May 23, 2018

Memorial Day Weekend

There's the obvious reason why we "celebrate" Memorial Day.

And then there's the secondary outcome ... an unofficial celebration of the start of Summer.

So let's get an early start on the unofficial start of summer.

I'll return next week ... and through the summer, don't be surprised to see me take a few days off. You should do the same. Who knows, you might recharge your batteries enough to come up with new merchandising ideas that help grow your business, right?

May 22, 2018

Lapsed Buyers

Here's a case where I can measure repurchase rates by year of recency - going back a whopping twenty-two years.

Tell me what you observe?

Say you are told that there is a "pot of gold" in your lapsed buyer audience. So you go apply some "vendor magic" to customers who last purchased 9 years ago, and the "magic" works 5% better.
  • 1,945 * 0.05 = 97 additional buyers.
A pot of gold worth 97 buyers.

This company acquires 230,937 new customers per year.

Now obviously it can be helpful to increase the number of 13-24 month buyers by 5% (34,292 * 0.05 = 1,715 buyers). But still ... it's 1,715 buyers. It's nothing.

Think about how much time you spend trying to "tickle the buying bone" of your lapsed buyer file.

Think about how much time you spend trying to acquire new customers.

Focus on the latter.

May 21, 2018

Empty Seats

One of the handful of accounts I follow on Twitter is Empty Seats Galore (click here).


Empty Seats in sports are comparable to missing your sales plan.

Why are there empty seats in sports?
  • Tickets are too expensive.
  • The stadium is lousy.
  • The team is lousy (most likely).
Why are you missing your sales plan?
  • Prices are too expensive.
  • Your store is lousy.
  • Your merchandise is lousy.
So why spend so much time trying to fix things via marketing tactics when you know full well why business is not meeting expectations?



May 20, 2018

Best Customers Will Hold You Back

Down the road we have somebody who likes to receive the print version of the newspaper.


Your best customers (unless you are a fashion brand) don't like change. They like you just the way you are ... just keep giving them what they want.

Now, if you are the media company lying in the driveway, what is best for your long-term success? Do you keep tossing the newspaper on the driveway of your "best" customer, or do you do the hard work of moving into the future?

May 17, 2018

Expanding The Assortment

You have a line of widgets, and they sell really well. A million dollars last year.

So you decide to expand ... you offer a cousin of widgets ... bidgets!
  • Widget Sales drop from $1,000,000 to $800,000.
  • Bidget Sales are $500,000.
  • Total Sales = $1,300,000.
You're frustrated a bit that widgets seem to be "dying", so you introduce quidgets!
  • Widget Sales drop from $800,000 to $700,000.
  • Bidget Sales drop from $500,000 to $400,000.
  • Quidget Sales are $400,000.
  • Total Sales = $1,500,000.
Here's another fun way to look at the dynamic.
  • 0 Product Lines = $0.
  • 1 Product Line = $1,000,000.
  • 2 Product Lines = $1,300,000.
  • 3 Product Lines = $1,500,000.
Transformed into an index gets us to this:
  • 0.000 , 0.000.
  • 0.333 , 0.667.
  • 0.667 , 0.867.
  • 1.000 , 1.000.
Guess what? That's another situation where a*(x^b) comes into play!!
  • a = 1.009.
  • b = 0.387.
And just like that, we have a forecasting tool to help us figure out how big or small our merchandising assortment needs to be!

May 16, 2018

Email Frequency

You test email frequency all the time, right?

RIGHT?

I know, you don't.

Here's the results from a test, post-attribution (after averaging the results of five different attribution vendors).
  • 0 Contacts / Week = $0.00 total.
  • 1 Contact / Week = $0.10 total.
  • 2 Contacts / Week = $0.17 total.
  • 3 Contacts / Week = $0.19 total.
  • 4 Contacts / Week = $0.20 total.
  • 5 Contacts / Week = $0.205 total.
I'll convert the metrics to fractions of the totals.
  • 0.000 contacts = 0.000 demand.
  • 0.200 contacts = 0.488 demand.
  • 0.400 contacts = 0.829 demand.
  • 0.600 contacts = 0.927 demand.
  • 0.800 contacts = 0.976 demand.
  • 1.000 contacts = 1.000 demand.
Guess what? We've just established another a*(x^b) relationship!!
  • a = 1.071.
  • b = 0.408.
Now, it turns out that a*(x^b) doesn't work great here ... the r-squared is north of 90% but it isn't reflecting reality great ... and that's because after 3 contacts a week you don't generate any additional demand.

This is why you execute email frequency tests. If you learn that only three contacts a week generate incremental volume, then you either kill two campaigns a week (which you'll never do) or you experiment like there is no tomorrow with two campaigns because there's absolutely no downside to doing that.

Test different creative treatments ... test new merchandise ... test no discounting ... test humor ... test content ... TEST SOMETHING!!!! If your test results look like these, then you have just been given the freedom to have a TON OF FUN. What would stop you from having a TON OF FUN?????

May 15, 2018

New Items, Demand Growth

Here's six years of new merchandise introductions, and the demand generated by the new merchandise introductions. That's the top of the table ... the bottom of the table converts the numbers to an index based on 2017 results.


Ok, let's plot the relationship between each index.


Oh - that's the a*(x^b) relationship!
  • a = 0.994.
  • b = 0.394.
With the relationship we can calculate how much demand we generate when we vary new item introductions ... cut back by 80% and we lose 55% of demand ... increase by 40% and we increase demand from new items by 16%.

Now we can plan what an appropriate merchandise assortment might be.

Fun!


May 14, 2018

Mix Of Unattributed New Customers

New customer acquisition comes down to two important components.
  1. New customers who are acquired via word of mouth, and are largely low-cost / no-cost in nature.
  2. New customers who you pay Google, Facebook, Digital Grifters, Television, Radio, Print, Podcasters, Influencers, and any other combination of thieves and honest middlemen to acquire.
It turns out that (1) and (2) are critically important ... especially (1).

When I analyze a highly successful business, it is common to learn that 50% (+/-) of new customers are unattributed (after working with your five attribution vendors and averaging their very different outcomes).

When I analyze a struggling brand, it is common to learn that 15% (+/-) of new customers are unattributed ... while 85% (+/-) are from clearly defined paid sources.

Here's what is really fascinating.

Word of mouth new customers can be explained by a simple equation:
  • Word of Mouth Newbies = C + X*(Change in Sales Last Year) + Y*(Marketing Efforts To Generate Word of Mouth).
  • C = Baseline.
  • X = Rate that change in sales last year impact word of mouth next year. Think of it this way ... if your sales are in decline (Sears) you get a bad reputation that hurts you the year after.
  • Y = Impact of marketing strategies designed to generate word of mouth.
Meanwhile paid new customers can be explained by a simple equation as well.
  • a*(x^b).
80% of the companies I work with have two significant problems.
  1. Inability to identify quality new merchandise.
  2. Inability to increase new customer acquisition.
And when you dig into the data, you learn the following.
  1. Word of Mouth new customer acquisition is fundamentally broken and Management has not hired the right people to fix the problem.
  2. Paid new customers ... via a*(x^b) ... has been optimized and is now being sub-optimized to keep sales at least flat while harming profit.
What percentage of your new customers are unattributed ... and are likely due to having a sound word of mouth program?

May 13, 2018

a*(x^b)

My entire career is based on a simple equation:

  • a*(x^b)
What the heck is Kevin talking about?

Let's think about this for a moment.
  • You spend $100,000 on paid search.
  • The average of your five attribution vendors say that paid search delivered $350,000 in sales.
  • You convert 40% of sales to profit.
  • Profit = $350,000 * 0.40 - $100,000 = $40,000.
  • Somebody smart in your company says "HOW MUCH SHOULD WE BE SPENDING ON PAID SEARCH?"
Somebody smart in your marketing department knows LTV and knows that you can actually afford to lose $25,000 instead of making $40,000 because the customer will pay you back within eighteen months.

How much should you spend so that you will lose $25,000?

That's where a*(x^b) becomes pretty darn important.

The simplest version of the equation is this:
  • 1*(x^0.5) ... better known as the SQUARE ROOT RULE.
  • You take the square root of what you previously spent and what you want to spend, and apply that to sales.
Here's what the table looks like in our example:


The equation suggests that we could spend $240,000.

Unless we have valid test results, we don't "know" that $240,000 is optimal. So we test our way north toward $240,000 until we find the right answer. And on the way, we actually learn what our version of "a" and "b" are in a*(x^b) ... we fit an equation and we know the answer.

Say we test spending $140,000 instead of $100,000 ... and after adjusting for seasonal differences we learn that we generate $391,000. We now have three data points that can be used to identify "a" and "b".
  • We know if we spend $0 we get $0, so that is point one (0,0).
  • We know that if we spend $100,000 we get $350,000 ... so this is our second point ... if we spend a 100% of our old budget we get 100% of old budget net sales (1,1).
  • We know that if we spend $140,000 we get $391,000 ... so this is our third point ... we spend (140,000 / 100,000) = 1.4 to get (391,000 / 350,000) = 1.117.
Three data points.
  • 0 , 0.
  • 1 , 1.
  • 1.4 , 1.117.
I plug the three data points into my "CurveExpert" software which I've been using since the mid 1990s. The fitted equation looks like this:


And the actual equation looks like this:
  • a*(x^b).
  • a = 0.985.
  • b = 0.407.
  • (0.985)*((new spend / old spend)^0.407).
The actual equation allows us to "optimize" paid search spend, after adjusting for seasonality.


This is the table we use to determine the "optimal" level of paid search spend.

Notice that our original guess ... using the "square root rule" ... well, that guess wasn't a bad guess at all, was it? All we knew was that $100,000 wasn't enough to spend, and we guessed that $240,000 was the "optimal" level. After testing a spend level of $140,000, we learned that $220,000 was the "optimal" level.

In other words, the square root rule was a wonderful starting point, wasn't it?

That's the power of a*(x^b).

It turns out that a*(x^b) is everywhere we look in e-commerce, retail, and old-school catalog marketing.

Copy every single line of this blog post ... print it and put it in your cubicle or your Executive Board Room. When somebody has a question about something, go back to this blog post and run your own analysis and answer hypothetical questions quickly and reasonably accurately.

Have fun, too!!

May 10, 2018

What Does It Mean?

I know, I know, you are thinking to yourself ... "what does all this mean?" You don't care that I just showed you two tables that demonstrate that the mobile experience is pushing customers out of retail stores (in this example ... your mileage will vary).

Ok, let's go back to the two tables. Here is 2018.

And here is 2014.

And you'll say "we know retail sales aren't great ... tell us something we don't know."

Here's what we don't know.

We don't know how long the shift will continue to accelerate before customers "calcify".

What does "calcify" mean?

We go back to old-school cataloging.

Customers bled into e-commerce for 10-15 years ... until the role of each channel switched. At first, the website supported catalogs. Today, the catalog supports the website. Customers calcified. Most shifted ... but a fraction didn't ... they just stayed loyal to catalogs and call centers.

See, there's a point where channel shift ends. Customers who shift will shift, leaving those who didn't shift in their preferred channel.

And so it is with retail. There's going to be a continued shift ... and then the shift is going to level out. When it levels out, things get really, really interesting!
  • Say that half of retail buyers move into mobile and/or desktop e-commerce and half remain in retail. The role of the store fundamentally changes (it has to, in order for all that square footage to continue to exist). If the store exists, the store exists to serve mobile commerce. This will be seen as a non-stop continuation of the "retail apocalypse" as some call it, but it's honestly so much more than that. It's a complete reinvention of what it means to have a retail store. And that's going to be fascinating, and for those who are given the latitude to truly innovate, it's going to be the most fun folks have had in a generation.
  • If the shift ends in the next 1-3 years, then I'm not saying that "nothing changes" ... but retail doesn't innovate the same way as above because it "won't have to innovate" as urgently.
Those who measure the shift properly are going to have a huge advantage over those who act without measuring the dynamics.

So measure the dynamic ... understand it ... and be ready to act if channel shift stops/calcifies.





May 09, 2018

Customers Are Shifting Their Behavior

Ok, here's the table from 2018:

And here's the table from 2014:

Now we have a handful of comparisons ... comparisons that tell us what is happening. What is happening, of course, is that customers are shifting their behavior.

Look down the Retail Channel column. These customers purchased in-store the year prior. What do you observe when comparing data four years apart?

  • The customer is 4.5 points less likely to buy in-store again.
  • The customer is 4.0 points less likely to buy via desktop e-commerce again.
  • The customer is 14.6 points more likely to buy via mobile commerce again.
You notice that the retail in-store customer changed his/her behavior. Repurchase rates decreased, and if the customer repurchased, the customer moved orders out of stores and out of traditional desktop e-commerce ... moving the orders into mobile commerce.

Across the board, customers are less likely to buy in-stores, regardless of the channel. So there is a change in behavior ... but the change is dramatic among mobile buyers. As customers increasingly shift to mobile, their likelihood of buying in stores drops significantly ... much faster than if the customer was a prior store buyer or an e-commerce / desktop buyer.

Also notice that the corporate rebuy rate dropped ... this company is less competitive today than four years ago. It's less competitive, and if the trends continue it means that customers are leaving the retail experience.

More on the two tables tomorrow.







May 08, 2018

What Is Happening Between E-Commerce And Retail?

I wrote about the Migration Probability Table back in 2006. It was a big part of the Multichannel Forensics book that I wrote in 2007.

And it's becoming important in 2018.

Look down the Retail Channel column. Of all customers who purchased in-store in 2017, 58.8% purchased again (corporately) in 2018. 43.6% bought from Retail, 14.8% bought from Desktop E-Commerce, and 12.9% purchased from Mobile Commerce (tablets, phones).

Then I create an index ... I divide the individual channel rebuy rate by the annual rebuy rate. Take the 43.6% retail channel rebuy rate into retail and divide it by the overall annual rebuy rate of 58.8% and you get 74.1%. It means that if a customer repurchases, the customer has a 74.1% chance of repurchasing into retail.

The table has meaning, of course, but it has more meaning if you compare the table across multiple years.

So tomorrow we'll compare the 2018 table (above) to the same table in 2014. And then we'll see what is happening between e-commerce and retail. 

May 07, 2018

The Interplay Between Retail And E-Commerce

Back in the stone ages at Eddie Bauer (1997), we'd measure how e-commerce customers behaved. When a customer purchased via e-commerce, the next purchase was most likely going to be via catalog marketing, with a retail store purchase much less likely and the e-commerce purchase the least likely to happen. In other words, the e-commerce channel was a "support" channel. It wasn't where the customer wanted to purchase.

The relationship wasn't much different when I worked at Nordstrom, circa 2005. We'd acquire a customer online, and the online customer had a 70%+ chance of placing the next order in a store. We knew we could spend a lot of money on paid search because the online channel was a "support" channel, one that would fuel retail growth.

When I started my consulting work in 2007, the relationship between e-commerce and catalogs had changed ... rather dramatically. E-commerce used to "support" catalogs ... but by 2007 catalogs were supporting e-commerce. The "next" purchase, regardless whether prior purchases came from print or e-commerce was most likely to happen via e-commerce.

A transition happens between channels. It's a transition from old to new. Catalogs used to be a major channel, and e-commerce used to be a support channel. From 2001 - 2006, the relationship flipped. Catalogs became a support channel and e-commerce became a major channel, when you actually measured the dynamic between the channels at a customer level. The period where the channels change roles is "confusing" ... for it seems like the customer is "doing everything" and therefore everything is important. You are led down the wrong path ... at the very time you should be focusing heavily on the emerging channel you instead embrace all channels and you work hard to integrate everything. Integration hurts your ability to capitalize on the emerging channel, while protecting the channel that is moving into a support role. Today we can see the impact of integration ... catalogers who treat marketing like it is 1993, and consequently earn customers who were 40 years old in 1993.

Back to retail and e-commerce.

In 2005 e-commerce was a support channel to retail.

In 2011, the relationship began to change. I would measure retail / e-commerce interactions, and I'd observe a new dynamic. Retail customers became increasingly likely to buy online and became less likely to re-order via stores. And e-commerce customers became increasingly likely to buy online and became less likely to re-order via stores. Our industry read this dynamic incorrectly - viewing this as a good thing. The phrase "omnichannel" dominated thought leadership pieces, pieces that encouraged marketers to integrate "everything". The added complexity helped nobody, and then thought leaders told everybody to create a seamless customer experience (using data of course). Complexity * Complexity = A Lot of Complexity!!

We should have learned from what happened between the interplay between catalogs and e-commerce, and we should have applied those learnings to retail. When the support channel becomes the primary channel, the world changes.

It's 2018 now, and Complexity * Complexity didn't work. Folks call it the "Retail Apocalypse". That's not accurate, of course. It's simply a failure to realize that e-commerce (some would call this "digital") was (is) becoming an equal to physical retail. It's nowhere close to being an equal yet, but if current analytics tell us anything they tell us that we're trending in that direction.

If e-commerce is slowly becoming an "equal" to physical retail, then the way you manage marketing in the offline/physical world changes. You might use your retail presence to find new customers, and then move those customers into your online/digital environment. This is where innovation happens, and it is where a re-definition of retail happens.

Always remember to get out in front of shifts between channels. You don't work to integrate the channels (that adds complexity) ... you work to find new ways to leverage what used to be the primary channel to help support the emerging channel.


May 06, 2018

Ashley Home Stores

There they are, delivering a couch or whatever. And as long as they have to deliver something, they may as well get some free advertising out of it, right?

So what is your version of the Ashley Home Store delivery truck? What do you have in your bag of tricks that acts as free marketing?

May 03, 2018

Goat Yoga


Read the thing from top-to-bottom. Lots of hustle, trial, and error. But in particular, lots of hustle.

This is different than what a generation of digital expertise teaches us. We're taught to push buttons ... spend money on Google/Facebook, partner or not with Amazon.

Consider what your version of Goat Yoga is. That becomes your awareness program. Your awareness program boosts all of the money you spend on Google/Facebook.


May 02, 2018

By Item Year Of Introduction

You recall our segments from earlier this week.

In this analysis, we look at the years when items were introduced, and then segment those items by our four classifications.

In particular, I care about the items that fall into the "Newer Customers / Modern Channels" category. These items are the future of any business. I don't see huge swings in this table.

But I do see one challenge.

Overall, look at the distribution of items sold that skew to "Modern Channels".
  • Old items (2013 - and before) ... 62% skew to modern channels.
  • Items From 2014 ... 74% skew to modern channels.
  • Items From 2015 ... 71% skew to modern channels.
  • Items From 2016 ... 69% skew to modern channels.
  • Items From 2017 ... 62% skew to modern channels.
This company is increasingly offering items that skew toward old channels and away from modern channels.

In other words, this company is electing to embrace old-school channels, aligning them with the most recent assortment of merchandise.

This can work in the short-term.

This is hard to pull off in the long-term.

May 01, 2018

MCI (Modern Channel Index) by Category

In a recent project I selected the top 500 selling items in the past year, and then classified the items into one of four segments.
  1. Newer Customers / Old Channels.
  2. Older Customers / Old Channels.
  3. Newer Customers / Modern Channels.
  4. Older Customers / Modern Channels.
Which segment represents the future of your business?
  • Newer Customers / Modern Channels.
Which segment represents the history of your business?
  • Older Customers / Old Channels.
I analyzed the top 500 items, splitting them out by merchandising category. Here's what I learned:

Widgets:

  1. Newer Customers / Old Channels = 4 items.
  2. Older Customers / Old Channels = 37 items.
  3. Newer Customers / Modern Channels = 6 items.
  4. Older Customers / Modern Channels = 8 items.
Pibits:
  1. Newer Customers / Old Channels = 8 items.
  2. Older Customers / Old Channels = 7 items.
  3. Newer Customers / Modern Channels = 26 items.
  4. Older Customers / Modern Channels = 5 items.
Widgets represent where the brand "was".

Pibits represent what the brand "will be".

Think about your marketing strategy for "Widgets".

Think about your marketing strategy for "Pibits".

When I talk about low-cost / no-cost customer acquisition strategies, I'm talking about leveraging Pibits in your digital channels and I'm talking about using Widgets in old-school programs to drive revenue among your long-term loyal customer base.

This isn't difficult stuff.

But it is readily actionable, isn't it?

What would stop you from doing this?

Memorial Day Weekend

There's the obvious reason why we "celebrate" Memorial Day. And then there's the secondary outcome ... an unofficial ...