMOs Out

According to AdWeek, CMOs with agency backgrounds (code for Brand-ing folks?  As opposed to real Brand Managers) are being replaced by those with a broader career experience across several disciplines.  Folks at the top want CMOs that can actually drive change down into the operational organization and quantify the results of their work.  That’s what a Product or Brand Manager, in the original meaning of these phrases, would do.

Sounds like a good idea to me.  The source for all this press commentary is here.

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6033% ROI, Defining Churn

The following is from the April 2007 Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

6033% ROI
—————-

Q: We have exchanged email a few times, and I don’t recall if I ever said thank you for your book.  While I had been experimenting with many CRM programs in my little dry cleaning shop, your book gave my thoughts order and clarity to refine what I had started.  Today, I see the world differently.

A:  Well, thanks for the thanks!

Q: You may or may not remember me.  Just after I sold my dry cleaning shop, I had bought your Drilling Down book.  I was the dry cleaner who had been doing rudimentary data mining and CRM with a point of sale system I had developed in Regina, Saskatchewan.

A: I do remember.  Internally, I was thinking, “Wow, this is going to be a real test of the Drilling Down concept”.  I mean, I have seen it work in many small businesses, but dry clean (seems to me) is a very tough, tough business.  Too many players, a lot of competing on price, etc.  A great environment for underground customer marketing in terms of beating the other guy – they will never know what happened to them.  But still, tough for small owner / operator to have the “will” and time to really make it happen.  So yea, I remember…

Q: Well, I’ve continued working within the dry cleaning as a marketing consultant.  The programs I had developed in my shop have now been transplanted into a few of my client’s shops, and are bearing fruit.

Tonight one of my clients reported a ROI of 6033% doing direct mail to certain customers in his market in California.  Another client of mine reported his fourth year of steady growth.  One of my first clients has been showing a 7 percent annual compound growth, and he is in a flat or declining market.  What began in my shop has been proven across North America, into Europe and Australia by my clients.

A: I can’t express how exciting that is.  Congratulations!

Q: Jim, data mining dry cleaner’s data is a blast.  You would be stunned at the quantity, and quality of data a dry cleaner gathers today.  Would you ever have thought data mining could be applied to suits and shirts?  Well yes, it can.

A:  I am stunned, and I bow to your most excellent Drilling!

Q:  Once again, thank you.

A: And thank you for sharing this, it’s very, very exciting to hear.  Like you said, no other word for it than “stunning”.  I remain most stunned!  Keep me informed.  Perhaps you should write a book?

Jim

Reader P.S.  If you own a service business that depends on repeat customers and would like to contact the fellow Driller responsible for producing the results above, let me know.  I’ll send you her e-mail address.  I can tell you these are not the only success stories she has to share with you…

Defining Churn
———————

Q: I work for an economics consulting firm based in Washington DC.  I am researching customer churn and customer displacement statistics across a variety of industries to try to establish a benchmark of what is considered high and low customer displacement.

A:  Nice to meet you, and a noble task!

Q: Do you happen to have any such churn statistics, or know if a place you could recommend?  I found plenty of statistics regarding churn rates within the telecom industry, but am most interested in companies that are involved in business-to-business relationships with their customers (relationship between a customer and a supplier).

In addition, I would also like to find churn statistics for customers who use multiple suppliers.  For example, a customer may go to several grocery stores rather than sticking with one dedicated store.  I would be interested in learning more about the statistics companies in these types of industries use to track customer displacement.

A: The reason you find a lot of churn info in telco / cable is the end of the customer life is easily defined by the disconnect, and these numbers are reported publicly as part of annual reports and so forth.  In many other businesses like the ones you describe, typically the companies have failed to define customer defection and so in their minds, there is no churn because there is no defection.

A “customer”, even though they have not contacted the company for 3, 5 or 10 years, is always still a customer.  If the company thinks like this there is no churn rate to be measured, by the definition the company has chosen for itself.

At the same time, defining defection is pretty easy to do by looking at the transactional data and defining the patterns of defection, for example “if a customer has not ordered from us in 3 years they are highly unlikely to order again”.  That’s defection defined; you just put a line in the sand and say “3 years no contact is a defection”.  The company then should declare customers in this status “defected” and then a churn rate could be found.  This is pretty easy to do, so if not executed, one of two situations exist: either the company does not have the data or they don’t have the “will” to discuss, internally or externally, the concept of customer defection.

A third possibility exists: the company in fact has the data and has defined defection, but would never, ever speak to churn or customer defection in any kind of public forum because this information is so critically important from a competitive and strategy perspective.  To discuss these numbers or the implications in public could have dramatic consequences for company positioning in the market or stock price.  So if they have the numbers, they’re locked in a safe.

As a result, I’m sorry to say, I do not have any broad-based “sources” for you, save one possibility: a book called The Loyalty Effect by Frederick F.  Reichheld (1996).  In this book, Reichheld goes through the business models of 25 different companies that excel at retaining customers in different industries , and proves out the financial model of customer retention using real data.  This is the book where the quote, “It costs 5x more to acquire a new customer than retain a current customer” (or the various bastardizations) came from.  So it might help you out.

The only other thing I can suggest is that “churn” is not always the word used to describe these stats but is most often used when the disconnect is easily defined, as in telco / cable; “displacement” is a rare use for this idea as far as I can tell..

“Customer Turnover” is a popular phrase in Europe and is used by some in the US; also “defection rate” is used quite a bit.  So if you’re pounding on Google to try to find these numbers, try those phrases and others you may find when doing these searches.  Banking / finance / insurance is another area where the “disconnect” is often easily defined, so you will find various defection rates in some of their case studies on the web.

Jim

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