Today I’m going to reveal my custom DTC Merchandising Dashboard that I use to help brands grow like The Oodie when I was CMO to:
- Inform merchandising decisions
- Benchmark new product launches
- Unlock category & collection insights
- Brings sales transparency to the team
As your brand moves from a single hero product to multiple categories you start to encounter challenges with merchandising complexity. Take True Classic for example, founded by @RyanBartlett who bootstrapped the men’s apparel brand from $3k to $200M+ in a few short years.
True Classic started out with a single hero product (crew neck t-shirts) and added hundreds of new products as they scaled.
The Playbook:
- Single hero product proof of concept
- Category and product expansion
- Acquire new customers and increase CLV
But “Mo products mo problems” right?
- How do merchandising changes affect sales?
- What hero products to feature across channels?
- What’s the new vs existing customer product mix?
- How do new collection launches perform relative to others?
These questions go unanswered.
The way I approach this analysis is by visualising Category, Collection and Product Attribute data using the Product Type field and custom naming conventions in Shopify. This is different from the Product Category that’s predefined for your product.
The Product Type field is a label you can use to describe your product. Most brands leave this blank or use it at the category level. The problem with this approach is that you don’t get a deeper understanding of collection performance within the category.
Or maybe you’re a merchandising analyst savant who expects everyone to operate in pivot tables and raw data. However, us mere mortals find it hard to visualise trends over time and unlock merchandising insights to prioritise for growth.
Coming back to the Product Type field in Shopify. I use the naming convention(s) Category – Collection – Attribute. Using True Classic as an example:
Tops – Crew Necks – Single Product
Tops – Crew Necks – Bundle
You can then export a custom sales report, filter by naming conventions using slicers and analyse any merchandising data you like. Unfortunately this change is retrospective and Product Type naming changes will only affect future sales data.
Using data from my Shopify Demo Store and True Classic’s navigation menu as an example, I’m going to go through a basic “HYPOTHETICAL” analysis of what I might uncover using my DTC Merchandising Dashboard.
Starting with Tops, Bottoms, Outerwear and Accessories. I always view data as total and %. This lets me see as we scale, does the category mix % hold or do other categories become more dominant. If so, what are the reasons?
Questions start to arise:
- Are we over or under-represented in certain categories?
- Do changes in sales reflect our marketing activities?
- What was the additional sales uplift for Outerwear?
- What Collections & Products represent the bulk of sales?
Outerwear launched as a new category at the end of Oct to take advantage of seasonal changes in buying behaviour. We see an additional 20% increase in Net Sales while also reducing the reliance on Tops as the dominant category.
With Jackets being the only Collection in Outerwear it includes Puffers, Blazers, Vests etc. We should consider launching these as new Collections to increase product discovery and data collection benchmarking.
Let’s dive a bit deeper into Tops.
We see that previously dominant collections like Crew Necks sales remained flat for the period, until Long Sleeves & Button Up Shirts accounted for around 65% net sales during Nov / Dec.
Let’s benchmark the new collection launches.
Jackets was the dominant collection, a strong launch with a total of $11m net sales, followed by Button Up Shirts and Long Sleeves with $7m net sales each. What can we learn from each launch?
We notice Sweaters ran out of stock early Dec.
- Are ads turned off?
- Have we backordered?
- When is the ETA for replenished stock?
- Are we capturing leads for back-in-stock emails?
- Was this above or below sell-through expectations?
Let’s look at Bundles vs Single Products. We notice 30% – 60% of 1st time customers are buying bundles.
We hypothesise that 1st time customers prefer to buy bundles because of the savings from tiered discounts.
Let’s take a closer look. We see a lack of bundle merchandising across categories.
Our 3 best-selling Tops Collections have Bundles and Single products, other collections don’t. Here we’ve found an opportunity to test some new bundles.
We can also filter for Top 10 Products across Categories or within a specific Collection.
Let’s take a look at Accessories > Hats.
We see Camo, Olive, Military and White Caps have similar Net Sales, however there’s an absence of any Cap bundles. Cross-checking Net Items we see people buying multiple Caps per order. This is an opportunity to launch Bundles.
This was a “HYPOTHETICAL” analysis using my DTC Merchandising Dashboard.
While Net Sales was the primary metric in this example, you should also use Net Quantity, Orders, Gross Margin, Discounts, Returns etc in combination with the above.
If you found this useful and would like to learn more about how I can help implement this for your brand I’d love to hear from you.