Customer cohort analysis is a helpful e-commerce tool to help you optimize your marketing and promotion efforts by focusing on customer experience and retention.
In simple terms, cohort analysis classifies customers into groups based on a common trait and tracks their behavior over time.
It looks at things such as the frequency with which your customers shop in your store, how long they stay, and how much they spend. Customers are typically grouped concerning their purchasing behaviors.
For example, customers can be grouped according to the type of campaign that converted them, the first product they ever bought from you, or even something as straightforward as conversion time.
What is a customer cohort analysis and what is its role in eCommerce?
In e-commerce, a cohort is a group of customers grouped based on a commonly shared characteristic over a period of time.
Here are some specific examples:
● Customers acquired via Instagram ads.
● Customers who purchased after a free trial.
● Customers who returned over the past three months.
Essentially, people can be grouped based on similar behavior exhibited over a specific amount of time. Customer cohort analysis looks at the ‘customer’ not just as one single entity with similar tastes and buying habits but as a diverse group of people with varied behaviors. Once you’ve pinpointed significant patterns and trends with regard to customer behavior, you will be able to formulate marketing campaigns targeting these specific groups.
This is often a better approach than launching a singular marketing campaign targeted towards a very broad audience and wishing it would land on the right people that you can convert as customers.
Types of Cohorts
These are groups divided by when they signed up for your product. For example, you can break down cohorts according to their sign-up date on your e-commerce app. You’ll then be able to measure retention by how long they continue to use your app since their sign-up date.
This helps you understand the percentage of users retained on your app until a certain, defined day. User retention can also be measured by how often and how regularly or rarely they come back to use your app or browse your online store.
These are groups divided based on the behaviors they exhibit within your app over a certain period of time. ‘Behaviors’ can refer to any number of actions performed within an app or site, such as sharing a photo, posting something, liking something, etc. You can then look at how long these cohorts are retained after performing such actions.
How Does Customer Cohort Analysis Help E-Commerce Businesses?
Predicts Future Customer Behavior
Customer Cohort analysis is very helpful in terms of predicting customer behavior. Because you have specific data that allows you to target potential customers with localized efforts (ads, promos), it allows you to convert leads to customers.
These insights allow you to understand customer behavior and anticipate their needs, thereby also increasing chances of retention.
Increases/Improves Customer Retention
Retention is king. If you lose customers, you lose sales.
Customer Cohort analysis gives you valuable insight that will allow you to retain existing customers and maximize revenue. Studies have shown that returning customers spend at least 67% more than new customers - that’s a lot.
Not to mention that it costs significantly less to keep existing customers happy than to keep on launching campaigns to attract new ones due to poor customer retention.
Helps to Understand Your Best Products and in Turn Create Offers
Customer Cohort analysis can give you insights into what your ‘best sellers’ are. Cohorts are often grouped according to ‘first product ordered,’ and from this alone you can see which of your products stand out the most to new customers.
You might also find which products customers buy often and repeatedly, thereby creating loyal customers. You can then highlight these products or create offers that will further improve customer engagement and retention.
Helps to Customize Your Marketing Strategies
Customer Cohort analysis is a powerful tool that will help you customize your marketing strategies based on what works and what doesn’t. Insights from cohort analysis will let you know how exactly you need to adjust your marketing activities and what you need to focus on.
For example, cohort analysis will help you determine whether or not you need to launch or bolster your loyalty or rewards program for existing customers. Insights from cohort analysis will allow you to find out if your existing clients are satisfied even without a loyalty program.
In this case, you can just continue your best practices and may not urgently need to launch a loyalty program.
Power your business with machine learning, without writing code.
Metrics for Customer Cohort Analysis That You Need to Know
Average Order Value
Average Order Value (AOV) measures the average total of every order placed with a merchant over a defined period of time. This is one of the most important metrics that merchants should be aware of because it drives important business decisions such as pricing, advertising budget, and store display. such as advertising spend, store layout, and product pricing.
AOV is determined by sales per order and not per customer. Even if one customer comes back multiple times to make a purchase, each order would still be factored separately into AOV. The formula for AOV is
Customer Lifetime Value
Customer lifetime value (CLV) helps to measure long-term business success as well as predict future revenue. CLV determines how much profit you can expect from a client over the course of their ‘customer lifetime’.
Depending on your margins, you can figure out how much you need to invest by estimating the lifetime value of a customer for your business.
Marketing Metrics reports that the probability of selling your product or service to a new customer is at 5–20% whereas your chance of selling the same to a regular customer is at 60–70%. That’s such a big difference, and it drives that point that retention is indeed more cost-effective than customer procurement. Retention is a lot cheaper than acquisition.
Time Between Orders
This refers to the time between successive orders. Depending on your type of product or service, this time period could be in terms of hours, days, weeks, or even months.
This is valuable to help you determine the timing of marketing emails to remind customers to repurchase or offer promotions that will continue to keep their repeat order rate high.
Repeat Rate Per Percentage to Second Order
Repeat rate is the most telling metric in proving how successful you are in retaining your customers.
This is the share of customers who patronize your business repeatedly (as opposed to cohorts who bounce after a single purchase).
Orders Per Customer
This is closely related to the previous metric. Simply put, the more repeat customers you have, the more orders they make each. High values in this metric likewise indicate a strong retention rate.
Compared to more popular analytics methods, customer cohort analysis tends to be more long-term and can provide slower feedback. It’s not a one-off analysis. Instead, it allows you to see patterns and insights on trends regarding customer behavior.
It takes time to observe, gather, and analyze data, which you will then translate into actual marketing and advertising strategies.
However, customer cohort analysis can be very rewarding in the sense that it provides real-world, reliable insights regarding customer behavior. It shows you ways to save money (ads, loyalty programs) in areas where you don’t need to spend, and also where you need to increase your investment.
It provides you with a more thorough, big-picture overview of your user journey, and creates long-term value for your company over time.
Graphite Note strives to provide businesses with automated customer cohort analysis models built with the help of predictive analytics to create a time-efficient, pro-tech solution to their data analysis needs. We’ve worked with real-life customers and standardized the ML models to be applicable for every customer.
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