Comprehensive Reports Allow an EdTech Startup to Analyze User Behavior and Refine Its Product Strategy: MentorShow Case
Leo Dubert
Co-founder, MentorShow
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:
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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.
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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.
All numbers are random and do not disclose any client’s information.
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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Leo Dubert
Co-founder, MentorShow
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