February 28, 2011

Dear Catalog CEOs: Chicos And Three Key Customer Trends

Dear Catalog CEOs:

By now, you've already read the comment from Chicos about catalog circulation.  If you haven't, here's a link to the article, and here is an important quote:
  • "Our CRM group [has] done a lot of very good work.  As a result, we were able to mail more efficiently.  We were able to increase business with less circulation."
This is the great mystery of our time, isn't it?  One set of experts tell you that catalog marketing is "dead".  And then you have the catalog vendor community demanding that you mail MORE catalogs, telling you that 85% or more of your online orders are caused by catalog marketing, suggesting that you must mail catalogs or you'll be out of business ... pointing to flawed matchback analytics to make their case. 

The reality is that there is a BOATLOAD of profit to be had by taking an average of each position offered by the experts.

In the past sixteen months, customer behavior seems to have shifted at an accelerated rate.  There are three key customer trends for catalogers to pay attention to.
  1. Frequency and Pages.  Two 64 page catalogs is better than one 128 page catalog.  The amount of profit to be made here is HUGE ... smaller books can be mailed far deeper than larger books, and the incremental contact generates more demand than one contact.
  2. Best "catalog" buyers (those who live in rural areas and shop via the telephone) are frequently UNDER-CONTACTED (from a frequency standpoint), if you can believe that.  There is a lot of profit to be had by optimizing the contact strategy among best catalog buyers who shop via the telephone in rural areas.
  3. Online buyers ... even those who buy online after being matched-back to a catalog, are most often being significantly OVER-CONTACTED.  There is a ton of profit to be had by greatly reducing the number of contacts to this audience.
By capitalizing on each of the three key customer trends (the trends are now accelerating, though they've been around for a decade), catalogers can become SIGNIFICANTLY more profitable.  In my projects, the average amount of profit is about a million dollars per year for a $100,000,000 demand-per-year (across all channels) business.

The Catalog PhD methodology capitalizes on the three key customer trends ... the methodology enables one to grow catalog sales while reducing catalog marketing expense.  Yes, it is possible!

Dear Catalog CEOs: How Customers Find Out About Products

Dear Catalog CEOs:

I've been harping on the fact that cataloging isn't dead, but it is becoming increasingly focused on a 55+ rural audience, with the under 35 audience orbiting different marketing channels.

That being said, please click here and view the catalog set of bars in this chart in a blog post from Greenlane SEO.  I'll let you draw whatever conclusions you like from the chart.

February 27, 2011

Dear Catalog CEOs: Do All These Channels Matter?

Dear Catalog CEOs:

Back in 1995, we managed 20 catalog mailings a year, and we liked it.

Back in 2000, we were being introduced to websites and e-mail marketing.

In 2011, we're drowning in channels.  Mobile (apps vs. websites, Apple vs. Android), Social (Facebook, Twitter, Foresquare, Blogs), Search, Affiliates, E-Mail Triggers, E-Mail Campaigns, on and on and on.  We are told to maximize offline, we're told to A/B test our way to online bliss.  Toss in geo-targeted email-based social commerce providers, and you've got a whole bunch on your plate!

And everybody has an answer how you use all of these channels to your benefit.  If you just follow the prescription, all is good.  Follow an online tactic and experience a 239% conversion rate increase, woo-hoo!  Forge deep, emotional connections with somebody on Facebook, and you're told that you will "reap the rewards", as the publications like to tell us.

With all of these 239% increases, we should be seeing unfettered, 100% growth per year, year-after-year, right?

Right?  Be honest.

This brings us to the graph at the top of this post.  Go ahead, click on it.  Across the 50+ brands I've worked with, this is an average, GLM-adjusted annual rebuy/repurchase rate for each of the past eleven years.

Annual repurchase rates are, in my opinion, the best indicator of customer loyalty.  I mean, if you keep 62% of your customers, and then you keep 55%, and then you keep 48% of your customers, then you have a loyalty problem, right?  So the metric quickly gets to the heart of the matter ... are you able to hold on to your customers.

Since we dove, head-first, into the multichannel pool of marketing bliss, our repurchase rates have, on average, decreased.  Sure, you can blame 2008 and 2009 on the economy, but you can't blame 2003, 2005, 2006, and 2007 on the economy, now can you?

There's one thing we can say ... the explosion of channels is not correlated with increased customer loyalty.  You could just as easily make the case that, after accounting for the collapse of the economy, that the explosion in channels resulted in a decrease in customer loyalty.

We were told we would be successful if we simply adhered to the best practices of every single new, burgeoning channel.  

My goodness.  How could it all have gone so wrong?

Make the rest of 2011 a year where you focus marketing efforts around merchandise.  Instead of trying to make channels work, why not spend some time working on making your merchandise work!??!

Make merchandise the focus of 2011.

Analytics Sunday: Segmentation

The vast majority of folks run a Segmentation system.  Segmentation systems are, in large part, responsible for the analytics mess we're in today.  And yet, Segmentation systems are very effective, enabling us to make decisions that help our businesses grow.

Segmentation systems are popular because they are easy to implement.  Take Google Analytics ... it takes you all of a few minutes to put Google Analytics on your website, it costs nothing, and you're almost instantly obtaining actionable feedback.

Be aware that there are forces in your company that are not supportive of your Segmentation system.  Because Segmentation systems are easy to implement, folks prefer their own Segmentation system over your Segmentation system.  The bigger the company is, the greater the likelihood that somebody is crafting their own Segmentation system in opposition to your work.

I go back to Nordstrom.  My team essentially ran a Segmentation/Forecasting hybrid system.  This system was in contrast to many other teams.  When a business issue came up, at least six different teams tried to obtain answers, using their own systems.
  • The Web Analytics team presented one-channel findings via Coremetrics.
  • The Credit team presented credit-centric customer findings via their database, a database that you weren't allowed access to.
  • The IT team had analysts that ran ad-hoc queries using SQL against the customer data warehouse, providing their own analytical viewpoint.
  • The E-Mail marketing team provided their results, based on data from CheetahMail, results that spoke to the performance of e-mail marketing campaigns.
  • Merchants used merchandising systems to report on the performance of their products.
  • My team ran our analytics against the customer data warehouse, trying to cobble together a story of customer behavior across all channels (a "Multichannel Segmentation System, if you will).
Who was right?  Everybody, of course.  And nobody, of course.  You could only believe part of the story the Web Analytics team told, but their story was only valid for 10% of the business.  You could believe part of what the Credit team shared, but it was valid for 20% of the business.  You could believe some of what the IT team suggested, as long as the IT team audited their own queries properly.  You could believe some of what the e-mail marketing team said, but their results were only valid for 3% of the business.  You could believe some of what the merchants said, because their systems tied out to Finance, so that gave them some credibility.  And you could only believe some of what my team said, because we weren't responsible for any marketing activities, so our results, while unbiased, were not accepted because we didn't reside in various marketing silos.

Now, an outsider might look at this and say, "... whoa, why not just come up with one version of truth, you have a multi-channel customer database, just have Kevin's team share the results and that's it, nobody else works on the problem ... this is the problem with a silo-based approach to marketing and analytics."


The problem is that Segmentation systems are easy to implement, so no matter what you do, it is human nature for folks to seek their own version of truth.  And trust me, folks will seek their own version of truth.

If you are going to rely upon a Segmentation system in your company, you better be ready for competition.  There will always be somebody out there who thinks they can do a better job than you can do ... that person is likely to reside in the IT department ... that person is likely to reside in a different business unit ... heck, I've even observed that person rising out of the finance department!

And you better be ready to be wrong.  The problem with running a Segmentation system that is easy to use is that folks in the Optimization camp, the Prediction camp, and the Forecasting camp all are ready to use advanced analytics to demonstrate that your data is "wrong".  You'll run a query that shows that "multichannel customers are the best customers", and somebody like me, running a Forecasting system, will blow your hypothesis out of the water.  Oh boy.

If you're going to run a Segmentation system, you almost have to focus your efforts on one word ... "profit".  Always be the person who knows how profitable every activity is/was.  The majority of your co-workers, folks running rogue Segmentation systems, are not going to go to the effort to calculate profit.  Profit gives you an advantage over everybody else, in fact, you instantly gain a valuable ally ... the finance department!!

February 24, 2011

Measuring Marketing Campaigns That Fail To Engage The Customer

We've all been there.

Somebody in the Marketing Department is charged with creating a campaign.  And oh boy, OH BOY, they absolutely hit one out of the park ... maybe they come up with some magical contest coupled with free shipping and 20% off in the middle of January, 2010.  For the next week, sales go BONKERS!

The marketer is celebrated.  She's nominated for a company award, and at the quarterly meeting, she's presented with $800 and a free plane ticket valid for travel anywhere in the continental forty-eight states.  Five weeks later, she's celebrating her genius at Hilton Head, while you're grinding through the analysis of one of an endless number of non-descript e-mail campaigns that are lucky to cause one in seven-hundred customers to purchase.

We're a short-term society.  We demand sales, NOW, and we reward people for what they've done lately.

Our businesses, however, thrive when we fertilize the customer file.

The truth is that many of our marketing campaigns fail to "engage" the customer.  In other words, when we measure the success of a marketing campaign, we take a "short-term" approach ... we simply ask if an e-mail marketing message or catalog or social media effort cause the customer to buy in the twelve, twenty-four, or forty-eight hours after the campaign.

Look at the image at the top of this post.

This is a "rolling-twelve-month" buyer analysis of the e-mail channel within a business.  We construct the query in parts.
  • Count all customers who purchased via e-mail from 1/1/2009 to 1/1/2010.
  • Then count all customers who purchased via e-mail from 2/1/2009 to 2/1/2010.
  • Then count all customers who purchased via e-mail from 3/1/2009 to 3/1/2010.
  • Repeat this process after every month, through current.
Plot the count of twelve-month buyers within the channel.  You end up with a graph similar to the one at the start of this post.

When a marketing campaign fails to "engage" the customer, you'll see a trend like the one illustrated in the image at the start of this post.  At month sixteen, the company ran a special e-mail marketing campaign, and customers LOVED IT ... twelve-month buyers increased by nearly 14,000.

Nine in ten Executives would say that the campaign "worked".

One in ten Executives would ask for a rolling-twelve-month buyer analysis a year later.  This rare set of Executives are rewarded with a very interesting finding!

Look at what happens in month twenty-eight ... the number of twelve-month buyers decrease by more than 12,000.

In other words, the vast majority of customers who purchased from the marketing campaign did not purchase again from e-mail, falling off the e-mail twelve-month buyer file.

Oh boy.

Now, granted, these customers may have purchased from other channels, so it's wise to run this analysis for your total buyer file, and for all micro-channels within your business.

This is common.  Marketers are rewarded for short-term efforts.  Our job is to analyze the long-term impact of our decisions.  Long-term, this campaign failed, because it harvested sales that did not translate into long-term business.

February 23, 2011

Forecast Forensics + Digital Profiles: Converting Factors

Here's where the rubber meets the road, as they say!

Yesterday, we created four factors.  Today, we assign customers to one of sixteen Digital Profiles.

WARNING:  Geeky math alert ... feel free to skim if you don't like math!

We score each customer, by standardizing each variable (remember, yesterday we calculated the mean and standard deviation of each variable on a twelve-month basis), then we multiply the coefficients in the Component Score Coefficient Matrix by the standardized variables.  Here's the Component Score Coefficient Matrix:

At this point, I have four factors.

Next, if a factor has a value greater than or equal to zero, we assign a value equal to one, otherwise zero.  Once we do this, we combine four factors by two values each, yielding sixteen Digital Profiles.  Here's my SPSS code, if you're interested (the four factors are f1, f2, f3, and f4).

compute d1 = -0.0000.
compute d2 = -0.0000.
compute d3 = -0.0000.
compute d4 = -0.0000.
compute dp =  00.
if (f1 ge d1) and (f2 ge d2) and (f3 ge d3) and (f4 ge d4)  dp = 01.
if (f1 ge d1) and (f2 ge d2) and (f3 ge d3) and (f4 lt d4)  dp = 02.
if (f1 ge d1) and (f2 ge d2) and (f3 lt d3) and (f4 ge d4)  dp = 03.
if (f1 ge d1) and (f2 ge d2) and (f3 lt d3) and (f4 lt d4)  dp = 04.
if (f1 ge d1) and (f2 lt d2) and (f3 ge d3) and (f4 ge d4)  dp = 05.
if (f1 ge d1) and (f2 lt d2) and (f3 ge d3) and (f4 lt d4)  dp = 06.
if (f1 ge d1) and (f2 lt d2) and (f3 lt d3) and (f4 ge d4)  dp = 07.
if (f1 ge d1) and (f2 lt d2) and (f3 lt d3) and (f4 lt d4)  dp = 08.
if (f1 lt d1) and (f2 ge d2) and (f3 ge d3) and (f4 ge d4)  dp = 09.
if (f1 lt d1) and (f2 ge d2) and (f3 ge d3) and (f4 lt d4)  dp = 10.
if (f1 lt d1) and (f2 ge d2) and (f3 lt d3) and (f4 ge d4)  dp = 11.
if (f1 lt d1) and (f2 ge d2) and (f3 lt d3) and (f4 lt d4)  dp = 12.
if (f1 lt d1) and (f2 lt d2) and (f3 ge d3) and (f4 ge d4)  dp = 13.
if (f1 lt d1) and (f2 lt d2) and (f3 ge d3) and (f4 lt d4)  dp = 14.
if (f1 lt d1) and (f2 lt d2) and (f3 lt d3) and (f4 ge d4)  dp = 15. 

if (f1 lt d1) and (f2 lt d2) and (f3 lt d3) and (f4 lt d4)  dp = 16.

With this logic ... and the instructions from above, I have sixteen Digital Profiles.

Next Week:  We describe each of the sixteen Digital Profiles that will be used in our Forecast Forensics analysis!  Contact me if you'd like to have your own customized Forecast Forensics / Digital Profiles analysis.

February 22, 2011

Forecast Forensics + Digital Profiles: Creation Via Factor Analysis

Here's the variables that I elected to enter into creation of sixteen Digital Profiles (contact me for your own customized project):
  • Data for the past twelve months ... using the scoring algorithm from the past twelve months to score prior years as well.
  • Frequency:  Orders in past year.
  • Items per Order:  Total annual items (20) divided by total annual orders (4) = 5.00.
  • Price per Item:  Total annual demand ($800) divided by total annual items (20) = $40.00.
  • 1/0 Indicator:  Did customer buy using telephone channel in past year?  1 = yes, 0 = no.
  • 1/0 Indicator:  Did customer buy using all other online channels in past year?
  • 1/0 Indicator:  Did customer buy using last-click attribution to e-mail in past year?
  • 1/0 Indicator:  Did customer buy using last-click attribution to search in past year?
  • 1/0 Indicator:  Did customer buy using last-click attribution to social media in past year?
  • 1/0 Indicator:  Did customer buy using last-click attribution to mobile in past year?
So, we create a dataset that has one year of data, with these attributes.

WARNING:  The rest of this post gets really "geeky" ... so if you don't like math, move along, there's nothing to see here!

Here are descriptive statistics for our variables:

The means and standard deviations are used later, when I want to create each of four factors.

Next, we run a factor analysis / principal components analysis, extracting four factors.  Here is the rotated component matrix:

In this analysis, we're looking for metrics with an absolute value greater than 0.20 ... this helps us identify the variables that contribute to each factor.
  • Factor #1 = Frequency, Mobile, and Social.  This factor likes loyal customers who have migrated to mobile and social channels.
  • Factor #2 = Telephone, Not Online.  In other words, this factor favors old-school shoppers who call the contact center to place an order.
  • Factor #3 = Many Items per Order, Low Price per Item:  These customers like cheap items, and they buy lots of cheap items!
  • Factor #4 = E-Mail + Search:  Customers who buy via e-mail and search, not necessarily other online channels, fall into this factor.  Kinda makes one wonder if e-mail causes search to happen, doesn't it?
Up Next:  We'll create sixteen Digital Profiles from the four factors extracted from this analysis.

February 21, 2011

Forecast Forensics + Digital Profiles: A Marriage Made In Heaven!

It's time to combine two fantastic methodologies, yielding a highly robust framework for understanding just what the heck is happening in your business!

Over the next several weeks, I will combine Forecast Forensics with Digital Profiles, illustrating how a multi-channel business has a customer base that his moving in many different directions, all at the same time!

Sound like the business you're managing?  Probably!

Tomorrow, we get started.  The database I'm using has several years of purchase history.  I will analyze six key channels:
  • Telephone:  Customers ordering via the phone, primarily from catalogs.
  • Online:  Pure online orders, minor online channels (affiliates), online orders driven by catalogs.
  • E-Mail:  Orders with last click attributed to e-mail.
  • Search:  Orders with last click attributed to search.
  • Mobile:  Orders with last click attributed to mobile app or mobile website.
  • Social:  Orders with last click attributed to social media.
I can hear some of you grumbling already, bemoaning the fact that I'm using last click attribution in this series.  Well, why don't you use this opportunity to take the methodology I'll share with you, and apply your attribution system to this methodology?  Better yet, why don't you freely publish the results so that everybody can benefit?

So, we'll take six channels, and we'll explore how customers are segmented (using Digital Profiles).  Then, we'll use the Forecast Forensics methodology to illustrate how the business is likely to evolve in the future, given what we've learned about customer behavior.

Forecast Forensics + Digital Profiles:  A marriage made in heaven!

February 20, 2011

Dear Catalog CEOs: Channel Trends

Dear Catalog CEOs:

One of the most interesting findings in my Catalog PhD projects (Print / Kindle) is the evolving nature of channels.

You remember the "multichannel era" ... i.e. 2000 - 2005 ... everything had to be linked together, integrated, in harmony, a direct response of the "wild west" internet of 1995 - 2000.

The evolution of the internet seems to have changed things.  It increasingly looks like 2005 - 2010 was a "sorting out" period ... customers found the channels that worked best for them.  In 2011, customers appear to be more "set" in their ways, as is evidenced by the Migration Probability Tables that I run.

Catalog / Telephone:  The 55+ rural audience really sorted themselves into this bucket.  They're comfortable with historical shopping habits, and aren't ready to do what pundits want them to do.  These folks party like it's 1999, if you will!  Be very careful if this is your primary source of new customers.

Catalog / Online:  This used to be a larger segment, but is increasingly fragmented.  A 45-54 year old suburban audience seems to skew to this choice.  This audience doesn't need 248 pages in the mailbox ... actively test sending 40% of your current page count to this audience, you're likely to get 90% of the demand on 40% of the pages.  Test, test, TEST!

Catalog / Retail:  Less effective than previously believed to be.  Even less effective among retail-only buyers.  Think about it ... the media has to motivate a customer to get in her car and drive to the store ... and that trip has to be unplanned ... that's HARD to do.  What would motivate you to get in your car to make an unplanned trip to J. Crew?  Be honest.

Online / E-Mail:  An increasingly dangerous combination of best customers and discount/promotion fanatics.  Reduce catalog pages to this audience ... once the shift migrates from catalogs to e-mail, you can significantly reduce page counts, and reduce frequency.  If you have a retail channel, then pay CLOSE attention to the ways that e-mail drives retail traffic, many retailers seriously undercount the impact of e-mail on retail.

Online / Search:  Search is surprisingly integrated with other channels, whether you know it or not.  Catalogs and e-mail cause searches to happen ... spending money on search is increasingly viewed as part of a two-step conversion process.

Online / Affiliates:  More incremental than previously believed to be, increasingly viewed as part of a two-step conversion process.  Surprisingly, customers who purchase via an Affiliate are unlikely to use an Affiliate on a subsequent purchase.

Sales Force:  I've yet to run across an instance where assistance from live human beings is anything but transformational, from a long-term value standpoint.  A relationship with an actual human being trumps any algorithmic channel-based experience.  Relationships with actual, live human beings represents all that social media ever aspired to accomplish.  Add humans to your business, as soon as possible!

Social:  Maybe the least impressive new channel ever created ... while at the same time being the most important channel since the advent of e-commerce.  Has almost no impact on annual spending behavior among a 50+ audience, has transformational impact on annual spending behavior among the 13-29 audience.  A channel buried in misinformation, hype, and mis-allocation of sales.  F-commerce (Facebook) could (or not) thrive as a sub channel (like e-mail thrives as a sub-channel within online).  Twitter ... not so much.  What we know is this ... what we see today won't be what we see tomorrow, everything will evolve and change.

Mobile:  Early returns suggest it will be transformational, in the way that e-commerce obliterated the traditional catalog marketing channel.  Early returns suggest that this transformation won't happen as fast as the pundits suggest it will.  Think of mobile as being in the Explorer/Netscape/Yahoo!/Hotmail/AOL phase that e-commerce was in back in 1998.  Generational differences are significant, critical to understanding how to leverage this new channel.  Mobile is likely to crush internal org structures, and is likely to cannibalize the living daylights out of the online channel.  Expect the mobile/online debate to rage like the catalog/online debate of 2000-2005.

Multi-channel:  Dead in the water.  Multi-channel customers do not spend more than single-channel customer.  Go back to 1995, and compare your annual retention rate and annual orders per buyer to comparable metrics in 2010 ... no difference (or worse than 1995), right?  Well, how is that possible?  I mean, you've added every channel under the sun, and your metrics have not fundamentally changed, right?  Adding channels does not translate to sales increases ... actually, adding channels might simply mitigate sales decreases.  Knowing demographics/lifestage is more critical, it dictates the effectiveness of channels.

Matchbacks:  Flawed.  Accounting for significant mailing waste.  Without a matchback, you're sunk.  With a matchback, you're sunk in the opposite direction.  Need something "in-between".  Catalog PhD sure helps!

Fragmentation:  This is what the promise of multi-channel became.  You'll have a veritable plethora of channels, and customers will self-select themselves into the channel combinations that satisfy their needs ... some will be anchored within a channel (a 63 year old New Hampshire woman anchored in the catalog/telephone channel), some will float between a half-dozen channels (a 32 year old suburban woman in Kansas City).  Our job is to segment (i.e. Digital Profiles), then to reduce/increase marketing expense and targeting strategy as appropriate.  Our job is not be everything to every customer, while at the same time offering many channels as appropriate ... a tough tightrope to walk.

Analytics Sunday: United Or Divided

We care about Analytics Systems, because by understanding where we stand, and where our Executive teams stand, we better understand why we succeed or fail.

Take this brief, two-question quiz.  You'll understand where you fall on the grid.  Answer how your Executive team might respond, and plot where they fall as well.
  1. When deciding who should receive a marketing promotion, do you like to segment customers into like groups (1), or do you like to create models that predict how likely customers are to respond to marketing messages (9)?
  2. When considering a new marketing strategy, do you like to run a series of simulations that predict how customers might behave in the future (1), or do you like to run a series of tests that reveal an optimal marketing strategy to employ against your customer base (9)?
Plot the answer to the first question on the following chart ... (1) on the left, (5) in the center, (9) on the right.

Plot the answer to the second question on the following chart ... (1) on the bottom, (5) in the center, (9) on the top.

If you and your Executive team aren't plotted near each other, you've got the potential for communication problems.

Analytics Systems matter, they represent the difference in philosophy that divides or unites us.

Here's an old-school example:  Catalog marketers often employed a Segmentation/Forecasting hybrid model to measuring advertising effectiveness.  For instance, Lands' End used a segmentation strategy with "current" customers, followed by 0-3 month recency, 4-6 month recency, 7-9 month recency, 10-12 month recency, and beyond in three month increments.  Eddie Bauer and Spiegel used a similar strategy ... "current" customers, followed by 0-6 month, 7-12 month, and beyond.  This segmentation strategy served dual purposes.  First, catalog performance was measured within each segment, and that was important (part of the Segmentation system).  Just as important was a subtle twist incorporated within a Forecasting system.  Customers were locked-into the segment at the start of a quarter/season, and only moved out of the segment into "current" quarter/season status if the customer purchased.
  • January 1 = 0 Current Season Customers, 100 0-3 Month Buyers.
  • June 30 = 67 0-3 Month Buyers.
  • Rebuy Rate For 0-3 Month Buyers = 1 - (67/100) = 33%.
This was a subtle, but very clever implementation of a Segmentation/Forecasting hybrid system.  The analyst knew how customers performed in each catalog (Segmentation --- understanding marketing campaign effectiveness), and just as important, the analyst was able to understand overall repurchase dynamics independent of marketing activities, and understand file strength implications for upcoming quarters/seasons (Forecasting --- understanding and forecasting customer performance independent of marketing campaigns). 

Back in 2001, I was trying to implement an Optimization/Prediction hybrid system, so I abandoned this type of segmentation scheme at Nordstrom ... the decision did not sit well with a faction of individuals who were loyal to the Segmentation/Forecasting hybrid system mentioned above.  Heck, it was as if I just obliterated all analysis reporting in the company!!!  My decision was not popular, at all ... when you and Management are far apart on the systems grid, it doesn't matter how bright you are, you're in big trouble.

Web Analysts frequently run into this reality, just read their tweets, calling Executives "HiPPOs", meaning "Highest Paid Person's Opinion".  You see, the Web Analyst often uses a Segmentation system that is hard-wired into the software solution used by the Web Analyst.  The person the Web Analyst is trying to convince frequently uses a Forecasting system ... take a merchant, who only cares if merchandise sells, pure and simple.  This person isn't evaluated on the performance of an individual catalog, this person is evaluated on customers loving her merchandise on an annual basis.  These are two individuals, running two different systems ... the merchant wants to know what is going to work in the future, the Web Analyst cares about what happened to conversion rates last month.  When individuals run different systems, disagreements happen.

It's really important to know where you stand on the Analytics System continuum.  It's just as important to understand where your Executive team stands on the continuum.  If you're in a similar place, you are united.  If you're in different places, you may be divided against each other.

February 17, 2011

Borders, Debt, Multichannel Marketing

Borders sought Chapter 11 protection earlier this week.

To blame bankruptcy on being behind the curve in the adoption of books as a digital enterprise represents a fundamental misunderstanding of multichannel marketing.

Let's start with the concept of debt.

You have a brand like Bed Bath and Beyond.  They don't have any debt.  Now, this prevents them from expanding and growing at an unfettered pace.  But they have a level of flexibility that allows them to ride out economic highs and lows.

Consider the financial situation of two individuals.
  • Individual #1 earns $100,000 per year, taking home $6,000 per month.  Individual #1 makes a $2,000 monthly mortgage payment (debt), and has financial commitments for $3,500 per month in expenses, yielding $500 each month that can be "saved".
  • Individual #2 earns $90,000 per year, taking home $5,400 per month.  Individual #2 does not have a mortgage payment (no debt), and has financial commitments for $3,700 per month in expenses, yielding $1,700 each month that can be "saved".
What happens if each individual has a health problem, requiring each individual to pay, say, $1,000 a month in insurance co-pays?

What happens if each individual is required to take a 10% pay cut?

This is what debt does to a retailer.  Or to anybody.  It cripples a person, family, or brand.

The multichannel vision was a simple one ... built on the premise that customers loved to shop, they loved to shop offline or online, and they loved online and offline advertising.  This required businesses to "do everything".  It's really hard to "do everything".  If you want to do everything, you almost have to borrow money.  And when you borrow money, you have no margin for error.

Multichannel marketing requires "margin for error".  Channels grow, and channels die ... heck, look at the music industry.  Flexibility is of the utmost importance.  Retail, unfortunately, is fixed, inflexible, and often debt-laden.

The retail channel limits innovation ... you simply cannot take a risk in retail that would cost you sales, or you'll need to borrow more money.  This is what causes a business to not be able to invest in digital books in the same way that a more solvent business, or a single-channel business (Amazon) can invest.

You cannot tell me that all Executives at Borders were so blind to digital innovation that they completely missed the Kindle/Nook revolution.  And let's not blame them for partnering with Amazon ... heck, half the pundits in 2000 thought it was pointless to have an e-commerce website when one could achieve "scale" by partnering with Amazon.  It's ok to make bets that are wrong.

The real issue is debt.

Wall St. wants retailers to take on debt.

Some vendors want retailers to take on debt, because that means that retailers will spend money with said vendors.

Focus, instead, on doing what is right for the customer.

And stay away from debt, if at all possible.  Multichannel marketing requires flexibility, debt takes flexibility away.

February 16, 2011

Forecast Forensics: Are Channels Complimentary?

Let me ask you a question:
  • "For every dollar you increase the amount of demand you generate in Search, how much additional demand do you generate in all of your other advertising/physical channels?'
Can you answer that question?

If not, why not?  I mean, that's about as basic a question as you're ever going to run across, right?

The Forecast Forensics framework can be used to answer these questions.  After all, all of this multichannel stuff says that channels work together in a multiplicative way to grow our businesses exponentially, right?

So, I ran the framework for data I have.  I ran four simulations, across five years.
  • Simulation #1 = Base Case.
  • Simulation #2 = Grow Telephone Volume by $1.5 Million in Year Five.
  • Simulation #3 = Grow Search Volume by $1.5 Million in Year Five.
  • Simulation #4 = Grow Mobile Volume by $1.5 Million in Year Five.
Let's look at how other channels grow, when these channels grow.

Grow Telephone Demand By $1.5 Million In Year Five:
  • Telephone = Increase of $1.5 Million.
  • Online = Increase of $0.2 Million.
  • E-Mail = Increase of < $0.1 Million.
  • Search = Increase of < $0.1 Million.
  • Social = Increase of < $0.1 Million.
  • Mobile = Increase of < $0.1 Million.
Grow Search Demand By $1.5 Million In Year Five:
  • Telephone = Increase of $0.2 Million.
  • Online = Increase of $1.0 Million.
  • E-Mail = Increase of $0.5 Million.
  • Search = Increase of $1.5 Million.
  • Social = Increase of $0.1 Million.
  • Mobile = Increase of $0.1 Million.
Grow Mobile Demand By $1.5 Million In Year Five:
  • Telephone = Increase of $0.3 Million.
  • Online = Increase of $1.0 Million.
  • E-Mail = Increase of $0.7 Million.
  • Search = Increase of $0.3 Million.
  • Social = Increase of $0.1 Million.
  • Mobile = Increase of $1.5 Million.
In other words, each channel yields a different result.
  • Growing Telephone orders (i.e. sending catalogs to rural customers) results in a simple increase in the Telephone channel, without helping other channels.  Each Telephone dollar adds $0.20 across other channels. Catalog orders over the phone are not complementary to other channels, in this case.
  • Growing Search orders results in $1.5 million in Search and $1.9 million in other channels.  Each Search dollar you add results in $1.27 being added to other channels, as well.  Search is complementary to other channels, in this case.
  • Growing Mobile orders results in $1.5 million in Mobile and $2.4 million in other channels.  Each Mobile dollar you add results in $1.60 being added to other channels, as well.  Mobile is complementary to other channels, in this case.
As a marketer, you probably want to know this fact, right?  I mean, knowing this helps you allocate marketing dollars appropriately ... your attribution models aren't going to tell you this, so it is a pretty important fact to know, right?

Give the Forensics Forecast methodology a try ... you're going to be fascinated by what you learn about how customers interact with the channels you support!

February 15, 2011

Marketing Campaign Performance: Kevin, Are You An Idiot?

  • "How is what you do different than what we do?"
In other words, the real question is this?
  • "What, exactly, is your "system"?
Good question!

The Multichannel Forensics "system" (a derivation of the Forecasting system I described on Sunday) ignores campaign performance ... that's how it is different than what you do.

Huh?  How can you ignore campaign performance?  Are you an idiot?  Have you ever looked at a funnel report?  Don't you know how important subject lines are in e-mail performance?  Have you ever measured the response rate of a postcard?  Have you ever used Klout to measure your online influence?  Are you an idiot?

That's the kind of response I sometimes get.

The best thing that can ever happen to a marketing/analytics professional is to work at a company that is failing, or to work for a channel that is failing.  For me, Lands' End 1995, Eddie Bauer 1998, Nordstrom 2001, or consulting work for 70% of current catalog brands qualifies.  Online marketers cannot relate to failure, for even during the Great Recession, online sales generally grew as online cannibalized existing channels.  Other marketers, however, are well-versed in failure.  Failure changes everything.

When you are failing, you evaluate every campaign, looking to unearth the reason why a business is failing.  When you do this, you learn critical truths about customer behavior.
  • Customers exhibit roughly the same loyalty on a year-over-year basis.
  • Customers purchase roughly the same number of times, per year, every year.
  • Customers increase spend, on a year-over-year basis, at roughly the rate of inflation.
In other words, marketing campaigns are little more than a big game of "whack-a-mole".
Look at this grid.  Each hole represents a marketing campaign.  Each mole represents success.  In total, you see six "successes".

When your business is failing, you notice that no matter what you do, no matter how hard you try, you're going to be stuck with six successes, total.  That's it.  Two things make the problem worse ... the six successes aren't necessarily predictable/repeatable, year-over-year, and overall, every campaign is 5% or 10% worse than last year.

In other words, it's very, VERY hard to fix a business by fixing campaign performance.

Catalogers know this.

In 1995, the cataloger mailed 20 catalogs a year ... that's it.

In 2011, the cataloger mails 26 catalogs a year, delivers 110 e-mail campaigns per year, manages 67,000 keyword combinations, partners with 22,000 affiliates, has 38,000 fans on Facebook, and 11,000 followers on Twitter.

In 1995, the cataloger exhibited the following loyalty metrics:
  • 45% annual retention rate among 12-month buyers.
  • 2.3 purchases per retained buyer.
  • $100 per order.
In 2011, the cataloger exhibits the following loyalty metrics:
  • 45% annual retention rate among 12-month buyers.
  • 2.3 purchases per retained buyer.
  • $135 per order.
The only thing that fundamentally changed, over sixteen years, is the average order value ... this grew at an inflationary rate.

In other words, marketing campaigns "don't matter".  Marketing campaigns don't change customer behavior.  Marketing campaigns simply follow a "whack a mole" style of speaking to a customer ... you are constantly trying to find which hole the mole (customer) will pop out of, and you're always a step behind the mole.

The Multichannel Forensics "system", if you will, ignores individual campaign performance, because history demonstrates that, for 95% of us, individual campaign performance does not lead to annual changes in customer behavior, rendering individual campaign performance meaningless.

The Multichannel Forensics "system" instead seeks to identify changes in customer behavior over time, using rolling twelve month metrics, annual repurchase rates, annual orders per buyer, annual spend per order, migration probabilities across channels, and five-year sales projections to identify "what customers are really doing", to identify "what is really working and what is not really working", across channels, across merchandise divisions, across brands.

In the Multichannel Forensics framework, you learn five critically important things:
  1. Merchandise REALLY matters.  Changes in the ratio of new/existing product, or changes in the mix of the assortment, result in changes in customer loyalty that manifest themselves in marketing campaign performance.
  2. New customers REALLY matter.  For 80% of my clients, success/failure is highly dependent upon a steady and ever-increasing diet of new customers that are subsequently managed in a profitable manner, after acquisition.
  3. Economic headwinds SOMEWHAT matter.  I've analyzed businesses that were barely impacted by the Great Recession.  I've analyzed businesses that were crushed by the Great Recession.
  4. Pricing/Promotions SOMEWHAT matter.  I'm not saying you should or should not be running free shipping promotions, that's a marketing campaign issue, once again.  What I am saying is if you have standard $14.95 shipping, and an online competitor offers free shipping 24/7/365 and delivers product to the customer faster than you, then you've got a big problem that cannot be solved via marketing promotions or new customer acquisition.
  5. Human beings REALLY matter.  In every analysis I've ever done, if a human being touches a customer, the customer spends more, both short-term, and long-term.  In many projects, when an algorithm touches a customer, the customer spends less long-term.
So, that's what's behind the Multichannel Forensics "system".  Actual customer data shows that, on an annual basis, marketing campaigns generally fail to yield incremental sales increases, instead, they shift demand between individual marketing campaigns, resulting in an endless and fruitless game of whack a mole.  The Multichannel Forensics system steps outside of the madness, seeking to understand and maximize the dynamics that truly drive increases in sales and profit,

Ok, it's time for your thoughts.  Leave a comment.  What "system" has worked best for you, over time?

February 14, 2011

Multichannel Forensics & E-Mail Marketing

I stumbled across this article about two months too late ... "E-Mail's Big Demographic Split".

The brief history of the internet illustrates a common trend among channels.
  • Newness.
  • Hype.
  • Monetization.
  • Outflow.
  • Maturity.
Somebody invents something (E-Mail, AOL, MySpace, Facebook, Twitter, Foresquare, Quora), the early adopters hype the living daylights out of it to attract an audience, the audience hypes the living daylights out of it to attract the mainstream, the mainstream attracts monetization, monetization drives the early adopters out, resulting in a mature channel.

In catalog marketing, this happened over more than a century.

In e-mail marketing, this is happening over the course of two decades.

For MySpace, this happened over the course of maybe eight years.

Facebook?  Twitter?  Get ready.

When I run my Multichannel Forensics methodology against company data, the trends are often similar to what we observe in the article illustrated at the start of this post, from the NY Times.  Mature channels exhibit outflow among a younger audience, while exhibiting increased participation among an older audience.

This is the reason you can read two articles with completely opposite points of view ... e-mail is dead ... and "don't ask is e-mail dead".  Both parties can be "right", with outflow happening among one audience and adoption among another ... age isn't the differentiator here, rather, lifestyle is.

If I were working at a brand like Nordstrom or Lands' End today, I'd score every customer on a technological continuum ... catalog marketing and e-mail marketing on the left, search/affiliates in the center, social media right-of-center, mobile on the far right.  In my Catalog PhD projects, this is essentially the same as the "organic percentage".

If you don't have age data appended to your file, you can run a classic Multichannel Forensics project against your channels ... pay attention to what e-mail customers do next.  Customers will eventually migrate out of a channel, and you'll have plenty of warning if you run the appropriate analytics against the data.

February 13, 2011

Dear Catalog CEOs: 3+3=4

Dear Catalog CEOs:

Back in the early 1990s, we knew that cannibalization existed.  If we mailed a title twelve times a year, and then we added a second title and mailed it twelve times a year to the same circulation quality/depth, we didn't get a two-fold increase in sales, did we?

And then the internet came along.

Today, catalog frequency and page counts are a highly sensitive topic.

I can't tell you how many meetings I've been in during the past four years, where the discussion goes something like this:

Merchant:  "The catalog is 144 pages, and I only have 24 pages to show my product.  I need 36 pages.  Please add 12 pages, make the catalog 156 pages.  Thank you." 

Kevin:  "The catalog will be less profitable at 156 pages.  In fact, the catalog shouldn't even be 144 pages, it should be 64 pages with call-outs to key landing pages."

Merchants:  "You can't sell product unless you show the customer the product.  I need an additional 12 pages.  Please add the pages to the assortment.  Thanks."

Kevin:  "What about e-mail?"

Merchant:  "What about e-mail?"

Kevin:  "Why don't you feature your products in e-mail campaigns.  Your e-mail campaigns go to over a million subscribers, three times a week."

Merchant:  "Real customers use catalogs, hardly anybody shops from e-mail campaigns.  Our catalogs generate $3.00 per book, we're luck to get $0.15 per e-mail, and that only happens when we offer 20% off and free shipping."

Kevin:  "What about your website?  Only 4.8% of your customers convert when they visit the website.  Why don't you spend time getting that rate up to 6.0% for your customers, you'll increase demand by twenty-five percent."

Merchant:  "Our IT team won't let us touch the website."

Kevin:  "What about search?  Why not partner with your vendor to maximize the search performance of your division?"

Merchant:  "Is that where we have to do all of that fancy bidding?  I don't even know what that's all about.  Just let me add the pages to the catalog."

Kevin:  "What about social media?  Have you built a deep, emotional connection with your fans?"

Merchant:  "Do you know how much work that requires?  And at the end of the day, I have fans, but I don't necessarily have sales. Just let me add the pages to the catalog."

Eventually, the merchant beats people down, getting his/her way ... sales increase, profit decreases, page counts increase, circulation depth decreases, prospecting decreases, the customer file erodes, and the business slowly sinks into a murky pool of warm tar.

In the post-internet era, page counts are death.  

Here's a typical relationship, one that I see over and over and over again:

Pages Demand Profit
0 0.00 0.00
12 1.46 0.48
24 1.89 0.56
36 2.19 0.60
48 2.44 0.61
60 2.65 0.62
72 2.83 0.61
84 3.00 0.59
96 3.15 0.57
108 3.29 0.55
120 3.42 0.52
132 3.55 0.49
144 3.66 0.46
156 3.77 0.42
168 3.88 0.38
180 3.98 0.34

As pages increase, demand increases at a ever-decreasing rate, yielding less and less incremental profit.  The problem, of course, is that the Circulation Manager sees 132 pages and $0.49 of profit and says, "yup, that works!"

It doesn't work.  It's simply wasteful.  These days, 3+3=4.  It's a relationship that subtly destroys the profitability of a catalog business in the internet era.

Testing indicates that the future of catalog marketing is all about small catalogs with a highly targeted assortment to a rural, older audience.

Penney / Search Fluff-Up: Is Marketing Bankrupt?

By now, you're read all about JCP and their little organic search fluff-up, as reported by the New York Times

In essence, JCP's search vendor participated in an epic series of black hat practices.  The NY Times article clearly states that JCP is distancing itself from the practices of the search vendor, SearchDex, firing the vendor last week, placing accountability squarely upon the vendor.

Allow me to draw a parallel.  Last night, I entered tasty treats in a dessert competition.  Folks paid $5 to sample treats, then were given the opportunity to vote for the treats they liked best.  The entry with the most votes would be declared the winner.

There were three ways to win the contest.  
  • One way was to have the best merchandise, to create something so delicious that voters had to vote for your product.
  • A second way was to present the merchandise in such a way that people were compelled to try the product ... fancy wrappers, spectacular containers, colorful toppings, that kind of thing.
  • A third way was to invite your friends and family at a cost of $5 per person, then ask friends and family to vote for the treats you made.
Guess which method won the contest?

The winner invited friends, friends voted as expected, and the entire team celebrated their victory while fifty other participants quietly left the building.

As business leaders, we're constantly being sold a seductive message.
  • Winning is easy.
  • You can win, now.
  • Nobody remembers losers.
  • Follow a simple set of best practices that anybody could employ.
  • Push the boundaries of "what is right".
  • Ask for forgiveness if you're caught.
  • Find ways to blame others when you are caught.
What's sad is that Penney didn't catch this, nor did Google.  An outsider caught the problem.
This story really isn't about JCP.  It's about us.  What the heck are we doing? 

Is Marketing Bankrupt?

Analytics Sunday: Which System Do You Run?

Do you know which "system" you run?

In football, there are many offensive and defensive systems.  Defenses run a 4-3 or a 3-4 scheme.  Offenses run a west coast offense, spread offense, spread option, option, wishbone, veer, run & shoot, I-formation, wing-T, on and on and on.

The system you run dictates the talent you need.  The system you run dictates your worldview, it dictates how you approach problems.

The same issues apply to the analytics community.  You may not know it, but you "run a system".  And your system, whether you know it or not, defines your relationship between your co-workers, your Executive team, and your public persona.

The most important part of defining your system is understanding what your primary area of concern is.  You can only align yourself with one of the following two statements.
  1. Your primary concern is to understand how marketing campaigns perform.
  2. Your primary concern is to understand how customers behave.
You do not measure marketing campaigns to understand customer behavior ... that is fool's gold.  Either you are trying to optimize marketing campaign performance, or you are trying to optimize customer spend.

And mind you, every analyst worth her salt uses pieces of every system.  But her first instinct, when any question arises, is to fall back on her primary system.

If your primary concern is to understand how marketing campaigns perform, then you are likely to run one of the following systems.

Segmentation:  The primary goal of the Segmentation system is to categorize customers into homogeneous groups, for the purpose of optimizing marketing performance.  Segmentation was popularized by the RFM, or "Recency - Frequency - Monetary" system.  RFM is seldom used to understand customer behavior ... you don't ever know what is in the mind of a customer when the customer is a 4-6 month, 2x+, $100 AOV buyer.  You don't know what is in the mind of a customer who is an "Existing Visitor" in Coremetrics.  You simply categorize people in like groups for the purposes of understanding the effectiveness of your marketing activities.  Segmentation has many cousins.  CRM blends automation with segmentation.  PRIZM clusters classify customers into similar lifestyle profiles.  Web Analytics is a modern fusion of segmentation in an OLAP environment.  If the primary goal of your analytics department is to classify customers for the purposes of measuring marketing activities, then you run a Segmentation system.

Optimization:  The primary goal of Optimization system is to use testing to significantly improve marketing performance.  The Optimization system is fundamentally different than the Segmentation system in approach.  The Segmentation system presumes that various segments outperform other segments, allowing the marketer to target a customer with a message.  The Optimization system presumes that testing strategies yield the optimal strategy to pursue within a customer segment.  Instead of believing that marketing is right, the Optimization system believes that testing methodology reveals truth.  If, when asked a question, you immediately think about "testing" the question against another strategy, then you run an Optimization system.  Optimization systems are highly popular among the online marketing community, as well as many in the database marketing community.

Prediction:  The primary goal of the Prediction system is to create statistical models to explain customer response to marketing, or to target individual customers for marketing efforts.  This system is different, in that it believes in one-to-one communications automated by mathematical equations.  Database Marketing, by and large, is a fusion of Segmentation and Prediction, with a skew toward Prediction.  Online Personalization is an outgrowth of Prediction.  E-Mail "relevancy" requires a strong "Prediction" system.  If you build statistical models to rank-order customers for catalogs, you might run a Prediction system.  If you create statistical models to measure online influence, you run a Prediction system.  If you build statistical models to identify customers for telemarketing campaigns, you run a Prediction system.  If you build statistical models to identify the impact of a loyalty program, you might run a Prediction system.  The key is your "first instinct" when approached with a marketing problem ... if you immediately think about models, you run a Prediction system.

If the primary purpose of your marketing activities is to understand the impact of customer behavior on a business, independent of marketing activities, then you are a bit different.  You run a Forecasting system.

Forecasting:  The primary goal of the Forecasting system is to holistically understand how customers evolve and change over time.  Those who run a Forecasting system move outside of marketing campaigns, instead seeking to link macro-level customer habits to current and future events.  The style of database queries is different ... using pre/post periods to understand customer behavior, coupled with forecasts/projections for future customer activity given prior trends.

Now, obviously, you use all four systems in your analytics work.  You skew toward one system, using elements of all systems.
That being said, why does any of this matter?

Well, it turns out that the system you run needs to be in harmony with the system that your Executive team believes in.

For instance, how would you approach answering the following question:
  • Conversion rates on your website are down eleven percent in 2011.  What is causing this problem, and what should be done to fix it?
Each analytics system approaches the question from a different perspective.
  • Segmentation folks look at conversion rates among buyers, existing visitors, new visitors ... they analyze conversion performance by traffic sources, trying to identify the underlying problem.
  • Optimization folks attempt to solve the problem first, looking to test different strategies that might resonate with the customer ... why look to the past when one can fix the problem by influencing the future?
  • Prediction folks attempt to solve the problem first, by targeting those who are visiting the site with relevant content.  Or, this system might "equalize" customers using a statistical model, in order to understand underlying problems in past marketing campaigns.
  • Forecasting folks combine macro trends with changes in customer behavior across channels, looking to identify trends (i.e. Mobile) that influence the future trajectory of website performance, forecasting the impact of lower conversion rates going forward.
Say your team runs the Prediction system.  Say your Executive team responds to the Segmentation system.  Well, you've got a disconnect, don't you?  Your Executive team wants to understand marketing performance, and you're out there promoting a personalization plan to boost conversion rates.  Ooops.  Or worse, say you promote a Forecasting system ... you are constantly talking about the impact that changes in long-term customer behavior caused by search/social/mobile will have on the business, while your Executive team craves campaign-based KPIs and Dashboard results of marketing campaigns.

Take a moment, and answer each of the following two questions:
  1. When deciding who should receive a marketing promotion, do you like to segment customers into like groups (1), or do you like to create models that predict how likely customers are to respond to marketing messages (9)?
  2. When considering a new marketing strategy, do you like to run a series of simulations that predict how customers might behave in the future (1), or do you like to run a series of tests that reveal an optimal marketing strategy to employ against your customer base (9)?
Plot the answer to the first question on the following chart ... (1) on the left, (5) in the center, (9) on the right ... then for the second question, plot a (1) on the bottom, (5) in the center, (9) on the top.

Repeat this exercise for the perceived answers your Executive team would provide you.

If you and your Executive team plot out in a similar manner, there's a good chance that you are aligned, that your Executive team understands what you are trying to accomplish.

If you and your Executive team are far apart on this graph, well, that doesn't mean you aren't going to be successful ... but it does mean that you come at problems from different perspectives, and that can lead to challenges and disagreements.  It can, by the way, also lead to spectacular breakthroughs, especially when each side respects the other.

P.S.:  Where do I (Kevin) stand, on this continuum?

I skew significantly toward Forecasting.  This is why my message is so different than what you hear in the Vendor community, is why my message is so different than those shared by leading analytics experts, and is why my message is not always welcome at leading conferences.

You really have to look back at the influences that shape each analyst, over time.  In my case, I was trained as a Statistician, which pushed me toward Optimization/Prediction.  Then I entered the Direct Marketing world, and noticed that Executives are not fans of Optimization/Prediction ... they tend to gravitate toward a Segmentation/Forecasting quadrant that was opposite of my training.  

In other words, I had to change my approach.

I borrowed heavily from things that were done at Fingerhut in the 1980s ... where they used a Segmentation/Forecasting bridge known as the rolling twelve-month file to demonstrate the health of the business independent of marketing campaigns.  I borrowed heavily from Jim Fulton, a former Lands' End co-worker who is now a Database Marketing consultant (Customer Metrics) ... he heavily leveraged the Forecasting system to demonstrate that new customers, not customer loyalty, lead to a healthy, growing business.  I borrowed from Lands' End, who skewed heavily toward a Segmentation/Optimization platform.  I borrowed heavily from Spiegel, a company that used a clever Segmentation/Forecasting bridge (I'll cover this in a future post) to measure file health.  I borrowed from Eddie Bauer, where pure Segmentation systems failed to improve the health of the customer file, pushing me toward channel interactions (Forecasting).  I borrowed from Nordstrom, a heavy Segmentation/Forecasting culture that "ripped the band aid off" on strategies without the need for Optimization.  I borrowed heavily from some of the early Forecasting/Prediction work done at the Santa Fe Institute in the mid 1990s, and borrowed heavily from Mathematical Biology (Forecasting/Prediction).

Notice that the word "Forecasting" keeps coming up in the activities that shaped my consulting focus.  I skewed farther and farther away from a focus on marketing campaigns over time, skewing more and more toward holistic customer migration patterns independent of marketing campaigns.

My experiences skewed me dramatically toward the Forecasting system.  I'm an enthusiastic advocate of moving beyond analysis of marketing campaigns.  I realize not many agree with me.  No worries!

Your experiences skew you to one system, or to a combination of systems.  No skew is right or wrong, all are appropriate.  You simply have to align your system with your core beliefs, and hope that your system is in alignment with those who are tasked with implementing your beliefs.

Alex Morgan (Yes, This Will Be About Merchandise)

When the USWNT Olympics Team was announced, all-time great Alex Morgan wasn't on the team. Nor was she on the four player alternate list...