If someone asked you “Where does your eCommerce demand come from?”…
…how would you answer that question?
The answer depends on the information available. If you’re using today’s standard eCommerce stack — Shopify and Google Analytics— it can be hard to truly answer the question.
Why? Because there are three dimensions of eCommerce traffic, and the standard stack only does a good job of explaining one of them. Traffic is the lifeblood of your business (assuming you’re selling something people actually want to buy 😉), so you need to get a handle on all three.
The three dimensions of eCommerce traffic are:
1. Channels & Products
This is the “eCommerce 101” view of your traffic — the information most easily accessible in standard web reporting tools. You know which digital channels drive the most traffic to your site, and you know which products are best sellers.
This information enables you to create better marketing campaigns: what channels should we focus on, and what products should we promote in those channels?
But channels and products don’t tell you much about who is buying from you and why. Channels and products won’t tell you much about the true profitability of your campaigns. “Original Price” and “Cost of Goods” are not standard Google Analytics data points, although both are critical to understanding how customers really interact with our merchandise.
Unit of Measurement: the order
Cost: GA is free, eCommerce platforms have various pricing schemes but many charge a percent of each transaction
Data Lift: low, simply paste a pixel onto your site and potentially troubleshoot the configuration
2. Acquisition & Retention
This is the CRM view, and getting access to it usually means employing a new piece of software to map your eCommerce transaction data to distinct customer identities.
Channels & Products give you the “what” and Acquisition & Retention give you the “who”.
Underneath the surface of your daily session count are real people — loyal customers, prospects, and everyone in between. If a marketing campaign or new product launch is not meeting expectation, it’s happening for one of two reasons: returning customers aren’t engaging at the expected level, or too few new customers are converting.
The CRM view helps you layer a customer lifecycle component into campaigns — acquisition, retention and reactivation all require different strategies. This view also helps you understand which products perform best with each group, enabling a more relevant approach.
CRM tools will help you begin to understand how your customers behave, but they are not great at helping you to understand how different groups of customers behave over time. And understanding profitability will still be challenging unless you have your data organized and are willing to push (and maybe pay) for that data to be included.
Unit of Measurement: the customer
Cost: varies — ESPs like MailChimp or Klaviyo can meet basic lifecycle marketing needs. An “ESP plus CRM” provides more granular insight, but it’s more expensive. If your data is clean and you’re a nerd, you can also code your own CRM reporting.
Data Lift: low-medium, depending on the complexity of the solution and how good your standard operating procedures are already
3. Simple & Complex Journeys
This is the ultimate goal: a full understanding of why your customers do what they do, broken down into causative clusters. What?!
Each customer purchase is a distinct journey, and you’re either a high intent shopper or a low intent shopper. As a consumer, you know this — you either find what you want and check out in five minutes or you browse around and maybe buy something, maybe don’t.
High intent and low intent shoppers exhibit different behaviors and have different needs. They are two fundamentally different groups, but they’re all lumped together in standard web analytics reporting. It’s hard to tell if an optimization is effective unless it addresses both groups, which is rare.
To grow profitably, you need to balance long/low intent and short/high intent journeys. Short journeys keep the lights on, and long journeys build loyalty and lifetime value.
After the customer makes at least one purchase, they enter their lifecycle with your brand. You want them to come back and purchase again, but there are a lot of factors influencing the decision. Do they have money? Do they remember you exist? Do they want to buy what you’re selling? Did you make them so angry the first time that they’ll never come back?
You can make sense of these various lifecycles by grouping customers into cohorts. A cohort is just a group of customers that share a common trait. For example: customers who were acquired in July 2020, or customers who purchased hats in their first order.
Customers can be as broad or granular as you want, but the valuable information comes from identifying cohorts that display significantly different behavior than the customer file on average.
When you have this information, you can start to understand which customers are worth focusing on, which strategic initiatives have a big upside, and what you may inadvertently be doing to sabotage your own growth.
Unit of Measurement: the cohort
Cost: probably high. You will need to bring in the big guns to map your data on a number of flexible dimensions. You may need to invest time and money in optimizing the way you clean and store your data. Again, if you’re a nerd with clean data, you can run some basic but critical analyses on your own to develop guiding strategic principles.
Data Lift: high. If you have ever re-platformed anything up until this point, you’re probably in for a lot of late nights and frustration.
What is the right approach?
No matter what size or stage your business is in — from startup to mature, from growing to struggling — you need to develop an understanding of all three dimensions of eCommerce traffic. But you also need to weigh the costs of your chosen solution against the potential benefits.
Software that is built to accurately monitor customer behavior as it changes over time is expensive, and it requires well maintained inputs (clean data) to produce accurate analyses.
Real talk: for smaller businesses, the upside of implementing this software is limited. A lot of the “low hanging fruit” for CRM systems and more advanced software is optimizing customer retention programs. If you’re working with a small customer base, it’s hard to earn back the price of the software this way.
SMBs are better off finding a trusted partner — agency, consultant or new hire — who can evaluate data quality and run some one-off analyses to inform strategic decisions. With limited resources, you need to focus on doing a few things well, and maintaining the uptime of a data pipeline is probably not one of them.
For more mature, more complex businesses, investing in the software is a no-brainer. A larger customer file means a larger return on basic optimization. And a more complex assortment means more upside opportunity for marketing personalization.
No matter your situation, a tool is never a panacea. Technology providers will pitch the amazing benefits of “personalized, data-driven AI chatbots on the blockchain”, but its up to you to make sense of the data and use it to make good decisions.