Lantern is a Y-Combinator-backed startup founded by Bastien and Guillaume, who met while they operated as Platform Engineers at Uber in San Francisco.
The observation that led to starting Lantern is that most people in product teams have workflows where analytics tools are decision support mechanisms and rarely the point of origin for exploration and decision-making.
In case you’re wondering, Lantern is a SaaS tool that automatically surfaces insights based on event streams from various areas of your business and delivers them straight into a dedicated Slack channel.
Bastien reached out to us as they wanted to understand how they could improve their understanding of their potential end-users, typically in a context where the platform’s activation was great, and they wanted to improve other leading indicators.
The team at Lantern was very eager to participate in a framed research activity with experimented practitioners, so they could improve their interview skills while ensuring they could get answers to their questions in a relatively short period of time.
Doubt is a good thing
Having never done this type of work with third parties previously, Bastien was initially sceptical about the quality of insights our discovery sprint would produce. We were able to show him past examples of insights, opportunities and recommendations our research would yield and how they could be pragmatically transposed into product strategy and initiatives.
After a very engaging research planning workshop with the whole team at Lantern, we stood up a discovery sprint focused on understanding the role product analytics tools played in a team’s/company’s decision process.
Next, we started recruiting participants across France and the U.S. where the product had seen early traction.
Light at the end of the tunnel
A few interviews in and we were already having clear signals of what truly mattered to end users as well as their pains and flipping moment i.e. the moment where they decide they need assistance in their workflow.
We were able to map out what a “chain reaction to an insight” looked like and typical user flows of participants searching for answers in their workflow from monitoring key metrics to making decisions.
On top of these, we were able to get a granular understanding of how the target group consumed analytics tools, the value of these tools had in their workflows, their perception of Lantern.
The insights, opportunities and recommendations our research yielded meant that the team finally had answers they could rely on to unblock their product exploration and prioritisation process. They were now equipped with the fuel they needed to
We were extremely happy with how our collaboration worked out from the get go. Lantern, despite being a small shop, has foundational culture blocks that mean the team always kept an open-mind when we suggested new ideas or offered.