Ibotta speeds up product iteration using Mode

As Ibotta’s Vice President of Analytics, Laura Spencer plays a key role on the company’s larger technology team, responsible for all of the data components such as data engineering, governance, security, and output. Since 2017, the company has grown from 17 data analysts to 50, organized into teams that support every department at the company, from marketing and client analytics to product.

Ibotta was previously using a well-known business intelligence (BI) tool for their high-level business metrics. The challenge was that the analysts were not able to drill into the data or do quick prototyping due to its lack of integration with Python or R. The data analytics team needed advanced analytics capabilities that were not part of this other solution.

For their advanced analytics, the analysts were using a separate platform, which allowed them to code in SQL or Spark. It was missing the data visualization capabilities they were looking for, however, which made it hard to share new findings with stakeholders and iterate quickly.

Laura’s team was looking for a solution that would bridge the gap between these two solutions, providing them with the flexibility to do deeper analysis and to visualize new findings quickly and effectively. As a SQL-based team, they wanted to be able to query their database, analyze in R or Python, spin up a visualization, and share it with a colleague to see if they were moving in the right direction.

Ibotta brought in Mode, which provided the analytics team with the combination of SQL, visualizations, and collaboration that they needed. This allowed the analytics teams to work more closely with their counterparts in other departments, driving more data-driven decisions throughout the company.

Using Mode, the product analytics team can now closely monitor the rollout of new features. They can now quickly identify anomalies that could indicate an issue and simultaneously, keep senior leadership apprised of key business metrics. They also help with A/B testing on new features, allowing them to weigh in on the impact of incremental changes to the app.

In addition, the product analytics team has also helped to develop frameworks to answer more complex questions. When the product team was working on a major change to the consumer experience, the product analytics team collaborated with them to design the experience from the ground up. Using Mode, they defined success metrics and measured how this new experience would impact new user activation.

For Ibotta, Mode has enabled faster iteration across the board.

Since implementing Mode, the analytics team has noticed more sophisticated questions coming from internal stakeholders. “That’s where the visualization component comes in,” explains Spencer. “Once people have an understanding of the data, they start to ask more complex questions and open up to new ideas.”

For example, teams have gone from asking predetermined questions, such as “How many purchases have been made?” and “What is our retention rate?” to much more complex ones, like, “What is our retention by different user segment and in-app experience?” Teams have even begun asking predictive questions about the hypothetical impact of a change, rather than only asking about historical change.

These shifts in data practices and perspectives have helped Ibotta gain a deeper understanding of their customers, and with this understanding, significant growth over the past 7 years. Ibotta has been named to the Inc. 5000 list of the fastest growing private companies in the U.S. for the last 3 years, and in 2019, became the first mobile consumer technology company in Colorado to achieve $1B in valuation.