## April 30, 2019

### 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?!!

## April 29, 2019

### 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?

## April 28, 2019

### 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!!

## April 25, 2019

### 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?

## April 24, 2019

### 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.

## April 23, 2019

### 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.
• No Prior "Anniversary" history = 1.0% April Rebuy Rate.
• Prior "Anniversary" history = 2.0% April Rebuy Rate.
• No Prior "Anniversary" history = 0.6% April Rebuy Rate.
• Prior "Anniversary" history = 0.9% April Rebuy Rate.
• 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?

## April 22, 2019

### 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?!

## April 21, 2019

### 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.

## April 18, 2019

### 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!!

## April 17, 2019

### Today's Presentation At Datamann Conference In New Hampshire

Maybe you're not joining me today (unlikely), and while I'm devastated by that fact, I thought you'd still like to see the presentation I'm giving while you toil in your office.

### Hillstrom's Targeting: Primary, Secondary, Tertiary

This week I've shared with you my "Weighted Quality" process. It's an important process when considering who to target, as demonstrated yesterday.

I use the same weighting process to create six additional variables.
• Primary Merchandise Category Preference.
• Secondary Merchandise Category Preference.
• Tertiary Merchandise Category Preference.
• Primary Channel Preference.
• Secondary Channel Preference.
• Tertiary Category Preference.
These variables are pure gold!!

Primary / Secondary / Tertiary represent the categories or channels that the customer historically spent the most weighted dollars.

Example:
• \$100 in Mens 0-12 Months Ago.
• \$100 in Womens 13-24 Months Ago.
• \$200 in Kids 25-36 Months Ago.
Weighted Values (recall earlier in the week?) yield the following:
• Mens = \$100.
• Womens = \$60.
• Kids = \$70.
We now have Primary / Secondary / Tertiary categories.
• Primary = Mens.
• Secondary = Kids.
• Tertiary = Womens.
Tomorrow, I'll show you just how valuable Primary / Secondary / Tertiary categories are to a targeting process!

## April 16, 2019

### Hillstrom's Targeting: Rebuy Rates by Weighted Quality

In a recent project, I segmented twelve-month buyers by "Weighted Quality". Each "Grade" represents 20% of the twelve-month buyer file.
• Grade "A" = Weighted History of \$665 or Greater.
• Grade "B" = Weighted History of \$343 to \$664.
• Grade "C" = Weighted History of \$192 to \$342.
• Grade "D" = Weighted History of   \$99 to \$191.
• Grade "F" = Weighted History of     \$1 to   \$98.
Then, I measured rebuy rates in the next month based on "Weighted Quality". Here's what the data demonstrated:
• 13-24 Month Buyers = 1.8%.
• 25-36 Month Buyers = 1.1%.
• 37-48 Month Buyers = 0.6%.
• 49-60 Month Buyers = 0.4%.
From a targeting standpoint, "Weighted Quality" does a spectacular job of separating customers ... good to not-so-good. We easily identify the "best" customers.

Your Homework Assignment:  Create a database attributed called "Weighted Quality". Create another database attribute called "Weighted Quality Segment" with values of A/B/C/D/F.

Tomorrow we'll add another step to the process. Our goal? We want to be able to segment and target customers liberally, in an effort to improve the following:
• Welcome Program.
• Anniversary Program.
• Optimization Program.

## April 15, 2019

### Hillstrom's Targeting: Weighting Variables

The secret to my targeting strategy is in the weighting of data, specifically, prior purchase data.

Now, you might have your own weighting strategy, and if so, that's fine, go with it. I like to discount older transactions.
• 0-12 Month Transactions =   100% Weight.
• 13-24 Month Transactions =   60% Weight.
• 25-36 Month Transactions =   35% Weight.
• 37-48 Month Transactions =   20% Weight.
• 49+ Month Transactions =      12% Weight.
What does this mean?

Let's look at a sample customer:
• 0-12 Month Spend = \$100.
• 37-48 Month Spend = \$100.
• Weighted Spend = \$100*1.00 + \$100*0.20 = \$120 Weighted Dollars.
Here's another sample customer.
• 0-12 Month Spend = \$25.
• 13-24 Month Spend = \$25.
• 25-36 Month Spend = \$100.
• 37-48 Month Spend = \$100.
• 49-60 Month Spend = \$100.
• Weighted Spend = \$25*1.00 + \$25*0.60 + \$100*0.35 + \$100*0.20 + \$100*0.12 = \$107 Weighted Dollars.
The first customer - even though the first customer spent just \$200 historically ... the first customer has more "weighted value" than the second customer.

In individual projects, I use a regression methodology to assign the weights. On average, the weights end up being similar to what is described above.

Tomorrow, I'll show you that the weights "matter", ok? We're in the process of building a targeting strategy to implement Welcome Programs, Anniversary Programs, and Optimization Programs.

## April 14, 2019

### Hillstrom's Targeting!

I know, I know, you're saying to yourself "... all of this theory you've been tossing at us is wonderful and all, and I'd like to have my own Marketing Management System, but I'm not sure how (from a targeting standpoint) I implement the ideas.

So it's time to change that.

Over the next several weeks, I'll talk about targeting opportunities, especially as they relate to email marketing.

Now, if you have a complex machine-learning process, the variables might be of interest to you, but everything else might be a bit simple. That's fine.

But for the rest of you, the concepts I'm going to talk about relate to "who" you target and "how" you target them. Rest assured, there are a lot of ways to positively impact your business via the following:
• Welcome Programs.
• Anniversary Programs.
• Optimization Programs.
Those are the three key areas where targeting works very, very well.

And specifically, the targeting tactics I'll discuss work best within your EMAIL MARKETING program!!!

So tomorrow, we'll begin with variable definition, and we'll go from there, ok? Let's make this information actionable!

P.S.: Yes, this will become a new product, one you are going to want!! Visit my project pricing page for cost details (click here).

## April 11, 2019

### Big Problems With New Merchandise

The bottom portion of our table tells a problematic story.

Look at the projected four-year demand totals, per item, by year.
• 4 Years Ago = \$6,599 per item.
• 3 Years Ago = \$5,612 per item.
• 2 Years Ago = \$11,749 per item.
• 1 Year Ago = \$2,388 per item.
Now look at total projected four-year demand, multiplying projections per item by total new items offered.
• 4 Years Ago = \$6,031,000.
• 3 Years Ago = \$5,966,000.
• 2 Years Ago = \$2,679,000.
• 1 Year Ago   = \$3,408,000.
Two years ago the merchandising team offered few new items ... customers craved the small number of new items, spending a projected ton on them ... but the multiplication yields sub-par projected demand. The problem was not fixed in the past year ... many new items but poor yield per new item, giving a modest gain in total projected four-year demand.

In the past two years, the merchandising team hurt this business.

You are a New Marketing Leader. Don't get blamed for problems you didn't cause. Clearly point out your role in fixing the problem (exposing new items in low-cost / no-cost channels like Email and Instagram to customers with pre-disposition to buy new items in the categories new items are offered). But always, ALWAYS know what role your merchandising team is playing in helping (or harming) the business, and communicate the impact to everybody, ok?

## April 10, 2019

### You Can't Just Throw Quantity At The Problem

We continue to explore our problem where the merchandising team leveraged an inconsistent approach to new merchandise.

Here I analyze four-year total projected net sales ... if you had one item generating \$10 you have a total of \$10 ... 2 items generating \$8 yields \$16 in total ... applied to our dataset.

Tell me what you observe.

The data shows that you can't just throw new items at a problem ... at some point you get past 250ish new items per quarter and then total demand is unchanged regardless how many more new items you throw at the problem.

The New Marketing Leader HAS to know the answer to this riddle. She must clearly communicate to all employees the limits of merchandising strategy. This brand has a point where there's minimal return on investment for new items. Are new items important? Yes! What is most important, of course, are QUALITY NEW ITEMS.

More on the topic tomorrow.

## April 09, 2019

### New Items and Demand Elasticity

Remember our table from yesterday?

I want to show you something ... look at projected four-year value of new items based on how many new items are introduced during a quarter:

What does the graph tell you?

Well, if you offer a small number of new items, you get a lot of demand per item.

If you offer a large number of new items, you get a small amount of demand per item.

There's a relationship here ... and tomorrow, we'll see how much long-term demand you get based on how many new items you offer per quarter.

## April 08, 2019

### So Much Juicy Data!!

The New Marketing Leader has a ton of challenges (and fun) awaiting ... but maybe the most important challenge awaits in understand what role the merchandising team played in the demise of the prior marketing leader.

In that process, the New Marketing Leader learns a ton about how merchandise strategy evolved.

Look at this example ... look down the "Quarter" column ... we can see how many new items were introduced in each quarter behaved (or are predicted to behave) over time.

Your merchandising team executed a highly inconsistent strategy, didn't they?

We'll be able to digest information from this table all week, but let's begin by looking at new items. Read down the "Items" column. Tell me what you see??
• By quarter, there were between 129 new items per quarter and 377 new items per quarter through 25-27 months ago.
• Then, somebody in the merchandising division decided that newness was a bad idea ... new items dropped from 273 to 106 to 52 to 45 to 25 in the quarter ending 13-15 months ago.
• Then, somebody in the merchandising division decided that newness was a GOOD idea ... new items increased from 25 to 149 to 631 7-9 months ago.
This type of wild behavior happens all the time ... and IS NOT THE FAULT OF THE MARKETER!!!!

Yes, marketers lose their job because of these issues all the time. But the marketer doesn't have to lose their job all the time ... or ever.

Spend some time analyzing merchandising issues.

Tomorrow, we'll dig more into this table, because there's so much juicy data there!!!

## April 07, 2019

Just soak it in, ok?

## April 04, 2019

### First Client To Say Yes Gets It

Last week I talked about measuring the downstream value of new items.

Let's try something ... instead of folding this into the typical Total Package framework in the short-term, I'll offer the following:
• First client to say YES gets a "Downstream Analysis" by Merchandise Category for the low price of \$3,000.
• Each additional client to say YES this week gets a "Downstream Analysis" by Merchandise Category for \$5,000.
Going forward, this analysis will be folded into my Total Package framework and cost structure (click here). So take advantage of this one-time opportunity!!!

## April 03, 2019

### Downstream Value Varies by Season

Look at four-year value for new items by season. Quarters 5/9/13/17 represent the first quarter ... 6/10/14/18 represent the second quarter, and so on.

Four-Year Downstream Value by Quarter Introduced:
• 1st Quarter = \$2,521.
• 2nd Quarter = \$2,533.
• 3rd Quarter = \$2,674.
• 4th Quarter = \$2,894.
In our example, new items introduced in the 4th quarter are worth about 7% to 10% more than new items introduced earlier in the year.

You'll work with your merchandising team to make sure that you give new items more exposure all year round (especially in free channels like email and Instagram), and you'll be willing to expose less-productive new items in Q4 simply because their potential value is better than any other quarter.

## April 02, 2019

### Now We Have Downstream Value

We take our annual totals, and then we (via the red numbers in this table) fill in estimates to get all new items to represent a four-year downstream value metric.

The bottom row represents four-year value, by year, for a typical new item.
• Year 1 = \$1,210 per item.
• Year 2 = \$1,850 per item.
• Year 3 = \$2,296 per item.
• Year 4 = \$2,656 per item.
Those are cumulative figures. Incrementally, we observe the following:
• Year 1 = \$1,210 per item.
• Year 2 =    \$640 per item.
• Year 3 =    \$446 per item.
• Year 4 =    \$360 per item.
Over four years, new items quickly lose steam, don't they?

At an approximate 9,000 new items per year, the brand will generate ...
• Year 1 = \$10.9 million.
• Year 2 =   \$5.8 million.
• Year 3 =   \$4.0 million.
• Year 4 =   \$3.2 million.
Let's say that your brand generates \$20.0 million from existing merchandise ... and your CFO says you need to hit \$35.0 million in total annual demand. You have evidence that the best you can hope for is \$20.0 + \$10.9 = \$30.9 million in annual demand. You're not going to hit your goals. And not hitting your goals is going to become your fault!!! That's how business works.

From here, you can calculate how many new customers you need to make up the difference ... and/or you can calculate how many new items you need to make up the difference.

You do this stuff, right?

Right???

## April 01, 2019

### Converting The Table

Once we know how much downstream demand (on average) a new item generates over time, we convert the table into a new table ... in this case, a table that illustrates quarterly demand based on quarters since the item was introduced.

Look at the red numbers ... those are estimates ... estimates that allow us to project how much downstream demand a new item generates on an annual basis.

Tomorrow, we'll convert this table into something usable.

### What The Customer Purchases Matters

Ok, back to our 18 month 1x buyer ... here, I filter for customers who bought only one (1) item in that first order, and then have not purch...