Dear Catalog CEOs: Do Multiple Channels Yield More Orders?

Dear Catalog CEOs:

One of the popular arguments of our time is the "multichannel/omnichannel customers are more valuable" argument, trotted out with increasing frequency by vendors and trade journalists who have a vested interest in convincing us that we can seemingly grow customer frequency infinitely.

This is a graph of the average number of orders per buyer per year for a "multichannel business".  The graph starts in 2001 and goes through the end of 2012.  Think how few channels existed in 2001.  Think how many channels exist at the end of 2012.

What do you observe?  The customer places the same number of orders per year, year after year after year - regardless of the explosion of channels between 2001 and 2012.

Run the analysis for your business.

You're going to learn that your customers have a natural rhythm ... they place the same number of orders per year (within a band) ... this order rate is directly correlated with the annual repurchase rate of your twelve-month buyer file.  These numbers do not change, they are fundamentally tied to the merchandise you offer (i.e. groceries are needed weekly, gifts are needed infrequently).

When you learn that this is the way the world works, a few things become painfully obvious.

1. Unless customers buy merchandise more than 4 times a year, loyalty programs are largely futile.
2. Customers who buy more than 4 times a year touch many channels - not because of the channels you offer, but because of the purchase frequency inherent in the merchandise your offer.
3. For 70% of businesses, the main driver of future success is low-cost customer acquisition programs that are maximized.

Run the analysis for your business.

Then craft strategies that align with the natural purchase rhythm of your business - and remember, the natural purchase rhythm of your business is determined by the merchandise you offer, and the frequency with which your customer needs your merchandise.

Item Profit And Loss - By Advertising Channel

You probably already run profit and loss statements for each item in your merchandise assortment, based on the ad costs of each channel that generate sales for items ... right?

The first item was featured in catalogs, and as a result, generated more total volume that the second item.  However, the second item was popular enough to generate demand without the aid of advertising - hence, the strong online demand component of this item.

You run a profit and loss statement for each item, subtracting catalog ad cost, email ad cost (which is virtually zero), and paid search ad cost.  The second item, after subtracting ad cost, is far more profitable than the first item.

You run this table for each item in your assortment, on a quarterly basis, right?  If not, click here to contact me via email to get this table run for you.

Merchandise Survival - Productivity Bands

When you're evaluating the success of your merchandising team, be sure to create what I call "productivity bands".

For instance, you might band items as follows:

1. Annual Sales of $100,000 or Greater.
2. Annual Sales of $10,000 to $99,999.
3. Annual Sales of less than $10,000.

Then, measure the survival of items within each band.

1. $100,000+ Survival Probability = 88%.
2. $10,000 - $99,999 Survival Probability = 46%.
3. $1 - $9,999 Survival Probability = 23%.

You've got interesting information, now.

Ask your merchandising team why 12% of high-selling items were discontinued.

Ask your merchandising team why 23% of low-selling items were carried forward.

Merchandise Survival

When you introduce a new item, how long can you expect the new item to survive?

A year?

Six years?

Businesses that introduce items that survive the first year at increasing rates tend to be healthy.

Businesses that consistently increase the rate that first-year items survive to a second year tend to be healthy.

In our example, 41% of first-year items are discontinued, they fail to survive to a second year.  There are three metrics to keep track of, over time.

1. Does the first-year item survival rate increase, or decrease, over time?  Hopefully, it increases.
2. Does the number of first-year items increase, or decrease, over time?  Often, I analyze businesses that struggle to produce enough productive first-year items, and this hurts the business, long-term.
3. Does the business generate enough first-year items with high levels of productivity?  The healthiest businesses tend to observe increases over time.

Give me a holler (click here) if you'd like for me to perform a Merchandise Forensics analysis for your business.

Big Data

Maybe a quarter of the questions I get these days are about what some call "Big Data".  For a brief primer, please refer to Wikipedia's definition of Big Data (click here).

There are two distinctly unique aspects to what the pundits call "Big Data".

• Technology.
• Applications.

I won't focus on Technology.  Rest assured that large vendors will develop slutions that promise to save the world.  You'll purchase the solutions, and you'll achieve varied levels of success ... just like you've been doing since we moved from mainframes to PCs in the late 80s.

I focus on Applications.

There are at least four key Application concepts to pay attention to.  They are:

1. Complex Adaptive Systems.
2. Dirty Algorithms.
3. Hyper-Optimization.
4. Brand Interaction.

Complex Adaptive Systems (click here):  This is what we fail to understand about our world.  Things connect, and they interact with each other, often yielding unpredictable outcomes.  In the catalog world, cataloger interaction with co-ops is representative of a Complex Adaptive System.  Catalogers volunteered customers to the co-ops, co-ops used algorithms to redefine the names, and then resold the names back to catalogers.  There are many participants in this system, dependent upon each other.  Their interactions yield unpredictable and unusual results (i.e. co-ops spinning 55+ customers to catalogers, accelerating the evolution of catalogers).

Dirty Algorithms:  This is my term, and it will be the bane of our existence!  Dirty algorithms seek to maximize the profitability of a portion of a Complex Adaptive System, without understanding how the Dirty Algorithm soils the entire Complex Adaptive System.  Example?  Easy!  Credit Default Swaps and their role in the meltdown of the global economy in 2008.  When a financial institution buys insurance to "spread the risk" of an investment, the financial institution is inserting a Dirty Algorithm into the Complex Adaptive System.  In the "Big Data" world, companies will routinely insert Dirty Algorithms into Complex Adaptive Systems.  9 times out of 10, this will not be done with malice, but rather, ignorance of how Complex Adaptive Systems work.  1 time in 10, this will be an act of pure evil.  We won't know the difference, we'll just be cleaning up messes all the time.

Hyper-Optimization:  We're at least a decade in to the era of Hyper-Optimization, and thus far, the results have not been pretty.  The best example of Hyper-Optimization happens in web analytics - earnest, honest, and well-intentioned analysts seek to increase conversion rates.  They take friction out of the system, spending time, resources, and money improving conversion rates, not realizing that the actual behavior exhibited by customers does not change ... that in reality, the web analyst caused a customer who visited the website 4 times before a purchase to visit 3 times before a purchase.  When the underlying behavior does not change, we are Hyper-Optimizing ... changing an outcome that does not fundamentally change the behavior.  This happens when we measure the wrong attribute.  If the web analyst measured annual frequency and annual repurchase rates, the web analyst would not Hyper-Optimize a meaningless outcome.  Email subject lines also fall under Hyper-Optimization ... here, marketers realize that conversion rates won't increase unless 20% off plus free shipping offers are provided.  The problem in this form of Hyper-Optimization is that the email marketer only attracts discount buyers, further fueling the need for future discounts.  If this behavior continues, the email marketer is no longer engaged in Hyper-Optimization, but rather, has introduced a Dirty Algorithm into the Complex Adaptive System.  Along these lines, Cyber Monday is the most disappointing version of Hyper-Optimization, whereby online brands now offer 30% off plus free shipping to yield the best final Monday of November in history, never minding that sales are depressed in the three weeks prior to Cyber Monday to wait for the discount.  Hyper-Optimization is a direct outcome of terrible measurement practices.

Brand Interaction:  Here's where most of us enter into our relationship with Big Data.  Most of us will treat Big Data as a glorified form of Campaign Management.  In Campaign Management, actions were linear and additive.  We mail 100,000 catalogs, we get $500,000 in demand ... we send 1,000,000 email messages, we get $200,000 in demand ... we buy 10,000 clicks for $0.50 each and we get 300 orders ... Cause and effect.  This is the world most of us honed our marketing skills in, in the 1980s and 1990s, a pre-Google world.  The reality is that we've always operated in a Complex Adaptive Ecosystem (called "the economy"), but we didn't have the data to help us understand the truth.  Most of the Big Data hucksters will operate in this realm, promising real-time decisions that dramatically boost profitability.  What they'll be doing, however, is a simple transfer of demand, from one party to another.  Yes, on a macro-economic level, growth can happen.  But by and large, on the level we deal with, we're trading demand among players.  Big Data solutions providers will simply push demand back and forth between those buying (or not buying) solutions ... and in some cases, will, by accident, interject a Dirty Algorithm that will cause all sorts of problems, or will Hyper-Optimize (pushing demand out of certain windows, into others).

Your Job?  Be smart, and I mean that with all honesty.  Most Big Data solutions will sound very seductive, on a Campaign Management / Brand Interaction level.  Your job is to ask solid questions, as you try to understand how a Big Data solution interacts on a Complex Adaptive System basis.  Are you being sold a Dirty Algorithm?  Who is demand being transferred from?  Are you simply Hyper-Optimizing a situation without yielding long-term growth?  As an example, remember that every time you use Dictionary.com, more than 200 cookies are placed on your computer.  Your simple level of inquisition at Dictionary.com results in hundreds of businesses harvesting information, pushing your inferences into the Complex Adaptive System called "Marketing", with Dictionary.com obtaining profit.  Those companies will attempt to influence you via Brand Interaction, in the form of Campaign Management.  You need to learn how this impacts you as a customer, and how it impacts the company you work for.

Go beyond the hype.  Study Big Data within the context of Complex Adaptive Systems, Dirty Algorithms, Hyper-Optimization, and Brand Interactions.  You'll find that Big Data is far more interesting at this level than what you read about in trade journals.

Dear Catalog CEOs: Sixth Anniversary Of MineThatData

Dear Catalog CEOs:

I started MineThatData six years ago this week.

Wow.

Show of hands ... how many of you thought I'd make it six years?  Most of you?  Good.  Because when I told folks I was starting my own business, the feedback was interesting.

• "Good for you, you're starting your own business, well done! ........ You said you're going to market your services with a blog?  Oh boy.  That's not going to work ........ What you do mean, you're going to give your methodologies away, for free, on your blog?  How does that work? ........ Wait a minute.  You're saying you're going to share everything you know for free, and then companies will pay you to do the very thing you just taught them to do on your blog? ........ Kevin, you know that nobody reads blogs, right?  People read MultiChannel Merchant, not you! ........ What do you mean, you'll build an audience, one subscriber at a time?  Tell me how you're going to do that, are you going to take out ads in DMNews? ........ Wait, you're not going to pay for marketing, people are just going to magically find you via word-of-mouth, or via Google?  You almost sound like one of those social media experts ........ You'll be working for the co-ops in a matter of months, they offer health insurance, your certainly not going to pay $1,500 a month for the right to enjoy a $3,000 per year co-pay, that's terribly expensive, especially when you're giving all of your knowledge away for free. ........ What services are you going to provide, anyway? ........ Multichannel Forensics?  Who needs that?  We already know customers who buy from multiple channels are worth eight times as much as single channel customers are.  Look at companies like Circuit City, they offer buy online and pickup in stores.  They have a multichannel approach that will crush Amazon. ........ Wait, did I just hear you correctly?  Did you just say you're going to tell catalogers that they can mail fewer catalogs and be much more profitable?  Oh, you're a piece of work.  You're going to tell catalogers to be more profitable by mailing less.  That's not how the world works. ........ Kevin, Nordstrom discontinued a catalog and grew sales because they are a retailer with a one-hundred year heritage, that won't work for anybody else. ........ no, Kevin, it doesn't matter what you experienced there, it is irrelevant. ........ the co-ops tell us to mail more. ........ What do you mean, co-ops have a vested interest in telling us to mail more catalogs? ........ What do you mean when you say that someday the USPS might be insolvent and it won't be cost-effective to mail catalogs anymore? ........ No, nobody would ever threaten to take Saturday mail delivery away. ....... Did I just hear you correctly?  You're recommending that catalogers focus on acquiring new customers?  Don't you know that it costs eight times as much to acquire a new customer as it costs to retain a customer? ........ You have a 5-year forecast model that proves your hypothesis?  Sure you do!  Who are you, Carnac the Magnificent?  ........ Are you saying that if we keep retaining the same customers, we'll end up with a customer base that is heading toward retirement and that's a bad thing? ....... These ideas are nuts.  You'll be lucky if anybody hires you. ........ Wait, what?  You're saying you'll charge less than vendors or industry consultants?  That's your business model?  Give away all of your intellectual property for free, then for the handful of people who do want to pay for your services, you'll charge less than everybody else charges?  I've heard enough.  You're nuts.  I'll put in a good word for you with the co-ops when you're homeless in a year. ........ No, Kevin, housing prices always rise, go look at the historical trends and make a data-driven decision. ........ Ok, good luck, you're going to need it!"

After 2,311 blog posts, 323,000 unique visitors, 14,500 tweets, 100+ brands analyzed, six years, nearly 3,000 blog subscribers, 4,530 Twitter followers, 2 books, and 9 booklets, I'm still standing, better than I ever did.

Thanks to my clients.  You believed in me.

Thanks to those of you who subscribe to my blog.  You, too, believed in me.

Thanks to those of you who follow me on Twitter.  That's where we ferret-out new ideas.  It's been a priceless experience.

Three hundred and twenty-three thousand unique visitors to the blog.  That's not a trivial number.  I thought you couldn't make a blog work that gives ideas away for free?  70% of my business is sourced from the blog.  Think about that, for a moment.

If I am going to make it six more years, I'd appreciate some feedback.  What do you need to learn about or understand?  What services should I provide for you?  What content do you want to read (for free, of course)?  After three million page views, we'll need to innovate going forward, so please use the comments section to provide feedback.

I appreciate your patronage.  I do not take your loyalty for granted, not for a moment.

Thanks,

Kevin

Work From Home

Maybe you heard ... there's a controversy out there about working from home (click here), and (click here) for a counter-argument.

This is one of those arguments where 95% of people can align and sound "right".  On the surface, they are right.  People should be allowed workplace flexibility, and employees should not be held to a "one size fits all policy".  

And on the surface, the 5% of employees who represent Management and align with Yahoo are "right" as well ... it is perfectly reasonable to expect that work-from-home staffers perform at the same level as the poor souls who are forced to trudge into work each and every day.  I used to manage 24 people, most of whom worked from home some of the time.  There were countless examples of folks doing great work, and folks taking advantage of me. I should have been allowed to expect everybody to give a fair effort, right?

This makes for great debate, it drives page views in trade journals and media outlets.  It's fantastic for Facebook and Twitter.

There isn't a right or wrong answer to this topic, just strongly worded opinions that do not make a difference.  We don't work at Yahoo, do we?  How could we possibly know if their choice is right or wrong for their unique circumstance with their unique employee base?  Imagine somebody from Yahoo waltzing into your office and beating up your corporate culture?  Who are they to know what's right for you and your company and your employees?

Have you ever listened to members of sport teams that win championships?  There are phrases that come up repeatedly.

• "We have great team chemistry."
• "We love each other."

How many of you feel this way about your co-workers?  Your boss?  Your CEO?  If you're in Management, do you feel this way about your team?

The issue isn't whether somebody is able to work from home or not.  The real issue is, of course, whether you can trust your employees/co-workers, and vice versa.  Because when people trust each other, there's the potential for team chemistry.  And when you have great team chemistry, all sorts of positive things can happen.

In a digital, sound-byte driven world where everybody gets to have an opinion, few people know how to foster great team chemistry, me included.

But if you do foster great team chemistry, you don't have to worry as much about policies and procedures.  Employees become accountable, they support each other.  The work, then, is more likely to be excellent, and the company more likely to thrive.

That's really what we're talking about ... how best to create a great culture, one where everybody trusts each other and performs at a high level, an accountable level.  Hard to solve that problem, no doubt.  But it is at the core of the work-from-home "conversation", and in an increasingly digital and less analog world, we seem uncomfortable to talk about it.