Comprehensive Reports Allow an EdTech Startup to Analyze User Behavior and Refine Its Product Strategy: MentorShow Case


MentorShow, an Edtech provider of exclusive online courses, had already set up data processing and analytical processes. However, the quality and accuracy of the calculations left much to be desired. The client set several project objectives:

  • To revise the existing KPIs and improve their quality
  • To find new optimized ways to obtain necessary data
  • To analyze a set of new metrics
  • To revise the logic of data processing
  • To build informative dashboards

The EdTech startup lacked the expert internal resources needed to complete the task. Hence, they turned to Valiotti, a Tableau expert with extensive experience in setting up SQL and ETL processes.


1. Refined data processing

After Valiotti’s team immersed itself in the Client’s subject area, we got acquainted with the available data, processes, and product features. At this stage, we worked both independently and together with the startup’s team.

We developed new raw data processing logic without using legacy code and created Python and SQL scripts. They run daily and allow the Client to obtain the necessary data for analysis (business KPIs and data tables). We also revised and updated the existing scripts for seamless performance.

2. A variety of data-rich dashboards for comprehensive decision-making

After schematization of data obtained from new sources, we built several dashboards with accurate calculations. They are regularly updated and allow for a comprehensive outlook. Previously, the Client needed to gather data from several reports manually. Now, the Client can track changes in indicators effectively to promptly make decisions on product development strategies based on meaningful insights.

This Edtech case study includes just a few examples of dashboards:

  • Retention report to analyze planned vs. actual subscription renewals, retention rates, and churn rates over time. For example, the dashboard gave the Client deep insights into when users churn and some possible reasons for it. Based on this data, the Client can make data-based decisions to improve user retention.

And Retention analysis report

● Retention report to analyze planned

  • Marketing KPIs report to analyse revenue, marketing expenses by platform, revenue and expenses per pass, ROI, CAC, and other KPIs.

Marketing KPIs report

  • Investor’s dashboard with such data as the number of subscriptions, including new ones, AOV/pass, CAC, LTV/CAC, payback time, retention, marketing expenditures, new signatures, courses, shootings, etc.

Investor’s dashboard with such data

  • Dashboard with information about subscribers, including their active periods, user name, email, ID, etc.

Dashboard with information about subscribers

  • A dashboard for a cohort analysis (Client consumption analysis)

A dashboard for a cohort analysis


  • Share the information about your product, features, and subject area for a data analyst to have a broad picture when calculating metrics.
  • Look for a simple code. Otherwise, those who will work with the code later may find it confusing. It’s best if a specialist leaves comments to facilitate further updates. Simple code also leaves fewer opportunities for mistakes and is easier to maintain.
  • Require clear documentation of your calculations and final reports. This will allow you, as a client, to better understand the applied method and easily understand what field was analyzed.

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