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

Client

MentorShow is a French startup that offers exclusive online masterclasses on different fields by successful speakers. The company is involved in producing and distributing courses via a website. In the future, another distribution channel – a mobile app – will be added.

Problem

The Client’s data processing processes were inefficient. They wanted the data analytics logic to be revised and updated, the KPI accuracy to be checked, and informative dashboards to be generated.

Results

As you will learn from this Edtech case study, we delivered refined data processing and set up Python and SQL scripts. New comprehensive reports allow for enhanced data accuracy. As a result, the Client gained meaningful insights about user behavior: for example, the reasons for subscription cancellations.

Technologies

Our Tech Stack

Leo Dubert

Co-founder, MentorShow

“Since about six months, Nikolay and his team are doing tremendous work for us. They're sharp, professional, efficient, and excellent communicators. They managed to bring clarity in a very complex and chaotic environment, with a patient, yet ambitious pace. Nikolay is also very present when needed, but can rely on his strong team. I definetly recommend!”

Goal

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.

Results

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:

  • Revenue KPIs dashboard to track essential metrics like Pre Refund Revenue, MRR, and ARR. By leveraging this tool, Mentorshow gains actionable insights into revenue trends, customer acquisition, and churn, empowering data-driven decision-making for sustained growth.

Mentorshow-Revenue-KPIs dashbord

  • Mentors Performance to evaluate individual mentor effectiveness. With these insights, they can identify top-performing mentors, refine product offerings, and enhance overall user engagement and satisfaction.

Mentors-Performance

All numbers are random and do not disclose any client’s information.

We also built two self-service dashboards for the marketing and product teams — no Tableau needed.

They wanted quick, self-service tools they could tweak without our help, so we used Apache Superset.

Reviews and Comments dashboard lets the client quickly check how students rated courses and individual lessons.

At the top: total number of reviews and the average rating across all classes.
Next to it: a class-by-class rating breakdown.
Below: student comments linked to specific lessons, with emails shown separately — so any issues can be addressed directly.Дашборд 1 Apache Superset

Classes dashboard is a bit more advanced — it tracks student activity by day and month.

The client uses it to spot peak engagement times and see which courses and lessons are most popular.

With 4 bar charts and 2 number widgets, it’s another case where a simple, fast tool works better than a complex one.
Дашборд 2 Apache Superset

Simple, clear, and fast — just what was needed.

Tips

  • 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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Technologies

Our Tech Stack

Leo Dubert

Co-founder, MentorShow

“Since about six months, Nikolay and his team are doing tremendous work for us. They're sharp, professional, efficient, and excellent communicators. They managed to bring clarity in a very complex and chaotic environment, with a patient, yet ambitious pace. Nikolay is also very present when needed, but can rely on his strong team. I definetly recommend!”

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