Data and Analytics Assessment and Roadmap:

iFit engaged with Analytics8 to develop a solution that would integrate all of its data and enable analytics so it could uncover the information the company needed to meet customer demands and grow its business.

We started by conducting a Data and Analytics Strategy Assessment. To kick off the process, we conducted interviews with business users to identify their daily processes and understand how they used their data. During these discussions, Analytics8 identified the key metrics iFit wanted to measure and profiled multiple source systems to outline the best system of record that would support their analysis.

With an understanding of iFit’s requirements and technical environment, the assessment concluded with a roadmap that detailed how to integrate data from iFit’s multiple systems into a cloud-based data repository to allow for unified analysis and simple addition of new data sources. The roadmap also outlined recommendations for the people, processes, technologies, and specific analytics that will help iFit use its data to better understand its customers, improve its offerings, and be able to answer questions from potential investors.

Data Warehouse and Analytics Implementation:

With the roadmap as a guide, Analytics8 deployed, configured, and implemented the recommended technologies and built iFit’s modern data architecture.

View the components of iFit’s modern data stack:

We moved very quickly from the roadmap to build the architecture and deploy the rest of the solution to enable end-users the ability to analyze their data in ways they hadn’t been able to do previously. We built a data warehouse in Snowflake, leveraged Fivetran to migrate historical data as well as current data, and used dbt to transform this operational data for source systems into the analytics model we designed.

As data was migrated into the Snowflake data warehouse, we were able to combine data from multiple source systems and model the data in a way that allowed for fast analysis. Within only two weeks, Looker was deployed “on top” of that data to further increase the end-user’s data analysis capabilities. We then built dashboards using Looker that allowed users to perform analysis on the fly and get immediate insight into their customer data. The dashboards tapped directly into the data warehouse, ensuring the data was accurate and up to date.

The dashboards we built analyze data about churn, workouts, membership, renewals, and retention—information that provided critical insights and analysis that outside investors were looking for.

Watch iFit’s CTO discuss the impact and results of their data modernization efforts:

The data and analytics solution we built allows iFit to move from disjointed analysis to drawing insights from a rich, unified dataset about its membership. iFit can now:

• Better understand the health of its subscription membership type segments by analyzing three main KPIs—paid users, net churn, and new users. This information is critical when courting new investors.
• Better understand its overall user base and subscription types (individual, family, monthly, yearly, etc.) and identify which segments were growing or shrinking. This information provides insights on how to structure and position its product offerings.
• Identify when customer churn is likely to happen and which factors predict when churn will likely to occur. Specifically, iFit is able to answer questions like:
  • When are customers upgrading and downgrading?
  • When are customers most often moving from one subscription type to another (for example, moving from an individual subscription to a family subscription)?
  • What workout behaviors and patterns indicate likely churn? For example, iFit discovered if a user only had one workout in the last three months, then they were 50% more likely to churn. Knowing this, the company then could initiate a campaign to get users to stay—sending a workout suggestion to users that have not worked out in one and a half months, before they hit the three-month threshold.
  • Identify specific workouts and trainers that lead to higher satisfaction and less churn.
• Track customers through the lifetime of their membership to understand patterns and build detailed user profiles, including information on what classes they take, what instructors they prefer, what time they like to workout, etc.
• Quickly and confidently answer questions from outside investors. Investors are keenly focused on new subscriber count, subscriber retention, and mobility. The solution that Analytics8 built gave iFit executives an interactive tool they can use to present to investors and respond to their inquiries with more detailed analysis.

Armed with its new data strategy and analytics solution, iFit is poised to take advantage of the virtual fitness market, grow its member base, ensure customer satisfaction, and be better positioned to obtain outside funding to grow its business and fulfill its mission.