Exploring user retention analysis

User retention is a critical metric for businesses aiming to understand customer loyalty and long-term engagement. Analyzing retention can provide deep insights into how users interact with a product or service over time, which can, in turn, inform strategies to improve user experience and increase lifetime value. One of the tools available for performing such an analysis is Deepnote, a collaborative data science notebook. Below, we explore a Deepnote notebook that focuses on user retention charts, providing a comprehensive look at how this type of analysis is conducted.

Data selection and preparation

The analysis begins by reading in the data, specifically targeting user sessions and their corresponding sign-up dates. The key variables extracted include user_id, signed_up_at_week, and the number of weeks that have passed since the user signed up. This initial data extraction is fundamental as it sets the stage for cohort analysis—a method where users are grouped based on their sign-up week, allowing for an examination of their behavior over time.

The notebook handles a large dataset with 190,335 rows, ensuring that the analysis is robust and encompasses a significant user base. This data will be critical for calculating retention rates and understanding user behavior patterns across different time periods.

Time range selection

Given that the data spans a full year, the analysis narrows its focus to a more manageable time range, from June 4, 2021, to September 30, 2021. This range is selected to make the analysis more relevant and focused on a specific timeframe, reducing noise and improving the clarity of the insights drawn from the retention data.

By limiting the time range, the analysis can concentrate on trends and patterns that are most pertinent to recent user behaviors, which can be more actionable for the business.

Retention calculation

Calculating retention involves several transformations:

Cohort size calculation: the number of users who signed up each week is counted. This forms the basis for understanding how many users are retained in subsequent weeks.

User activity counting: for each week, the analysis counts how many users from each cohort were active. This count is essential for determining the retention rate.

Retention rate computation: By dividing the number of active users by the cohort size, the notebook computes the retention rate, providing a percentage that reflects user engagement over time.

An additional step ensures that the analysis only includes complete data for each cohort. If the cohort has not yet completed a full week, it is excluded from the data to avoid skewing the results.

Visualization

Visualization is a powerful tool in any analysis, and this notebook leverages it to make the data more accessible and interpretable. It suggests plotting a time series of each cohort's retention rate, which can highlight trends and changes in user behavior. For instance, the analysis may reveal that cohorts from September performed significantly better than those from earlier months, indicating improvements in user retention strategies or changes in the product that resonated with users.

In addition to retention rates, visualizing the absolute number of users can provide context to these percentages, helping to identify whether high retention rates are due to a large initial cohort size or other factors. A matrix visualization might also be used to spot outliers and trends across different cohorts, offering a more granular view of user retention patterns.

The Deepnote notebook on user retention charts provides a structured approach to understanding how users engage with a product over time. By carefully selecting the data, focusing on a relevant time range, calculating retention rates accurately, and employing effective visualizations, the analysis offers actionable insights. Businesses can use these insights to fine-tune their strategies, improve user experience, and ultimately increase customer loyalty and retention.