A comprehensive guide to cohort analysis
A comprehensive guide to cohort analysis
Understanding customer behavior is vital when creating successful marketing strategies. Cohort analysis is a powerful way to do exactly that. It’s a method in which users are grouped together based on shared characteristics. This grouping makes it easy to identify patterns and trends to help businesses make informed decisions.
Cohort analysis involves a series of steps and a reliable data pipeline tool like Fivetran that makes the process of data collection and ingestion easy. Businesses can then be more focused on accurately analyzing data and getting insights out of it, instead of spending time collecting and managing data. In this article, we’ll learn the benefits of cohort analysis, different types of analytical methods and how to perform the process.
What is cohort analysis?
A cohort is a group of people who share common characteristics, attributes, likes and dislikes for a specified period of time.
The similar attributes and experiences are what differentiate one cohort from another. These attributes could be anything: place, behavior, age, gender or profession that helps group people in a cohort.
Some examples of cohorts are:
- Customers who sign up for a membership program in a specific month
- Users who signed up for a free trial in the third week of July
- Users who signed up for a product via organic search
- Customers who subscribed for a newly launched paid feature after the first email
- Employees who took more than five days off in the third quarter
Cohort analysis segregates a larger pool of customers into smaller cohorts to track and analyze their behaviors and trends over a specified time range. It’s a powerful way to get insights into how and why groups behave in a certain way, which lets businesses make better decisions.
For example, with cohort analysis, a marketing manager can understand that customers who signed up for a product after attending a product demo had a better retention rate than customers who didn’t participate in the demo.
Cohort analysis is applicable in any department: marketing, human resources, sales and even healthcare. For instance, marketers can create targeted campaigns for better retention and lower churn rates. Likewise, HR teams can validate if their training and strategies are useful or not.
Types of cohort analysis
There are two types of cohort analysis: acquisition-based and behavioral-based.
Acquisition-based cohort analysis
Acquisition-based cohort analysis tracks trends and patterns in customer behaviors after they first purchase your product (i.e., when your brand first acquires customers). For example, if an organic food brand launches a mobile app, you can decide to track trends in user behavior when users first download the app. Then you can have different cohorts for each month or each week.
You can also measure each cohort's acquisition, conversion and retention rates and see which cohorts have the highest engagement and simulate similar strategies across cohorts.
So, you may see that the highest conversion rate is among users who downloaded your app in December. This may mean that your marketing efforts during that month were successful and can be repeated.
Behavioral-based cohort analysis
This type of analysis focuses on monitoring the behaviors of cohorts after they perform a specific action, such as purchasing your product. Here, cohorts can be grouped according to their date of first purchase. You can monitor metrics like average purchase value, churn rate, customer lifetime value and purchase rate to check which cohort of users is the most engaged.
For example, you may see that users who bought your product in March have the highest purchase value, indicating that they are more likely to be your loyal or active users. Or, an ecommerce company can validate how different groups of users behave after they collect items in their cart but don't purchase any item. You can then track metrics like how much time they spent on the website and the cart abandonment rate.
This can help you identify why users spend time on the website but still don't purchase anything. This can help you create targeted campaigns or marketing techniques to convert them into paid customers.
Which type of analysis is good? Well, that depends on the goals of your analysis. If you want to enhance user acquisition rates and track the effectiveness of various marketing channels, acquisition-based cohort analysis is a preferred choice. But if you want to understand the various ways to increase customer retention and revenue, behavioral cohorts will help.
Benefits of cohort analysis
Cohort analysis brings a group of customers together based on their shared behaviors and monitors their engagement over a specific time frame. Here are some benefits:
Measure the impact of changes
When analysts want to know if a specific feature or a new marketing campaign has increased sales, they use customer cohort analysis. For example, they can track the behavior of the users who used the new feature and compare it with the actions of the users who don’t use the feature. This may help them determine whether the feature should be continued or whether a marketing campaign is worth investing in.
Maximize marketing efforts
Businesses can easily spot the most effective campaigns with the help of cohort analysis features. For example, let’s say you have multiple streams of advertising your product: social media channels, paid ads, organic search and email marketing. You may want to know which stream works the best.
With cohort reports, you can find that customers acquired through paid ads have a higher retention rate than any other stream. This information lets you allocate your budget in the right direction.
Conversely, if you find that customers acquired through social media channels churn quicker than other channels, you may want to investigate the reasons and improve retention rates.
Spot behavioral trends
Identifying customer behaviors over time can help spot patterns and trends, like how much time they spend on your website, how often they purchase similar products, where they spend the most time and what their average spend is. For example, a cosmetics business may notice that customers who purchased their first product in April make repeated purchases throughout the year. You can then target these repeat customers through promotions like discounts and special offers to increase sales.
Or if you are a SaaS product owner and notice that new users aren’t getting engaged, you can offer them extra support like product tours, user guides or in-depth documentation.
Track customer satisfaction
You can also check if customers are happy with your product or service with a cohort analysis report. For example, say you are an ecommerce company. You track the behavior of users who have been with you over a period of six months and one year. You notice that users who have been with you for a year have lower engagement levels — you can fix this by improving your service. For instance, you may want to send out a simple survey or a special promo code to them so that they connect back to you. Cohort analyses are performed by cohort analytics tools like Google Analytics, Kissmetrics, Amplitude and SQL. To ingest data into any cohort analytics tool, you need a data pipeline solution like Fivetran.
These allow you to gather raw data from disparate sources and centralize it into a single location. Then, it cleanses and normalizes this data and loads it into a data warehouse like Redshift. Once the data is ready, it's put into the business intelligence (BI) and analytics tool for further analysis.
Our other core features include:
- Fully managed data pipelines, no coding required.
- Quick setup that can be done without any previous technical expertise.
- Visibility into pipeline activity so you know what’s happening.
- Soft deleting data so that you can still run analyses on data that’s no longer in your source system.
- Data blocking and column hashing that protects and anonymizes sensitive data in your database (without sacrificing its analytical value).
- Pre-built data models for connectors that allow you to standardize your data and generate tables that can be linked to your visualization tools.
- Easy-to-use dashboard with options for users and permissions, notifications and alerts
- Sync can happen as often as every five minutes.
- 300+ data sources and supports all major data destinations. Users can also implement their own connectors using Fivetran cloud functions. Plus, when data sources change their APIs and schemas, Fivetran automatically adjusts its connectors.
- SOC 2, ISO-27001, HIPAA, GDPR, PCI DSS Level 1 certifications + Data is encrypted at rest and in transit.
- 24/7 access to a team of technical specialists who quickly troubleshoot issues via live chat, calls or emails. Also supports users through its extensive documentation.
- 99.9% uptime so you always have the most updated data for analysis.
How to perform a cohort analysis: steps and process
Every cohort analytics tool has a different method for performing analysis. However, here’s a general process that can get you started.
Specify your goals
The first step is to have clarity in your goals. What do you want to understand from the data that you have? What do you want to learn from this analysis and how will it help your business?
Do you want to understand more about customer behavior, like how they landed on your website or how much time they spent on an app? Or do you want to understand at which point they are churning? These answers will help you identify which cohorts to track and which metrics to choose.
Choose the metrics you want to measure
Once your goals are clear, the next step is to know what cohort metrics you want to track. Some examples could be:
- User acquisition rate
- Conversion rate
- Retention rate
- Average purchase value
- Customer lifetime value
- Engagement rate
- Churn rate
- Customer satisfaction
Define relevant cohorts and gather data
Now, choose which cohorts you want to measure the metrics for. After that, you need to consolidate relevant data like the date of first purchase or the total amount spent. You can do this by using data integration tools like Fivetran that help you collect data based on its source.
Analyze and interpret data
Once you have collected the data, you can use different methods of analysis like time series or regression to spot patterns. This will allow you to notice any critical changes that may have happened.
Use Fivetran for your cohort analytics tool
Cohort analysis is an effective way to help businesses get clear insights into customer behavior and actions. It’s important to have reliable and accurate data to perform cohort analysis. This is where data integration and data pipeline tools like Fivetran help — allowing access to accurate, error-free and complete data.
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