Build Customer Relationships Using Cohort Analytics

Build Relationships Using Analytics

It’s no secret that acquiring new customers can be difficult—not to mention expensive. With more competition and marketing channels out there than ever, trying to stretch your business’s dollars can feel like an uphill battle.

That’s why it’s so important for business owners to build and nurture the relationships they already have with existing customers. It’s not only more cost effective; it also improves customer loyalty which has a positive impact both on lifetime value and Average Order Value (AOV).

Fortunately, Shopify provides the data you need to closely examine your current customer relationships, helping you to identify which are at risk and how to turn returning shoppers into loyal, life-long customers.

Study Your Loyal Customers

Shopify defines loyal customers as those who have placed more orders with your store than your average customer and have a high probability of placing another order. These are your superstars, your reliable supporters.

It helps to look at these loyal customers as a kind of template for what you would like all your customer relationships to look like. Who are these customers? What types of products are they buying from you? Where are they located?

And because your loyal customers have demonstrated their affinity and preference for your store, marketing higher-margin products to them may yield better results than with other customers—improving your bottom line.

Turn Returning Customers Into Loyal Customers

The Returning customers report shows customers who have purchased from your store at least twice.

Studying these customers’ order histories can give you a great understanding of what caught their eye, so you can market similar products to them in the future. And once you know what they like, you can begin to advertise more premium and up-market products in the same category to them. This gives you a great (and informed) opportunity to increase their AOV.

Compare At-Risk Customers to Loyal Customers

If your store is on the Advanced or Shopify Plus plan, you can pull up the At-risk customers report. At-risk customers are returning customers who have not placed an order with you recently, and who have a medium-or-worse probability of placing another order with you as determined by Shopify’s machine learning models.

Compare these customers side-by-side with your loyal customers. Do you notice any patterns? Maybe your loyal customers are purchasing more premium products than your at-risk customers, leading to a difference in their satisfaction with the experience. Maybe your loyal customers have qualified for free shipping more frequently. Take note of these trends and try steering your at-risk customers in a new and better direction.

This can also be a great opportunity to offer perks like free shipping or discount codes to your at-risk customers to bring them back into the fold.

Leverage Customer Relationships to Improve Average Order Value

You can also sort your customers by AOV. What do your biggest spenders have in common? Is there a pattern you can identify and try to replicate with your other customers?

If increasing AOV is one of your strategic goals this season, here are a few tips and strategies:

  • Create an order minimum to qualify for free shipping and other incentives
  • Bundle products to create packages and sets
  • Promote up-selling and cross-selling of similar products
  • Create a points-based customer loyalty or rewards program

Coming Soon: Analyze and Compare Customer Cohorts of New Customers

In the coming months, you’ll have more power and control over how you can browse your customer data.

Customer cohort data will help you get a clear view of your retention rate—the percent of customers who return to your store to make a purchase. You can also analyze differences in retention rate across different customer cohorts based on when they made their first purchase.

The new report gives you additional metrics to help you dig into the details, too. Sort your cohorts by how much customers are likely to spend on your store (Predicted Spend Tier) and where they’re located (customers by location), and even visualize the overall retention curve for your store to compare it with previous time periods or different customer cohorts.

You can lean on cohort analysis to make smarter decisions about your store.

  • Identify your most valuable (or least valuable) customer cohorts in order to engage with them at appropriate times with specific email campaigns.
  • Identify which activities contribute to more valuable customer cohorts in order to double down on these successful levers.
  • Identify the right time after a customer’s first purchase to nudge them to make another purchase.

Cohort analysis will be available in your Shopify admin under the Reports tab, in the coming few months!

Cohort analysis FAQ

What do you mean by cohort analysis?

Cohort analysis is a subset of behavioral analytics that takes the data from a given dataset and divides it into related groups for analysis. The cohorts can be defined by a common characteristic such as time period, customer type, or product. This approach allows businesses to analyze user behavior and trends over time, helping them to better understand their customers and how they interact with their products or services.

How do you conduct a cohort analysis?

  • Define cohorts: Cohorts are groups of users who have something in common, such as signing up for an app or website at a particular time, making a certain kind of purchase, or having a certain type of device.
  • Define the criteria for segmenting the cohorts: Once you have identified the cohorts, you need to decide how to segment them. This could be done by time, age, location, device type, purchase type, etc.
  • Collect data: Once you have identified and segmented your cohorts, you need to collect data about them. This could include user engagement metrics, purchase behavior, usage patterns, etc.
  • Analyze the data: Once you have collected data, you need to analyze it to identify trends and correlations. This could include looking for correlations between different user groups and their behaviors, or identifying when certain user groups have higher engagement and purchase rates than others.
  • Report findings: Finally, you need to report the findings of your analysis. This could include presenting data visualizations or summary reports to stakeholders, or providing insights to inform decisions.

How many types of cohort analysis are there?

There are three main types of cohort analysis: cohort retention analysis, cohort conversion analysis, and cohort comparison analysis.