Dear Mr. Comer

Dear Mr. Comer:

I once worked for your mail order business, from 1990 to 1995.  I recall being taught one of your most important principles of doing business (click here):

• Principle #2 = We price our products fairly and honestly.  We do not, and have not, and will not participate in the common retailing practice of inflating mark-ups to set up a future phony 'sale'.

Now, I know you have more important things to tend to in Heaven, things like averting the end of the world on December 21 and helping our leaders avoid the fiscal cliff, but I thought you might want to see how Principle #2 evolved in the past decade.

Wow, that's some serious action!  Let's do the math on a $105 purchase.

• Normal Business = $105 merchandise + $14.95 shipping and handling = $119.95.
• This Promotion = $105 merchandise - $40 discount + $0.00 S/H = $65.00.
• Savings = 46%.
• Merry Christmas!

If the cost of goods sold is 40% and shipping/handing is assumed to truly cost about $7 per order (ignoring the human costs to pick/pack/ship merchandise), then we're looking at about $71 profit under normal business conditions, and $16 profit via this promotion ... requiring the promotion to drive a 340% increase in orders to equalize profit.

If my assumptions are off, they're not off far enough to fundamentally change the story.

Mr. Comer, you might be surprised to learn that, in 2012, this is considered a "best practice" in the e-commerce industry.  Trade journalists, bloggers, vendors, research organizations, and consultants achieved consensus on this topic around the time of the Great Recession.  Because we have consensus, it has to be a best practice to accept $16 profit per order over $71 profit per order in order to maintain market share.  

Experts will point to JCP, who eliminated discounts and promotions in favor of fair, everyday pricing, and saw same-store sales drop 25%.  Experts conveniently ignore companies like Apple, who adhere to your principle and have more cash on hand than the US Treasury.

Mr. Comer, I recall spirited discussions that leadership had with you in 1992.  They asked me to analyze tests where we priced mock turtlenecks at $12 each, or 2 for $23.  We had extensive debates whether this strategy violated Principle #2.  Those were great times.  In 2012, with the advent of Cyber Monday and now Green Monday, our industry believes that Principle #2 places the industry at a competitive disadvantage.  We used to argue about ways to discount one product out of ten thousand by 4%.  Twenty years later, we discount all products by 40%, plus free shipping.  

At the current rate of promotional acceleration, we'll be discounting by between 75% and 80% in the year 2032.

Thank you, Mr. Comer, for taking a few moments from your busy schedule to hear this update about modern e-commerce.

E-Commerce Simulations: Here's My Framework

Use of simulations are commonplace.  We wouldn't be having vibrant discussions about global warming unless somebody ran a simulation illustrating a forthcoming train wreck.  

And nearly every day, your television station provides you with a four or five or seven day weather forecast ... and it's reasonably accurate.  Weather forecasters are running a myriad of simulations, then they forecast the future.

Remember the movie Apollo 13?  The simulator was pretty important in getting folks back to Earth.  And that was more than 40 years ago.

Formula 1 race car drivers hopped into the simulator to test the new race track in Austin, Texas.

Why, in e-commerce, do we not use simulations?

We don't have to over-complicate the creation of simulations.

Let's use a very simple example.  I take two snapshots of the twelve-month file, one from October 10 - November 9, then another from November 10 - December 9.  I record key attributes about each customer ... recency ... frequency ... monetary value ... channel preference ... merchandise preference ... Christmas shopping preference ... price point preference ... free shipping preference ... discounts/promo preference.

Allow me to over-simplify for a moment, to demonstrate how I run my simulations.  Let's pretend that in the 10/10 - 11/9 timeframe, I segment customers as good, average, and poor.  Then let's pretend that in the 11/10 - 12/9 timeframe, I segment customers using the exact same criteria ... good, average, and poor.

Toss in new customers, assigning them to good/average/poor, and we have a 4x4 matrix.

With this simple matrix, I can calculate how a customer will migrate in the next year.  Look at average customers.  If I start with 1,000 average customers ...

• 1000 * 0.40 = 400 will become good customers next month.
• 1000 * 0.40 = 400 will still be average customers next month.
• 1000 * 0.15 = 150 will become poor customers next month.
• 1000 * 0.05 = 50 will not be active, and will leave the simulation next month.

This process is repeated for each row, giving us the count of customers starting next month in the simulation.

This process is repeated, month after month after month, yielding simulated results for your business over time.

This is a 4x4 example.  My methodology utilizes a 1,000,000,000 x 1,000,000,000 matrix ... the matrix takes no extra work to program in a computer than a 4x4 matrix used in our example (though many of my models work on a 500 x 500 matrix, FYI).

I append 12-month spend values across channels and merchandise categories, yielding simulated sales totals for the business.

See, this doesn't have to be terribly difficult.  And yet, simulated results yield discoveries that are not easily obtained via normal queries.

Yes, we need to execute e-commerce simulations.  We're decades behind other industries.  The time is now.  Get busy!

Dear Catalog CEOs: Looking Ahead

Dear Catalog CEOs:

Who do you look to, when trying to conceive what the future might look like?

We have research organizations, folks who tell us that "77% of website visits will be on mobile devices in 2016".  Might be true.  Anybody can conceive a future where more people are using mobile devices.

Few people seem to be able to articulate what that metric means for a business, like, say, a catalog business.

My favorite example of this takes us back to January 2000, at Eddie Bauer.  I created a simulation tool that forecasted the sales trajectory at Eddie Bauer between 2000 and 2005.  I had three key points in the presentation.

1. Online sales would surpass call center sales early in 2003.
2. When online sales became more than 50% of the direct channel total, both customers and employees would perceive the online experience differently, changing expectations both within the company and outside the company, causing all sorts of in-house employee arguments.
3. Call center sales would skew to older customers who liked the catalog, causing a disconnect in marketing strategy (i.e. catalog productivity suffers, the products that still work are preferred by older customers, thereby further alienating younger customers, thereby driving catalog productivity even lower).

I presented the findings to Eddie Bauer Management and Spiegel Management (at the time, Spiegel owned Eddie Bauer).  My comments elicited one response.

• Laughter!  Lots, and lots, of laughter.

They found my imagination curiously humorous:

• "Online sales won't surpass e-commerce sales for a decade."
• "Customers love catalogs, they find the e-commerce experience on a 56k modem to be a clunky and untrustworthy."
• "Catalogs tell better stories than websites tell."
• "Are you really expecting email to matter?  Email is spam."
• "Retail customers cannot carry a website into a store, but they sure can carry a catalog into a store."
• "Online customers are disproportionately male, but our sales are disproportionately female, so your logic doesn't make any sense."
• "Catalogs are the driver in a multi-channel experience, so there will not be a disconnect in the age of customers across channels."
• "Your forecasts have a high margin of error, so we shouldn't trust them, right?"
• "What if we listen to you and you are wrong?  Then what?"
• "What could possibly make up the sales lost if catalogs are reduced?  Are you suggesting customers will voluntarily click on a bookmark and visit a website without being prompted by marketing?  Ha!"
• "Look at Amazon.  They're an online brand that is about to go out of business.  They don't have a catalog to drive customers to their website, in fact, outside of low prices, they don't have any way to drive traffic to their website.  Researchers say they only have enough cash to make it to the end of 2001.  They're finished, multi-channel bricks 'n clicks strategies are the future."

Hard to argue with the logic, back in January 2000.

Hard to look at those comments in 2012 without wanting to yell "WAKE UP" at those folks back in 2000.

Which is why I asked you what information/advice you pay attention to as you look ahead? We actively shape what 2016 will look like, based on our responses to business challenges in 2012.

Do you have tools that allow you to see what 2016, 2017, or 2020 will look like?

Does your Executive Team have an active imagination?  Do you allow them to fertilize their imagination without being attacked by the kind of small thinking outlined in the quotes listed above?

Do you run various simulations to aid your ability to imagine the future?

Thoughts?

Multiple Touchpoints

Multiple touchpoints (frequently referred to as multi-channel or omnichannel) happen when the customer is in a state of transition.  And when a customer is in a stage of transition, those who manage old-school channels try to stitch together a version of the future that includes the old-school channel.

If you actually analyze your own customer behavior, you'll see a couple of interesting trends.

• Old-school multi-channel transition is largely over ... customers shifted from catalogs and call centers to e-commerce and search and email.  55+ rural customers held on to old-school channels, while 35-51 year old customers moved to e-commerce, search, and email.  This transition largely happened between 2002 and 2008.  It is over.  Run a migration probability table, it will tell you the truth!

• Classic e-commerce transition is beginning ... customers age 19-35 are transitioning from e-commerce, search, and email to local/social/mobile.  Because we measure this in aggregate, it looks like "everybody" is making the change.  Not true.  Overlay demographic data on top of these trends ... omnichannel is much more about 19-35 year olds moving away from e-commerce/email/search than it is about everybody doing everything.  We're just measuring the trend incorrectly.

This "omnichannel" or "multiple touchpoint" transition will happen for several years.  Because research organizations, trade journalists, and bloggers don't have access to data within companies, they will report on overall trends, not segmented by demographic cohorts.  Their observations will suggest that "everybody" is doing "everything".

Not true.

Dig in to your own data ... perform demographic overlays on your customer file ... and observe unique trends.  Run a migration probability table!

• Judy is largely sticking with old-school tactics, but will dip a toe in e-commerce.
• Jennifer prefers classic e-commerce, but will dip a toe in mobile/social/local.
• Jasmine is transitioning to newer channels.  When you hear media buzz about omnichannel, think Jasmine.

Filtering Signal From Noise: Big Data

In a "Big Data" world, we're told that we need to collect all sorts of data from all sorts of sources, yielding an "omni-channel" view of the world.

That may be true.

Now, I want for you to watch this time lapse video.  Essentially, one image is taken every twenty seconds during the course of the day ... in other words, the majority of the data has been stripped out of this video.

Watch.

Artistically, the video is interesting, right?  But more important, look at what happens to our understanding of that day when we strip out the vast majority of information.  By removing data (not by adding data), we are left with a unique story to convey.

The same thing happens with that hyped-up fad known as "Big Data", doesn't it?  We spend all of our time trying to combine data from different sources, so that we can find nuggets of actionable insights.

Now, sure, a Big Data advocate would say that you could collect all of the data, and then just accelerate the data so that the end result is the same as the video above.  Have at it.

But what would happen if we do the opposite?  What happens if we strip out all of the junk, the noise, so that we're left with only the good stuff, the "signal"?

Food for thought.

Are My Customers Permanently Switching To Mobile?

We all lived through the transition from old-school direct marketing to e-commerce.

1. E-commerce was a fad (customers tried it, then went back to old-school direct marketing).
2. E-commerce became mainstream (customers switched, in large numbers, and did not go back to old-school direct marketing).
3. E-commerce became boring (customers returned to normal behavior, just in a new channel, with some customers choosing to not make the switch).

We're beginning the same process with mobile.  While the pundits hound you about the myriad ways your business is stuck in the stone ages (e-commerce), your peers are doing research to see whether we're in (1) (2) (3) above.

It's easy to see if we're in (1) (2) (3) above.  Go back to our Multichannel Forensics framework of 2007, and apply it to e-commerce vs. mobile.

Query:  Capture all customers who purchased via e-commerce or mobile between November 2011 and October 2012.  Within this audience, tabulate how many customers purchased via e-commerce or mobile in November 2012.  Then, we calculate the classic website (e-commerce) and mobile index metrics (remember, the migration probability matrix is the probability of buying from a channel in the future, divided by the probability of buying in the future).

This table shows what happens when customers are trying mobile, but are not adopting it as a primary channel.

As you can see, customers who purchased via mobile last year switched back to e-commerce.  Customers are not comfortable making the switch.

Look at this table.  This is what it looks like when customers are getting ready to make the switch to mobile --- website customers are not defecting to mobile, but mobile customers are not switching back to the website.

Remember, an index > 20% means customers want to switch.  When the table looks like this, mobile has taken hold.  Mainstream customers are not willing to make the switch, but mobile advocates have switched, and are not as likely to go back to the e-commerce experience.  This is such a key transitional phase ... it has to be measured and understood.  When this happens, you make organizational changes ... you move the focus from e-commerce to mobile, even though e-commerce is where the sales still happen.  Your customer is making the change, at this point.

When your query results look like this, customers have made the shift, and mobile is about to become the dominant channel.

Mobile customers have moved beyond e-commerce, they are unlikely to switch.  Conversely, e-commerce buyers are migrating to mobile.  When your query looks like this, mobile is truly your future.

Which of the three scenarios fits your business?  It's easy to run the query.  Your analytics expert, digital analyst, or web analytics professional should be able to pound out this query in 15 minutes.  Run the query this afternoon.  Share the results with your Executive Team.

It is not hard to understand where your customer is in the mobile transition.  Stop listening to trade journalists and bloggers, folks who don't even have access to your customer database.  

Analyze your own data, and make your own decisions!!

Dear Catalog CEO: Your Target Audience

Dear Catalog CEOs:

In 1994, the target audience for catalogers was "everybody".  Good times, indeed!

Back in 2008, I ran across a curious finding, when analyzing the most responsive catalog names.  I was used to finding responsive names in New England and the Midwest.

I wasn't expecting what I learned next.

After mapping the most responsive catalog names for numerous clients, I noticed that urban and suburban areas had highly valuable customers, just not highly responsive to catalog marketing.

Between 2008 and 2010, demographic overlays illustrated another interesting trend.  The most responsive catalog buyers were getting older, significantly older in fact than the rest of the customer file.

You can validate each finding with your own customer database.  Run the queries, you're likely to observe similar findings.

So here, at the end of 2012, we know two things.

1. Catalog response is greatest among rural, 55+ customers.
2. Tremendous profit opportunities existing by mailing even more catalogs to the responsive audience, while greatly eliminating catalogs among customers not in the target audience.

Mail/holdout tests frequently validate these findings.  Urban customers under the age of 45 tend to purchase from your e-commerce website at high rates, even when catalogs are discontinued.

As you put together five year business plans, keep the image above nearby.  Develop a marketing plan for each cell in the grid!