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?

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!

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?????

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!

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?

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!!

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.