Case studies
How Tradezella retained 2.5-times more customers with the use of data
Industry
SaaS, Fintech
What we did
Data automation, product dashboard
Outcomes
Activation growth from 28% to 65%
Industry
SaaS, Fintech
What we did
Data automation, product dashboard
Outcomes
Activation growth from 28% to 65%
Problem
Tradezella helps traders make data-driven decisions and build healthy trading habits. Through reports, insights, and feedback, its users can analyze their trading style, learn from their mistakes, and improve their performance in an informed way.
We worked with Tradezella when the product was in its early stage, with only an annual subscription available. The management team used Stripe to monitor basic business metrics - ARR, churn, retention rate, etc. However, they felt that they were missing out on some essential data, so they asked us to help them dive deeper into their data.
We found out that, due to the nature of annual billing and limited usage stats, they had trouble differentiating between active and churned users. It was only towards the end of users’ subscription periods when the team would realize they may have lost those customers long before. At that point, it was close to impossible to re-engage them.
Tradezella tried implementing the industry’s best practices to onboard and retain its customers. A marketing textbook would recommend adapting the communication to each user’s particular situation - sending relevant content, employing appropriate engagement instruments, etc.
However, due to the lack of data on users’ activity, Tradezella would be forced to serve identical content to all users, whether they would upload their trades daily or hardly ever use the app. They knew they could get a lot more out of these interactions but lacked the means to do so.
Lastly, the management team at Tradezella really needed to tap into their product funnel. So far, it was nearly impossible with just the data from Stripe and limited signup records. They wanted to know how users move from one stage of a funnel to another and where they drop out. With that knowledge, they could address the bottlenecks and better measure the impact of their campaigns.
“We were generating lots of data but had no easy way to access it. The Coupler.io team helped us gather it all into one place and derived lots of valuable insights. The improvements in business metrics were almost instant.
Solution
As the first step, we helped the Tradezella team define different stages of the funnel - what constitutes an activation, when we count a user as churned, and the desired activity level that will likely keep a user coming back.
The next step was about setting up automated data flows. Using Coupler.io’s JSON importer, we synced data from their application to BigQuery and retrieved all historical data. We processed the data further and set up data syncs into Google Sheets. Every morning, an up-to-date set of data is loaded into the spreadsheet, and the Tradezella team can take it further.
The marketing team is now served a fresh list of users entering a particular funnel stage. They can treat them with relevant communication - show the product benefits to those that haven’t paid yet, offer help setting up when it’s most needed, promote regular product usage among those slowly dropping out, etc. They can focus on the marketing side of the project while the automations set them up with appropriate contact lists.
Once Tradezella implemented the personalized email sequences, they didn’t have to wait for long to see the results. In the very first month, they managed to re-activate 2.5-times more users than in any of the previous months that year. What’s more, the activation rate after signup skyrocketed, from 28% previously to around 65% in each of the following months.
With the data pulled daily from BigQuery, we also set up a comprehensive dashboard in Google Sheets. It aggregates the funnel data, visualizes the conversions, and shows the bottlenecks in the process. The team at Tradezella uses it to track the performance of campaigns, prioritize their projects, and measure the basic metrics of the business. They can pick up stats for only specific groups of users, compare any two periods, and a lot more.
Throughout our cooperation, we strived to empower the Tradezella team with our skills and expertise - not only in data analysis but also in building SaaS products. It paid off for them financially and gave them the framework they can follow to make their business data-driven.
Tools & technologies
Data sources
