April 30, 2015

Avoid Excessive Optimization

I'm tired of "optimization" ... and I have a thirty-year track record of using statistics to optimize everything! Absolutely tired of it.

See this character here? That's an analyst ... an analyst telling you that what you do doesn't work. I was an analyst, back in the day. It was fun to point out that stuff didn't work. I spent more energy pointing out what everybody did wrong than I spent praising everybody who did well. What a mistake. What a lost opportunity.

Optimization can paralyze a business. Heck, I've paralyzed businesses. When I got to Nordstrom Direct, we were losing more than thirty million dollars a year, so something had to be done. But immediately, I paralyzed the organization. I told everybody how many catalogs we would mail. I told everybody how many pages there would be in a catalog - when somebody wanted to add pages, I said "no". When somebody wanted to add a merchandise category, I said "how will it perform", and when the answer was "not great", I said "no". My team determined the version (out of 5-10 per campaign) of an email message a customer recevied ... and when my boss told me that my algorithm was wrong to send her merchandise that a fifty year old loves, I essentially said "too bad".

Worse, though I told everybody what to do and how to do it, I demanded the opportunity to have a "testing budget" where I got to play all I wanted, without having to optimize the efforts of my team. I held everybody else accountable - but I got to play and have fun. #Optimization!!

This last point is really important. The idea of a "testing budget".

Let's say that you're going to "hold your merchandising team accountable" (good luck with that). Well, let's say that there is a merchandise category that is forecast to do $10,000,000 in sales, and is forecast to generate $2,000,000 of variable profit / contribution (before subtracting fixed costs). Say you're going to spend $2,000,000 marketing this merchandise category. At the start of the year, work with your finance team to "build a cushion" into the budget. Forecast $10,000,000 in sales on $2,300,000 of marketing cost, for $1,700,000 profit. This gives your merchandising (and marketing) team an opportunity to play with different strategies, without having to be "held accountable" or having to "optimize" anything.

With $300,000 of testing opportunity, the merchandising team can feature a series of merchandise not expected to perform well on key landing pages. The merchandising team can get these items in various email campaigns. The merchandising team can get some of these items prime catalog real estate without ruining the integrity of the catalog marketing program.

Make sense?

Give your merchandising/marketing teams a "budget cushion". This allows you to optimize the business, but it also allows everybody the freedom to test different ideas without the punitive "thumbs down" featured in the image at the start of this post.

April 29, 2015

airbnb Commercials

Have you seen the TV commercials? Here's one (click here if you don't see the image).


There's two lines of reasoning out there. There's the folks who get paid by saying silly things that generate page views that ultimately attract advertisers. They tell you that television is dead (and it may be dead ... what happens when cable is unbundled and ESPN cannot charge every household in America $5 a month ... or what happens when 90% of the public chooses to not get Fox News or MSNBC because they find political rhetoric offensive ... how does Fox News / MSNBC survive when 22 year olds are no longer subsidizing programming designed to agitate 65 year olds?)

The second line of thought is different. It's not public. It's common sense business people working in an evolving ecosystem. Advertising is an evolving ecosystem, make no mistake about it. The ecosystem changes all the time. One day you have a free audience on Facebook, the next day you're paying Facebook pennies to speak to the free audience you cultivated on your own, and the day after it is too expensive to talk to your audience on Facebook, causing you to shift advertising dollars to (gasp) television.

This doesn't mean that television is back, much in the same way that it doesn't mean anything that JCP mailed one (1) catalog. It's just an evolving, bubbling ecosystem, where smart people are looking for inexpensive solutions to reach people. The smart people are first, they get any (if any) benefit that exists, and then the masses come, driving up prices, getting less for their dollar. When the masses arrive, the smart people have already moved on to something different. That's why they are smart people!

In my projects, I see those who analyze, and those who "do". Those who analyze are always late to the party. They worry about getting an attribution algorithm just right. They spend four months evaluating attribution partners. They argue about the right way to define things. Those who "do" try television. They just try it. They don't even bother to see if sales should be attributed to television, they just move, constantly. If television works, sales increase, and every person in the company can plainly see it ... no need to parse the solution out in a complex mathematical attribution algorithm that may well be wrong in the first place, an algorithm that ultimately feeds a larger data-intensive ecosystem that truly benefits the algorithm creator more than the algorithm user.

Hard work is coming, folks. Twenty years ago, the internet was new and exciting and new business models were created upon it. Growth was easy. Key innovations since has been less effective at driving sales increases (search, social), requiring the internet to cannibalize offline activity to grow. We have no idea how mobile will turn out. So until we see how mobile turns out, there's a period of intense, hard work required out of all of us, hard work that won't necessarily yield positive results.

Here's an example of the hard work that is coming ... Uber delivering for companies like Neiman Marcus. Will Neiman Marcus experience a sales increase? Highly unlikely. Are the testing something? Are they following the herd?

Do not follow. Following is expensive.

Do not wait. Waiting is expensive.

Try something. You need new customers.

Try something different. When it doesn't work, move on, don't bemoan the fact that it didn't work, celebrate that your have a culture willing to try things. If Airbnb tries television, and they're lauded by the digerati, shouldn't you try something different as well? Try.

April 28, 2015

What You Show A Customer Influences Future Customer Activity

Take a look at this email marketing campaign.


Are the top two pair of shoes sold out? Who knows? Who cares?

What we know is that this is a marketing tactic designed to get you to buy either of the bottom two pair of shoes, right?

What happens after the customer purchases one of the pair of shoes? Well, World Soccer Shop generates profit. Nike generates profit. The email marketing team gets closer to earning a bonus. The merchandising team gets one step closer to earning a bonus. The analytics team measures the future value of the customer. Vendors attribute the order independent of bonus structure.

But what happens to the customer? What does the customer purchase next?

Few people know the answer to that question, and for good reason. Almost nobody measures the big 'ole bubbling ecosystem known as our "brand".

In project after project, the act of causing a customer to buy a specific item changes the trajectory of the customer ... usually for good ... sometimes the outcome isn't so good ... but almost always, the impact is not measured.

Measure the impact. Contact me (kevinh@minethatdata.com) if you cannot do the work yourself. It's important work, no doubt about it.

April 27, 2015

Email Marketing Changes Response Patterns

Alright friends, take a look at this one. Here's a customer who last purchased online 7-9 months ago. Our prediction, for the next year, is as follows:



The customer is pre-disposed to buy via online and search channels, going forward. Nothing wrong with that. But your email marketing team, well, they care, they care deeply about getting the customer to buy via email marketing.

Let's try something. Let's pretend that the customer was going to buy online for a second time. Here's what the customer trajectory would look like:



Nearly eight in ten subsequent purchases would happen via online/search, if the customer were allowed to buy online. However, your email marketing team want the next order to happen via email marketing. They crave it. They're going to make it happen! And when they make it happen, they change the trajectory of future customer behavior.




There are two things worth noting, in this example. First, when evaluating share of future spend, we observe that share has been shifted - away from the online channel, more into the email channel. Going forward, email is going to be more important to this customer.

Second, and more important - look at the individual row-based repurchase rates by channel ... what do you see? Clearly, the customer is more valuable now. Email, in this case, caused the customer to simply become more valuable to the business.

For this business, you want the email marketing team aggressively going after customers, because when the customer buys from email instead of the online channel, the value of the customer simply improves.

Your business is a big, bubbling ecosystem. You are not "omnichannel", you are an ecosystem ... and within the ecosystem, there are good choices and there are bad choices. You want to pursue good choices. In this case, email represents a good choice.

Contact me (kevinh@minethatdata.com) for your own, customized triggers & grids project.

April 26, 2015

How Does The Catalog Influence Sales? 2015 vs. 1995 ... A Cataloger Must Read

Ok, folks, this is becoming a really big deal. In nearly every project I work on, the way customers, channels, and merchandise all interact has changed, and the changes are not well understood. So let's explain the changes.

Let's pretend you have three product lines ... WIDGETS ... WUDGETS ... and WEDGETS.

Let's pretend you mail one catalog a month ... a 96 page catalog.

In 1995, your catalog merchandise assortment was as follows:
  • 64 pages of WIDGETS.
  • 32 pages of WUDGETS.
  •   0 pages of WEDGETS.
In 1995, based on your advertising / merchandising strategy, here's what you sold:
  • $5,000,000 of WIDGETS.
  • $2,500,000 of WUDGETS.
  • $0 of WEDGETS.
  • $7,500,000 total.
Do you see what happened there? Your sales were roughly similar to the page distribution offered. You didn't offer any WEDGETS in your catalog, and you didn't sell any WEDGETS. 

A generation of catalog marketers grew up with these rules. The rules became hard-coded. If you are in a meeting, and you are dealing with a catalog professional older than sixty years old, the hard-coding becomes problematic, doesn't it?

In 2015, the world is fundamentally different. Let's review what I see across all of my projects.
  • 64 pages of WIDGETS.
  • 32 pages of WUDGETS.
  •   0 pages of WEDGETS.
But in 2015, sales happen two ways (assuming you don't have a retail store ... if you do, then the story gets even more interesting). First, we have sales attributed to the catalog.
  • $2,300,000 of WIDGETS.
  • $1,300,000 of WUDGETS.
  • $300,000 of WEDGETS.
  • $3,900,000 total.
What do you see in that summary? What is different? Discuss!

And then, second, we have sales that happen online, without the aid of a catalog. Yes, a ton of sales happen even if catalogs are not mailed. Your mail/holdout tests prove that this happens.
  • $1,500,000 of WIDGETS.
  • $1,500,000 of WUDGETS.
  • $600,000 of WEDGETS.
  • $3,600,000 total.
What do you see in that summary? How did sales vary?

So we add catalog-driven sales to pure online marketing and organic sales, and we get the following story for 2015:
  • $3,800,000 of WIDGETS.
  • $2,800,000 of WUDGETS.
  • $900,000 of WEDGETS.
  • $7,500,000 total.
And to repeat, here is the story that's been hard-wired into the minds of many catalog professionals:
  • $5,000,000 of WIDGETS.
  • $2,500,000 of WUDGETS.
  • $0 of WEDGETS.
  • $7,500,000 total.
Do you understand what happened there? Several things happened ... several moving parts, right?

In 1995, if you didn't feature an item, you didn't sell the item. In 2015, if you don't feature an item, you will still sell the item online ... and I know, I know, there's going to be an outcry of anger from many catalogers ... "Kevin, you're wrong and I'm going to tell you why, we have internet-only items and they don't sell at all, so you're an idiot." Might I posit that you aren't featuring internet-only items properly? How often are they featured on the home page? How often are they featured on key landing pages? How often do they appear in email campaigns? Do you feature those items in magazine ads? Television? Pop-up stores? Amazon? When you bury items on the website, of course the items won't sell ... but they aren't selling because they aren't in the catalog ... they aren't selling because you aren't even trying to sell them!!!!

In 1995, 100% of the sales happened because you mailed catalogs. In 2015, 50% of the sales happen because you mail catalogs (20% of those catering to a 40 year old customer, up to 70% for those catering to a 70 year old customer).

In 1995, you had one audience ... a catalog audience ... and that audience had a consistent merchandise preference. In 2015, you have multiple audiences, different audiences by channel (be very skeptical of the omnichannel crowd's suggestion that you have one audience ... look at sales by channel ... sales are fundamentally different by merchandise category ... strongly suggesting different audiences). Why not serve multiple audiences with different merchandise assortments?

In 1995, WIDGETS dominated, capturing 67% of sales. This was because there was one audience and one advertising strategy. In 2015, WIDGETS capture less than 50% of sales. This happens because you have multiple advertising strategies, varied customers across channels, and featured WIDGET items in the catalog drive WEDGET sales online. When I worked at Nordstrom, we learned that you didn't have to feature MENS merchandise in a catalog ... ever ... mail/holdout tests by product category showed that featuring WOMENS items in the catalog caused customers to shop MENS online and in the store. Have you performed a comparable analysis for your business?

In 1995, the catalog merchandise assortment determined everything. In 2015, the catalog merchandise assortment may well be holding back the growth of your business, especially if your merchandising and marketing teams ignore the true merchandise assortment sold online without the aid of a catalog.

I see these trends in nearly every project I perform. The longer we hold on to our catalog heritage, the longer we stunt growth of merchandise categories that appeal to online buyers. We hurt the future by holding on to the past.

Does this make sense to you?

Do you have reporting to help you understand this dynamic?

Contact me (kevinh@minethatdata.com) if you want to better understand what is happening in your business.

April 24, 2015

Cabela's Campaign

Two things that need to be pointed out (click here):
  1. The article header says "amid strong sales" ... then the author buries at the bottom of the article the fact that retail comps were -1.3% (i.e. the opposite of strong). Though to be fair, currency changes do impact business.
  2. The ad agency running the "disconnect from mobile" campaign then encourages users to use mobile to share their "disconnect from mobile" experience with their friends.
#Sigh.

April 23, 2015

Your New Department - First 90 Days

Maybe you just took over a department? It's your first crack at dealing with ten or more individuals. You probably feel overwhelmed, like the new Director of Marketing in this image. But you're also optimistic and talented, like the new Director of Marketing in this image.

The first ninety days in this new job are the most important ninety days, make no mistake about it!

I took over a department at Nordstrom Direct, way back in 2001. On my very first day in the job, a pair of individuals entered my office, and asked me to fire a person. Within a half-hour, the person who these folks wanted me to fire entered my office, and asked me to fix the department. Who do you think I sided with? And what do you think those introductory statements said about each employee?

Make it clear to each employee you inherit that you are using week one to "evaluate and plan". You are not making any decisions that first week. You are getting to know people. Observe work habits. See who leads in meetings. Who gets along? Who hates other people? Who gets run over by other departments? Who produces accurate work, and who produces inaccurate work? Who produces, period?! Document everything.

Sit down with your Human Resources team, and ask for all performance reviews. Also ask for documentation about each employee ... stuff like interpersonal problems, office relationships, problems with the law, the whole nine yards. Some of it will read like an episode of Downton Abbey.

Then craft a plan.

Create annual goals and objectives for each employee. These are the goals and objectives you will use to evaluate each employee. Set expectations from day one, folks. Then, every Monday morning, produce a dashboard that shows how your department is progressing against your goals and objectives.

If you take over on a Monday, make sure you have a mandatory department-wide meeting the following Monday. Have somebody from Human Resources in the room - this sets a tone that you are dead serious about having a well-functioning department that works with honesty and integrity. It also prevents inaccurate gossip - you don't want the malcontents on your team to go out there and publicly lie about what was said in the meeting.

In this meeting, set expectations. Be firm. When I took over the most dysfunctional team I'd ever observed (Nordstrom 2001), I made it very clear what I expected. In my case, this is what I shared:
  • We are a family. We love each other.
  • When we disagree, we disagree internally. Publicly, we are a unified and confident team more than willing to defend the work of each individual in the department.
  • Say whatever you want in my office and get it out of your system.
  • We are here to serve.
  • We are here to generate a ton of profit (I then outlined goals and objectives and told everybody what work they would focus on for the next year).
  • We are going to work hard - no more three hour work days.
  • We pursue excellence and innovation.
  • Our work is accurate and trustworthy.
  • We do not have a political agenda.
  • If you are not on-board with this agenda, please go find a new job as soon as you can.
You'd think a message like that would be well-received.

You'd be wrong.

The message was received much in the same way a hive of bees would receive a dozen rapidly fired darts at their queen. Oh, the message created a buzz alright. One employee asked me to "make things like they used to be". Another quit. Then another. Then another.

After your first two weeks, you can begin the process of building the department in your image. Do not copy Steve Jobs, or your Mentor. Do it your way.

Pay attention to those who are not "onboarding", to use a term a former boss enjoyed using, and determine why they are not onboarding.

Each week, reinforce your message via your dashboard. Show every employee where the department stands against goals and objectives. Reward those who adopt your program. Overpay for talent now, because your compensation department will put the clamps down on you soon. Make it clear to everybody else that they are not meeting expectations. Hire folks who you trust, who are talented, and who implement your program.

Never waver.

Always communicate - and repeatedly communicate the same message. Your employees seldom hear what you want to communicate - so the same message said 137 times has a better chance of being understood how you want the message to be understood.

Once you get past your first ninety days, you've begun to put your program in place, and you'll learn quickly whether you are going to be successful or not.

But those first ninety days are critically important - the most important of any period. Make good use of your first ninety days.

April 22, 2015

Lousy Website Optimization

This problem keeps coming up in the projects I work on.

I create a model that predicts how many orders a customer will place in the next month.

Then, I create a model that predicts how many days (called "monthly visits" in this example) a customer will visit your website next month.

Finally, I segment customers based on the two predictions.

The lower left cell (green) represents customers who are unlikely to purchase, and are unlikely to visit. Notice that this cell has the second best daily conversion rate (purchases / days visited).

The upper left cell (orange) represents customers are are likely to purchase, but are unlikely to visit. Notice that this cell has the best daily conversion rate (5.5%). In many ways, this is the customer your visit-centric metrics are trying to optimize ... their conversion rate is good, but they don't use your website much.

The lower right cell (blue) represents customers who are not likely to purchase, but are likely to visit. You'd expect a lousy daily conversion rate, and you get precisely that (1.9%). Marketers HATE this cell ... they do anything they can (discounts/promotions/begging) to get this customer to purchase. You won't get this customer to purchase ... this customer likes to visit, to browse.

The upper right cell (yellow) represents customers who are likely to purchase, and who are likely to visit your site often. This is the dream customer - fully engaged with the site and very willing to purchase. What is the daily conversion rate for this segment? A paltry 2.1%. Your best customers have horrible conversion rates, in this example (happens all the time, folks).

Your staff will work overtime to get the customers in the upper right quadrant to purchase NOW!! Cart abandonment programs, programmatic approaches & retargeting, keyword bidding, discounts/promos in email campaigns. STOP IT! Just STOP IT! These customers already love your brand - they just love it so much that their visits overwhelm their purchases. You DON'T CARE that this customer has a low conversion rate ... so what?! Collect the profit.

Show of hands ... how many customers do you have that fit into each cell above? If you don't know the answer, send me an email (kevinh@minethatdata.com) and I'll get busy creating the cells for you.

April 21, 2015

Triggers & Grids: Combining Attributes

Let's look at an example ... in the table below, I illustrate Logistic Regression coefficients for the likelihood of a customer buying from email marketing in the next twelve months. The rows represent recency  ... the columns represent prior purchase channels.


Let's pretend we have a customer who has only ever purchased one time, and it was via email marketing, and the customer spent $100, and the purchase happened one month ago. For that customer, the probability of buying from email marketing in the next year is ... (-5.216 + 2.423 + 1.841*(100/1000)) ... we obtain a logit value of -2.519, which when transformed (EXP(-2.519) / (1 + EXP(-2.519)) = 7.5%.

Now let's pretend that the customer also purchased ($100) via Amazon in the past month ... the equation changes to (-5.216 + 2.423 - 0.031 + 1.841*(100/1000)) = -2.550, which is then transformed (EXP(-2.550) / (1 + EXP(-2.550)) = 7.2%.

Do you see what happened there? The very fact that the customer just purchased from Amazon actually caused the customer to become less likely to buy via email marketing. #OhBoy.

Each of our businesses possess customers who exist within an ecosystem. A very interesting ecosystem! We've been trained that each incremental purchase is good. Most of the time, each incremental purchase is good! But sometimes, odd dynamics occur. We need to understand odd dynamics.

In subsequent posts, I'll show you how these models can be used to combine attributes, and when attributes are combined, we can understand how customers behave and evolve as we promote different channels. The activity leads us to create different trigger strategies for various customers.

For instance, here are a set of customer attributes (the customer bought via the phone 10-12 months ago, spending $100), and the predictions by channel.


This customer has a 9.9% chance of buying via phone next year ... that's 60% of all outcomes. Now, look at what happens when the customer reactivates via Amazon ... becoming a 0-1 month $100 Amazon customer and staying a 10-12 month $100 Phone customer.


In the short term, the Amazon purchase helps catalog responsiveness ... trigger off a catalog, folks!

But not so fast. Let's let one more month pass ... now the Amazon purchase is 2-3 months ago.


Within just a month, the Amazon purchase helped phone response (catalogs), then hurt phone response (catalogs). Also, look at what happens to Search response in each grid ... low ... then high ... then higher. Yes, the Amazon purchase pushes the prior catalog-centric buyer online, where the prior catalog-centric buyer "engages" in online activities that will cost you even more money.

In other words, the Amazon purchase should trigger a catalog, and then, it is quite possible that you might cut back on mailings because response drops and paid search response increases, costing you more money.

So this is the stuff we're going to talk about ... your customers interact with your business in fascinating ways, yielding an ecosystem that is not well understood. By combining attributes in a Logistic Regression equation, we set off all sorts of interesting ecosystem findings, don't we?

April 20, 2015

The Customer Resume

Allow me to tell you a brief story. Let's go back to November 1990. Yes, 1990. I was in my first week of work at Lands' End. One of the aspects of my job (statistical analyst ... today that job would be titled a digital analyst or a data scientist or a growth hacker) was to audit my statistical models.

I submitted a request each Friday to print the customer purchase resume of one of every ten-thousand customers. Over the weekend, the customer file was scored, and when I arrived at work on Monday, a stack of printed customer records magically appeared on my chair. Every Monday morning, I thumbed through each record, hand-calculating that my scores were being applied properly.


The best part of the process was to watch how customer behavior evolved. Not overall customer behavior, but rather, the behavior of an individual customer. I can still remember customer # 1000004 ... her record was always on the top of the stack (no, I am not going to share her name with you, though I can still remember her name). She hadn't purchased in a long time, so every Monday there was drama ... had she reactivated over the past weekend?

Every marketing employee should be required to look at a customer resume. Every one.

What do you see when you look at the resume at the beginning of the post? Be honest. Study it. How does this customer behave? This isn't much of a "digital" customer, is it? Or a seasonal customer. But this customer is a very, very good customer, make no mistake about it. So enjoy this customer - quit demanding that this customer engage with you digitally. Collect profit. Ok?

By the way, there was a Friday back in 1992. I submitted my request to print one out of every 10,000 customers ... except I typed in 1 in every 100 customers. On Monday morning, our IT team, who clearly saw the request and knew I made a mistake, elected to initiate a "teachable moment" and dutifully printed the resume of one out of every 100 customers. They deposited the 40,000 sheets of paper in my office. You learn a lot when you audit your model scores, don't you?

Catalogers - Read This


This person talks about his startup and finding customers on Facebook. When performance (due to cost) struggled, he decided to go find new customers elsewhere.

The next time your favorite co-op shows even further reduced performance and takes your names for free and sells them to an online brand to help the online brand put you out of business, why not do something about it other than complain? Do what this individual did - try something different.

April 19, 2015

Triggers & Grids: Grading Strategies

Modern customer behavior is perceived to be much more complicated than it was twenty years ago. That may or may not be true.

Twenty five years ago, you created several hundred RFM segments and you called it good. Today, your three hundred RFM segments would overlay against twenty marketing channels, getting you ever closer to an infinite number of segment combinations. Is it any wonder folks want you to have a "one-to-one" relationship with a customer? It's become hard to segment the customer in a meaningful way.

It is perfectly reasonable to simplify the number of dimensions you deal with. I create simple models ... in the case above, a model for a product line called "Widgets", and a model for a product line called "Wudgets". The predictions for each model are reduced to five segments ... sort of like when you were in school ... A/B/C/D/F.

Then, I measure future "Widget" spend across the A/B/C/D/F grades for "Widgets", and A/B/C/D/F grades for "Wudgets". In other words, let's pretend that the customer is a prior "Widgets" buyer, but has never ever purchased "Wudgets". We'd look across the "A" row for Widgets, and then align with the "F" column for Wudgets ... the customer is worth $44.38 to the Widget merchandise category.

Say you have an email marketing campaign for Widgets ... you know that the customer must be worth at least $30 of future spend to generate a productive Widget email campaign. Even if the customer has not purchased Widgets in the past ("F"), a recent Wudget purchase might move the customer into A/B territory for Wudgets ... and when that happens, the customer has sufficient value to email the Widget campaign.

Better yet, you can build Wudget purchase intelligence into the Widget model ... and by doing so, you eliminate the need for the grid altogether ... you trigger/target based on the Widget grade.

April 16, 2015

Sports Commerce Session #2: Draft and Develop

In our second session, I describe how "draft and develop" strategies in sports are similar to "customer acquisition and customer retention" in e-commerce / retail. Click here if you do not see the video box below.


April 15, 2015

Incremental Value

Let's say I want to move 13-24 month 1x buyers to a second purchase.

How much might I be able to spend to make this happen?

In our table, the average 13-24 month 1x buyer is worth about $13.50.

In our table, the average 0-12 month 2x buyer is worth about $71.65.

Intuitively, you might say that you gain $58 of value, so you can spend a fortune.

Nope.

There are two things we need to know.
  • Actual repurchase rate of 13-24 month 1x buyers.
  • Incremental percentage that we will convert with incremental marketing activities.
  • Percentage of demand that flows-through to profit.
Let's pretend that the annual repurchase rate of 13-24 month 1x buyers is 10%. Let's also pretend that we can increase the 10% annual repurchase rate to 12% by performing various marketing activities. Finally, let's assume that 35% of demand flows-through to profit. We now have the pieces necessary to determine how much we can invest.


Look at the "Marketing Cost" line ... you could invest an additional $0.77 per customer, and if you can achieve a 20% increase in repurchase rate, the math yields more profit.

This is how a modern marketer can leverage lifetime value style metrics to improve business performance. In our example (assuming that we can truly get a 20% increase in repurchase rates by marketing more to a customer), we are able to grow the active 2x buyer file - and that's a good thing, right? These customers will spend a ton of demand next year.

Have your analytics team run this analysis for you today. If they cannot run the analysis for you, contact me (kevinh@minethatdata.com), and I'll gladly do it for you.

April 14, 2015

But A Recent Buyer Is A Good Buyer, Right?

For the most part, a recent buyer is a good buyer ... right?

Let's take a second look at the grid from last Friday.



This table measures direct-channel value for a business that has retail stores and an e-commerce website. So read down the 7-12 month direct recency column ... if the customer has never purchased from retail, the customer is worth $42.66 demand to the direct channel. But now, the customer (surprisingly) walks into the store and buys something. Everybody is happy! Everybody, that is, except for those who manage the website.

Why?

The value of the customer to the website went up, quite a bit, from $42.66 to $85.97. But the customer is nowhere near as valuable as a recent online buyer (between $141.77 and $303.42).

I know, I know, the omnichannel experts will tell you that this doesn't matter.

It matters if it is your job to forecast online sales, right?

It matters if it is your job to forecast online inventory, right?

It matters if it is your job to forecast online traffic, right?

The omnichannel movement is sweeping vital business details under the carpet. Omnichannel advocates don't care where the customer buys.

Your CFO cares deeply where the customer purchases ... where the customer purchases dictates future sales, and future sales require staffing and inventory and proper forecasting techniques ... and ... they require proper triggers/targeting strategies. In this case, the customer just bought from the store ... why send emails demanding that the customer take advantage of a free shipping offer when the customer just bought from a store?

Our future includes a full incorporation of purchases across all channels ... we encourage the customer to purchase anywhere ... but we react VERY DIFFERENTLY when the customer buys from a specific channel. The omnichannel thesis of "do everything digital" simply makes no sense in a world where we can clearly see how the customer will behave in the future.

Send me an email (kevinh@minethatdata.com) if you need assistance with grids and triggers.

April 13, 2015

Catalog Fact Check

Two examples requiring fact checks:
  1. Via the NEMOA website (click here for the article), you are told that co-ops "insist" that e-commerce brands contribute their names/transactions to the co-ops. "Insist" is an interesting word, because it gives pure online brands the opportunity to say "no", which they do (they tell me they say no), and when they say no, they get to rent the names you freely contribute - and the co-ops keep all the money. At least two co-ops sell your names to e-commerce brands. Ask a CMO at an e-commerce brand - fact check the issue for yourself! Then ask yourself why you are happy being ripped off?
  2. Via the print industry (click here for the article). The article misleading states that catalogs in the mail are down 84% since 2007 (the DMA says the total is about 40% ... so either the DMA is understating the problem or the print-centric article is overstating the problem, either way, you have a trust issue, don't you?), and then the author of the article blames you, the cataloger, for not having a strong enough presence on Capital Hill. We know the metrics are factually untrue. Catalogers struggled for two reasons ... the collapse of the global economy ... and e-commerce growth that rendered cataloging unnecessary for many customers. The reason is clearly not about having a strong presence on Capital Hill. My goodness.
There are way, way too many good, honest vendors, printers, paper reps, trade journalists, industry conferences, boutique consultancies, and independent consultants out there. Way, way too many good ones. Kind ones. Honest individuals. Folks who work nights and weekends to make sure your campaigns get out the door. Folks who work hard to make sure that there are good presentations at conferences. Their reputation should not be soiled by biased, misleading, inaccurate statements that protect the financial agenda of co-ops and/or printers.

April 12, 2015

The Masters

Some of you watched one of the four major golf tournaments during the past few days. It's labeled as "a tradition unlike any other". Yes, there's a marketing point coming ... for you ... so hang in there!

For me, the event signals the formal transition from winter to spring (unless you live in Northern New England). There's something special about putting winter in the rear view mirror, don't you think?

For those who have watched The Masters on network television for the past twenty-plus years, the event theme song is as cherished as the event itself. Click here if you don't see the box below, and enjoy Dave Loggins (yes, Kenny's brother, and if you don't know who Kenny Loggins is, go Google "Kenny Loggins Pooh Corner").



One of our loyal readers attended the event a few years ago, and passed along this image. Yes, I promise, I'm getting to a point about marketing. Please look at the image.



A Pimento Cheese sandwich folks. And it's only $1.50. Notice the Masters Logo (which you saw in the frozen frame on the video of the song) next to the sandwich ... they're daring you to try it!

Let me ask you a question. What is the product that I (Kevin) sell, the product is priced so low you have to try it, a product comparably endorsed by The Masters Logo? Couldn't name one, could you? That's a marketing failure on my behalf.

What is the product you sell, a product that is at a permanently tempting price, a product that you endorse on your website above all others, a product you dare your customers to try?

And what is the tradition you fold around that product? God knows I don't have a tradition to fold around my products.

All of us, me included, need a version of a Pimento Cheese Sandwich. I'm going to work on one. How about you join me? What is your version of the Pimento Cheese Sandwich? And what is the tradition you'll wrap around the Pimento Cheese Sandwich?

April 09, 2015

Triggers And Grids

Our future includes a shift from pure campaign-centric work to grid-centric triggers. Oh, I know, countless pundits have been telling us this for decades ... they tell you how their cloud-based machine learning application will uncover patterns you'd never anticipate. 

But the reality is that we must combine business experience with math ... unfortunately, we tend to omit the business experience portion of the equation.

Take a look at this grid ... the grid illustrates how much a customer with various recency-oriented direct-channel and retail-channel attributes will spend online in the next year:



Grids help us understand customer value. Grids help us understand whether we should change our behaviors or not.

Look down the "Direct Recency = 25 to 36 Months" column. This column shows us how much a 25 to 36 month direct channel buyer will spend online, in the next year, by in-store retail recency segments.

Let's pretend that we have an online buyer with no retail history ... that customer is expected to spend $14.51 online in the next twelve months. Now, look at what happens when the customer buys from a retail store ... the customer moves up the column, and moves into the "0 to 1 Month" recency segment. Online value increases ... from $14.51 to $37.58.

Conversely, read across the bottom row ... let's pretend that the customer buys online instead of buying in-store. Now, the customer moves from $14.51 of online value to $141.77 of online value.

The retail purchase has very little influence on online value. The retail purchase would likely trigger a different email cadence - one that encourages the customer to learn more about the store.

The online purchase has significant influence on online value. The online purchase would trigger a different email cadence - one that encourages the customer to continue to shop online.

Grids quickly become complicated ... it's hard to move beyond two dimensions. A good idea, then, is to create models that combine numerous attributes into one dimension. You predict, on an A/B/C/D/F basis, the value of the customer online, and you predict, on an A/B/C/D/F basis, the value of the customer to a retail store. Then you have a 5x5 grid ... and better yet, you have multiple 5x5 grids that show retail value, online value, and company value. Based on customer value, you trigger the customer into different marketing programs.

In the coming months, we're going to talk more about grids, about models that combine attributes, and the triggers we use to capitalize on customer behavior. The goal is to expand business knowledge ... in terms of your career, business knowledge > machine learning applications.

April 08, 2015

Sports Commerce Series: Customer Metrics

Many of our readers - especially those who follow along on Twitter, are actively involved in Sports Commerce. They perform analytics for sports teams, or they are part of sports organizations.

So I am going to do something for those folks ... each Thursday for awhile, I am going to run a series of sessions on Sports Commerce. In today's session, I will draw comparisons between Sports Analytics and Sports Commerce. Subsequent sessions will build upon prior content, as I explore how Sports Analytics Experts can generate significant profit for their teams/organizations by applying analytics techniques to their commerce divisions.

If you can see the embedded video below, please click it to view the session.



If you cannot see the embedded video, then please click here to visit YouTube to watch the session. 

If you work for a Professional Sports Franchise or a Collegiate Organization and don't have the resources to analyze your own Team Shop or E-Commerce Store, send me an email message (kevinh@minethatdata.com) and we'll talk about how I can help.

April 07, 2015

Williams Sonoma


Look at page 25, where you will observe selected financial data. Yesterday, a trade brand pointed out that their omnichannel strategy is clearly working. Let's evaluate their hypothesis. Look at the metrics for 2010 (the year before a research brand invented the term "omnichannel"), and compare the metrics to 2014. What do you see?
  • Slower growth.
  • Lowest gross margin percentage in the past five years.
  • Slowest e-commerce growth of the past five years.
  • Second slowest retail growth over the past five years.
  • If selling square footage have increased (+2.6%), then the retail growth metric (+2.4%) implies a necessary drop in comp store sales.
Clearly, expenses are being managed well, because profit as a percentage of net sales is good. And the metrics we see in 2014 are outstanding ... most retailers would beg to have metrics that look like this.

But my goodness. This isn't the outcome of an omnichannel strategy that is clearly working. This is the outcome of a company that has done things well for three decades.

Williams Sonoma is worth emulating, no doubt about it. But not because of omnichannel excellence ... for if they were excellent at performing omnichannel strategies, their metrics suggest that omnichannel excellence is resulting in a slow degradation of business performance.

April 06, 2015

Lifetime Value (LTV)

When I started working at Lands' End, back in the early 1990s, I was surprised how few companies calculated lifetime value.

When I work with companies in 2015, I am surprised how few companies calculate lifetime value.

#Sigh.

I know, I know, you don't have all the proper cost metrics and you use web analytics software that only allow you to evaluate campaigns and as a result you perceive that it is impossible to perform lifetime value analytics and you don't care anyway as long as your campaigns deliver an acceptable ad-to-sales ratio so so what?

All of the magic in your business comes from understanding how customers behave. No, not how they behave in campaigns ... but how they behave.

Do you have the table illustrated above? Do you review the table, at least quarterly? No? Why not?

The query is terribly simple.
  • Identify all customers who purchased as of April 6, 2014.
  • Segment those customers based on recency (three months increments) and life-to-date orders (1x, 2x, 3x, 4x-6x, 7x+).
  • Then, for each segment, calculate the average amount of demand spent by the customer in the next twelve months.
  • Produce the table outlined above.
How hard can that be?

And yet, there's a lot of value to be had from reviewing the table.

Notice how few high-value customers there are?
  • 0-12 Month 7x+ Buyers.
  • 0-6 Month 4x-6x Buyers.
  • 0-3 Month 3x Buyers.
That's it.

Now look at all the low-value buyers (green and blue). Notice that a first-time buyer quickly moves into low-value status if the customer does not repurchase within six months. Don't you think that's something your whole company should know? Shouldn't every employee know that you have six months to convince a first-time buyer to purchase again, or the customer will descend into low-value status, requiring you to acquire another customer? Wouldn't your marketing team want to craft programs to convert the customer to a second purchase within that vital six-month period of time?

You don't have to perform a full lifetime value analysis to do the right thing. Just replicate the table above. It's not hard to replicate the table - the queries are terribly easy to replicate. Then identify the blue/green segments above, and do something to prevent customers from falling into the blue/green segments. By doing just this small amount of work, you replicate 80% of the value of a lifetime value program. Take the table above and convert it to profit, and you are 90% of the way there. And you've done virtually no work!

If you cannot produce the table, contact me (kevinh@minethatdata.com) and I'll do it for you.

April 05, 2015

Influential Books

When you begin your career, you gobble up information ... everything is new and interesting and you are a sponge, soaking up knowledge.

If your career is measured in fifteen year thirds, then there is a point somewhere in the latter half of the first third of your career where you realize you have to find your own identity. You stop doing things the way everybody else does them, and you chart your own course (or not).




There are six books that influenced my path.
  1. Statistical Methods / Snedecor and Cochrane (click here). I have a version from 1938. The book talks about the kind of tests that modern online marketers claim to have invented. Without Snedecor and Cochrane, Google wouldn't be what Google is. Did I mention that these folks wrote about stuff back in 1938 ... stuff that digital gurus love talking about today? Maybe everything new is old.
  2. Correspondence Analysis in the Social Sciences / Greenacre and Blasius (click here). The book illustrates ways to take a simple cross-tab table in Excel and map the relationships between the rows and columns. There is no better lesson than one where you learn that complex information can be reduced to simplified information.
  3. Modelling Biological Populations in Space and Time / Renshaw (click here). The most influential book of my career. The book explains how species interact with each other ... how wolves need/eat rabbits until there are no rabbits and the wolves starve, for instance. Everything in this book relates to modern channel interactions. You learn that e-commerce needs stores, but stores don't need e-commerce ... you learn that e-commerce preys on stores, driving store performance down, causing stores to close ... stores are rabbits and e-commerce is a wolf (and by the way ... e-commerce will be a rabbit to the mobile wolf, it's coming, and e-commerce folks choose to not recognize this, to their detriment). Once you draw the comparison, your view of the world changes - you are able to see the future clearly.
  4. Permission Marketing / Seth Godin (click here). You may have noticed that everything about my business model has roots in this book. The book offered a blue print for building a small business. Still does. Mr. Godin was a beloved guru - then he refused to play the social media game of engagement (like he could engage with 400,000 readers on a one-to-one basis) and so many marketers abandoned him ... but that's their loss.
  5. The Demography of Corporations and Industries / Carroll and Hannan (click here). This book parallels "Modelling Biological Populations in Space and Time", but looks instead at how industries evolve. For me, the "ah ha" moment was how you start with a ton of small businesses ... the small businesses eventually coalesce around a handful of winners who become huge ... and those winners are then defeated by a ton of small businesses who capitalize on a new technology or change in the system ... and then those small businesses coalesce around a handful of winners who become huge. This pattern repeats ... and is in the process of happening once again (mobile).
  6. Moneyball / Michael Lewis (click here). I don't like this book because of the metrics. This book is important because of how it discusses culture. You can have all the metrics in the world, but if the culture isn't aligned, well, tough, you're not going to make progress. And here's a hint for the analytics community - you need an evangelist at a high level - people don't just accept your arguments and do what you say - the world doesn't work that way. Best of all - the book outlines how you can do everything right, and in the end, you don't succeed - and somebody with more resources steals your ideas and gets all the glory.
I think it is really important to have your own "system", your own set of beliefs that you implement where you work, and then you experience success, success that you use to further build upon your own, unique system. You need to have your own way of doing things. Of what possible good is it to be a "Google Analytics" expert or an "omnichannel expert" when you are doing things that ten thousand people can do equally well? Craft your own way of doing things, provide value, and you'll go places.