Loyalty Lizard Logic

I've measured this phenomenon more times than I care to mention.

"Brand X" is upset that customer loyalty isn't great ... so instead of doing the hard work to offer merchandise that customers have to have, "Brand X" decides that it must instead create a loyalty program, and then offer discounts/promotions and other nonsense in an effort to increase customer loyalty. The marketing analyst points out the following:

• 100,000 12-month buyers ... they spent $240 last year.

Then, an analyst mucks up the measurement process. She quickly determines the following:

• 10,000 12-month buyers are in the loyalty program / they spent $600 last year.
• 90,000 12-month buyers are not in the program / they spent $200 last year.
• Average customer spend last year = $240.

Powerpoint slides are quickly assembled ... and the analyst proudly proclaims that the loyalty program is creating customers who are worth 3x as much as non-loyalty customers. Everybody offers a round of applause ... they loyalty vendor, sitting in the room, winks at the CMO with an "I told you so" level of arrogance that makes the CFO want to vomit.

Why does the CFO want to vomit?

Because the CFO spent money on the loyalty program ... but still has a housefile of 100,000 customers spending $240 a year.

Nothing changed.

Well, two things changed.

• A loyalty vendor is getting paid.
• A marketing analyst is writing terribly biased queries.

But the organization doesn't care ... they put down the gas pedal. A year later, the results look something like this.

• 17,500 12-month buyers are in the loyalty program / they spent $580 last year.
• 82,500 12-month buyers are not in the program / they spent $170 last year.
• Average customer spend last year = $241.75.

Once again, everybody but the CFO misinterprets the data ... they see the $580 / $170 ratio, and they proudly proclaim that the loyalty program is causing a 3.41x increase in spend. #Wow.

But the CFO is doing slightly different math.

• 100,000 customers spending $241.75 = $24,175,000.
• 100,000 customers used to spend $240 = $24,000,000.
• Incremental value of the program = $24,175,000 - $24,000,000 = $175,000.
• Profit Factor = 40%.
• Cost of the Program = $100,000.
• Program Profit = $175,000 * 0.40 - $100,000 = ($30,000).

Technically, the CFO's math is also wrong. The CFO is assuming that all of the gain in 12-month buyer spend is attributed to the loyalty program, and that's not a fair assumption. More/less demand may have been generated, after accounting for changes in marketing strategy, discount/promo strategy, and merchandise productivity.

Regardless, you get the picture.

It is quite likely that the loyalty program actually caused Lifetime Value (LTV) to decrease, because the sales gain did not offset program costs.

And it is quite likely that the marketing executive will speak at a conference next year, and will tell the audience that they achieved a 3.41x increase in spend in their loyalty program. The audience will enthusiastically cheer, knowing that they've proven once again that loyalty programs work. They'll enthusiastically endorse lizard logic, won't they?

P.S.:  The marketing analyst and the CFO analyzed the data incorrectly. I could share how I'd analyze it, but you'd criticize me, suggesting I was measuring things incorrectly as well. Then, you'd share with me how you'd analyze the data, and I'd tell you that you were wrong. We'd all go in circles yelling at each other, all of us wrong. The key is to realize that as we triangulate toward an accurate answer, we all notice that sales are essentially not increasing, and that is the only metric that matters.

Romance Novels

Click on this little ditty ... and learn that 4% of print books are in the romance category, while 45% of e-books are in the romance category.

Our analytics are all messed up. We attribute the difference to channels.

The difference has nothing to do with the channel. The difference has everything to do with the content. In your case, study the merchandise that sells best in each channel. There are many times that low-margin, low price-point product sold via email discounts/promotions end up driving down lifetime value - has nothing to do with the channel, has everything to do with the product and promotion.


P.S.: If you like baseball, give this article about the Milwaukee Brewers trading away a bunch of current talent for what the GM calls "future talent" (click here). When your business is bad (similar to Milwaukee's record this year), do you plan for the future, or do you cut back on marketing expense to save money? Because if you do the latter, you reduce the future value of the business if your marketing cuts hurt customer acquisition activities.

But Their LTV Isn't Very Good!!

Who cares?

I ran into this one recently. The Marketing Executive held a report from a noted catalog boutique agency. The report suggested that customers acquired from "digital" sources were worth much less than customers acquired from "catalogs".

• Catalog Sourced Buyer = $50.00 Lifetime Value.
• Digital Sourced Buyer = $20.00 Lifetime Value.

The Marketing Executive said, earnestly ...

• "We don't want the Digital buyer ... they're worth less."

#OhBoy.

You know what this is?

Yup ... it's Lizard Logic!!

Old school catalog leaders are notorious for turning away "digital" buyers because LTV is not as good.

Who cares?

Do you think your CFO, if you told her that you could acquire 100,000 customers who would each generate $20 profit in the next five years, would say ... "nah, I don't want an additional $2,000,000 profit"?

Your CFO would flog you if you didn't generate $2,000,000 additional profit.

Here's the next line of reasoning I hear.

• "But the digital buyer doesn't like buying from the catalog, so that's a bad thing."

Who cares?

You know what? If the digital customer doesn't like buying from the catalog ... don't send the digital customer catalogs!!

Here's the next line of reasoning I hear:

• "But we are a catalog brand. It's our job to mail catalogs."

No.

You are a merchant. It is your job to sell things.

You want every dollar of profit you can find. Period. It is SO HARD to generate profit. Why would you turn profit away simply because the customer is worth less than another customer?

Impacting LTV

You just acquired a new customer. You lost money on the transaction (that's common).

You want your money back.

Which of the following two strategies is most likely to get your money back.

1. Market normally to the customer ... wait until the customer hasn't purchased in 18 months, then use vendor-centric reactivation strategies coupled with discounts and promotions to "win-back" the customer.
2. Immediately work hard to make sure the customer is happy with the first order, and encourage the customer to purchase for a second time within the first ten weeks following a first order?

The answer isn't even close.

It's (2).

Vendors push for (1) because that's how they make money.

You need to push for (2) because when the customer purchases for a second time (especially within 10 weeks), the customer is worth A LOT MORE than when you wait 18 months to convert the customer to a second purchase. You earn all of that incremental profit in the short-term, and, you push the customer to 3x or 4x or 5x status faster, so you earn even more profit in the long-term.

Any lifetime value simulation makes it perfectly clear that you have to do everything possible to get a customer to place a second order within 10 weeks of a first order.

What stops you from taking advantage of this fact?

What stops your call center from calling this customer to make sure the customer is happy? It's called a "call center", right? So call the customer!!

Ten weeks ... 70 days.

That's the window when you can make the biggest impact on LTV.

It's Fun To Acquire A Customer Via Discounts And Free Shipping!

It sure it!

But is it the right thing for your business? How does LTV vary between a customer earned the honest way (full price, no promotions) and a customer earned via cheating (discounts, promotions, free shipping)?

There are times when the customer earned via cheating is worth more, long-term.

There are times when the customer earned the honest way is worth more, long-term.

Your job is to do the math. No more theoretical arguments.

Let's run through a brief example. I typically create twelve-month profit value models for folks. The models might reveal the following:

Customer Earned The Honest Way.

• Probability of Purchasing, Next 12 Months = 40%.
• Amount of Net Sales Generated if Customer Purchases = $200.00.
• Gross Margin Percentage = 60%.
• 12 Month Ad Cost = $20.00.
• Pick/Pack/Ship Expense, as a % of Sales = 5%.
• 12 Month Profit = 0.40*$200.00*(0.60 - 0.05) - $20.00 = $24.00.

Customer Earned Via Cheating.

• Probability of Purchasing, Next 12 Months = 45% (better).
• Amount of Net Sales Generated if Customer Purchases = $215.00 (better).
• Gross Margin Percentage = 53% (worse due to discounts/promos).
• 12 Month Ad Cost = $20.00.
• Pick/Pack/Ship Expense, as a % of Sales = 10% (worse due to free shipping promos).
• 12 Month Profit = 0.45*$215.00*(0.53 - 0.10) - $20.00 = $21.60.

Well isn't that fun?!

• The discount/promo buyer is more loyal.
• The discount/promo buyer spends more.
• The discount/promo buyer generates less profit.

Now, as long as the brand is able to acquire 12% more customers via discounts/promos, then the math works out well ... you'll generate 11% less future profit per customer but you'll generate 12% more customers yielding more profit.

It is entirely possible that the discounts & promotions & free shipping are the right thing to do for your business.

But you have to do the math to know, don't you?

Are you running the math? Or are you being a "strategic marketer"?

Please ... run LTV math. I'm beggin' ya!

Just Show Me How To Calculate LTV

I know, I know, you just want simple math, something you can calculate on your own. 

Let's do something at a 30,000 foot level. Doing something at this level is better than the 90% of you who are not calculating LTV in any way, shape, or form.

Step 1:  Select any customer who purchased for the first time between 20100700 and 20110700.

Step 2:  Record the profit/loss generated by that customer on the first purchase. Don't have that data? Go get it. You wanted to do this yourself, so there are certain data elements you have to have. You'll have to speak with somebody in Finance. You'll have to speak with somebody in Marketing. You'll have to be proficient at calculating profit. You can do this!

Step 3:  In the next 12 months following the first order, please record the following data elements for each customer.

1. Total net sales generated.
2. Total gross margin dollars generated.
3. Total ad cost spent against this customer.
4. Margin Factor (percentage of margin that flows through to profit, independent of ad cost).

Step 4:  In months 13-24 following the first order, please record the following data elements for each customer.

1. Total net sales generated.
2. Total gross margin dollars generated.
3. Total ad cost spent against this customer.
4. Margin Factor (percentage of margin that flows through to profit, independent of ad cost).

Step 5:  In months 25-36 following the first order, please record the following data elements for each customer.

1. Total net sales generated.
2. Total gross margin dollars generated.
3. Total ad cost spent against this customer.
4. Margin Factor (percentage of margin that flows through to profit, independent of ad cost).

Step 6:  In months 37-48 following the first order, please record the following data elements for each customer.

1. Total net sales generated.
2. Total gross margin dollars generated.
3. Total ad cost spent against this customer.
4. Margin Factor (percentage of margin that flows through to profit, independent of ad cost).

Step 7:  In months 49-60 following the first order, please record the following data elements for each customer.

1. Total net sales generated.
2. Total gross margin dollars generated.
3. Total ad cost spent against this customer.
4. Margin Factor (percentage of margin that flows through to profit, independent of ad cost).

Now, this isn't going to get you to lifetime value, but it will get you to 5-year value, and that's a big deal.

Let's say that you have the following averages for Step 3, Step 4, Step 5, Step 6, and Step 7:

• Step 3 = $80.00 Sales, 50% Gross Margin, $20.00 Ad Cost, 10% Margin Factor.
• Step 4 = $60.00 Sales, 50% Gross Margin, $16.00 Ad Cost, 10% Margin Factor.
• Step 5 = $45.00 Sales, 50% Gross Margin, $12.00 Ad Cost, 10% Margin Factor.
• Step 6 = $35.00 Sales, 50% Gross Margin, $10.00 Ad Cost, 10% Margin Factor.
• Step 7 = $25.00 Sales, 50% Gross Margin, $8.00 Ad Cost, 10% Margin Factor.

For each step, we calculate annual profit.

• Step 3 = $80.00 * 0.50 - $20.00 - $80.00 * 0.50 * 0.10 = $16.00 profit.
• Step 4 = $60.00 * 0.50 - $16.00 - $60.00 * 0.50 * 0.10 = $11.00 profit.
• Step 5 = $45.00 * 0.50 - $12.00 - $45.00 * 0.50 * 0.10 = $8.25 profit.
• Step 6 = $35.00 * 0.50 - $10.00 - $35.00 * 0.50 * 0.10 = $5.75 profit.
• Step 7 = $25.00 * 0.50 - $8.00 - $25.00 * 0.50 * 0.10 = $3.25 profit.

Alright friends, we've got LTV:

• Year 1 = $16.00 profit.
• Year 2 = $11.00 profit, $27.00 cumm profit.
• Year 3 = $8.25 profit, $35.25 cumm profit.
• Year 4 = $5.75 profit, $41.00 cumm profit.
• Year 5 = $3.25 profit, $44.25 cumm profit.

At a high level, you have LTV at a cumm level for each of the first five years a customer is on your housefile.

Remember yesterday when we calculated payoff horizons? Apply that logic to your customer base after knowing how much profit you generate from a first order, and you've got everything you need to perform a 30,000 foot level LTV analysis ... remember, 90% of folks aren't calculating anything, so by following the simple steps here, you are well ahead of the curve. From here, you can follow your favorite University Professor and dig deep into advanced LTV theory.

But at least you'll be ahead of 90% of the competition.

That counts for something, right?

And you didn't have to pay a penny to obtain the information.

Payoff Horizon

Let's work through a very simple example.

Let's say that there are only two customers I can acquire each year.

• Each newly acquired customer generates $20.00 profit in year one, $12.00 profit in year two, and $8.00 profit in year three.
• Customer #1 can be acquired at a loss of $10.00.
• Customer #2 can be acquired at a loss of $25.00.

What should the strategy be for this company?

1. Do not acquire either customer, because the company loses money acquiring the customer?
2. Acquire only Customer #1, since Customer #1 is generated at a better profit rate than Customer #2?
3. Acquire both Customer #1 and Customer #2 each year?

This can be answered easily, by simply doing a bit of math. Let's run a three year scenario for each strategy.

If we do not acquire either customer, then here's what happens.

• Year 1 = $0.00 profit.
• Year 2 = $0.00 profit + $0.00 profit.
• Year 3 = $0.00 profit + $0.00 profit + $0.00 profit.
• Three Year Profit = $0.00.

Let's say for each of the next three years, we only acquire Customer #1 each year.

• Year 1 = -$10.00 + $20.00 = $10.00 profit.
• Year 2 = $12.00 (from year 1 customer) + (-$10.00 + $20.00) = $22.00 profit.
• Year 3 = $8.00 (from year 1 customer) + $12.00 (from year 2 customer) + (-$10.00 + $20.00) = $30.00 profit.
• Three Year Profit = $10.00 + $22.00 + $30.00 = $62.00.

Let's say that for each of the next three years, we acquire both Customer #1 and Customer #2. We already know the following for Customer #1:

• Year 1 = $10.00 profit.
• Year 2 = $22.00 profit.
• Year 3 = $30.00 profit.

So let's run the same scenario for Customer #2.

• Year 1 = -$25.00 + $20.00 = $5.00 loss.
• Year 2 = $12.00 (from year 1 customer) + (-$25.00 + $20.00) = $7.00 profit.
• Year 3 = $8.00 (from year 1 customer) + $12.00 (from year 2 customer) + (-$25.00 + $20.00) = $15.00 profit.
• Three Year Profit = -$5.00 + $7.00 + $15.00 = $17.00.

In total, then, the company that acquires both Customer #1 and Customer #2 generates the following:

• Year 1 = $5.00 profit.
• Year 2 = $29.00 profit.
• Year 3 = $47.00 profit.
• Three Year Profit = $5.00 + $29.00 + $47.00 = $71.00.

Which business do you wish to manage?

• Do Not Pursue Customer 1/2 = $0.00 + $0.00 + $0.00 = $0.00 three year profit.
• Pursue Only Customer #1 = $10.00 + $22.00 + $30.00 = $62.00 three year profit.
• Pursue Customer #1 + #2 = $5.00 + $29.00 + $47.00 = $71.00 three year profit.

If your goal is to never lose money acquiring customers, you go with the first strategy, and in three years, you have no incremental new profit.

If your goal is to maximize 12-month return on investment, then you go with the second strategy, and three years from now you have a customer file generating $30.00 of incremental profit, and for the three-year period, you generated $62.00 profit.

If your goal is to maximize the long-term health of your business, then you go with the third strategy, and three years from now you have a customer file generating $47.00 of incremental profit, and for the three-year period, you generated $71.00 profit.

THIS IS WHY YOU HAVE NO CHOICE BUT TO CALCULATE LIFETIME VALUE.

NO CHOICE.

Don't you think your Executive Team wants to have a discussion about whether they should pursue just Customer #1 or pursue both Customer #1 and Customer #2 each year?

Ever wonder why your business doesn't grow? Maybe you are not pursuing Customer #1 and you are not pursuing Customer #2.

Ever wonder why your business grows slowly? Maybe you are not pursuing Customer #2.

Ever wonder why profit as a percentage of sales is sluggish but total profit is good? Maybe you are pursuing both Customer #1 and Customer #2.

Show of hands ... 

1. How many of you calculate Lifetime Value?
2. How many of you know specifically which of the three strategies outlined above your business should execute to achieve company goals?

If you didn't raise your hand to #1, it's time to hire somebody like me, or hire your favorite vendor. There are many credible vendors who can help you measuring long-term value.

If you didn't raise your hand to #2, don't you think it is time to learn what your business will look like in the future based on different payoff horizons? Contact me now (kevinh@minethatdata.com) and let's get busy.

Lifetime Value Week

On Twitter, I get a lot of questions about lifetime value.

Lifetime Value, or "LTV" as some call it, is simply a measure of the future profit generated by a customer acquired today. There are 22,493 ways to calculate LTV ... so the point of this week isn't to teach you how to calculate LTV, though I will certainly share my thoughts about how I calculate it. Too many pundits, vendors, and analytical gurus will complain about any calculation style ... they're missing the point ... less than 10% of the companies I work with even bother to calculate LTV in the first place. Why have an argument about how to calculate LTV when almost everybody chooses not to calculate LTV?

Here's the interesting thing ... almost any statistical model created on an annual basis has LTV essentially built into it. Smart marketers who avoid all of the nonsense about individual campaigns tend to elevate the value of LTV to (at minimum) Marketing Leadership and Finance Leadership.

LTV requires the marketer/analyst to measure profit. Without profit, LTV has no meaning whatsoever. Who cares that a customer will spend $90 in the next twelve months if only 30% of sales flow-through to profit and the company will spend $30 in ad cost marketing to the customer?

Why was the last paragraph important?

• Profit = $90.00 * 0.30 - $30.00 = $27.00 - $30.00 = ($3.00).

See what I mean? That $90.00 LTV value is meaningless because it represents sales, not profit. Profit equates to a loss of $3.00. #OhBoy.

LTV, when measured in terms of variable profit (what some call "contribution" ... essentially it is profit prior to subtracting fixed costs), takes one of two trajectories.

1. LTV is low, and tends to drop off quickly when annual repurchase rates are low.
2. LTV is plentiful, and can actually increase over time if the customer has a 75% or greater annual repurchase rate and purchases 6+ times per year.

(1) is the reason that so many catalog brands are stuck and cannot grow. They cannot harvest enough long-term profit out of the customer (due to low annual repurchase rates and high ad-to-sales ratios) to overcome the steady decline in co-op response rates.

(2) is the reason that Wal-Mart, Amazon, Starbucks, McDonalds and many other "mega-brands" are what they are ... the customer purchases over and over and over and perpetually generates a mostly constant level of profit ... and this is what leads to the loyalty gurus claiming credit for their efforts (though their efforts have minimal impact on loyalty).

The worst companies spend to a cost-per-new-customer, independent of profit.

Good companies tend to calculate 12-month future profit for new customers, and invest in new customers to optimize twelve-month total profit among first-time buyers.

The best companies calculate future value for EVERY CUSTOMER in the database, regardless of where the customer is in the customer lifecycle. This allows the best companies to maximize future profit from EVERY CUSTOMER in the database.

More on this topic tomorrow.

Items And Pricing In A First Order Matter

Recall our example?

Watch what happens when a customer purchases 2 items at $90 each, instead of 4 items at $45 each.

This is a common outcome. Your merchandising team raises prices, the customer trades off by purchasing fewer of the expensive items, and the net result is a less profitable customer.

See what happens when the customer purchased 3 items at $45 instead of 4 items at $45.

It's helpful to have more items, isn't it?

Now, let's try 4 items, and instead of having them at $45, we'll buy them at $60.

So the secret is to somehow increase prices and increase units.

Unfortunately, it is terribly hard to do both.

At least be aware of the tradeoffs that occur between prices and units. As a former CMO once told me ... "units equal customers".

October 2014 vs. December 2014

Ok, here's the same data as illustrated yesterday, but for recently acquired buyers, illustrating their downstream repurchase rates.

1st Time Buyer in December 2014:

• Cumm Rebuy Rate at End of 12/2014 = 2.4%.
• Cumm Rebuy Rate at End of 1/2015 = 3.9%.
• Cumm Rebuy Rate at End of 2/2015 =  4.9%.
• Cumm Rebuy Rate at End of 3/2015 = 6.1%.
• Cumm Rebuy Rate at End of 4/2015 = 6.9%.
• Cumm Rebuy Rate at End of 5/2015 = 7.9%.
• Cumm Rebuy Rate at End of 6/2015 = 8.9%.
• Cumm Rebuy Rate at End of 1 Year = 15.2%.

1st Time Buyer in October 2014:

• Cumm Rebuy Rate at End of 10/2014 = 3.8%.
• Cumm Rebuy Rate at End of 11/2014 = 7.4%.
• Cumm Rebuy Rate at End of 12/2014 = 9.9%.
• Cumm Rebuy Rate at End of 1/2015 = 11.0%.
• Cumm Rebuy Rate at End of 2/2015 = 13.7%.
• Cumm Rebuy Rate at End of 3/2015 = 15.5%.
• Cumm Rebuy Rate at End of 4/2015 = 16.9%.
• Cumm Rebuy Rate at End of 1 Year = 21.8%.

It's the same story for this company, simply condensed down to a year instead of a five year window.

Do the analysis - it's gonna be enlightening, and it will cause you to question what you are doing.

Or, send me a message and I'll analyze the issue for you (kevinh@minethatdata.com).

More On Weak Performance Among New Buyers From December

Let's review some data from a business from 2010 ... this allows me to evaluate long-term value for five years. But in the short-term, I want to show you the cumulative probability of a customer purchasing for the second time, by month. Watch this:

1st Time Buyer in December 2010:

• Cumm Rebuy Rate at End of 12/2010 = 2.7%.
• Cumm Rebuy Rate at End of 1/2011 = 4.7%.
• Cumm Rebuy Rate at End of 2/2011 = 6.1%.
• Cumm Rebuy Rate at End of 3/2011 = 7.9%.
• Cumm Rebuy Rate at End of 4/2011 = 9.4%.
• Cumm Rebuy Rate at End of 5/2011 = 10.9%.
• Cumm Rebuy Rate at End of 6/2011 = 13.1%.
• Cumm Rebuy Rate at End of 1 Year = 21.7%.
• Cumm Rebuy Rate at End of 2 Years = 26.9%.
• Cumm Rebuy Rate at End of 3 Years = 29.8%.
• Cumm Rebuy Rate at End of 4 Years = 31.8%.
• Cumm Rebuy Rate at End of 5 Years = 32.9%.

1st Time Buyer in October 2010:

• Cumm Rebuy Rate at End of 10/2010 = 4.1%.
• Cumm Rebuy Rate at End of 11/2010 = 9.5%.
• Cumm Rebuy Rate at End of 12/2010 = 13.3%.
• Cumm Rebuy Rate at End of 1/2011 = 15.1%.
• Cumm Rebuy Rate at End of 2/2011 = 16.5%.
• Cumm Rebuy Rate at End of 3/2011 = 18.7%.
• Cumm Rebuy Rate at End of 4/2011 = 20.6%.
• Cumm Rebuy Rate at End of 1 Year = 29.1%.
• Cumm Rebuy Rate at End of 2 Years = 34.4%.
• Cumm Rebuy Rate at End of 3 Years = 37.6%.
• Cumm Rebuy Rate at End of 4 Years = 39.8%.
• Cumm Rebuy Rate at End of 5 Years = 41.5%.

Do you see what happens? In this case (your mileage will vary), the December buyer starts off with low repurchase rates (Jan/Feb are not big demand months), and the customer never catches up. Look at what happens to customers acquired in October 2010 ... they are immediately responsive ... you get the same percentage to repurchase in two months that you get from December 2010 buyers in six months. Why? In large part, this happens because Nov/Dec are highly responsive months. Look at the October 2010 data, especially once you get to January ... cumm repurchase rates slow down dramatically, because Jan/Feb are not big demand months.

You obtain an advantage when you acquire customers just before big demand months. If I were a catalog co-op, Google, or Facebook, I'd be selling that message very hard - heck, it isn't hard to run a life table to measure the dynamic, is it?

And if I were you, I would assign my analytics team this challenge ... they need to explain the dynamic to the whole company. Maybe your peak season is in April ... then you need to acquire customers in February so that those customers can purchase for the second time in April, right? Have your analytics gurus analyze this issue thoroughly. Pass the information along to your customer acquisition team. Pass the information to your Brand Response Marketing team. Then craft tactics that capitalize on this dynamic.

P.S. Critics will say that this is old data, and I should focus on new buyers from 2014. Ok. Tomorrow, we'll do that.

Speaking Of All Those Worthless Christmas Newbies

This from a recent project:

1st Time Buyers, November 2010:

• 1st Full Year Demand = $44.40.
• 2nd Full Year Demand = $19.66.
• 3rd Full Year Demand = $15.61.
• Three Year Demand Value = $79.67.
• Three Year Profit = $13.87.

1st Time Buyers, December 2010:

• 1st Full Year Demand = $25.36.
• 2nd Full Year Demand = $10.62.
• 3rd Full Year Demand = $9.25.
• Three Year Demand Value = $45.23.
• Three Year Profit = $3.09.

Your Brand Response Team takes advantage of this dynamic. They plant all sorts of customer acquisition seeds during the year. They harvest new customers in September/October. Those new customers pay back, immediately, in November/December.

Make sense?

Merchandise Categories Matter in Lifetime Value Calculations

Here's a typical story.

Now, let's assume that the customer purchased from Merchandise Category 3 instead of Merchandise Category 1.

#OhBoy.

Let's swap out Merchandise Category 3, moving Merchandise Category 6 in instead.

Now that you know this, there are several things you would be willing to do, right?

1. You feature Merchandise Category 6 items in your prospecting catalogs (you use prospecting catalogs, right?).
2. You gladly pay more for keywords from Category 6.
3. You pay less for keywords from Category 3.

But If I Cannot Attribute New Orders Properly, I'm Sunk, Right?

Wrong.

Attribution is a mirage. We've all done it wrong for decades and managed to grow businesses to ten percent pre-tax profit.

When I analyze an e-commerce-only business, I get to see unique and interesting trends.

For instance, it frequently takes a half-dozen visits to harvest a first purchase. Those visits tend to come from:

• Social Media.
• Facebook Advertising.
• Google / Paid Search.
• Email Marketing and/or Company Blog.
• Direct Load.
• Visit Resulting in Purchase.

Now, you give this problem to 10 attribution vendors, and you'll get 10 different answers. Every answer is right. Every answer is wrong. Worse, give this problem to Google, and they'll prove that advertising works and therefore they'll prove that you should spend more money, and then they'll ask you to use Google Analytics to prove that digital marketing works.

I'm not saying you shouldn't use attribution algorithms. If anything, I'm saying you should pay for three of them and have your in-house team execute a fourth routine and then average the results.

But what I'm saying is more important ... work every source you have ... work the low-cost or no-cost sources VERY HARD ... and seek to obtain new customers as a function of all activities, not as a function of a handful of optimized channels. When I worked at Nordstrom, we generated more than 10% pre-tax profit and acquired more than a million new buyers in stores each year and couldn't attribute almost any of them to any one marketing activity ... and because we didn't spend more than a percent or two of net sales on marketing, the new customers just kinda rolled in because we focused on the important stuff.

The same thing happens in your business ... many vendors don't want you to know this, of course, because they don't get paid. If you work your platform and low-cost / no-cost channels VERY HARD, you will get a ton of new customers independent of marketing activities. Focus on getting the details right, and the return on investment takes care of itself. Then you can have an argument about which attribution algorithm gets closest to your biased version of the truth.

But The Customer Is Worth Less

Here's what you keep telling me.

Source #1 = Catalog Co-Op.

• Profit per New Customer = ($11.00). You lost $11.00 profit to acquire the customer.
• Year 1 Profit Per New Customer = $17.00.
• Year 2 Profit Per New Customer = $12.00.
• Two Year Net Profit = ($11.00) + $17.00 + $12.00 = $18.00.

Source #2 = Some Form Of Digital Marketing.

• Profit per New Customer = ($13.00).
• Year 1 Profit Per New Customer = $13.00.
• Year 2 Profit Per New Customer = $9.00.
• Two Year Net Profit = ($13.00) + $13.00 + $9.00 = $9.00.

At this point, you tell me that the catalog co-op customer is worth twice as much as the customer acquired from digital marketing.

At this point, you tell me that you don't care about acquiring customers via digital marketing, because they are "worth less".

Two years from now, you'll call me to tell me that your co-op sourced names are all 65 years old and that they are driving your merchandising assortment into an array of merchandise preferred by retired customers ... and the merchandise assortment is no longer appealing to customers sourced from digital marketing ... and therefore, you cannot afford to leverage digital in a meaningful way.

Oh, wait, this all actually happened between 2010 - 2015.

Here's how thinking can evolve.

Be content with acquiring two customers that generate the same profit as one customer. Treat these customers differently downstream. You'll end up with a bigger customer file, but one that is less productive. Regardless, profit is the same, right?

And isn't profit what matters most?

I know, I know, I just talked about how December buyers tend to have lower lifetime value. You'll tell me you just need to ramp-up your efforts in December. But for most catalog-centric brands, market share is being lost in December to online brands, to Amazon, and to retailers who really stepped-up their e-commerce efforts. We need to find other times of the year to find new customers.

And if we cannot do that, and if our primary source of new names is shrinking, well, then we have to think long and hard about finding new customers from other sources, regardless of reduced lifetime value.

Profit per New Customer

Ok, you have your "war room" plastered with five years of customer acquisition history, right? You publish all of your customer acquisition metrics from all sources on the wall in your "war room", don't you?

On the walls, you have key metrics you share with your company, by source of acquisition.

1. Total Demand Generated by Year.
2. Ad Dollars Spent by Year.
3. Total New Customers, by Year.
4. Total Profit, by Year.

And then, you have these important metrics:

1. Marketing Cost per New Customer (Total Ad Dollars / Total New Customers).
2. Profit per New Customer (Total Profit / Total New Customers).
3. Year 1 Profit per New Customer.
4. Year 2 Profit per New Customer.
5. Year 3 Profit per New Customer.
6. Year 4 Profit per New Customer.
7. Year 5 Profit per New Customer.

You are probably saying to yourself, "Hey, idiot, when we acquire a customer from a new source, we don't know anything about years one through five." True. But it is your job to estimate those figures. That's what you are being paid to do. So make a guess, based on what you see with other sources of acquisition.

Compare profit per new customer at the point of acquisition with profit in year one. Do you lose twelve dollars of profit acquiring the customer, and then make twenty dollars of profit in year one? Yes? Then it might be a good idea to acquire that customer, right?

Sit down with your CFO and share your data - heck, it's posted on the walls of your "war room", so just invite her in to take a look. Ask her how deep she is willing to invest in a new customer ... you might be surprised to learn that she is willing to lose money for up to three years in order to grow the business. Or, you might learn about the financial distress your company must deal with ... and that's the reason you can only prospect to break-even.

The key, of course, is to use profit per new customer as the driving metric in this analysis. Don't use marketing cost per new customer, as that metric does not cleanly align with future profit (yes, if you do the math, there is 100% correlation between profit per new customer and marketing cost per new customer, but most people don't do the math to learn the relationship, you included, so just use profit per new customer).

Make sense?

Lifetime Value - Free Shipping

This one is interesting. Look at a customer acquired in June.

Ok, now let's see what future value looks like for those who took advantage of free shipping.

This company generates an average of $10.00 of shipping/handling revenue.

In other words, free shipping caused the company to lose $10.00 ... and the customer paid back $6.15 of incremental profit in year one.

If I ran the analysis forward another year, we'd probably see that all ten dollars have been recouped. But it took two years to recoup the profit. Two years! And in so many of my projects, the profit is NEVER recouped.

Make sure you are measuring what you lose up-front with free shipping.

Make sure you are measuring what you gain, downstream, from a customer acquired via free shipping.