Measurement and analysis are the bedrock on which a successful Digital Marketing campaign relies upon because having a detailed view of people’s behavior gives insights to a Marketing team. Furthermore, having an in-depth analysis about visitors/users/customers’ behaviors based on groups formed by common/shared characteristics of these customers leads to valuable discoveries about user actions or inactions thereby greatly improving campaign strategy.
This article aims at showing you how to set up a Facebook Cohort Analysis pipeline and why Cohort Analysis is a useful tool you should have in your armory when measuring any Marketing campaign. It also gives you a sneak peek into the different use cases where a Facebook Cohort Analysis pipeline can be useful.
Table of Contents
Introduction to Facebook Cohort Analysis
A Cohort by definition is referred to as a group of people who share common characteristics over some time. This common factor maybe age, particular use of a product, population demography, size, etc.
Cohort Analysis on the other hand is behavioral analytics that enables you to identify the relationships between the characteristics of a population to the behavioral activity of that population. It breaks the data into subsets of related groups called Cohorts, these Cohorts usually have similar qualities and experiences within a stipulated time frame. Cohort Analysis also allows you to see patterns from the groups as it relates to their traits rather than making assumptions from the general sample of the population.
Cohorts data can be obtained from e-commerce platforms, web applications, ads on websites, games online, and many more. It shows engagement metrics of users over time so you know if users are interacting favorably with your service or taking relevant steps towards being loyal. These metrics show if conversion and retention are getting better and not just appearing to improve because of overall growth due to new customers, and this would in turn help in shaping the company’s Marketing policies.
By using Facebook Ads campaigns, visitors to your website typically increase through ads displayed on Facebook. As user behavior varies from one individual to another over a while, ads can be a subtle way of convincing clients to engage on your website. Placing targeted ads on Facebook gives you the leverage of measuring user action against a predefined activity and having a critical assessment of this will ensure you have an idea of how to enhance the steady growth of your Customer Lifetime Value (CLV).
A typical example of a Cohort on Facebook shows a user’s initial and subsequent action like the number of people that clicked on “add to cart” and the subsequent ones that clicked on “purchase”.
In succeeding sections, you will be shown how to set up a Facebook Cohort Analysis pipeline.
Understanding the Importance of Cohort Analysis
Cohort Analysis is of utmost importance as it allows you to ask specific or targeted questions about your user’s behavioral patterns and in return draw useful deductions from them through analyzing the data.
Below is a list of possible benefits you can acquire from Cohort Analysis:
- Understand how User behavior Affects your Business: With Cohort Analysis, you can see how actions are taken or not taken ultimately impacts your business metrics. This simply means you can analyze how customer behavior affects conversion, churn, retention, revenue, etc.
- Customer Lifetime Value Calculation: Cohort Analysis is also used to know how much customers are worth to your company over a specified time. Grouping customers based on acquisition periods such as size, segments, etc. gives you the avenue to discover which acquisition channel leads to the best Customer Lifetime Value.
- Increasing Efficient Customer Engagement: Having seen ways in which Cohorts engage with your product or service, you can find new ways to make customers take effective actions to increase engagement.
- Conversion Funnel Optimization: One of the benefits of Cohort Analysis is the ability to improve on your conversion funnel. You can accurately predict how a user’s experience affected the conversion rate from the top to the bottom of your funnel. This is done by comparing customer engagements at different times to your sales process in a funnel analysis.
- Assessing Customer Churn: You can test to know if an activity or inaction of a customer leads to a different outcome in your chain process and relevant feedback can be gotten immediately. For example, if you assume an action taken on your site would lead to sign-ups, you can define your Cohort based on that criteria to get verifiable data and the results can be compared with other Cohorts to see which does better.
- Break Down Customer Acquisition By Channel: Cohort Analysis allows you to get very specific about optimizing your conversion channels. You can segment per-channel revenue for Cohorts over a set time where you can see which optimization is most effective and areas where improvements are necessary.
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Steps in Setting up a Facebook Cohort Analysis Pipeline
To create a Facebook Cohort Analytics pipeline, carry out the following steps:
- Step 1: Log into Facebook Analytics.
- Step 2: On the menu found on the left, click on Activity, then select Cohorts.
- Step 3: Click Create Cohort.
- Step 4: Select the first event you wish to use in determining your Cohort. By default, New User Activity is the set value.
- Step 5: Proceed to select the second event that will track your Cohort. By default, User Activity is the set value.
- Step 6: Choose a breakdown to apply to the chart. This is optional though and values may include Age, Language, etc. but the default value is Overall.
- Step 7: You can choose to refine your event but this is optional too. If you choose to refine your event, click on the event, then click on Refine to access further parameters. Apply is clicked to affirm your refinement selection and x is used to remove a parameter.
- Step 8: Click the ellipsis icon and select Save from the list.
- Step 9: Finally, enter a name and click Save.
It is important to note that anyone who has access to a Facebook Cohort Analysis pipeline can create Cohorts but only Admins and Developers can edit, save, or delete Cohorts.
Understanding the Facebook Cohort Analysis Use Cases
This article has covered how to set up a Facebook Cohort Analysis pipeline in layman’s terms. In this section, you will get an understanding of the different use cases for a Facebook Cohort Analysis pipeline through valuable reports.
Facebook Cohort Analysis reports can be used for a vast number of reasons like determining when the last buyers come in, the number of buyers to expect after a specified period of time and how long does it take for the leads to buy your product. Here are 3 key use cases for the Facebook Cohort Analysis reports:
Use Case 1: Purchase Time Duration for Users from Online Stores
For the online stores, the time users take for a purchase is an important question. Now the time to purchase a product from an online store can vary from days to months. To find this out you will need to select this specific Cohort and choose “New Web Users” as the first event and “purchase” as the second event. The report obtained from the analysis can elicit the following points:
- On average, what percentage of new users bought a product in the first-week post-click. You can compare this with the number of users buying in the second week, third week, and so on.
- Development of Cohorts for specific weeks along with the overall trend of buyer behavior are two metrics that can be super helpful for the Marketing campaign for your Facebook Cohort Analysis pipeline.
You can run this report every alternate week and adjust your SOPs accordingly. For instance, if your Cohort takes a week to mature fully, then you should be looking at the last 7 days of mature data, which means data that dates back 8-14 days ago.
Use Case 2: Purchase Time Duration for Leads
Irrespective of the size of your enterprise, you need to be aware of the time it takes for your leads to buy your product(s). If you remain oblivious of this simple fact, you run the risk of turning the ads off a little too late, which might lead to ad spend going down the drain for nothing. You might also be running the risk of turning the ads off earlier than you would have wanted to make money.
To create the report for Facebook Cohort Analysis that gives you this information go to the Facebook Analytics link, under the “Measure and Report” section in your ad account, and then scroll to the left and click “Cohort”. Choose Lead as your first event, and Purchases as your second event. The table obtained shows the following observations:
- Percentage of leads that have become buyers in the first, second and third week after buying in and so on.
This information can be leveraged to set KPI (Key Performance Indicators) benchmarks for your ad sets in the Facebook Cohort Analysis pipeline.
Use Case 3: Conversion Time Duration for Prospects
This is a crucial piece of information for eCommerce advertisers. A prospect adding a product to the cart signals buying intent. Understanding how long it takes for it to convert into a purchase can tell you how to structure your remarketing audiences. It also helps you structure your cart abandonment email campaigns. To create a report for this information in the Facebook Cohort Analysis pipeline choose “Add to Cart” as your first event and “Purchases” as the second. The table obtained can give you the following valuable insights:
- Exact locations of significant drop-offs in purchases.
- Duration of time passed before “Add to Cart” users stop buying altogether.
This can help you understand how recent your add-to-cart remarketing audiences should be. It can also help you realize when should you stop advertising to tire kickers.
This article brought to light the importance of measuring and analyzing a Digital Marketing campaign such as Facebook Ads through setting up a Facebook Cohort Analysis pipeline. You were shown how to set an event that would determine your Cohort and how to track the Cohort in your Facebook Cohort Analysis. This article also touched upon the various use cases of a Facebook Cohort Analysis pipeline before wrapping up.
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