Teikametrics helps online retailers maximize sales and profitability. Its platform uses data science to deliver insight on what to advertise, and what new products to release to market, and when. It has grown rapidly in the last 3 years from 20 employees and 4 engineers to over 100 employees and a 30 person global engineering team across 3 locations. Its platform supports over 3000 sellers, and manages $5B+ of gross merchandise value and 10+ TB of data.
Teikametrics relies on great products to create successful, happy customers and grow its business. But Teikametrics’ rapid growth introduced new engineering challenges that impeded consistent delivery of new roadmap features, threatening the continued success of its customers and ultimately its business. Without a formalized approach and system to measure engineering performance, Teikametrics was lacking the insight they needed to identify the source of the problems and optimize its engineering organization.
Aatish Salvi, CTO of Teikametrics, set out to solve two major challenges. The team first needed to segment and quantify the level of effort for various engineering initiatives so they could deliver accurate roadmap forecasts. “We didn’t have a way to identify exactly what was derailing our roadmap efforts. We are a startup growing so fast that it was hard to segment workstreams and understand what was contributing to overall engineering workload,” Salvi stated. Second, Salvi wanted to be efficient with engineering investment, “It’s the easy answer to blindly hire more engineers when you aren’t getting enough done, but we wanted to articulate the value every new hire would provide, and how they would power improved engineering performance.” Teikametrics initially tried to solve these challenges by creating new processes and reports within their issue tracking and project management systems.
But that introduced problems of its own. Salvi commented, “We were very unsuccessful with this approach. There was no framework in the tools to support what we wanted to do, and there was no guidance or consultation on if or how we could actually do it.” Salvi and his team felt that the process change would place a large burden on individual engineers. “We quickly realized using our existing tools was an unrealistic expectation, and an unnecessary distraction to the engineering teams,” Salvi concluded.
Salvi realized he needed more than just a new tool that would provide quantitative metrics on engineering performance. Teikametrics needed guidance on how to best organize and measure its engineering teams to achieve its objectives. “Once we saw the Jellyfish demo and met with their team I knew this was exactly what we needed. They not only offered a great engineering management platform, but also a best-fit framework on how to get value from it, including proven practices that would work for a company of our size and shape.”
After just a few months working with Jellyfish, Salvi and the Teikametrics team identified the root cause of their challenges and achieved significant improvement in their overall engineering performance, delivering 89% of planned roadmap features per quarter. These new insights showed that interdependencies between teams were causing significant roadmap delays: “Jellyfish showed us how entangled our teams were, and how we could restructure teams for meaningful performance gains,” Salvi recalled. Salvi and his team were also surprised to learn how much engineering bandwidth was being consumed by one-off customer success requests that could be automated. “It was a transformative moment for us to see how many tickets were being assigned to these [one-off customer success] requests. With the data from Jellyfish, we justified the addition of a dedicated resource to automate these requests and reallocated the engineering effort directly to the roadmap for these same customers.” As they begin planning for the next year, Salvi’s team has been able to provide the Finance team with a clear picture of how many engineers and what teams are required to deliver the company’s strategic product plan.
Six months after starting with Jellyfish, Salvi’s engineering team has restructured their teams to optimize performance, which has resulted in quantifiable gains in engineering throughput and roadmap forecast accuracy:
As Teikametrics looks to nearly double engineering again in the next year, Salvi and his team are confident they have the right tools, process, and expertise in place to scale their operational successes, “Jellyfish is devoted to solving the exact types of problems we were facing, and they are putting together a community of knowledge in a way that no one else is trying to do, which gives us the confidence to grow this high performing engineering organization.”