Hillstrom's Targeting: Calculating The Organic Percentage

I recall analyzing this test at Eddie Bauer ... in 1996.

1996. Twenty-three years ago.

Of course, we didn't have mobile in the mix, so we just removed that column and analyzed the rest.

You have your mailed segment, you have your holdout segment, and you run the test for three months or six months or preferably a year. Then, by channel, you compare what you sold in the mailed group vs. what you sold in the holdout group. Look at the results (bottom arrow).

• Mailed-In Checks = 10% Organic (90% catalog driven).
• Call Center / Phone = 30% Organic (70% catalog driven).
• Desktop / Laptop = 60% Organic (40% catalog driven).
• Mobile = 80% Organic (20% catalog driven).

The overall average was 49% Organic (51% catalog driven).

Now, you go into your database and you use weighted demand and weighted organic percentages and you calculate the historical organic percentage for each customer. Here's an example:

• 1 Purchase for $100 0-12 Months Ago, via Desktop / Laptop.
• 1 Purchase for $100 13-24 Months Ago, via Mobile.
• 1 Purchase for $100 25-36 Months Ago, via Phone.
• 1 Purchase for $100 37-48 Months Ago, via Mail.

In this project, weighting is as follows:

• 0-12 Months Ago = 100%.
• 13-24 Months Ago = 60%.
• 25-36 Months Ago = 35%.
• 37-48 Months Ago = 20%.

Therefore, I have the following amount of weighted dollars:

• 100*1.00 + 100*0.60 + 100*0.35 + 100*0.20 = $215 weighted dollars.

And each channel has an associated organic percentage, yielding weighted organic dollars.

• 100*1.00*0.60 + 100*0.60*0.80 + 100*0.35*0.30 + 100*0.20*0.10 = $120.50 weighted dollars.

The calculation for historical organic percentage is straightforward:

• $120.50 / $215.00 = 56%.

In other words, 56% of weighted historical spend is "organic", and 44% is driven by catalog marketing.

This percentage (56%) is stored in your database ... it's calculated in real-time or weekly or whatever works best for you. But it is scored for every single customer in the database, regardless.

It's a good idea to create another variable .. .an "organic percentage segment" that has Low / Medium / High designations for the organic percentage.

• 0% to 33% Organic = Low.
• 33% to 67% Organic = Medium.
• 67% to 100% Organic = High.

The designation allows you to appropriate target customers at a simple level. It also allows you to store the segment in post-campaign analytics, allowing you to measure if high-organic-percentage customers generate incremental profit when you mail catalogs.

Use the template above, and combine the template with your mail/holdout test results and you've got something interesting, don't you?!!

Hillstrom's Targeting: It Works For Catalogs Too!

You've heard me talk about the "Organic Percentage" ... over and over again. It's the percentage of sales that are not caused by catalog marketing. Smart catalogers figured this stuff out fifteen years ago (we studied this at Lands' End in the early 1990s ... we called it Cannibalization back in the day).

From a targeting standpoint, you want to do the following:

1. Mail catalogs to customers with a LOW organic percentage.
2. Greatly reduce catalogs to EVERYBODY ELSE.

It turns out that our targeting framework works very, very well when evaluating the Organic Percentage.

In our dataset, here are customers with Quality = "A" ... they're the very best customers. I segmented the customers based on prior Weighted Organic Percentage, and then measured in the next month how much customers spent ... organically and via print. Here's the table.

Again, these are the best customers ... and look at what happens in the High Weighted Organic Percentage segment ... those customers generate 79% of future demand organically. Now, because these are best customers, you'll still mail 'em catalogs.

Here's the same table for customers with Quality = "C".

Look at the High Weighted Organic Percentage segment ... they generate 75% of future demand organically. This means they'll only generate $1.19 because of catalogs ... whereas Low Weighted Organic Percentage customers generate $4.26 because of catalogs. If you mail the High segment twice a month, you're doomed!!!!

So please, get a High / Medium / Low Weighted Organic Percentage variable into your targeting framework ... and then capitalize on it!!!

And if you don't have the resources to do that, contact me (kevinh@minethatdata.com) and I'll do it for you, ok?

Hillstrom's Targeting: Combine Anniversary Events and Email Clicks

Last week we talked about the importance of recent email clicks ... and we talked about the importance of "Anniversary Events".

Combine the two and you're really got something!

Let's look at April repurchase rates by Customer Quality, 30-Day Email Click, and a Prior April Purchase. Ready? We'll simply by looking at Customer Quality = "C", ok?

• 3.7% for No Anniversary, No Recent Email Click.
• 6.2% for Yes Anniversary, No Recent Email Click.
• 7.0% for No Anniversary, Yes Recent Email Click.
• 10.2% for Yes Anniversary, Yes Recent Email Click.

Looks like the combination of targeting variables yield a highly meaningful result, don't you think??

If you have a customer approaching an Anniversary Event and showed interest by clicking through an email campaign in the past month, you better use all of the targeting tools at your disposal to encourage a purchase.

Right?

Go get busy, right now!!

Hillstrom's Targeting: Recent Email Clicks Matter ... A LOT!

A visit to your website in the past 30 days matters.

A visit to your website via email marketing in the past 30 days matters more!!

Let's look at rebuy rates for the next month, based on customer quality (A/B/C/D/F) and an email click designation (None, Old Clicks, or Click in the Past Month).

Customer Quality = A

• None = 16.9% Rebuy Rate.
• Old Click = 16.8% Rebuy Rate.
• Recent Click = 26.3% Rebuy Rate.

In other words, your email marketing program causes a click, and the click causes a customer to become much more likely to repurchase in the next thirty days.

Also notice that the old clicks are meaningless. Recent clicks matter.

Recent clicks matter.

Customer Quality = B

• None = 7.7% Rebuy Rate.
• Old Click = 7.3% Rebuy Rate.
• Recent Click = 11.9% Rebuy Rate.

Customer Quality = C

• None = 4.3% Rebuy Rate.
• Old Click = 4.2% Rebuy Rate.
• Recent Click = 7.7% Rebuy Rate.

The trends are consistent, aren't they?

Customer Quality = D

• None = 2.6% Rebuy Rate.
• Old Click = 2.4% Rebuy Rate.
• Recent Click = 5.2% Rebuy Rate.

Customer Quality = F

• None = 1.4% Rebuy Rate.
• Old Click = 1.3% Rebuy Rate.
• Recent Click = 3.5% Rebuy Rate.

Even among lapsed buyers, the trends remain constant.

Customer Quality = 13-24 Months of Recency

• None = 1.3% Rebuy Rate.
• Old Click = 1.9% Rebuy Rate.
• Recent Click = 5.2% Rebuy Rate.

Look at that!  If a customer has not purchased in, say, 18 months, but the customer clicked through an email campaign last month, that customer is more likely to repurchase (5.2%) than a customer with Average Quality (C) who has not clicked through an email campaign ever. Yeah, that's a big deal!!

Customer Quality = 25-36 Months of Recency.

• None = 1.0% Rebuy Rate.
• Old Click = 1.1% Rebuy Rate.
• Recent Click = 3.5% Rebuy Rate.

Customer Quality = 37-48 Months of Recency.

• None = 0.5% Rebuy Rate.
• Old Click = 0.6% Rebuy Rate.
• Recent Click = 2.3% Rebuy Rate.

Customer Quality = 49-60 Months of Recency

• None = 0.3% Rebuy Rate.
• Old Click = 0.4% Rebuy Rate.
• Recent Click = 1.8% Rebuy Rate.

It's clear that you need (at minimum) an email click targeting segment, right? A simple yes/no indicator for whether a customer clicked through an email campaign in the past thirty days will get you started. Every time that indicator goes from 0 to 1 your marketing automation program should do something to encourage this customer to purchase.

Right?

Click here for pricing for Hillstrom's Targeting, should you not have the data summarized properly in your database.

Hillstrom's Targeting: Welcome Program Segmentation

Let's think about it this way ... the first-time buyer is part of a Welcome Program if Recency = 0/1/2/3 months old. It's the prime development period in the life-cycle of the customer. This is it!!

Because the customer is new, the customer ranks "low" in the quality segment. Here's what it looks like for the business we're analyzing.

• "A" customers = 0.08% are in a Welcome Program.
• "B" customers = 0.5% are in a Welcome Program.
• "C" customers = 2.6% are in a Welcome Program.
• "D" customers = 8.9% are in a Welcome Program.
• "F" customers = 23.6% are in a Welcome Program.

In other words, this is the place where you move a customer "up the ladder", if you will.

Make sure that Merchandise Preference is adequately incorporated into your email marketing program, especially when it comes to your Welcome Program.

NaviStone

NaviStone was birthed by Cohere One (Cohere One is now owned by Midland Paper and integrates solutions with NaviStone). A new privacy-based lawsuit against NaviStone emerged in recent days (click here). Recent lawsuits have been dismissed (click here).

If you want to see what has been argued about NaviStone, click on this link to read more. 

Whether you agree or disagree with their practices as a Professional is irrelevant. I'm simply asking you to take a few minutes today and think, ok? Think about the business opportunity lost by not leveraging technology that harvests personal information unintended for third-party consumption. Conversely, think about how you might be messing with a customer when you harvest personal information unintended for third-party consumption.

Hillstrom's Targeting: Folding In Your Anniversary Program

Recall that we have grades for customer quality:

• A
• B
• C
• D
• F

Now, remember when you were in school and you earned an A+ or a C-? We can apply comparable logic to our A/B/C/D/F framework. Add a "+" if the customer ever bought from the month we're currently in. For instance, if the customer ever bought in April, add a "+" to the segmentation variable. This means that the customer is likely to be "extra responsive".

How do I know that the customer will be "extra responsive"? Well, I've got data on my side! So do you!!

Here's an example, for "A" customers.

• No Prior "Anniversary" history = 13.2% April Rebuy Rate.
• Prior "Anniversary" history = 24.6% April Rebuy Rate.

For "B" customers:

• No Prior "Anniversary" history = 7.0% April Rebuy Rate.
• Prior "Anniversary" history = 11.0% April Rebuy Rate.

For "C" customers:

• No Prior "Anniversary" history = 4.2% April Rebuy Rate.
• Prior "Anniversary" history = 6.8% April Rebuy Rate.

For "D" customers:

• No Prior "Anniversary" history = 2.6% April Rebuy Rate.
• Prior "Anniversary" history = 4.3% April Rebuy Rate.

And for "F" customers:

• No Prior "Anniversary" history = 1.4% April Rebuy Rate.
• Prior "Anniversary" history = 2.2% April Rebuy Rate.

It even works for lapsed buyers. Here's the 13-24 month file:

• No Prior "Anniversary" history = 1.6% April Rebuy Rate.
• Prior "Anniversary" history = 3.8% April Rebuy Rate.

25-36 month buyers:

• No Prior "Anniversary" history = 1.0% April Rebuy Rate.
• Prior "Anniversary" history = 2.0% April Rebuy Rate.

37-48 month buyers:

• No Prior "Anniversary" history = 0.6% April Rebuy Rate.
• Prior "Anniversary" history = 0.9% April Rebuy Rate.

49-60 month buyers:

• No Prior "Anniversary" history = 0.3% April Rebuy Rate.
• Prior "Anniversary" history = 0.5% April Rebuy Rate.

Yup - the methodology works. The simple fact that the customer has an "Anniversary Purchase" 12 months ago yields rebuy rates that are 70% to 100% better. Heck, this isn't even an "Anniversary Program" ... which would REALLY cook via this framework.

In email marketing, this tactic works well. Feature what the customer wants to see, and show 'em stuff that aligns with an Anniversary Purchase. Simple! Now go do something with this knowledge, ok?