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Fireside chat with Orangetheory Fitness on their data journey to drive marketing efficiency at scale.

Operationalize your data. Measure the KPIs that matter.

The Switchboard founders Ju-kay Kwek and Michael Manoochehri met with Ameen Kazoureni, the Chief Data and Analytics Officer at Orangetheory Fitness, over a fireside chat. Ameen took us through Orangetheory’s accelerated customer and data journey that drives marketing efficiency across 1400 studios and 24 countries globally.

Watch the entire webcast below or scroll down for video snippets of key insights.

Video 1

Core data challenge

Solving for the core data challenge - "Marketing data has strategic importance for growth, and the best place to start. But getting it to unified and structured for effective decision making is a heavy lift and a distraction"

— Barry C., Head of Advertising Data Operations

Video 2

Cross-functional team collaboration

On the system of engagement to collaborate with the CMO and define concrete deliverables

Level of work and diligence in driving the outcome

There are many steps involved in getting the needed visibility into Marketing funnels. Also, considerable engineering overhead and time to deliver it.

Thinking beyond traditional metrics

Unlocking the data to go beyond traditional metrics to find those custom metrics for the business that adds value.

Pre-ETL prep work before anything

Another core data challenge - prep work to clean up the data before ETL.

Advice to new data officers

Ameen's advice to the new Chief Data and Analytics Officers on setting a strong data transformation foundation with stakeholder interviews.

Case in point - how complex data sets work

Adobe Analytics provides a good example of why understanding stakeholder needs and specific domain expertise are the keys to cutting through the complexity of arcane datasets.

Unifying data silos requires data normalization clarity

Data normalization is a critical step for successfully unifying data silos. This is because, in most cases, teams have to work with datasets that may have been built for something else.

Think clearly about build vs. buy

To get maximum velocity, think 'build vs buy', and focus on what is truly a differentiator. Then have internal teams focus on only the downstream data transformations that are proprietary.

Applying an incremental approach to data assets

Incremental approach with data assets - don't boil the ocean to industrialize every data source. Release and monitor usage for one to ensure what you're building is valuable for the business team, and connect it well to other sources to prevent the silos problem.

Build vs Buy and how to understand the ROI associated with both.

Ameen on ROI and build vs. buy decisions when it came to operationalizing the data assets.

Initial stakeholder interviews are important

2 key lessons: (1) Don't underestimate the importance of a robust stakeholder interview process; (2) Take an interactive approach to data projects because people will want more.

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