Data analytics is the practice that determines the success of a business. While cohort analysis is a kind of data analysis in which users are assembled into sections depending on common attributes. Organizing a set of people’s data into cohorts enables you to analyze and optimize campaigns based on extensive factors.

Cohort analysis proves to be a robust tool for marketing analytics services as it provides insights based on consumer behavior. In this article, we’ll take you through the steps of executing cohort analysis. Let’s begin!

STEP 1: Ensure the availability of the needed data

The very first thing required for any kind of analysis is reliable data. You can look forward to trusted sources like Shopify, Magento, or WooCommerce for needed data. But in case, there is the availability of an in-house database, then ensure it consists of the following:

  • User-level data: Meticulous data is the base of cohort analysis or any type of data analytics. It starts with a unique identifier for each customer. Email addresses or customer IDs can also serve the purpose here. 
  • Transaction dates: The orders you have processed must have a date assigned. Oder dates can be attributed to the customers.
  • User acquisition dates: Customer acquisition dates or first-order dates can help determine the outset of customers’ activities. This information is included in the orders report as well. For cohort analysis based on behavior, you can add information like age group, nationality, etc to the report.
  • One or more report metrics: This is generally the order value represented in the currency you prefer. However, cohort analysis can also be carried out using metrics like order count.

If there is a lack of any one of the above-mentioned requirements, it won't be possible to carry out the cohort analysis.

STEP 2: Select your Cohort identifiers and compute the time elapsed

After you have ticked off all the information mentioned above cohort data analytics, you ought to identify.

Cohort identifiers

Cohort identifiers can be optimized for arranging cohorts in groups established on a certain time frame. For instance, you can group your customers based on the customer acquisition date.

  • Daily cohort (Format YYYY-MM-DD): Daily cohort is optimal for daily basis business analysis. 
  • Weekly cohort (Format YYYY-WW): It is perfect for monthly analysis.
  • Monthly cohort (Format YYYY-MM): It is good for analyzing year-long progress.

Time elapsed

Time elapsed is used to compute the time since the date of customer acquisition (or since the cohort identifier you have selected).

  • Time elapsed in days- Format XXX, e.g. 015, meaning 15 days passed.
  • Time elapsed in weeks- Format XX, e.g. 03, meaning 3 weeks passed.
  • Time elapsed in months- Format XX, e.g. 04, meaning 4 months passed.

STEP 3: Maintaining cohort analysis

Before getting to the visuals part, one must ensure that the data pipeline is optimized for basic processing requiring the least manual maintenance. Assure:

  • Automate updates: Periodic collection of data to make sure that the most recent updates are reported in the analysis.
  • Overwrite data: Practice overwriting rows using the transaction date column. It ascertains that old entries are not being overwritten with the import of the new data.

STEP 4: Create a cohort analysis table

All in place, now you can start creating a cohort analysis table in the Business Intelligence (BI) tool of your choice. You need to start by adding the cohort identifier, followed by the time elapsed. Further, you can operate the table to combine the metric you picked for data analytics.

STEP 5: Additions to cohort analysis table

Now when your cohort analysis table is ready, you can get more insights by adding:

  • Dimension filters
  • Cumulations of cohort
  • Totals per column

Wrapping Up

Data analytics using cohort analysis is the best way to get insights into the behavior of the cohorts. You can also compare two cohorts. Moreover, it allows you to identify patterns in the behavior of particular groups. Further, you learn about the impact created by a specific variable on a different set of audiences.

However, creating a cohort analysis table is not as easy as it sounds. You need proven Data Analytics Services to do the task for you. There are many companies in the market from which you can choose and get going!