May 16, 2019

Cracks in the Foundation

I'm full of praise for the marketing strategies used by Duluth Trading Company. But you already knew that.

Now go read their Annual Report (click here).

Growth is generally coming via retail ... and profit as a percentage of sales is eroding ... still amazing for a traditional catalog brand expanding into retail ... but eroding.

You're seeing the first cracks in a sound foundation. Nothing to stress about, heck, most retailers would kill to have Duluth Trading Company performance.

But cracks are beginning to show. This is the inflection point (in a business) where things become really fun, the point where a smart Management Team gets to demonstrate their skills.

May 15, 2019

New Item Spacing

It's common to see "Over 369 New Items" proudly pronounced. That's a good thing!!

It's an even better thing to "premiere" these items ... stagger 'em. Give each item attention, and give each item some space to breathe. Use email marketing to do this.

May 14, 2019

Email Marketing / Spacing

Recall this image from a few days ago? Look how the offense, via spacing, created a situation where one player ends up with the ball and doesn't have anybody within 10 feet of him.

When the player is this wide open, the probability of a made three pointer increases from maybe 30% to maybe 40%.

A vendor recently sent out a study about email opens and email clicks (click here). The study suggested that opens and clicks are in decline while volume increases. This is a classic example of Marketing Spacing.

In other words, if you send five email campaigns a week, you are not doing a good job of creating spacing. Everything is crowded.

Want an example?  Here are the messages from a brand you know and love ... from three days last week:
  • Friends and Family Savings for our Best Customers.
  • Spectacular Savings.
  • Save Up To 40% Off.
  • Special Invite - Free Shipping.
  • Up To 50% Off Key Items.
  • Good News: Friends and Family Savings.
  • Save Up To 50%.
That's eight messages (8) within three days.

And you wonder why email marketing "doesn't work anymore" as a Professional recently told me??

The article goes on to talk about segmentation as a key strategy. And if you're going to send out three campaigns a day, I suppose that is a tactic, sure.

A strategy, however, might include not pummeling the living snot of out a customer with three %-off offers PER DAY.

Spacing is critically important in marketing ... it's something more of us should be practicing.



May 13, 2019

Join Me In London!!

How about joining me in London on June 13??

I'll spend an afternoon going through the magic of the "Great Eight", talking about attribution, discussing Merchandise Productivity and evangelizing the importance of New Customers.

Later in the session there will be a handful of case studies that the audience will work through. I know, fun, right??!!!!!



Oh, did I mention ... the afternoon session is being held at Twickenham Stadium ... how cool is that???

So come on out on June 13th, ok??!!





May 12, 2019

Marketing Spacing

I've been a big fan of "Marketing Spacing" since joining Lands' End back in 1990. That's when Bush 41 was in office. That's a long time ago.

What is Marketing Spacing?

Let's look to the sports world for an example ... in this case, last week's Bucks / Celtics game. With the Bucks clinging to a 5 point lead in the 4th quarter, the Bucks inbounded the ball. The Celtics defended well, messing up the play.


Finally, Milwaukee begins the play, three seconds later.


It's often a good idea to get the ball to your best player ... and that's exactly what Milwaukee did. Look at the Spacing ... five players all 23 or more feet from the basket, all spaced out equally across the court.


Milwaukee's best player drives toward the free throw line. Look at how the defense collapses around him.


When multiple Celtics collapse to stop Giannis, somebody is open. That somebody is Brook Lopez.


Do you see what Giannis did there? He traded a 50% chance at 2 points for a 40% chance for 3 points.  0.40*3 = 1.20 points expected.  0.50*2 = 1.00 points expected.

The result?  BANG!!


A couple of things to think about.

First, the marketer would try to attribute credit for the three point shot. The marketer would spend endless hours debating whether the act of shooting (bottom of the funnel) deserves credit or whether the inbound passer (top of the funnel) deserves credit ... or whether Giannis gets credit for drawing two defenders. Naturally, the marketers spend days talking and analyzing and go absolutely nowhere. Yay, Marketers!!

Much more important, however, is the concept of Marketing Spacing and Optimizing Purchases. The concept is identical to what you observe above. Most marketers are pummeling customers with contacts. Not smart. And most marketers are not optimizing anything properly. Think of it this way ... you have an email marketing program ... do you want Giannis shooting a double-teamed 2 point shot or Brook Lopez shooting an open three pointer? You want the latter. Same thing in email marketing. You want the customer to get an email campaign that hasn't been crowded by three other email campaigns in the past thirty-six hours ... and you want to increase the odds of a purchase by personalizing the merchandise assortment to the customer.

Right?

Right??

Marketing Spacing is critically important. In our modern digital age, we bathe customers in a slurry of marketing contacts. It's not the right thing to do, we all do it, and we've got to install sports concepts into our marketing programs to avoid pummeling the customer.








May 09, 2019

Dear Management Analytics Consultant: Promotions

Dear Management Analytics Consultant:

Our email campaigns feature deep discounts. I'm talking about 40% off plus free shipping, or 50% off one item, that kind of thing. I reviewed the past 400 campaigns (we run five a week), and 369 of the 400 campaigns offered at least 30% off. This has to damage our brand. Our sales haven't increased in the past two years, but the average promotional discount increased from 33% off to 43% off. That can't be good. Here's my question: How do we get out of this mess?

Thanks,
Randall



Dear Randall:

I don't think your company wants to get out of this mess. If your company wanted to get out of this mess, you'd have clear communication from Sr. Management stating that you will only offer 20% off for the next year, no more, and your sales plan would include a 20% sales decline to account for the business you'll lose.

You are the marketing expert, so change the game. If you have to offer a huge discount, offer a huge discount on ONE ITEM ... an item that has meaning to your business. Or one category. Work closely with your merchandising team and focus on underperforming items, and offer discounts only on those items. Offer a discount for getting a customer to do something that is beneficial to your brand ... if the customer has switched to e-commerce and won't set foot in a mall anymore, offer an incentive for the customer to set foot in a mall. You do not have to have the same promotions in all channels.

Change your discounting strategy into one that pushes the customer places that are beneficial for both the customer and your brand. Back off your promotional percentage by 5% and see what impact that has on sales.

Now, if you want to do all of that stuff and Sr. Management tells you that it's 40% off plus free shipping in perpetuity, then maybe it is time to look for a new job.

May 08, 2019

Dear Management Analytics Consultant: The Right Email Merchandising Strategy

Dear Management Analytics Consultant:

Here's a problem. I have a segment of customers who love buying Men's Footwear. It's their preferred category. However, when I slice and dice the data, I observe that the category the Men's Footwear buyer is most likely to purchase next is Women's Dresses. This kind of makes sense, because a third of our business is Women's Dresses.

So how should I target this customer? Does this customer get Men's Footwear, other Men's Categories, or Women's Dresses?

Thanks,
Ashley



Dear Ashley:

This isn't an "optimization" problem. This is a "marketing strategy" problem. You are a marketer. It's your job to have a strategy.

Here's a tip. In the vast majority of my projects, the most valuable customers (after equalizing for recency/frequency/monetary/channel-preference) are those who buy from multiple merchandise categories.

This means you need to have a multi-pronged strategy for communicating to this customer. Let's use email as an example. This might be a "personalization strategy" for this customer, via email marketing:
  • Monday = Primary Category (Mens Footwear).
  • Tuesday = Primary Division (Mens).
  • Wednesday = Most Likely Response Category (Womens Dresses).
  • Thursday = Weekly Promotional Strategy.
  • Friday = New Merchandise Friday.
This means that Monday/Tuesday/Wednesday campaigns exploit "merchandise personalization" based on prior purchasing behavior.



May 07, 2019

Dear Management Analytics Consultant: How To Evaluate This Stuff

Dear Management Analytics Consultant:

How the heck do we evaluate how our catalog is working? Seriously? If I have twelve items on pages 8-9, how do I know if those twelve items worked in harmony and delivered sales/profit?

Thanks,
Darren



Dear Darren:

That's a good question. Each catalog should have a mail/holdout panel. But instead of measuring success at a catalog-level, we're going to measure success at an item level. Measure the sales the mailed group generate for a SPECIFIC ITEM and compare the sales to the control group (for that SPECIFIC ITEM) ... a group who did not receive the catalog.

Sum the results for all items on pages 8-9.

Look at items not offered in the catalog, compare mailed vs. holdout for those items, and allocate the difference equally across all spreads.

Now run a profit and loss statement on the incremental sales (mailed totals - holdout totals) for items on pages 8-9.
  • RED = Profit vs. Net Sales Between 30% and 39%.
  • ORANGE = Profit vs. Net Sales Between 20% and 29%.
  • GREEN = Profit vs. Net Sales Between 10% and 19%.
  • BLUE = Profit vs. Net Sales Between 0% and 9%.
  • PURPLE = Profit vs. Net Sales < 0%.
Repeat this for every spread in your catalog.

On the wall in your Omnichannel Seamless Customer Experience Conference Room (formerly known as Conference Room 1A), post each spread and color-code the spread.

Tell me what you see.

You'll see why the catalog worked or did not work.

Not only is this analysis highly valuable, it's FUN to run!!!

P.S.:  This analysis also works for email marketing.

May 06, 2019

Dear Management Analytics Consultant: Merchandise Control

Dear Management Analytics Consultant:

You frequently talk about the importance of merchandise. But honestly, I don't have any control over merchandise. I'm just a marketer. Shouldn't I just assume that the merchandise is great and then focus on my role in creating a frictionless customer experience?

Thanks,
Elmer



Dear Elmer:

You couldn't be more wrong. You play a major role in determining the merchandise that succeeds and the merchandise that fails. Who owns email marketing? You do! Why can't you feature new merchandise, personalized based on prior customer purchases, every Wednesday and Friday? If you give customers a chance to buy new merchandise today, those items will become winners tomorrow. This stuff isn't rocket science. Use Instagram to share the new items that have the best chance of success, and create an in-house Influencer to convince customers to buy the stuff. That's your job, and your co-workers are waiting for you to do something. So do something!! Put your best winning items in expensive marketing channels, put new items in inexpensive marketing channels.

P.S.:  Spend less time creating a frictionless customer experience and instead cause a customer to crave your merchandise.

May 05, 2019

Dear Management Analytics Consultant: Attribution

Dear Management Analytics Consultant:

Our Marketing VP is as old-school as they come. She strongly believes that the catalog is responsible for all sales, telling me that without the catalog we wouldn't have acquired the customer in the first place, meaning that all downstream orders should be credited to the catalog. At minimum, she'll accept matchback results that give credit for all online orders to the catalog recently mailed to the customer. Meanwhile we've A/B tested our catalogs (without telling her) and we learned that 42% of demand is catalog-attributable while 58% is organic and is generated without catalog mailings.

Here's my question. How do I convince my boss that we have actual data that proves the catalog has limited value? I've even hired a pair of attribution vendors to analyze a month of data, and their analyses are wildly different and are unreliable. How can I get my boss to trust me when I can't trust attribution vendors because their algorithms disagree?

Sincerely,
Sophia



Dear Sophia:

You probably won't convince your boss that you are right and her worldview is wrong. Your boss built an entire career based on a worldview not unlike the J. Peterman character on Seinfeld had. If she accepts your view of catalog marketing (which is based on data, by the way), she has to come to terms with the fact that the value she brings to the organization is diminished.

There are several things people in your position do. If Management demands that a lot of catalogs be mailed, then mail them ... but not necessarily to housefile customers. Mail them to prospects and hold out online housefile buyers. Stuff as many catalogs into the mailbox of a first-time buyer for three months. Use email marketing to communicate to pure online buyers with high organic percentages. Recommend smaller catalogs (which are more productive - chalked full of winners of course) and then run scenarios showing that you can mail MORE of the smaller catalogs, which should be pleasing to your old-school boss.

A final note. All attribution models are flawed. They are all based on assumptions, though some of the assumptions (and math behind the assumptions) are better than others. The digital age promised us clarity. Instead, the digital age delivered confusion. Use your A/B test results to inform investment decisions. Work with a trusted attribution vendor and help shape their work with your A/B test results. Vary your search and Facebook spend on a monthly basis, giving attribution vendor algorithms more opportunities to detect key relationships.




May 02, 2019

Organic Percentage as a Key Targeting Variable

I spoke at a conference a few weeks ago, and the topic of "attribution" was popular. Folks wanted to know "how" to attribute orders to marketing activities.

The most important variable to store in your database (for attribution purposes) is the "organic percentage". At a customer level, you calculate the percentage of demand that your mail/holdout tests show happens regardless of catalog mailings (in a print environment). When evaluating your matchback results, you discount the results by the organic percentage.

In other words, if your matchback analytics (a lousy form of attribution) show that you generated $3.50 for a segment of customers, you discount the matchback analytics by the 50% organic percentage (in the example above) ... and that means that a $3.50 matchback result is actually a $1.75 incremental outcome based on mail/holdout testing.

And the difference in results is staggering. When you run your simulations for optimal page counts (and yes, the ad costs above are inaccurate due to printing efficiencies, but they're outlined as they are to prove a point), you see dramatic differences between matchback analytics and the far more accurate outcome calculated via the organic percentage.
  • Via matchback, your optimal page count is between 80 and 192 pages ... fatten-it-up and let-er-rip!!!
  • Via the organic percentage, you are limited to a tiny 32 page catalog.
The former is inaccurate (and benefits the entire print ecosystem).

The latter is far more accurate (and benefits your brand).

Store the organic percentage in your database at a customer level ... you know how to do this, you have your mail/holdout tests to calculate the organic percentage.

Yup, this targeting stuff works!

May 01, 2019

Hillstrom's Targeting: Consequences of Targeting

You analyze hundreds of companies and you see recurring themes. One of the themes is the impact of digital messaging on a retail audience.

Here's how this works ... you have a previously retail customer who you bombard with digital messages (frequently via email or social) ... and guess what? The customer buys online.

When a former retail customer buys online, customer behavior changes. I've run countless simulations that demonstrate what happens ... but you don't need the simulations, you've got real data to prove the point.

Here's an example ... a group of equalized customers (weighted historical spend of $450 - $499, 0-12 month buyers). There are four segments. Then, I measure next-twelve-month spend based on the segment the customer belongs to. Here's the data.

The details tell a fascinating story.

If you have a retail-only buyer, that customer will spend $184 in the next year (in this dataset, your mileage will vary).

Now you convert the customer to a digital buyer instead of getting the store purchase you normally would have obtained. Look at future retail/online spend.
  • Retail spend drops from $149 a year to $126 a year ... -15%.
  • Online spend jumps from $36 a year to $87 a year ... +142%.
  • Total spend increases from $184 a year to $213 a year ... +16%.
In this case, we find the beloved "omnichannel gain" that vendors constantly scream at us about ... the "multi-channel" buyer spends 16% more.

But ... BUT ... spend in retail is -15% to what it would have been.

If the customer eventually converts preference online (a minority currently do this, the dynamic will change over time), then look at what happens to retail spend ... it drops from $149 to $126 all the way down to $54.

In other words, if your targeting strategy constantly screams benefits of online buying, you'll get prior retail-only customers to embrace your digital messaging ... and then what?
  • You close stores.
What happens when stores close?  Well, the retail history will disappear ... and then look at the future value of the online-only buyer ... it's $175 ... in our case, it's the lowest of the four segments. You close the store and the customer spends less (Macy's has gone on the record publicly that this dynamic happens) and you might be more profitable but customers are less loyal.

This is the consequence of targeting in a retail environment. Your targeting strategy shifts customers in/out (usually out) of stores. When that happens, the store dies, and when the store dies, customers who used to shop in that trade area become less valuable.

#Omnichannel!!!!!

You might have a great targeting environment ... and you might do a spectacular job of targeting. But short-term ROI measurement is feckless if you don't understand the consequences of targeting. Run simulations and understand what the long-term impact of targeting strategies are. You HAVE to do this, right? RIGHT??

Run the simulations.

Understand what the consequences of targeting are.

Then make better decisions.

Contact me (kevinh@minethatdata.com) if you need help. Pricing information is outlined here.





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.
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?

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.

Cracks in the Foundation

I'm full of praise for the marketing strategies used by Duluth Trading Company. But you already knew that. Now go read their Annual...