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.

So click here and enjoy!

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!

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:

• Grade A = 19.8%.
• Grade B =   8.2%.
• Grade C =   4.7%.
• Grade D =   2.8%.
• Grade F =   1.5%.
• 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.

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.

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).

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?

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.