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?

Hillstrom's Targeting: Extending The Grid

Ok, let's extend the grid concept for email targeting.

Let's say you have a customer who has a Primary Category of 2 and a Secondary Category of 12, based on Weighted Historical spend. What is the probability of this customer buying from other categories in the next month?

• Category 00 = 1.2%.
• Category 01 = 0.3%.
• Category 02 = 2.8%.
• Category 03 = 0.6%.
• Category 04 = 1.5%.
• Category 05 = 0.4%.
• Category 06 = 0.4%.
• Category 07 = 1.3%.
• Category 08 = 0.7%.
• Category 09 = 0.5%.
• Category 10 = 0.5%.
• Category 11 = 1.7%.
• Category 12 = 6.4%.
• Category 13 = 1.8%.
• Category 14 = 1.2%.
• Category 15 = 0.3%.
• Category 16 = 1.4%.
• Category 17 = 0.4%.
• Category 18 = 0.5%.
• Category 19 = 3.0%.
• Category 20 = 1.2%.
• Category 21 = 1.2%.

Clearly Categories 2/12 are important ... and there's a bump in Category 19 as well. But clearly, the Primary / Secondary framework matters ... it matters a lot.

This gets you thinking about how best to contact the email subscriber. Here's a possible framework:

• Monday = Key Brand Message (same message sent to everybody).
• Tuesday = Feature New Merchandise from the Primary Category.
• Wednesday = Feature New Merchandise from the Secondary Category.
• Thursday = Feature New Merchandise from the Tertiary Category.
• Friday = Key Winners From Across The Brand (same message sent to everybody).

Using this framework, you expose every single email subscriber to an outstanding cadence. Each customer gets to see a key brand message. Each customer gets to see what your winners are. And each customer gets to see new merchandise from their Primary / Secondary / Tertiary categories.

We'll extend the concept tomorrow.

P.S.: Yes, I get it ... some of you dynamically load products in a personalized manner into your campaigns, while others just chug out 40% off plus free shipping messages with a man and woman looking warmly at the image of a t-shirt. Y'all do something different. I'm encouraging you to partner with somebody, in-house or outside, to target appropriate merchandise to the right customer. People have been doing this for twenty years. Pick up all the dollars lying there on the floor, ok?!

Hillstrom's Targeting: A Big 'Ole Grid!!

Take a look at this monster!

Go ahead, click on it ... I'll wait for you.

Welcome back!

This table is for the best customers ... a grade of "A". Each row represents a preferred Weighted Category ... if a customer spent more weighted historical money on Category 11, then you read across the row for Category 11 ... the numbers are the probability of a customer buying from any category in the next month.

Read across the row for Category 11. Tell me what you see??

I'll simplify it for you.

• No customer is more likely (by a long shot) to buy from Category 11 next month than customers who have spent the most weighted historical dollars in Category 11.
• For these customers, their preferred future categories are Category 11 and Category 12.

What should your email campaigns focus on, for this customer?

• Category 11.
• Category 12.

Now go do something about it!

This targeting methodology makes it really easy to do the right thing for a customer. If the customer prefers Category 11, give the customer what the customer asks for! Feature new products from that Category, and heck, add some of Category 12 for the customer as well.

Hillstrom's Targeting pricing can be found here.

Hillstrom's Targeting: Value of Primary / Secondary / Tertiary

Ok, let's look at a practical example of Primary / Secondary / Tertiary categorization of merchandise categories.

In this case, we look at next-month repurchase rates based on Weighted Customer Quality and Primary / Secondary / Tertiary for a specific merchandise category (say Home merchandise). We use historical customer data to segment customers based on quality and if the customer's spend on Home merchandise yielded Home as a Primary / Secondary / Tertiary favorite, or none of those.

Ready? Here's the targeting table. The targeting table illustrates the probability of a customer buying from Home in the next month, based on Weighted Customer Quality and Primary / Secondary / Tertiary preference for Home merchandise.

The cells that are red are cells that, if you were to target Home merchandise in an email campaign, you'd ultimately target.

You'd target any 12-month "A" customer, period.

You'd target 12-month "B" customers who like Home as a Primary or Secondary preference.

You'd target 12-month "C" customers who like Home as a Primary preference.

You have what you need to execute an Email program that features Home merchandise. Those are the cells you need to target, if you want outstanding response.

If you want to add segments?

Add "B" customers with Tertiary preference.

Add "C" customers with Secondary / Tertiary preference.

Add "D" customers with Primary preference.

It's really quite simple!

You've just added a component to your Optimization Program ... good for you!!