How This Company Is Managing Price Increases

When you look at the average price per item purchased by merchandise class, you see interesting things. Look at this brand:

This table is full of interesting findings. Look at old items. Old items are considerably cheaper in 2022 than are items from other classes ... though prices have generally increased on the items that remain.

Look at the diagonal ... the cells represent the price of items introduced that year. In 2020 new items averaged $30.11 ... in 2021 new items averaged $38.04 ... in 2022 new items averaged $45.29. Each year new items are introduced at ever-higher prices.

Read across each row ... this brand increases prices on the items that remain as time passes, or the brand discontinues low-priced items.

This dynamic creates all sorts of odd customer-based outcomes. Customers looking for lower prices either wait for discounts/promotions, or customers gravitate to items that are cheaper (i.e. the older items). These dynamics make new items appear unattractive according to company reporting, pushing continuance of older items, which hurts the future of the brand.

These dynamics are happening everywhere in late 2022, and the dynamics are problematic.

Price Point Resistance

In an inflationary environment, your newer customers are more tolerant of higher prices than are your long-time customers. Here's an example from recent work:

• $36.29 average price point for customers acquired 49+ months ago.
• $38.58 average price point for customers acquired 37-48 months ago.
• $39.19 average price point for customers acquired 25-36 months ago.
• $39.09 average price point for customers acquired 13-24 months ago.
• $42.10 average price point for customers acquired 0-12 months ago.

This brand generates 40% of sales from customers acquired 49+ months ago. That's a problem for this brand, because this brand is trying hard to increase prices to cover cost of goods sold increases ... but the long-term customer base is balking at price increases.

If 15% of your sales come from customers acquired 49+ months ago, it will be easier to pass along higher prices to customers.

It's common to observe price resistance among long-term customers.

Discounting by Merchandise Class

Here's a fun one for a merchandise category recently analyzed. The metrics below show the percentage of sales attributed to items selling below their historical average price point.

• 31% for this year's merchandise class.
• 36% for last year's merchandise class.
• 30% for merchandise class from two years ago.
• 29% for merchandise class from three years ago.
• 20% for merchandise class from 4+ years ago.

This data is interesting. Items being sold from merchandise classes 4+ years ago are not being discounted. Items from the class two years ago are being discounted the most.

The stuff that is still selling from 4+ years ago tends to be best items, and therefore those items do not need to be discounted, do they? 

In my work, it is common to see one (1) merchandise class discounted more than other classes. You've been there, you know why this happens ... there are cases where a merchant is fired, and the new merchant just doesn't like what the prior merchant sold. Consequently, the prior merchant's items are discounted ... get rid of 'em!

Of course, there is a customer aspect to this dynamic. As a brand clears out of a merchandise class, the brand creates a customer who wants to buy the merchandise being cleared out, and wants to buy that merchandise at a discount. What happens to the customer who buys merchandise being cleared out at a low price? Often, that customer becomes less responsive.

Merchandise Category: Customer Composition

Have you ever looked at "how" a merchandise category generates customers? It's fun stuff!

Let's evaluate a category.

• Last Year's Buyers = 21,315.
• Rebuy Rate This Year = 6.5%.
• Existing Buyers Repurchasing = 1,386.

The merchant might believe that his/her customers are loyal. They are not loyal. Nope.

How about all of the other customers who bought from the brand last year but did not buy from this category?

• Last Year's Buyers = 669,200.
• Rebuy Rate This Year = 0.85%.
• Brand Buyers Repurchasing = 5,696.

Almost none of the brand buyers from last year (but no category purchase) decided to purchase from the category this year. Seriously ... it's less than 1%. And yet ... more than 4x as many customers came from this segment as came from existing category buyers repurchasing from the category. In other words, it's important for this category to generate success from last year's brand buyers who did not buy from the category.

How many customers were new/reactivated and bought from this category?

• 6,701.

Let's summarize the three segments.

• 1,386 buyers were existing category buyers purchasing from the category again.
• 5,696 buyers were existing brand buyers purchasing from the category.
• 6,701 buyers were new/reactivated to the brand, in total, and bought from the category.

This category is fully dependent upon new/reactivated buyers and from brand buyers who did not buy from the category in the past year. As are most categories, to be honest.

Know these facts, and guide your category marketing approach appropriately.

Category Development: Product Offering

In the next four(ish) weeks, I will launch a new product called "Hillstrom's Category Development". The product will build upon Customer Development work from 2021, looking at your merchandise/product categories as mini-businesses within your brand ecosystem.

Each "mini-business" has different pricing levels (and price increases or decreases) that help us understand what role pricing has had on customer response.

In the example above a 10% price increase results in an 8% drop in rebuy rates. A Category Development project helps us see the impact on rebuy rates, spend per repurchaser, new/reactivated buyers, and gross margin dollars.

Customers buy merchandise, and merchandise belongs to categories. Given equal attributes, we commonly see that buying from more categories leads to more future value.'

The table shows many cases where you'd rather have a customer buy fewer items from more categories than many items from one category. This trend happens often across my client base, and can be accelerated via email personalization (for instance).

Each category delivers new customers at varying levels ... some categories are good at this, some are not. Subsequent, some categories deliver high-value customers, while other categories deliver duds.

A Category Development project focuses on discounting/promotions. For some clients, discounts/promotions yield more profit in the long-term via development of "more" customers who generate "less" profit in the future ... if the "more" more-than-offsets the "less", you're good. In a recent project, I measured future gross margin dollars.

• Each dollar spent on full-priced merchandise last year = $0.19 gross margin this year.
• Each dollar spent on discounted merchandise last year = $0.16 gross margin this year.

That's a case where discounting hurts the future. There are cases where discounting helps the future. A Category Development project looks into this issue and provides answers.

A Category Development project illustrates changes over time. One category is the same size today as it was in Summer 2020, contracting after the COVID-bump. However, the category is fundamentally changed.

• Annual Items Sold Today = 104,193.
• Annual Items Sold 6/30/2020 = 124,568.
• Price per Item Purchased Today = $57.44.
• Price per Item Purchased 6/30/2022 = $47.70.

Yeah, units equal customers, and for this category they are trading customers to generate margin dollars at a higher price per item purchased.

• % of Sales Sold Below Historical Average Price Point, Today = 29%.
• % of Sales Sold Below Historical Average Price Point, 6/30/2022 = 32%.
• % of Sales from New Items, Today = 6.8%.
• % of Sales from New Items, 6/30/2022 = 7.6%.
• Gross Margin % Today = 44%.
  
• Gross Margin % 6/30/2022 = 39%.

We see minimal changes in discounting and new item development. We see a big jump in gross margin percent. How about gross margin dollars?

• Gross Margin Dollars Today = $2,642,656.
• Gross Margin Dollars 6/30/2022 = $2,340,423.

We see the tradeoffs ... customer response (via units sold) is dampened at higher prices. However, gross margin dollars increased. The company is more profitable, with fewer customers, which will impact future sales.

These are the kind of tradeoffs analyzed in a Category Development project.

As you know, when I am introducing a new product, I introduce it at a lower price for my clients and for blog subscribers. I test the code, to make sure that everything is working properly. You get comparable results to a full project, but at a significantly lower rate.

Pricing for Category Development:

• $30,000 will be the full project cost when the product is launched.
• $14,000 will be your price for participating in this test.
• If you pre-pay to spend your budget prior to year-end, I'll do the work for $11,500.

Take advantage of this opportunity, given that I don't discount my prices ... I only offer opportunities like this when testing the code of a new product.

Contact me now ... kevinh@minethatdata.com!!

Loyal Buyers Are Different

There are a lot of things you have to be careful about when evaluating loyal buyers vs. new buyers. Price and merchandise category data help shape your thought process.

Let's look at a handful of metrics, comparing first-time buyers vs. loyal buyers.

AOV, Items, Price.

• Loyal Buyers = $123, 4.3 items, $28.92 price per item.
• New Buyers = $106, 2.5 items, $42.67 price per item.

Already our brains should start clicking. Why would new customers purchase a small number of expensive items while loyal buyers purchase many inexpensive items? By the way, this trends happens frequently in the data I analyze.

% of Sales Spent on Items Selling Below Their Historical Average.

• Loyal Buyers = 30%.
• New Buyers = 25%.

Looks to me like this brand prefers to offer discounts/promotions/incentives for loyal buyers, doesn't it?

% of Sales Spent on New Items (new in the past year).

• Loyal Buyers = 20%.
• New Buyers = 13%.

This is also common. Sometimes traditional catalog brands feature long-term best-selling items to new customers. Sometimes Google has a lot of history with long-term best-selling items, steering traffic accordingly.

% of Sales From Category 6.

• Loyal Buyers = 17%.
• New Buyers = 7%.

You will find 2-3 categories that clearly skew to new buyers, and you will find 2-3 categories preferred by your loyal customer base. Be careful here!!!! Don't make the mistake of assuming that because one segment of customers likes a subset of your assortment that all customers will like your assortment ... that's just not how this stuff works in reality.

% of Sales Submitted via Call Center.

• Loyal Buyers = 31%.
• New Buyers = 14%.

This is a classic (and dangerous) situation. This is the signature of a classic catalog brand that is battling the past while trying to move into the future. When loyal buyers do "old school things", it's hard to move into the future. When your new customers do "modern things", your instinct as a brand will be to shove "old school things" at customers who want to do "modern things". You'll see this in modern e-commerce as well ... compare mobile vs. desktop.

Your loyal buyers are different. Analyze the differences, and then personalize "how" you treat loyal buyers. You can have one merchandise assortment, but you can also "feature" the stuff that loyal buyers like (and do the same thing for your prospects).

A Category Making Progress

Over time I've changed how I evaluate winning items.

In the past year, I've analyzed items sold on a daily basis. Each day is analyzed separately. If a bad item is featured in an email campaign, the item may well be a winner for a day or two. May as well credit the item as a winning item for that day or two, correct?

I rank-order all items during the course of a day, then cumm total sales. If the best selling item represents 10% of sales that day, that item is assigned a value of 0.10 for that day. If a lousy item sold 1% of total sales and was in the 74% percentile, the lousy item is assigned a value of 0.74.

Each item is coded separately, each day.

There's a thousand things I can do with this data.

For one category, I analyzed the percentage of sales on an annual basis for top 20% of items, 21% to 40% of items, 41% to 60% of items, 61% to 80% of items, and 81% to 100% of items. Here's the table.

This category doesn't have a lot of best sellers - if it did, 20% or more of annual sales would be from items in the top 20%.

However, this category is changing. Look at the 41% to 60% column - average selling items. The fraction of sales have increased each of the past three years ... from 17.2% to 18.8% to 20.7% to 22.0%. Same thing with items in the 61st to 80th percentile. Meanwhile, items selling in the bottom 20% of the sales distribution used to comprise 40% of sales (three years ago) but only comprise 34.2% today.

The merchant in charge of this category is doing something right - the results are not dramatic, but there is clear improvement here. Good job!