When you’re running a Shopify store, it’s easy to get lost in a wormhole of analysis - of merchandising, product page UI, checkout funnels, you name it.
And while you’re stuck in that wormhole (hello down there!), your competitors are likely eating your lunch on FB Ads or organic search.
So let’s step carefully around that wormhole, and focus on one key question to propel your shop:
Who are our best customers, and what marketing channels are helping them find us?
To help you answer that fundamental buyer segmentation question, we teamed up with the eCommerce marketing experts over at Growth Engines, who help stores (like AutoAnything.com and Robbins Brothers) grow.
They find buyer segmentation to be a critical analysis step before and during marketing campaigns - as Drew Sanocki of Growth Engines puts it:
Customers are not all alike. In a typical retail business, 20% of the customers drive 80% of the revenue. The quicker retailers realize this, identify that 20%, and build the business around that 20%, the faster they can impact revenue.
Together, we produced an analysis template that you can clone and run yourself for any Shopify store with Google Analytics enabled.
Read on for the full breakdown of the process, or dive right into the recipe on the CIFL Template Vault on Trello.
How it’s Made
Warning: analyzing Shopify data will take you beyond the humble Google Sheet, given Sheets’ 2 million cell limit, and the pure size of transaction-level Google Analytics and Shopify data.
Instead of Sheets, we’ll use the Agency Data Pipeline stack:
Stitch to Push Data into BigQuery
A code-free (and often cost-free) tool to pipe your data from Shopify + Google Analytics into the database.
Open-source SQL models
Written in the DBT framework, these models can be pulled off the shelf to transform your raw data into answers.
A Data Studio dashboard template
To visualize your answers and share them with colleagues.
Don’t worry, you can copy this process without any coding knowledge, although knowing some SQL will allow you to build on top of our foundation.
Learning from Buyer Segmentation
Buyer segmentation is not a prescriptive analysis - it doesn’t output one ‘magic bullet’ to improve your revenue.
Instead, it’s a way of seeing the shape and flow of your customer base - the current running through your business.
You can see this buyer flow as a matrix of two variables: metrics and segments.
For most Ecommerce shops, a handful of core metrics can drive decision-making. For this analysis recipe, we’ll focus on three:
Retention rate: Are our top buyers coming back?
Average order value (AOV): How much do buyers in each segment spend in an average purchase?
Frequency: How often do they purchase?
Ultimately it’s up to you, using the intuition you’ve built around your store, to decide which of these metrics to focus on.
If you’re selling a consumer staples like razors, you’ll want to focus on frequency.
If you’re selling a rarely-purchased items like mattresses, maximizing AOV will be your bread and butter.
We find that focusing in on fewer key revenue drivers like Frequency and AOV greatly simplify marketing. Too often clients get lost in “vanity” metrics like social likes or predictive metrics like open rates or bounce rates while they ignore metrics that directly drive revenue – Frequency and AOV.
Like we mentioned above, buyer segmentation is an art, not a science.
Generally it’s necessary to segment buyers in a few different ways, to discover which method exposes the true distinction in your customer base.
In this analysis recipe, we slice by three different segments:
By Revenue Percentile
By breaking down buyers into spend buckets, you can get a sense of the distribution of your customer base.
Do the top 10% of buyers account for the bulk of your revenue, or do you have a more even distribution?
Knowing this can guide whether you focus your efforts around finding and keeping these ‘whales’ happy, or whether broad-based marketing is a bigger win.
From this example, we see that buyers who spend in the 90th+ percentile drove over ⅓ of revenue last month.
*A simple segmentation around total spend will tell you a lot about your business. First, you will see how much of an 80⁄20 you have going on in your customer base. Do you have a small segment of “whales” that drive the top line? *
Second, you will notice that they often come disproportionately from one marketing channel or buy disproportionately from one product category. Those are key insights you should use to direct your acquisition and merchandising effort.
Note that you can change these segment definitions with a one-line change in the underlying SQL, to add or remove granularity as needed:
Do one-time buyers drive most of your revenue, is does your shop depend more on repeat business?
Knowing the answer will help you allocate resources across retention (customer support) or acquisition (marketing) efforts - or it might expose a gaping hole in either of them.
In our demo shop, we see a couple eye-popping distinctions: a mere 5% of our most active buyers (with a 3+ frequency) drive over 20% of revenue.
They’re already being retained quite a high clip (40%+) though, so it’s probably best to focus on turning the 80% of buyers who purchase one-time to purchase a second time.
By Marketing Channel (from Google Analytics)
How you spend time and money across channels might be the primary tool in our revenue-growth toolbelt.
First, we want to check the overall health of each marketing channel - roughly how many buyers + revenue are each driving, and how do they stack up across metrics?
In the demo shop above, we see that Organic Search is outperforming both Adwords and FB Ads in terms of retention rate, frequency and AOV.
Reminder: you can grab a copy of the recipe (including SQL models and Data Studio template) from the CIFL Template Vault.
We can also mashup our marketing channel segments against the revenue and frequency segments, to see which channel brings in our best (or worst) customers.
This buyer index normalizes the success of each channel to a score of 1.
A value greater than 1 means the channel drove, relative to its total revenue, a higher percentage of ‘Frequency = 2’ (or any segment) buyers than you’d expect.
A value less than 1 means the channel drove less revenue through that segment than you’d expect.
So looking at the above example, we again see that Organic Search did a solid job of driving top 10%-spending buyers, whereas FB Ads did a less-than-stellar job.
Putting Segments to Work in Campaigns
Thanks to Data Studio’s CSV export functionality, we can use our buyer segments to build email lists for campaigns.
Say you wanted to build a lookalike audience of buyers who purchased twice or more via FB Ads. From the second page of the recipe’s Data Studio template, you can do that with a few dropdown clicks:
Creating custom audiences and lookalike audiences of only your best customers works. Why attract more bad customers to your business?
Dive in for yourself
In the CIFL Template Vault on Trello, you’ll be able to copy the entire process we used to build out these Shopify buyer segmentation models.
The recipe includes a few ingredients:
A Google Sheets tracking plan, containing step-by-step instructions on getting set up
A Data Studio reporting template, which you can copy and connect to directly to your BigQuery database
If you’d like help setting up this Shopify buyer segmentation analysis on your store’s behalf, just drop a note to email@example.com.
Thanks again to Michael and Drew at Growth Engines for their inspiration and support in building out this analysis recipe.