Kevin Hillstrom: MineThatData

Case Study: All Paths Lead to Apparel Tops

Yesterday I talked about Entertainment customers shifting dollars over time to Apparel Tops.

By the time the customer orders for the third time, 34% is in Apparel Tops, just 9% in Entertainment.

Does this happen to all categories? In this case, yeah, it happens to all categories.

Here are first order categories and migration through a third order.

Apparel Bottoms: 47% / 21% (Bottoms vs Tops) in first order, 16% / 35% in third order.

Fashion: 34% / 21% (Fashion vs Tops) in first order, 9% / 33% in third order.

Home: 47% / 19% to 18% / 32%.

Jewelry: 40% / 17% to 6% / 35%.

Workplace: 27% / 21% to 3% / 36%.

Outside: 51% / 12% to 17% / 27%.

Having Fun: 37% / 20% to 15% / 32%.

Seasonal: 34% / 19% to 7% / 31%.

Decorations: 31% / 20% to 9% / 32%.

Two interesting things happening, of course. First, customers on a first order do buy from multiple categories, with Apparel Tops being right there at the top. Second, as the customer evolves, the customer spends more and more money with Apparel Tops, less and less money with the category of a first purchase.

As a marketer, tell me how you plan on using this information to make different decisions? If the answer is "I'm not changing anything", that means something.

Tomorrow, we'll explore an email exchange discussing "doing something different" given we have knowledge of customer behavior.

Case Study: Entry Points Into A Brand And Subsequent Migration

It's so much fun to study categories ... you learn how your business actually works in ways that Spotify reporting just can't replicate.

Let's look at Category = Entertainment. I selected all first-time buyers who purchased from Entertainment in the first order, measuring how their merchandise preferences evolve as the customer advances down the customer life cycle.

Tidbit #1: If the customer purchased Entertainment in a first order, the customer only spend 33.6% of first order AOV on Entertainment. In other words, the Entertainment item was likely an add-on (Apparel Tops and Home comprised 31.3% of first order AOV).

Tidbit #2: As the customer evolves, the customer leaves Entertainment, shifting into Apparel Tops. There's not much sense to keep hounding this customer to buy Entertainment when the customer has a natural gravity toward Apparel Tops.

Every brand has gravity. If we see more examples of new buyers ultimately gravitating to Apparel Tops, we know something valuable about how to market to customers in email marketing (for instance) moving forward.

Case Study: Off-Season Purchases

We've learned that "Beans: The Internet's Only Variety Store" is heavily skewed to the November/December timeframe.

I've learned across nearly twenty years of consulting project that it's not healthy for a business to skew so heavily to Christmas. You want to offer products that customers purchase all-year. If there's a reason that subscription-centric brands are coveted by investors, there's the opposite reason that Nov/Dec businesses aren't coveted.

When I see a skewed business, I run a regression model to test the dollar contribution of Oct/Nov/Dec orders vs. orders generated during the rest of the year. For twelve-month buyers, spanning four years of purchase history and one year of "prediction", here's the simple regression equation.

1.257 + 0.079*($ Spent in Oct/Nov/Dec) + 0.095*($ Spent Jan-Sep).

In other words, dollars spent in "off months" are 20.2% more valuable than are dollars spent in Oct/Nov/Dec.

This fact supports encouraging the Merchandising Team to invest more effort into January - September.

Case Study: Simple Forecasts

If we're going to analyze the intersection of category performance and customer performance (which - FYI - is really what our businesses all come down to), we should also be able to forecast how a category is likely to evolve next year based on current customer trends.

Here's a simple forecast I put together for Apparel Tops ... the largest category that Beans manages.

I know, small numbers ... click on it for details, ok?

Categories yield Teachable Moments. Look down the "PctDmd" column. This is the percentage of annual demand likely to be derived by four customer segments.

9.4% from last year's Apparel Tops AND Other Category buyers.
6.4% from last year's Apparel Tops only customers.
4.7% from last year's Other Category buyers.
79.5% from New/Reactivated buyers this year.

In essence, the Merchant responsible for Apparel Tops is generating 80% of his/her revenue from customers who haven't purchased in the past year.

If you know that fact, how would you work with a Marketing Professional to grow Apparel Tops? Yes, you know the answer. Hint - the answer applies to most of you.

We can see the performance difference for three twelve-month buyer segments.

Category Yes, Other Yes = $5.98 in the next year.
Category Yes, Other No = $4.58 in the next year.
Category No, Other Yes = $1.44 in the next year.

If the customer didn't buy Apparel Tops last year but bought something else, the customer is worth 1/3 as much to 1/4 as much as a customer who did buy Apparel Tops last year.

Look at the demand forecast on the far right - if everything is similar to last year, the category will contract by 4.4%. No bueno.

Your Chief Merchandising Officer and Chief Financial Officer need to know what is coming ... by category. If categories are forecast to contract, inventory buys need to be adjusted accordingly.

These category analysis are darn important.

Case Study: When The Crabby Merchant Is Right ... And Horribly Wrong At The Same Time

In the email dialogue shared yesterday, the Chief Merchandising Officer suggested that her customers had long repurchase cycles, therefore, it's not fair to measure future value across only twelve months.

For "Beans: The Internet's Only Variety Store", she is both correct and horribly wrong at the same time.

Her customers have long repurchase cycles. I use my Life Table Methodology to measure repurchase activity/cycles. Here is what the data looks like.

The green table shows incremental repurchase probabilities by month, as well as cumulative repurchase probabilities by month, for 1x buyers, 2x buyers, 3x buyers, and 4x buyers. The graph next to the green table maps out incremental repurchase probabilities for first time (1x) buyers. Look at that stupid-high spike at month = 12. What do you think that is?

It is customers coming back and repurchasing at high rates exactly one year following a first purchase.

In other words, there are a metric ton of customers who buy in November/December, are responsive in Dec/Jan/Feb, then are inactive for most of the year ... then they magically reappear 11-12 months following a purchase to buy again. The customer then disappears for nearly a year before reappearing in months 23-24.

It's a hugely seasonal business ... the Chief Merchandising Officer is CORRECT in suggesting that her customers have a long purchase cycle and measuring things over time is more appropriate.

The Chief Merchandising Officer is also HORRIBLY WRONG at the same time. Her customers are simply not generating reasonable amounts of future demand no matter the timeframe looked at. Remember what I shared yesterday?

New customers spend maybe $11 in year one, $7 in year two, and $4 in year three. In total, that's $22 of future demand ... it's nothing.

It is terribly difficult to run a business when customers have virtually no future value! The secret to a successful business is to manage acquisition costs while maximizing future profit yielding a wildly profitable relationship. If you cannot maximize future profit? No bueno.

The job of a great merchant is not to smoosh all sales into November/December. A great merchant creates reasons for customers to buy products all year long. The Nordstrom Anniversary Sale proves this is possible. Amazon Prime Days (where do you think they got that idea from) prove it is possible. The merchandising team at Beans should also know it is possible ... and chiding an analyst for not viewing customer response on an appropriate time horizon does not absolve the merchant for failing to create a thriving business in, say, June.

Does that make sense to you?