Among methods like A/B test, which can be used by marketers to quantify the effectiveness of a selling strategy for a certain targeted segment of customers, cohort analysis distinguishes itself as one of the best. A cohort simply describes a group of users that share the same reaction to external stimuli.
Because we are talking solely about marketing, those stimuli are marketing strategies. Therefore, a basic cohort analysis will provide data about the number of actual customers interesting enough to be retained. In fewer words, cohort analysis data will look deep into a certain`s group buying behaviors over time and follow the changes that occur. This is a powerful and extremely useful tool and is most often segmented by user acquisition date, so businesses can more easily understand the concept of the customer lifecycle and the efficiency of promotional efforts.
There are many cohort analysis examples but two of them are the most relevant: we have behavioral cohorts or behavioral analytics and acquisition cohorts.
Behavioral cohorts analyse users based on the actions they engage in within the app during a given period. On the other hand, Acquisition Cohorts group users based on the exact time they first signed up for a product or service.
Performing your own cohort analysis is simply a more efficient way to use data that you already have access to. eCommerce companies can use this tool to identify the products that are more likely to be successful, while in digital marketing it can distinguish the web pages that perform the best based on conversions or time spent on that website. Cohort study can also be used with mobile apps, online gaming, website security, or cloud software to name a few because all of these industries have a deep need to identify the reasons behind customers leaving and the solutions for this problem.
A guide to cohort analysis and how to use it to measure Customer Lifetime Value (CLV)
A particular case for using cohort analysis is using this tool to measure Customer Lifetime Value (known as CLV or LTV). There are four simple steps to follow for completing this task while using data provided by Google Analytics.
First of all, you have to make sure you chose the proper settings. You should select a Date Range, Cohort Type, and Cohort Size depending on what your marketing goal is. Next, you will have to measure the customers` margin in that particular group. Measuring the user retention rate of customers is going to be our next step, and we will get our result right after measuring the discount rate and the acquisition cost.
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How to Use Cohort Analysis to Measure Customer Retention?
Let’s start with the first one: how cohort analysis allows you to measure and impact user retention rate. First of all, it is crucial to understand the formula for CRR and what story that value will tell us. To measure CRR we first need a true picture of the retained customers. To find the percentage of those who have been retained since the beginning, we divide the result with the number of clients at the beginning to finally have the user retention rate.
The higher CRR value, the higher will be the customer loyalty rate. If you are a marketer, you will find yourself promoting product features or running different campaigns, and performing cohort analysis is the right way to evaluate their success. Data collected from cohort analysis will help you prognosticate future user behavior, recognize features that will increase user retention, set-up a data-driven marketing system, or plan for customer engagement activities.
You need three types of data to perform a cohort analysis that will help you track user retention rate (CRR): you have to know who you are tracking, the metric value on all subsequent dates after signing up, and the sign-up date.
Google Analytics is the go-to tool when in need of a trustworthy cohort analysis on conversions, certain key metrics, or just website traffic. To that, you will have to choose the “cohort analysis” function from the Audience menu, and at the top of that screen, you can make a wide range of settings that will help you generate your cohort report. You can adjust metrics like Cohort type, cohort size, metric, and date range. Each has its use: for example, the cohort type functionality lets you set the group of data or clients you want to examine. Cohort size indicates the time period you want your analysis to be done, which could be anything from one day to one year. Date rate stands for the time period you want to perform the cohort analysis and the available options with Google Analytics are from one to three months. Nonetheless, your cohort analysis on this platform can be focused on distinct metrics like user retention which is the default metric or pageviews per user, revenue per user, transactions per user, goal completions per user, session duration per user, and sessions per user.
While using cohort analysis for user retention, certain metrics are particularly important. First of all, make sure you focus your work on the repeat rate because this one metric will give you lots of data regarding your customer lifecycle. Repeat rate is an indicator of the share of customers who transact with your company repeatedly in contrast with those who are on-time buyers.
Orders/customer is that one metric tied to the repeat rate that will increase its value if the customer will make more buys. If you are into subjective-but-still-relevant metrics, the time between orders can be used to target where a reactivation email is needed and, most importantly, when. Last but not least, the average order value is a metric that will help you target the high-spenders that arrive at your doorstep from time to time. It is useful because you will never have the time and the resources to target everyone in the same way, and you don’t want to miss out on low average order value customers.
How to leverage Cohort Analysis to maximize CRR?
In certain situations, connecting the dots is simply too hard and can prove an impossible task especially if you are a marketer trying to consider all the behavioral traits of your customers. But data is never a curse if you have the right tools to navigate, and cohort analysis is the right instrument for dark times like that. The smart approach is to break down your cohort analysis into various campaigns and follow one or more of these four strategies.
Using cohort analysis you can harvest data that will indicate to you what kind of customers buy the most and what products or services are preferred so you can come up with the perfect targeted offers. These offers can go from coupons to free shipping and from discounts to all sorts of incentives because the important thing here is to retain the existing customers.
Another way to maximize customer retention rate is by implementing loyalty programs. Loyalty is one of the most appreciated values among people and it is particularly important in the sales industry especially now, when so many brands with so many offers are available, so it is harder than ever to stay in one place. Gamification is more and more part of the loyalty programs, but using rewards, tier programs, or loyalty points can be a successful strategy as proven before in numerous marketing campaigns. Even though we all like to be rewarded for our commitment to the brand, loyalty programs can be challenging simply because it is difficult to measure and unpredictable. The good news is that cohort analysis can help you target the exact customers who are more susceptible to be retained for the long haul.
No matter how many amazing campaigns you make to create a customer spotlight, sometimes you will identify idle customers. It is a good thing though because now you will be able to use a plan that includes reactivation mails. This is the perfect way to gently nudge your clients and steer them to the point when they will start buying again. Pay attention to those time intervals metrics that give you information regarding how much time passed between two purchases so you can properly plan a reactivation campaign and target it with extreme precision.
Probably the biggest problem that cohort analysis can solve is spotting the exact time and place where a user`s journey ended on your website. Having this information at your disposal will give you the ability to solve the problem immediately, prevent other events like this from happening, and use one of the above strategies to win your customer back.
In conclusion, a cohort analysis will provide a better perception of your users’ behavioral traits and will bring you users engagement and increased revenue per user. In addition to that, such an analysis can harvest useful insights into the health of your marketing strategy and business in general. Estimating the CRR as a cohort analysis will help you better understand each customer and how can he be retained for long-term growth.