How Fabi.ai helped obé Fitness cut turnaround time for ad hoc analysis by 75%
Fabi is the AI analyst we've been waiting for. It's an intelligent assistant that's embedded right in our data stack. It supercharges self-service for stakeholders and saves me hours of analysis.
Summary
obé Fitness, a leading at-home fitness platform for women with more than 17,000 on-demand classes, faced challenges handling the high volume of internal data requests. In addition to being high volume, requests were unique in nature, posing a challenge for existing self-service data solutions.
By implementing Fabi.ai, they significantly improved their data team's productivity, enhanced stakeholder self-service capabilities, and accelerated data-driven decision-making across the organization.
Bringing world-class fitness expertise to homes across America
obé Fitness is committed to bringing world-class expertise from trainers and fitness classes directly to users across the country.
Their mission to provide engaging, effective workouts relies heavily on data-driven decision making, from content creation to in-product personalization to user experience optimization. This approach requires a deep understanding of user engagement metrics, content performance, and predictive modeling for user retention.
We want those folks who are managing our trainers and managing our content creation schedule to know what types of content are going to hit and where we might expect a little bit less engagement.
Navigating the data maze: obé fitness's analytics challenges
The at-home fitness industry has seen explosive growth in recent years, driven by technological advancements and changing consumer preferences. As demand from consumers has increased, companies offering on-demand fitness options have faced intense competition, evolving user expectations, and the need to continuously innovate their offerings to stand out.
As a result, good, accessible insights from user data is more important than ever before. It allows companies to understand user behavior, optimize content, personalize experiences, and make informed decisions about everything from class scheduling to marketing strategies.
A critical component of running a successful data team is making sure that folks have the information they need as soon as possible when they're asking a question.
obé Fitness has always had a data-forward culture, but found themselves grappling with the limits of their existing data infra and analysis capabilities, including:
- High costs associated with their existing BI tool (Looker)
- Overwhelming workload on their single-person data team
- Slow turnaround times for ad-hoc analysis requests
- Static analysis outputs that quickly became outdated
- Limited ability for non-technical stakeholders to interact with data independently
These challenges weren’t just operational headaches; they were slowing down the speed of future innovation in a competitive market.. The company wanted to get ahead of it and needed a solution that could address these issues comprehensively so they could get the insights they needed to improve retention, analyze engagement, and make personalized recommendations to members.
So, Michael Bartoli, director, data and analytics, set out to find a better solution.
Fabi.ai: The AI-powered solution to streamline analytics
I remember our onboarding flow being incredibly smooth. Really all I had to do was create a user and credentials in Snowflake, authorize Fabi on those credentials, and then send over the dbt documentation that we already had.
To address these challenges, obé Fitness implemented Fabi.ai as a key component of their data stack, alongside Snowflake, dbt, and other tools. The integration brought several key advantages:
- Fabi.ai was seamlessly integrated with their existing data stack, including Snowflake and dbt, and gave Michael a single environment to work in to avoid endless context switching between tools.
- The AI capabilities of Fabi.ai allowed for natural language querying of data, reducing the need for manual SQL and Python writing.
- Stakeholders were empowered to perform their own analyses, with the option to have Michael review their work.
- Fabi.ai could generate Python visualizations and provide context-aware follow-up answers that improved the quality and speed of analysis.
I'm a single-person data team, but Fabi makes it feel like I have at least doubled, maybe tripled or quadrupled my own efforts.
Transforming data operations: Fabi.ai's impact on obé Fitness
The implementation of Fabi.ai led to remarkable improvements in obé Fitness's data operations:
Quantitative results:
- Accelerated analysis: Ad-hoc analysis time reduced from a full day to 2-3 hours, with some processes seeing a 75% decrease in turnaround time.
- Empowered stakeholders: Self-service analytics capabilities reduced data team requests by 80-90% (from 1-2 per week to 2-3 per month), freeing up resources for more complex analyses.
Qualitative results:
- Increased productivity: The data team's productivity doubled or tripled, allowing focus on high-value tasks and strategic initiatives.
- Enhanced decision-making: Faster insights delivery enabled more agile, data-driven decision-making across the organization.
- Greater trust and communication: The ability for stakeholders to independently verify data and easily request reviews fostered increased trust and open communication between the data team and other departments.
- Improved code quality and consistency: AI-generated code and visualizations led to higher quality, more consistent reporting across all analyses, reducing errors and misinterpretations.
- Increased stakeholder independence: Non-technical team members gained the ability to perform their own data queries and basic analyses, reducing reliance on the data team for routine tasks.
Fabi.ai empowers me, as a non-technical user, to dive into my own data in real time. It's fast, intuitive, and eliminates the need for constant back-and-forth with the data team. Whether it's late at night or early in the morning, I can get the insights I need right when I need them.
Key takeaways
Fabi really shines because it's natively already plugged into our data warehouse, so it has access to live data all the time at the click of a button.
- AI-powered analytics tools can significantly reduce the workload on lean data teams.
- Empowering non-technical stakeholders with self-service analytics improves decision-making speed and data-driven culture.
- The right AI tool can improve both the productivity and the quality of work for data professionals.
- Seamless integration with existing data stacks is crucial for successful adoption of new analytics tools.
Looking ahead: Expanding Fabi.ai's impact at obé Fitness
As obé Fitness continues to leverage Fabi.ai, they are focusing on three key areas for future development:
- Enhancing predictive models: The team plans to use Fabi.ai to further improve their recommendation engine and user retention prediction models.
- Expanding self-service analytics: obé aims to increase the adoption of Fabi.ai among non-technical teams, further reducing the burden on the data team.
- Advanced data science applications: With time saved on routine tasks, the data team intends to explore more sophisticated data science projects to drive business growth.
These forward-looking plans demonstrate obé Fitness's commitment to maximizing the value of their data assets and continuously improving their analytical capabilities with Fabi.ai as a key enabler.
Ready to improve your data workflows? You can get started with Fabi.ai for free in less than five minutes and make complex analysis a breeze.
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