Original Melding Of Ideas - Your Own System

There's an episode of Seinfeld where Mr. Lippman opens a muffin store (Top of the Muffin to You) ... at one point, Elaine is thoroughly exasperated with Mr. Lippman, and proclaims (click here), "You stole my idea" ... then Mr. Lippman responds, with an earnest condemnation of innovation ... "these ideas are all in the air, they're in the air."

Well, sure. Go ahead and copy the experts. Happens all the time. Especially in my industry.

For many, it is prudent to let somebody else absorb all the risk, waiting to see what works and what doesn't work (ask Apple - introducing a stylus after Samsung does - introducing a large phone after Samsung does, introducing wearables after others introduce them).

On my analytics podcast, I am working through a series about having an analytics "system" or a "program" ... much like Oregon in college football or Green Bay in professional football  or Oakland in baseball or Houston in professional basketball our Southampton in the EPL ... a way of doing things that is unique/different than everybody else ... a methodology that creates a competitive advantage.

In my case, you come to me because my system is different than what you find elsewhere. No campaigns - annual analytics. If your business is down 10%, somebody might tell you to "be more omnichannel" or to "be social" or to "leverage digital" or "take advantage of Big Data" or "use overlay models from the co-ops". Those are the solutions that work within their system.

I'm going to take a different approach. I'm going to identify issues other people miss ... well intended experts will tell you that they are copying what I'm doing, but they're going to miss a lot of stuff, because they don't run my system. But to be fair, because they have their own system, their own way of doing things, they have the potential to find things I don't find. That's what is great about having your own system - you identify things other people do not identify.

In the next several blog posts, I'm going to talk about my system, my way of doing things. Let's think about how our systems influence how we approach problems. I strongly recommend that you create your own system. Why imitate somebody else? What's the point? You can most certainly borrow from others, picking and choosing what works for you - no problem with that - but ultimately, you need to pioneer your own system. We'll talk about my system over the next several days. You'll think about your system during this series of posts. Sound good?

The Algorithm Says So

Back in the stone ages of email marketing (2004), my team at Nordstrom would assign one of up to ten different email creative treatments to a customer, on a twice-weekly basis.

We used transactional data and customer preferences, back in the day ... we wouldn't have clickstream data integrated with retail and e-commerce purchases until late 2006. Based on testing, we knew that purchase transactions were about 10x more important than customer preferences. In other words, if the customer said she wanted to see merchandise tailored to a 30 year old customer but purchased merchandise tailored to a 55 year old customer, the purchase data was 10x as effective at predicting the future. That's a big deal. Customers spent more when they were sent content aligned with their purchase history. Customers spent less when they were sent content aligned with their personal preferences.

My boss at the time wanted to see merchandise tailored to a 30 year old customer.

My boss at the time purchased merchandise tailored to a 55 year old customer.

And so, one lazy afternoon, my boss asks me why she keeps getting "old person emails"? She's not happy. She expressed her preferences online, and she wants to see product aligned with her preferences.

"Why do I keep seeing old person emails?", she asks.

I said, "because the algorithm says so".

Here's what I want for you to think about today. Which outcome is "best"?

1. Give the customer what the customer wants, making less profit in the process but making the customer happy.
2. Give the customer what the customer is likely to buy, making more profit in the process.

Discuss how you would approach this issue. Describe the cases where you let algorithms make decisions. Describe the instances where you override algorithms, even if it costs you profit. Most important - describe your thought process. How do you balance profit with customer desires and company success?

What Happens When Prices Are Too High?

Here's what your Merchandise Forensics work will indicate, when prices are too high.

1. When prices are too high, annual repurchase rates will likely decrease.
2. When prices are too high, annual orders per buyer may or may not decrease.
3. When prices are too high, items per order will likely decrease.
4. When prices are too high, price per item purchased will significantly increase.
5. When prices are too high, average order value grows.

Repeatedly, you'll see Executives who are ok with this dynamic. They'll accept a world where 90 customers spend $120 per order when they would have a world where 110 customers spend $98 each. There's two problems with this thesis ... first, you only have 90 customers purchasing instead of 110, hurting future business potential ... and second, you dissuade new customers from buying product, causing you to acquire fewer customers, hurting future business potential.

This is a common problem in cataloging ... in pure e-commerce, prices sometimes decline over time ... and in retail, prices are artificially inflated in an effort to create a fake sale opportunity, yielding a net price that accurately reflects what customers want to spend.

We must measure Merchandise Productivity - and more important, each element of Merchandise Productivity. We need to understand these dynamics before the dynamics negatively impact our businesses.

Price

Start your post-Labor Day reading with this one ... click here.

Sure, the paper folks were protected (just read any catalog trade article for examples of paper folks protecting their interests) ...  but customers said "no no no". As the price of an e-book closed in on the price of a physical book (omnichannel pricing strategy, no doubt, prices need to be the same in all channels), customers cut way back on the purchase of e-books. Ooops.

Customer spend is truth. If customers aren't buying, then the customer isn't the problem ... we're the problem. Yup, us!

So we learn that if we raise the price of a digital good, customers spend on the digital good decreases.

One of the most common outcomes of a Merchandise Forensics project looks like this:

1. A catalog business struggles.
2. Cataloger doesn't want to raise prices.
3. Cataloger introduces new items that are more expensive, a version of price increases subject only to new merchandise.
4. Customers don't like expensive new items, customer doesn't buy them.
5. Cataloger must discount/promote to move inventory.
6. Customer gets used to discounts/promotions and the deflationary impact on pricing.
7. Cataloger raises prices on new items to overcome discount/promotion impact on gross margins.
8. Fewer new items succeed.
9. Cataloger has a long-term new product issue, which greatly hurts merchandise productivity.
10. Only the long-time winners work - further driving the customer base older and older - further pressuring sales as long-time winners slowly die.

Your pricing strategy yields a lot of unanticipated consequences. Be sure to thoroughly analyze multi-year trends. Use the trends in other industries as a cautionary example of what happens when you want control over the customer experience.

Find The Merchant At Your Company Who Is This Passionate

Look at this image ... do you see merchandising passion?

Ok ... because it is the Friday of Labor Day weekend and you're not doing anything meaningful at work anyway ... how about taking eight minutes to watch this video ... click here please!

There are two examples of merchandising brilliance in this video.

• The bread guy ... do you think this person loves the merchandise (bread/butter) he sells?
• How about the professional who created the video ... Liza de Guia (follow her on Twitter here)? As she signs off, she labels herself a "Storyteller". Amen!

Find the merchant at your company who cares as much as the guy baking bread. Find the merchant at your company who cares as much as the professional who made the video. Let that person run wild!

Product Breadth

It is very difficult to see the impact of product breadth via digital analytics. Very, very difficult.

Here's how a lot of this stuff works. Let's say you only offer one product line (Widgets). Last year, you had 100 customers purchase widgets. In the next twelve months, of the 100 customers who purchased widgets, 40 purchase again. Your job, as a marketer, is to find at least 60 new+reactivated buyers to keep widgets moving forward.

Somebody in Management, or somebody in your merchandising organization ... somebody decides it is time to sell Wudgets.

It is here, now, that things get interesting.

No longer do 40 of 100 widget buyers purchase widgets. Nope. Now, 25 of 100 widget buyers buy widgets. 10 of 100 widget buyers purchase widgets and wudgets. And, importantly, 10 of 100 widget buyers switch over to wudgets. Also interesting ... it becomes harder to find enough new+reactivated widget buyers to fuel the business ... in the short term, because now customers have to choose between widgets and wudgets. Finally, the wudgets product line brings in new customers as well.

The following year, the two product categories find and equilibrium. In most cases, one of the product lines becomes primary, the other secondary. In most cases, one of the product categories does the new customer heavy lifting, while the other product category siphons off customers from the category doing the heavy lifting.

It is the management of these dynamics that is so very interesting. When done well, the impact is multiplicative. When done poorly, the impact results in cannibalization of product categories.

Once you understand the dynamic, you can grow many categories by simply growing one category. When I worked at Nordstrom, the family and team intuitively knew how these dynamics worked - they didn't need my geeky math to tell them that Womens Shoes and Cosmetics brought the customer into the business, satisfying customers that then migrated into Accessories or Apparel or whatever.

Amazon takes product breadth to amazing extremes. They are an example of a multiplicative pattern.

Most retailers are going in the opposite direction ... the omnichannel movement results in significant inter-channel and inter-category cannibalization. Few of the experts understand this dynamic - but it is lying there in plain sight - for if the dynamic were multiplicative, retailers would experience overall 20% gains, year-over-year. Instead, online grows and retail shrinks - certain in-store product categories die as their demand is pushed online - and the slow death experienced there results in fewer customers buying from other in-store categories - resulting in smaller and smaller store formats. Again, too few of the experts understand this dynamic. Almost nobody measures it. And it lies there, in plain sight, waiting to be discovered.

New and Risky

I once worked with a company that launched a new business. The new business went from three million to eight million to seventeen million to twenty million ... and then ... it stalled. The brand killed the business, as the business was losing money. We'll never know if the business could have made it through the dip.

Way back in the late 1990s, when I ran circulation at Eddie Bauer, we introduced new creative - younger models, branding, less product density. It was a disaster ... -10% across the board. It is very painful to lose $60,000,000 demand and $15,000,000 profit because of creative changes. Very painful. No bonuses for managerial employees because of the decisions of two or three people. Twelve to eighteen months of pain is all it takes before you reverse course ... and then it's terribly hard to make up the $60,000,000 demand and $15,000,000 profit. But, again, we'll never know if you lose $60,000,000 demand every year, or if in three years, you are posting $90,000,000 positives once customers get used to the change.

The great sadness of the "data driven" era is that we simply cannot afford to measure success on a long-term basis. And as a result, we''ll never know if changes can work, because we kill off the changes before they every get a chance to work.

One company told me that they instituted a huge change to their website ... posted -20% for about three months, then flat performance, then positives, so that by the end of the year, the results were positive. Now, in a short-term data-driven world, you'd never ever get the opportunity to see that the outcome could become positive, because some data-driven analyst would kill off your initiative and potentially cost you your job in the process.

New = Risky. We may need to give new a longer chance to succeed.