But They Don't Want Cheap Items

Last week I expressed my contempt for a customer base that wanted discounts / promotions. It's not the way I want to run a business (take a $50 item and sell it for $50 and then cheat all those that bought it for $50 and sell it for $35).

But it works, of course.

As a marketer, you want to make sure you understand the difference between customers wanting a $40 item for $25 and customers wanting $25 items. There is a difference.

For the brand I'm analyzing, the top three deciles (which represent customers who, after being acquired, have an average rebuy rate in the next year of 54%) generate 11% of their volume from items selling for between $20.00 and $29.99. Meanwhile, the bottom three deciles (which represent customers who, after being acquired, have an average rebuy rate in the next year of just 31%) generate 38% of their volume from items selling for between $20.00 and $29.99.

What does this mean?

It means the new buyers don't want cheap items. They want quality items sold at cheap prices.

It's the old JCP issue from a decade ago ... customers didn't want everyday low prices of $30 ... customers wanted 40% off of a $50 item.

This impacts "how" you sell stuff on your website, if your brand has customers with similar characteristics.

Items Selling Below Their Historical Average

I can't believe I even have to write this given how much I dislike promotions, but here we are. I'm looking at data for the brand we've reviewed over the past two weeks. Remember ... new customers in the best decile had a 58% chance of buying again in the next year ... new customers in the worst decile had a 28% chance of buying again in the next year.

So you want to acquire customers in decile 1/2/3. You want to avoid customers in decile 8/9/10.

I looked at the percentage of merchandise in the order sold below the historical average price point for the item. In other words, if an item was $50 and then was lowered to $40 the historical average might be $48 and if it sold for $40 it sold "below" the historical average.

Top three decile results for percentage of sales sold "below" the historical average?

• 68% ... 72% ... 68%.

Bottom three decile results for percentage of sales sold "below" the historical average?

• 46% ... 45% ... 59%.

The best new buyers tend to like items where they are getting deals, according to the analysis.

If you are a "data-driven" professional, you don't look for facts to confirm your bias. You tell the client what they need to do. This client needs to recognize that getting a deal is important to their prospect base.

Marketers Determine Loyalty Levels Based On The First Purchase

For the brand I'm analyzing, there are numerous marketing channels used to acquire customers.

These channels deliver above-average new buyers.

• Marketplaces.
• Refer-A-Friend.
• Points Program.
• Postcards.

These channels deliver low-value new buyers.

• Affiliates.
• Email Marketing.
• Catalogs.
• Search.
  
• Space Ads.

Your mileage will vary (wildly) ... but you have clear relationships, and you know (for instance) that Amazon buyers aren't coming back to your website to build a relationship with you ... their relationship is with Amazon.

So if you knew that search buyers had low long-term value and you knew that December newbies had low long-term value, wouldn't you do something about search in December?

Wouldn't you?

Marketers determine if customers will be loyal, and they do it based on how they acquire a customer. Acquire a customer via search in December, and the marketing makes life difficult going forward.

I know ... here come the unsubs.

New Buyer Quality: Logistic Regression

Ok, a geeky detour for today in case you want to do this work yourself.

Every first-time buyer in "Hillstrom's Newbies" purchasing for the first time between a date range is analyzed ... did the customer purchase again within twelve months?

We use a Logistic Regression framework - the methodology allows us to see the impact each variable has on subsequent purchase activity.

Now, the numbers are kinda hard to read here, so don't really focus on the numbers. Focus on the bullet points below.

• I measure the impact of each additional item purchased in a first order.
• I measure the impact of the price of each item purchased in a first order.
• I measure the impact of discounted/promoted items in a first order.
• I measure the impact of new/existing items in a first order.
• I measure the impact of canceled items in a first order.
• I measure the impact of returned items in a first order.
• I measure the impact of the month of acquisition.
• I measure the impact of the share of a first order in each merchandise category.
• I measure the impact of marketing channels in a first order.

There are other attributes worth analyzing, but for the brand being studied, this is sufficient to tell a story ... the story of why a first-time buyer purchases again.

When you get a "Hillstrom's Newbie" project, you get the Logistic Regression analysis as the foundation of your customer behavior. From there I rank-order the new buyers from most likely to purchase again to least likely ... and report to you what I learn.

New Buyers and Returned Items

Pundits love to hound you about returns, about making things right for the customer.

Maybe you don't have to do that.

In the business we've reviewed for the past two weeks, first-time buyers who lost their entire first order to returns (i.e. the customer returned everything) were 58% more likely to buy again than were customers who returned nothing.

Oh.

Always remember that when a customer returns something the customer might well replace the item. Not exchanges ... but the customer actually / physically buying something different to meet the needs of the customer.

The company I'm analyzing learned three fascinating things:

• Canceled Items caused new buyers to be MORE likely to purchase in the future.
• Returned Items caused new buyers to be MORE likely to purchase in the future.
• Discounts/Promotions caused new buyers to be MORE likely to purchase in the future.

You've got enough information there ... without anything else I'm sharing in upcoming posts, to put your entire merchandising / operations / marketing / creative teams into action. They all have a purpose. They could all work in an integrated manner, with a purpose.

P.S.: This is the stuff I work on in a "Hillstrom's Newbies" project. Now I know that some of you are going to unsub right now ... "HOW DARE HE SELL US STUFF???" is the feedback I get. So go do the work. Go talk to your analyst and get busy ... you get the benefit of what I'm saying and you pay nothing. How do you beat that?

Canceled Orders / Items

Many of you are dealing with this very issue. You planned to sell Blue Widgets for nine months, you have them on your website, but most of the sizes are sold out and replacements are sitting on a ship outside of Long Beach.

Do those canceled orders or canceled items hurt future customer behavior?

Maybe.

The company I'm currently analyzing (and this was pre-COVID, so I get it, we live in a different world now)?

No.

In fact, canceled items helped ... helped A LOT!

Customers were 2.36 times more likely to repurchase if they had an item canceled than if the order was properly fulfilled for the company I analyzed for this series.

2.36 times more likely to repurchase!

In other words, when a customer was disappointed the customer became persistent. The customer looked for something else to buy, causing the brand to get two orders in a short period of time.

What am I asking you to do with this information?

Don't give up. Give the customer alternatives. Give the customer choices. Especially first-time buyers. Work hard to aid their persistent behavior.

This Is Where Kevin Says OH BOY

As you know, I hate discounts and promotions. It's the realm of the unimaginative ... when you don't have a single creative idea to grow your business you just give away hard-earned gross margin dollars. It's brain-dead easy and doesn't require any actual marketing chops.

But that doesn't mean the tactic doesn't work.

If you analyze data, you need to be honest when the data doesn't go the way you think it should go.

Such was the case with the brand I just analyzed. Here are first-time buyers by decile (1 = best, 10 = worst). The y-axis shows the percentage of customers who purchased via a discount and/or promotion.

Oh boy.

42% of the customers in the best decile purchased via discounts/promotions.

About 12% of the customers in the worst decile purchased via discounts/promotions.

So this company clearly has an opportunity ... to pursue discounts/promotions.

Of course, this company needs to perform a profitability analysis, because it is possible that at 40% off you are making less money than at full price.

In this specific case, it was more profitable long-term for the brand to offer discounts/promotions.

And I hate that.

But I have to be honest and I have to follow the data, wherever it leads me. In the case of this company, the data leads me down a path I don't like but have to honor.