ACTION REQUIRED FOR CATALOG PROFESSIONALS: NAME YOUR FAVORITE MATCHBACK VENDOR

This is a picture of the Red Cross.  They are in the process of responding to a disaster.

If you had to attribute events that caused the Red Cross to be here, you'd have a challenge on your hands, right?  It would be easy to demonstrate that the disaster gets most of the credit.

It would be hard to show how individual contributions and corporate contributions led to this response.  It would be hard to show how the actions of a Red Cross volunteer or employee led to the purchase of the vehicles, which contributed to the response.

Similarly, in catalog marketing, we have to attribute response.  In the rest of the marketing world, this is called "attribution".  In catalog marketing, catalogers frequently take credit for actions that should be attributed to online marketing ... as a result, the methodology is called "matchback", not "attribution".

There are many matchback vendors.  Often, I'm asked to comment on the strengths and weaknesses of matchback vendors.

Instead of my opinions, let's focus on your thoughts.

In the comments section, please leave an anonymous comment.  Here's what I want you to share.

1. Name of the Matchback vendor you use.
2. Grade you would give their services ... "A", "B", "C", "D", or "F".
3. Tell us what your matchback vendor does best.
4. Any additional considerations.

If you wish to put your name behind the comments, go ahead.  If not, please leave an anonymous comment, and give your thoughts to the catalog community.  Via the magic of search engine optimization, this can become the post that folks look to for your thoughts about matchback vendors.

Why Attribution Efforts Fail Miserably: A Basketball Example

I want to walk you through a series of images from a basketball game.  The images will explain why attribution efforts fail miserably.

Look at the first image (below).  The player in the middle of the court just secured the rebound of a missed shot (the number on his jersey is "4").  He is passing the ball to the point guard (the number on his jersey is "0").

Now, we move a few seconds ahead in the action.  I positioned two arrows on the two players we are following.  The player with the ball (number "0") dribbled to the top of the key.  Next, he will pass the ball to the right wing, where his teammate is wide open.  But more important, look at the left arrow.  This is player number "4".  He has hustled down court, passing all of his teammates.  Because of his hustle, he had drawn two defenders near him.  And because he drew two defenders near him, his teammate (number "12") is wide open on the right wing.

Ok, we'll look at one more image, at the end of the play.

Because player #4 drew a defender close to him, his teammate on the right wing (#12) was wide open, and received a pass from player #0.  A defender rushed to the teammate (#12), blowing right past a shot fake.  Now, the teammate (#12) has a wide open three point jump shot attempt, which he buried!  The crowd went crazy, because this player hit a wide open shot that counted for three points, he's credited with one more point than he would be credited for on easier shots taken inside the three point arc.

But look at our player, player #4 ... he is just above the black arrow on the image.  If the shot is missed, player #4 has hustled into position, and has blocked out the defender.  Most long shots rebound to the opposite side the ball is shot from.  In other words, player #4 is in good position to get the rebound if the shot is missed ... and if he doesn't get the rebound, he has a defender blocked out, so that his teammate (to his left, #35) will get the rebound.

In the box score (basketball's version of a KPI attribution dashboard), here is what is documented from this offensive series:

• Player #0 = 1 assist.
• Player #12 = 3 points.
• Player #4 = No credit for anything.

This is exactly the same situation we observe in all of our attribution/matchback work.  We'd give player #12 credit for closing the deal (last touch attribution).  We'd give player #0 credit for an assist (multi-touch attribution).  We'd give player #4 no credit for anything other than a defensive rebound.

Player #4, who is truly the reason for the success of this offensive series, gets no credit.  In large part, he gets no credit because we don't have a measurement system to assign value to his efforts (hustle, positioning, basketball IQ, fundamentals).

Coaches, however, do assign value to his efforts.  They watch film, and can visually see his value to the team.  That's why he is playing in this game.

We, as a measurement community, will fail until we find a way to provide attribution to the activities that truly make a purchase happen (customer service, merchandise, creative).  Today, we give too much credit to channels, discounts, and promotions (i.e. player #12 and, to a lesser extent, player #4).  

We don't give credit to customer service, merchandise, and creative (i.e. player #4).

Attribution Week: Your Website and Paid Search

Take a good, hard look at the "All Other Online Demand" row in this table.


Which column has the best performance for the online channel?


The website is pulled in two opposite directions.

1. Catalogs cause website demand to happen.
2. Emails significantly cannibalize website demand.

Pure website demand is the most elastic of any channel.  Some forms of marketing drive a customer to a website, while other forms of marketing drive the customer away from the website.

It is easy to understand the impact of Catalogs and Email Marketing on website demand.  All we have to do is execute mail/holdout tests.

In Paid Search, it's important to geo-target.  You see, we want to measure the impact that Paid Search has on all other channels.  To this point, we've described the impact that Catalogs and Email Marketing have on Paid Search.

You can set up a brief test ... geo-target specific areas, then execute normal Paid Search activities vs. minimal Paid Search spend, and overlay this with Email mail/holdout test results.

This will clearly tell you how important Paid Search is to all other channels.

Ok, given what you've learned this week (and it is a lot), answer these questions.

1. Do you currently have this level of business intelligence at your company?
2. If the answer is "no", are you willing to execute the tests necessary to answer these questions?
3. If the answer to both questions is "no", please describe in the comments section why you don't think you need to answer these questions?  Go!!

Attribution Week: Channel Impact

This is our table from earlier in the week, measured via a 90 day long mail/holdout test of catalog marketing and email marketing.


There are many nuggets of information in the table.  Let's summarize how each channel is impacted by catalog marketing and email marketing.


Paid Search:  We generate $2.00 per customer via Paid Search in the quarter.

• Catalog causes $0.72, or 36% of Paid Search volume to happen.
• Email causes $0.18, or 9% of Paid Search volume to happen.
• 55% of Paid Search demand is independent of Catalog or Email marketing.
• We have to attribute 36% of Paid Search volume/cost back to catalog marketing, and we have to attribute 9% of Paid Search volume/cost back to email marketing.

Natural Search:  We generate $2.00 per customer via Natural Search.

• Catalog causes $0.58, or 29% of Natural Search volume to happen.
• Email causes $0.18, or 9% of Natural Search volume to happen.
• 62% of Natural Search demand is independent of Catalog or Email marketing.

Affiliates:  We generate $0.50 per customer via Affiliates.

• Catalog causes $0.34, or 68% of Affiliate demand to happen.
• Email causes $0.10, or 20% of Affiliate demand to happen.
• 12% of Affiliate demand is independent of Catalog or Email marketing.
• We have to attribute 88% of Affiliate marketing costs back to catalogs or email campaigns.
• Since Affiliate marketing is directly tied to Catalogs/Email, we need to strongly consider if the demand will continue to happen if Affiliates are dropped?

Display:  We generate $0.25 per customer via Display:

• Catalog causes $0.15 per customer of Display demand to happen (60%).
• Email causes $0.01 per customer of Display demand to happen (4%).
• 36% of Display demand is not driven by other marketing activities (36%).
• We have to attribute 60% of Display costs back to the catalogs that caused the demand to happen.

Social Media:  We generate $0.15 per customer via Social Media.

• Catalog causes $0.01 per customer of Social Media demand to happen (7%).
• Email causes $0.04 per customer of Social Media demand to happen (27%).
• 66% of Social Media demand happens independent of Catalog/Email.
• Given the strong link between Email and Social Media, there should be some level of integration between the two channels.  Social Media, to some extent, depends upon a strong email marketing program.

Tablets:  We generate $0.65 per customer on Tablet devices.

• Catalogs cannibalize $0.06 of demand from Tablet devices.
• Email cannibalizes $0.04 of demand from Tablet devices.
• In other words, when Catalogs/Emails are sent, customers shift their focus away from Tablet devices, and instead spend money in other channels.  When Catalog/Email activities are not happening, customers shift their attention back to Tablets.  This strongly suggests that, in the future, when customers spend more and more time with Tablet devices, this business will be able to scale back a bit on Catalogs and Email marketing campaigns, because the demand will be recaptured by Tablet devices.

Mobile:  We generate $0.15 per customer on Mobile devices.

• Catalogs cannibalize $0.03 of demand from Mobile devices.
• Email has no impact on Mobile devices.
• In other words, when Catalogs are sent, customers shift their focus away from Mobile devices, and instead spend money in channels congruent with Catalogs.  At this time, Mobile is a very tiny portion of the total story.  In the future, we need to watch this relationship, to see if it continues.  If the relationship continues, the data strongly suggest that we can cut back a bit on Catalogs, because demand will reallocate to Mobile devices.

At minimum, the attribution process should include three things.

1. Mail/Holdout Testing, where applicable.  For catalog marketing and email marketing, this couldn't be easier.  For Display Ads, this couldn't be easier.  For Paid Search, you can vary your budget and compare the impact.
2. Modification of Ad Cost by Channel.  In our example, paid search costs should be moved from the paid search budget to catalog marketing, and to email marketing.  Too often, we don't focus on this aspect of attribution.  Mail/Holdout tests illustrate why we need to do this.
3. Business Intelligence.  The goal of any attribution project should not be to just allocate demand/expense, but should be to teach every employee how channels fit together.  We just don't do enough of this, do we?  How often do we step back, and try to teach employees how our business works?

Contact me (click here) for additional assistance with attribution work.

Attribution Week: Email Productivity

If you ask 100 email marketers the following question, how many do you think will answer "yes".

• Question for Email Marketers:  Do you measure the success of email marketing by executing holdout groups, not mailing customers email campaigns for up to three months at a time?

Ask 100 email marketers this question, and fewer than five will answer "yes".

What a shame.

You see, all of the ways that email marketing interacts with the rest of your business are illustrated by email holdout tests.

Take a look at our example:

Here's the deal.  Email marketers typically measure performance via opens/clicks/conversions, adding average order size to the strategy to yield demand per email delivered.  In our case, over the course of a three month period of time. we get $4.94 demand per customer ... across 26 email campaigns (2x per week by 13 weeks) ... each campaign is generating $0.19 for a total of $4.94.

We run a profit and loss statement.

Demand	$4.94
Net Sales	$4.20
Gross Margin	$2.31
Less Email Cost	$0.08
Less Pick/Pack/Ship	$0.42
Variable Profit	$1.81

That's some sweet action!

Except, of course, that this analysis is providing us with the wrong answer.

When we do not send email campaigns to customers, as measured via mail/holdout tests, we generate just $3.33 demand per customer, not the $4.94 as measured via opens/clicks/conversions.  We know this, because in our example, one set of customers did not receive a single email campaign for three months, and continued to generate incremental demand.

Here's what the profit and loss statement looks like, with our new and more accurate style of measurement.

Demand	$3.33
Net Sales	$2.83
Gross Margin	$1.56
Less Email Cost	$0.08
Less Pick/Pack/Ship	$0.28
Variable Profit	$1.20

Now, there's nothing wrong with $1.20 profit per customer ... but it is a lot less than $1.81 per customer, right?

Let's take a look at what happens to other channels, when email marketing is discontinued for ninety days.

Paid and Natural Search results decrease, by 11% and 16% respectively.  In other words, email marketing causes searches to happen.  In theory, you have to take 11% of your paid search marketing cost, and allocate it back to your email marketing budget.  Yes, you have to do this, this is what attribution is all about.

Demand	$3.33
Net Sales	$2.83
Gross Margin	$1.56
Less Search+Email	$0.15
Less Pick/Pack/Ship	$0.28
Variable Profit	$1.13

Now, that doesn't make a difference in this case ... but when your email marketing program lacks a lot of productivity, well, it is enough to push the whole program under water.

Look at all other online marketing demand.  When you don't mail email campaigns to a customer, that customer changes behavior.  The customer re-directs demand away from email marketing, back to the website ... spending $6.57 per customer online instead of $4.35 per customer online.  In other words, customers are using email as a navigational tool to get to the website.

Look at affiliate marketing demand.  When emails are stopped, customers spend $0.24 each at affiliates ... but spend $0.34 each at affiliates when emails are delivered.  Now, the affiliate is probably getting a cut of each order, right?  Well, you have to attribute that cut on the $0.10 incremental difference, attributing that to email marketing, not to affiliates, because email marketing caused the affiliate order to happen.

Demand	$3.33
Net Sales	$2.83
Gross Margin	$1.56
Less Marketing	$0.16
Less Pick/Pack/Ship	$0.28
Variable Profit	$1.12

Display / Retargeting are not significantly impacted, are they?

Look at social media ... 28% of social media demand is caused by email marketing.  In other words, if you take email marketing away, your brand advocates don't have as much to yap about, and consequently, they spend a little bit less.  In this example, email marketing causes social media demand to happen, so you want to know that, don't you?

Email has minimal impact, when it comes to tablets/mobile.

We learned that email marketing isn't as effective as we believe it to be, when measured via traditional channels.  That being said, it's still highly profitable.

Tomorrow, we look at each channel, decomposing the role of each channel with/without advertising.

Attribution Week: Catalog Profitability

One secret to marketing success is knowing how customers behave if you stop marketing to them.


When you stop marketing to customers, you are left with brand recognition and merchandising love.


By the way, both of those things are really, really important.


So we execute a four-panel mail/holdout test.  One set of customers is treated "as usual" ... that's the catalogs+email column.  One set of customers is not mailed email campaigns ... for three months.


Yes, three months.


One set of customers is not mailed any catalogs ... for three months.

Yes, three months.


One set of customers is not mailed anything ... no catalogs, no email campaigns ... for three months.


Yes, three months.


You learn an awful lot by doing this.


We know that catalog marketing, when measured via the traditional mail/telephone channels, doesn't look very profitable.

Demand	$4.00
Net Sales	$3.40
Gross Margin	$1.87
Less Book Cost	$2.25
Less Pick/Pack/Ship	$0.34
Variable Profit	($0.72)

This is the outcome, over the course of a quarter ... it looks like we are losing $0.72 per customer ... and if you have 200,000 customers, well, that's more than $140,000 per quarter, more than $560,000 per year.


Well, that's doesn't work.  So we created the "matchback".  Any online order that happened within "x" days of sending a catalog is credited back to the catalog.  This "changes everything", as they like to say on Twitter!  Look at the table at the start of this post.  If we sum up all demand across all channels, we get $19.70, not a paltry $4.00.  Now look at the profit and loss statement.

Demand	$19.70
Net Sales	$16.75
Gross Margin	$9.21
Less Book Cost	$2.25
Less Pick/Pack/Ship	$1.67
Variable Profit	$5.29

Wow, catalog marketing works!  And that's where we quit.  This all happened back in 2000/2001/2002, our industry realize that we were "multi-channel" and we just stopped in time. We didn't innovate.  We just assumed that all of these online orders were caused by the catalog.


Not true.


Look at the results across catalog mail and catalog holdout groups.

Well, the test tells us something different.


Look at the incremental lift ... the difference between mailed results and holdout results.  We only generated $7.74 of incremental demand per customer.


In other words, if we don't mail any catalogs, the customer continues to generate 57.9% of the demand the customer was going to generate anyway.


I know, you don't want to believe this, you want to believe that customers crave catalogs.  Well, these days, customers are influenced by customers --- demand will still happen, regardless.


What does the profit and loss statement look like?

Demand	$7.74
Net Sales	$6.58
Gross Margin	$3.62
Less Book Cost	$2.25
Less Pick/Pack/Ship	$0.66
Variable Profit	$0.71

Ok, there's nothing wrong with this.  But the outcome is not what matchbacks suggest.  Instead, the results are 58% of what matchbacks suggest.


In this case, your job is to discount your matchback results, multiplying whatever your vendor tells you by 0.58.  Then, you run your profit and loss statement on what remains.


Later this week, we'll dig into the specific channel-based results, showing what happens to other channels when you stop mailing catalogs.

Dear Catalog CEOs: Attribution Week

Dear Catalog CEOs:


This week, we're going to tackle a concept known to the catalog industry as the "matchback", or to the rest of the world as "attribution".


As you already know, our industry incorrectly associates many online orders with the mailing of a catalog.  Based on mail/holdout tests, we know that some customers will continue to spend money, half or more of their dollars, when no catalogs are mailed.


This is not something that the vendor community wants for you to know.  The entire vendor ecosystem benefits when we over-attribute online orders to catalogs.

This week, we'll look at examples of mail/holdout tests.  We'll more accurately understand how catalog marketing impacts a business.  You'll be much more profitable if you adhere to the knowledge learned in the series of posts that will be published this week.


Good luck!

Two For One

I've never understood the logic.

When confronted with two customers, the folks I speak with routinely prefer Option 1:

• Option 1 = Acquire a customer via a preferred advertising channel for $10 profit, customer pays back $20 in the next twelve months, net profit = $10.
• Option 2 = Acquire two customers via online channel or offline mass-media channel for $10 profit, customer pays back $16 in the next twelve months, net profit = $6 * 2 = $12.

I'll take the two-for-one trade any day of the week.  Most people won't.  They'll simply work harder trying to find more customers who pay back $20 in the next year, and they'll pay more to acquire that customer.

Our future requires us to take on more "two-for-one" relationships.  We need to acquire two customers at a lower value (and no, I'm not talking about 20% off plus free shipping as an incentive), and prioritize that over acquiring one customer at high value.  

Honestly, we determine long-term value.  Catalogers, in particular, sabotage long-term value by mailing online buyers twelve catalogs a year, then incorporate 30 day and 60 day matchback windows, meaning that every future order is tied to a catalog, when the customer could be far more profitably mailed four times a year if the business factored in the organic percentage.  This alone changes the long-term value equation in favor of an online customer.

Again, we're going to need to focus on finding two-for-one deals, two customers with lower value that are greater than one customer with high value.

Worst Practices: Catalog Matchbacks

Sometimes, we need to look again at an old concept, to see if it is still viable.

Today, the concept is the "matchback". This is the catalog process where online orders over, say, a sixty day window, are "matched back" to the customers who received a catalog. The assumption is that when a customer orders online, the customer orders because something stimulated her to order. More often than not, the cataloger believes that the catalog caused the order.

The best way to validate whether matchback results are accurate or not is to execute mail and holdout tests. Take your house list of 200,000 twelve month buyers, randomly sample 40,000 names, then split those names into two groups. One group receives your next catalog, one does not receive your next catalog.

Pay close attention to online demand in the three weeks after the catalog is mailed. Let's assume you see this outcome:

• Mailed Group Online Demand = $4.00 per customer.
• Holdout Group Online Demand = $0.20 per customer.

When you observe this outcome, you know that catalog matchbacks represent a "best practice" for your business --- online orders simply would not happen if the catalog is not mailed.

But if you see this outcome, you've got issues:

• Mailed Group Online Demand = $4.00 per customer.
• Holdout Group Online Demand = $3.00 per customer.

When you observe this outcome, it means that your matchback algorithm is taking credit for all of your online orders, when in reality, 75% ($3.00 / $4.00) of the orders would happen anyway, regardless whether the catalog was mailed.

In this situation, catalog matchbacks are a "worst practice". They are causing you to dramatically over-mail your customer file, matchbacks may be costing you 15% to 50% of company profitability.

Seriously.

I mean it. That is what happens! I analyze a lot of customer data across a lot of companies. I see this on a routine basis.

Long-time readers are looking at this and are saying "... Kevin, you've been harping on this topic for more than four years, when are you going to stop?" Well, I'll stop when we, as an industry, begin to avoid "worst practices".

I want your business to be highly profitable.

I don't want for you to waste money on cataloging, when you could save that money and re-invest it in customer acquisition or re-invest it testing new methodologies or new technologies.

Execute a test. Determine for yourself if you are executing "best practices" or "worst practices".

Catalog Matchbacks: FAQs About Why They Cost You Profit

You had questions about my thesis that catalog matchbacks are costing you profit. Let's answer your questions.

Question: "What do you have against the vendor community? How can you criticize folks who've helped us through tough economic times, and are always there for us?"

Let me be clear. I am not being critical of members of the vendor community who have your best interests at heart. Plenty of folks in the list industry and the co-ops make the right decisions for you every single day. In fact, I recommend many folks from Millard or ALC or Abacus to you (as examples), when asked.

Always remember, however, that most in the catalog vendor community make more money when you mail more catalogs. There is a disincentive for the vendor community to share best practices in housefile contact strategy testing, because doing so is detrimental to their business, and could result in the vendor employee losing his/her job. I've witnessed this conflict first hand, where the vendor gives you an answer that ultimately benefits the vendor more than you. So do the right thing for your business, ask valid questions of your vendor reps, and closely monitor their response.

Question: How can you mail catalogs and get negative demand, I don't understand?

One of the myths of the "multichannel era" is that everything fits together, is additive or even multiplicative! This simply isn't true, and is clearly illustrated every time you execute a catalog housefile mail/holdout test. Advertising doesn't necessarily cause customers to spend more. Sometimes (often), advertising causes customers to change behavior. Some catalogers find that when you don't mail catalogs, demand from e-mail marketing doubles ... in essence, the lack of a catalog causes the customer to switch loyalty to e-mail marketing. You can't see that outcome unless you execute the test. That is an example of "negative demand". In other cases, mailing the catalog causes customers to spend more on e-mail marketing, this is the classic "multichannel era" outcome of 1+1=3. It does happen. But you cannot know it until you execute mail/holdout tests.

Question: If I execute a mail/holdout test, I'll lose demand, and I can't afford to do that when the economy is so bad. Why are you constantly recommending that I do something that hurts my business?

I wouldn't recommend that you do something that hurts your business. I am asking you to execute the test so that you can identify the most profitable catalog mailing strategy. Take a look at the image at the start of this post. On the left-hand side of the table, you see what happens when you follow your matchback results. You'll mail 12 catalogs to this customer segment. On the right-hand side of this table, you see what happens when you test different numbers of catalog mailings to a customer. You see that demand happens whether you mail catalogs or not to a customer. You see that 8 catalogs is the optimal strategy. You see that if you mail 8 catalogs, you increase profit by +/- 20%, per customer. Show me what other strategy you have in your toolkit to immediately increase customer profitability by 20% today?? If you want help executing/analyzing/implementing a contact strategy view of catalog customers, contact me!

Question: Are matchbacks invalid for outside lists?

No, go ahead an use matchbacks for outside lists, that's a good way to evaluate prospects.

Question: We go to great lengths to capture key codes on online orders. Why do we need to execute mail/holdout tests when we ask the customer to enter a key code?

Here's a neat finding from mail/holdout tests ... if you don't mail a catalog, a customer is likely to use an older catalog and will enter an older catalog key code. Or other times, the customer will just visit your website because she loves your brand. Imagine that? She'll simply come to your website unprompted by a catalog mailing, and will place an order, sans key code. That's the best measure of customer loyalty ... a customer willing to shop without advertising. How will you ever be able to accurately measure customer loyalty unless you execute the mail/holdout test?

Question: We know that 80% of our online orders are matched back to a catalog, so we know that matchback analytics are right for our business. Why would you ever recommend abandoning a methodology that is so useful?

There are two issues with your comment. First, when a catalog brand gets 80% of online orders from catalog mailings, the catalog brand is not doing a good job of online marketing. Second, the matchback algorithm is incorrectly allocating orders to catalogs. As stated earlier, your most loyal customers will always shop from you, regardless whether you mail catalogs or not. In other words, you don't need to advertise as often to your best customers ... they already love you!! How can it possibly hurt to execute a mail/holdout test and learn the optimal number of catalogs to mail to a customer?

Question: In your examples, if you don't mail catalogs, catalog demand decreases. We cannot support top-line sales declines. Why would you advocate hurting our business?

I'm advocating a strategy that makes you more profit. Profit dollars matter. My strategy means you end up spending less on catalog marketing, generating a decrease in top-line sales. I recommend re-investing that money in customer acquisition, online marketing/search, mobile, and in some cases social media strategies, in order to grow your business. If done right, top-line sales won't decrease, and you'll be more profitable.


Ok, it's time for your questions. What do you want to learn?

Catalog Matchbacks Are Incorrect, Costing You Profit

During the past ten years, I've analyzed, plus/minus, a hundred different contact strategy tests. In these tests, we hold out a group of customers, to understand how much the customer will spend if we don't mail catalogs.

In every case, the holdout tests indicate that the incremental sales generated by a catalog mailing are less than what is illustrated in a traditional matchback algorithm.

The matchback incorrectly assumes that just because a customer was mailed a catalog, then any online order was "caused" by the catalog.

This, my loyal readers, is simply not the case.

The image at the top of this post is typical of what I usually see. This would be a modestly-sized $60,000,000 catalog business with thirteen annual mailings. As you can see, when comparing the results of a matchback with the results of a mail/holdout test, demand is overstated by about 40%.

And as a result, profit is completely mis-stated. Your matchback reporting tells you that you generated more than $600,000 profit ... when in reality, you only generated a little over $300,000 in profit.

This means that you are grossly over-circulating to your housefile segments.

This happens all of the time. In the case of a $60,000,000 business, it is reasonable to expect that matchback analytics are costing you a million dollars of annual profit, per year.

Yes, you are losing a million dollars of profit on a sixty-million dollar business, per year, because you are using matchback analytics instead of mail/holdout results to evaluate your business.

Always remember that there are audiences that benefit from matchback analytics. This audience is not likely to help you evaluate your business in the most profitable way possible, because use of matchbacks benefits their business model.

• The USPS
• Your Printer
• Your Paper Rep
• Your Co-Op
• Your List Vendor

Always remember that there is an audience that benefits from mail/holdout test methodology.

• You

Which audience do you think you should focus on?

Please start using mail/holdout methodology, and improve the performance of your business. As always, I am here to help you.

Dear Catalog CEOs: Matchbacks

Dear Catalog CEOs,

During the past decade, matchback analytics have become an indispensable part of catalog marketing. Without matchback analytics, it is possible you would not have a catalog channel.

The age of the "matchback" changed our perception of marketing.

Do you remember the good 'ole days, like way back in 1994, before we had an e-commerce website, back in the stone age of catalog marketing?

Back in 1994, we cared a lot about the concept of "cannibalization". We executed a lot of exotic multi-variate tests to detect cannibalization. For instance, say we mailed two catalogs, one on September 1, one on October 1.

• September 1 Catalog = $5.00 per catalog.
• October 1 Catalog = $5.00 per catalog.
• Total Demand = $10.00 per customer.

And then, we got excited! If we could generate $5.00 per catalog, maybe we should add a third catalog. So in 1995, we added a third catalog, on September 15.

• September 1 Catalog = $4.00 per catalog.
• September 15 Catalog = $4.00 per catalog.
• October 1 Catalog = $4.00 per catalog.
• Total Demand = $12.00 per customer.

Remember what we thought? We were happy with the new catalog, but we were concerned with the performance of the two existing catalogs. It was almost like they weren't working well anymore! And in fact, if we ran a profit and loss statement, we found that $12.00 of customer demand across three catalogs was less profitable than $10.00 of customer demand across two catalogs.

We thought about cannibalization, a lot. We were genuinely concerned about how one marketing activity cannibalized another activity.

Then matchback analytics came along. The data enabled the catalog vendor community to change our minds about how we thought about marketing activities.

We stopped thinking about "cannibalization". Heck, these catalogs didn't cannibalize business. Nope, these catalogs "added" business, they "drove" business to other channels.

The industry script (supporting an additive model vs. cannibalization) benefits the catalog ecosystem. The co-ops and database vendors created reporting that illustrated how catalogs drove sales across all channels. Their reporting supported the notion that we should rent more names from the co-ops. In other words, matchback reporting fuels the financial success of the co-op and list industry. The paper industry supports the concept of matchbacks. Printers support the concept of the matchback. The USPS supports the concept of the matchback. Your favorite Catalog Consultancy that helps you with mailing plans benefits from the matchback. Even third-party opt-out services benefit from matchbacks ... without matchbacks, they serve fewer customers who are getting unwanted catalogs.

The industry script benefits the entire catalog ecosystem.

Now let's focus on you, the Catalog CEO. Do you benefit from matchback algorithms?

As we head into the Holiday season, I'd like to ask you to do our industry a favor:

• Randomly sample 5,000 or 10,000 customers from the universe you would mail your best-performing Holiday catalog to.
• DO NOT mail these customers your best Holiday catalog.
• Code these customers as a unique segment, and enter these customers into your matchback routine with your favorite matchback vendor. Remember, these customers were not mailed a catalog, so your matchback vendor should show that no orders are matched back to the catalog that you did not mail.

If your matchback vendor matches online orders back to a catalog that was not mailed, then you have an estimate for how much your matchback vendor is over-stating the results of your catalog mailings.

Catalog CEOs, this is a very important topic. If your matchback vendor is over-stating your catalog performance because your vendor fails to take cannibalization into account, then you are over-mailing your customer base, and in all likelihood, you are wasting marketing dollars, squandering profit.

Increasingly, I am hearing of big discrepancies between matchback results and real-world results obtained via holdout tests. One company told me that every phone order was paired with one online order matched-back in their matchback algorithm. And yet, when they executed a holdout group, they only saw a 5% drop in total demand --- almost no phone demand or online demand was lost when the catalog was not mailed.

In other words, cannibalization was so significant that the catalog was basically adding no incremental demand. This is an important concept --- cannibalization testing shows no additional demand, while matchback algorithms show that catalogs drive online business, forcing you to mail more catalogs.

Do you understand the distinction?

Catalog CEO's, please ask your marketing folks to give this test a try. The entire catalog industry ecosystem benefits from matchback algorithms, and they aren't supporting matchback to be evil ... it's the best available reporting folks have. I'm asking you to question your results, to execute a test and validate that the matchback algorithms are giving you honest results. I don't benefit from doing this test, I have no financial interest in positive or negative results. Only you will benefit if you find that orders are being mistakenly attributed to catalog mailings.

So see for yourself! Run a holdout test, code the customers as a segment, run them through your matchback algorithm, and see if there is a bias that is causing you to over-state your results.

Thank you for your consideration,

Kevin Hillstrom, President, MineThatData

E-Mail Marketing, Search, Matchback, Attribution

One of the mysteries of marketing in 2009 is the concept of attribution, a process where we matchback orders derived in one micro-channel to the advertising micro-channel that drove the order.

For whatever reason, the e-mail blogosphere and vendor community fails to capitalize on this opportunity.

My Mutichannel Forensics projects repeatedly indicate that e-mail marketing and search marketing play a unique micro-channel role. E-Mail marketing is a "love" channel, if you will. The 10% to 50% of your twelve-month buyer file that subscribes to e-mail marketing "loves you" more than the average customer. These customers have better "RFM" characteristics, not because of e-mail marketing necessarily, but because the customer is a good customer who wants to learn more.

And then we have search, which works in the opposite direction. The customer who "loves you" doesn't implicitly trust you. As a result, she wants to make sure that she's getting the best deal possible, the best combination of merchandise and value.

When you have customers who want to see your e-mail campaigns and then want to use search, you have a classic micro-channel combination that must be tabulated in your database, and analyzed going forward.

At minimum, we need to run matchback algorithms for e-mail marketing. Catalogers have been running matchbacks for the past fifteen years, taking credit for orders that were not necessarily driven by catalogs. E-Mail marketers, however, have been exceptionally slow to embrace attribution and matchback programs. I don't understand why.

It's a fairly simple process. Say you deliver an e-mail marketing campaign on a Tuesday. Take all customers who ordered on Tuesday, Wednesday, Thursday, and Friday, and match them back to your e-mail campaign. And by the way, make sure you have a holdout group, a group who did not receive the e-mail campaign, and do the same process --- subtracting the difference between mailed and holdout group for true incremental value.

Now, any orders that are generated by search marketing are matched back and attributed to the e-mail marketing campaign. And here's where we need to make an adjustment ... we need to make a guess at all of the unconverted searches that were caused by e-mail marketing, and allocate the cost of those unconverted searches back to the e-mail marketing campaign. If the typical search conversion rate is, say, 3%, you have to multiply converted searches by 33, and then multiply that total by the cost-per-click, in order to get at the right advertising cost.

Two things usually happen, two things that are highly relevant to e-mail marketers.

1. E-Mail marketing causes search activity, and that search activity results in orders that are normally credited to search and should be credited to e-mail. This can result in e-mail marketing being more productive that usually measured to be.
2. E-Mail marketing causes the "search audience" to do a bunch of unproductive searches. As a result, the "search segment" is actually unprofitable --- causing the e-mail marketer to withhold e-mail marketing campaigns to customers who search all of the time.

The latter point is worth noting ... the e-mail marketer should be creating segments in the database of customers who utilize search on a frequent basis ... electing to develop a different contact strategy for the "E-Mail / Search" micro-channel combination.

Online Buyers: An Easy Catalog Circulation Tip To Save Money

Catalog marketers looking to reduce expense often decide to not mail catalogs to online buyers.

This can be a challenge, because so many of those matchback algorithms suggest the catalog played a role in the online order (though we can not prove the catalog caused the order).

When I'm conducting a Multichannel Forensics analysis, I like to categorize every one of the 365 days of the year as a "catalog day" or an "online day". In other words, I sort every day based on sales totals ... days where telephone orders are dominant are called "catalog days". Days where telephone orders are not dominant are called "online days".

Not surprisingly, online buyers purchasing on "online days" are less responsive to catalog marketing than are online buyers purchasing on "catalog days". Toss in Google activity or e-mail activity, and you've got a recipe for catalog cost savings!

Modern Segmentation, Modeling, And Planning

Much of the segmentation/modeling/planning process involves predicting a future purchase, followed by the determination of an appropriate targeting strategy.

For instance, in this catalog example, we predict two things.

1. We predict the Response Rate to a future catalog.
2. We predict the Average Order Size for a segment being mailed a future catalog.
   

Based on these two predictions, and a forecast for the cost of mailing a catalog, we arrive at the following segment-level mailing prediction and profit/loss statement (after online/retail matchback):

	Prediction
Response Rate	1.8%
Avg. Order	$125.00
$ Per Book	$2.25
Flow-Through %	35.0%
Flow-Through $	$0.79
Book Cost	$0.70
Profit	$0.09

The marketing world of 2009 requires a different level of sophistication.

In the future, we will change the planning and prediction process. This segment will be split into two sub-segments.

1. Subsegment #1 = Customers with the same RFM-style classification, but never historically purchased using Paid Search, Affiliates, or Shopping Comparison Sites.
2. Subsegment #2 = Customers with the same RFM-style classification, but historically purchased using Paid Search, Affiliates, or Shopping Comparison Sites.

In each case, we'll measure future response, but we'll also predict the expected marketing cost associated with self-service customers using Paid Search, Affiliates, or Shopping Comparison Sites. If the catalog or e-mail drives customers to these micro-channels, we incur additional marketing expense. Here's the sub-segment prediction:

	Subseg #1	Subseg #2
Response Rate	1.8%	1.8%
Avg. Order	$125.00	$125.00
$ Per Book	$2.25	$2.25
Flow-Through %	35.0%	35.0%
Flow-Through $	$0.79	$0.79
Book Cost	$0.70	$0.70
Pred. Search/Aff/SC Cost	$0.02	$0.18
Profit	$0.07	($0.09)

In this example, Subsegment #2 generates additional expense, because they like to use Paid Search, Affiliates, and Shopping Comparison sites after receiving a catalog. Therefore, we have to predict what the amount of incremental expense is likely to be. The same level of prediction is required to properly manage future e-mail campaigns.

For Statistical Modelers, this opens up a whole new area of exploration --- it's like drilling for oil in areas where exploration was prohibited.

For the Catalog Circulation Director, this gives you the opportunity to fundamentally change the contact strategy for self-service online shoppers, while generating a boatload of profit for your brand.

For the E-Mail Marketer, you have a once-in-a-lifetime chance to motivate your Executive team to deliver e-mail campaigns to unprofitable customers less often --- and you'll have the proof!

For the vendor community, especially for matchback vendors, you have a whole new product you can develop --- one that integrates purchases and expenses in a holistic and actionable manner. Or maybe the folks at Coremetrics or Omniture can get a jump on the catalog vendor community, and take ownership of this new opportunity.

Best of all, all of you e-mail vendor employees who regularly read this blog have a chance to build an application that improves the profitability of e-mail marketing efforts for your clients --- a good thing!!!

Catalog ROI Is Overstated Because Of Search

Last week, we chatted about how E-Mail ROI is mis-calculated. My stats tell me that you found the article interesting.

Catalog advertising causes the same issues that e-mail marketing causes, often on a larger scale.

The typical catalog marketer matches paid search orders that occur within 30/60/90 days of a catalog mailing back to the catalog that the circulation team believes is responsible for creating the order.

However, the typical catalog marketer does not match back unconverted paid search expenses to the catalog responsible for causing unconverted paid search to happen.

Take a look at this profit and loss statement.


This is a fairly typical catalog profit and loss statement.

Notice converted paid search orders. These orders are matched-back to the catalog. Some catalogers match the paid search expense of those orders back to the catalog.

Almost nobody matches the unconverted paid search clicks back to the catalog that caused paid search to happen. In this example --- a reasonably honest assessment of a catalog profit and loss statement, the catalog caused 3,200 paid search orders to happen. However, at a 3% conversion rate, the catalog caused about 100,000 paid search clicks to happen.

The average cataloger does not allocate the cost of the incremental 96,800 unconverted clicks back to the catalog that caused the clicks to happen.

So three things happen.

1. The cataloger significantly over-circulates the catalog, because the additional expense is not allocated to the catalog driving paid search. The catalog marketing effort is less profitable than it appears.
   
2. The cataloger significantly mis-understands the impact of catalog marketing. In this case, circulating 1,000,000 catalogs caused 100,000 paid search clicks. The marketer fails to see that the catalog caused a 10% "engagement rate". This is a big deal --- the catalog is causing far more customer engagement than is typically measured.
   
3. A portion of the 100,000 paid search clicks result in purchases with the competition, reducing your Net Google Score.

Eventually, we'll create a database infrastructure that allows us to capture appropriate customer interactions. This will fundamentally change how we market to customers.

• We will attribute unconverted paid search clicks back to the customer/catalog combination, in our promotional history files. Instead of recording an $0.80 cost for the catalog, we'll record a $0.80 + $0.50 = $1.30 cost to the customer, incorporating the cost of the search. Ask your database, co-op, or web analytics vendor if they are able to do this for you.
  

• When we make mailing decisions (e-mail or catalog), we will make the decision based on the historical paid search expenditure of the segment we're considering. We won't send as many catalogs or e-mails to customers who augment their experience with unconverted paid search. This is a big deal, folks ... we'll be much more profitable when we make this transition.

• Example: Say your break-even on an $0.80 catalog is $2.50. Now you have a customer who loves to click on paid search ads when she receives a catalog. Your "real" cost of mailing the catalog is $1.30, driving your break-even over $4.00.

• Example: E-Mail marketing is essentially free, until it isn't free! The new e-mail marketing discipline will require us to make e-mail marketing decisions, at a segment level, based on anticipated paid search expense. All of a sudden, e-mail marketing is fundamentally changed --- the discipline becomes nearly identical to catalog marketing.
  

• Another Issue: We have the same problems with Affiliate Marketing and Shopping Comparison Sites. If catalog marketing drives a customer to an affiliate, and that affiliate skims 7% off the top of an order, the catalog needs to receive an expense penalty for driving demand to the affiliate.
  

We've spent a decade doing matchback analytics. Now, we need to provide the vendor community some leadership, so that matchback analytics account for the expense side of the ledger. We are continually making bad decisions because our database infrastructure fails to capture important information.

Who do you see doing this type of work out there, and what was the impact of this style of analysis?

Prove It: Matchback Attribution

One of our loyal readers is trying to goad me into ripping a recent article on Multichannel Merchant. I won't link to the specific article, because 95% of the content the author writes about will increase your profits, and I don't want for this to be a criticism of the author, who is simply trying to give advice that will help you.

But I do want for us to consider one of the quotes, the exact quote sent to me by a loyal reader.

• "Correlation analysis suggests that as much as 90% of the unallocated orders that come to the Web for the multichannel marketer are directly related to catalog mailings".

We need to consider three phrases in the sentence.

1. Correlation analysis suggests (this means we cannot prove the findings that follow).
   
2. As much as (this means the real number is always lower than what we will be quoted).
3. Directly related (this means we cannot prove causation).

Based on the linkage of three phrases, an entire industry moves forward with an agenda, an agenda based on a linkage of assumptions that may or may not be correct.

Do you realize the leap of faith required to believe in this sentence?

We listen to quotes like this because we want to believe the sentence.

Now it is entirely possible that those who adhere to the tenants of the sentence quoted above are right, and will experience unbridled success. I grant you this point.

But are you willing to grant me my point ... what if one, two, or three components of the sentence are wrong? What does that mean to the success of the businesses we manage?

Matchback attribution. Prove it!!