Software house
> V1 case study - MVP product development based on data from analytics tools V1 case study - MVP product development based on data from analytics tools

Data wpisu
Tomasz Kozon
Tomasz Kozon

Below you will find descriptions of the development and changes made to the MVP (Minimum Viable Product) version based on data=driven customer guidelines. In this post, we'll discuss how heatmaps, recordings of user sessions, and data from Google Analytics helped us improve the new version of the product.

In part 1 of the case study, I went over how to create MVP versions, so if you haven't read it yet, you should first check out this article: Part 1 of the GrowCreator case study.

Production implementation and traffic generation for MVP version

It was assumed from the start of the project that the MVP wouldn't be the last release of the product. It had no comparable products on the market, and a large part of the implementation was innovative and required validation in a production environment, generating traffic of real users. Most of our assumptions were verified in practice, but the analysis also revealed problems users had, which we hadn't anticipated when designing the application.


The application generated traffic from multiple sources. Our strategy involved creating groups of recipients from people who were seeing the client's brand for the first time as well as running remarketing campaigns among previous visitors of their shop online. GrowTent is one of the top companies on Poland's market in its industry and a popular choice for shoppers from all over Europe, so we had a large amount of data to use to develop the project with the client.

Data from analytical tools

Before launching the configurator, we configured basic tracking scripts via Hotjar, Google Analytics, or Facebook Pixel. It was all implemented with Google Tag Manager.

The applications collected data for several months. During this time, we optimized campaigns and developed a package of changes to be implemented in subsequent sprints together with the client.

The implementation of GrowCreator V1

A client was actively involved in the development from the beginning of the project. Based on the analytical data and more than 10 years of industry experience and knowledge, the client's team was able to determine users' needs and translate them into the application's logic. We were tasked with providing feedback on the suggestions for changes and recommending UX solutions, as well as creating a prototype and the final version of the configurator.

growcreator v1

The main suggestion from the client involved dividing the configurator into two paths:

  1. Basic - a set of 8 steps, some of which had ready-made product packages from a specific section. The crop set configuration takes five to ten minutes and is meant for users who do not require full customization of the crop set.
  2. Advanced - a set of 21 steps. It takes  20-35 minutes, and is meant for advanced users who want full control over the selection of components as well as full access to the whole offer. Additionally, this path allows users to add parameters of parts they already own. Using this approach, the configurator is able to match the components with the appropriate products in subsequent steps - the algorithm displays the specific components based on the inputted data.

We have made significant improvements to the UX too. We rebuilt the header so that users could see what the next steps were, as well as changed the way filters are presented and how the most important information is displayed.


It was necessary for the product's second version to undergo both front-end and back-end changes. However, these modifications did not significantly affect the whole logic and concept of the application, so we managed to implement it in 3 two-week sprints. Its smooth implementation and rapid development can be largely attributed to the precise guidelines from the client, as well as to a solid foundation laid out in the MVP version of the project.

Currently, in the new version, we have implemented analytics, and we are analyzing the first data from tools to make further enhancements to the configurator.

Other Case Study