The Marketing Research and Insight Excellence Award winner Firefish, The Numbers Lab and Pinterest.

Editor’s note: Firefish, The Numbers Lab and Pinterest are the winners of the 2022 Groundbreaking Research Project Award which is a category in the Marketing Research and Insight Excellence Awards. To find out more about the awards click here. 

With 102 million data points and a visual research design and execution, Firefish, the Numbers Lab and Pinterest partnered to realize a unique commercial opportunity to differentiate Pinterest. The teams created pictorial research outputs for teams across Pinterest, sharing tactical examples of how to create inspirational content and talk to brands and advertisers. “By articulating an easily understood framework, and bringing along dozens of cross-functional stakeholders, the project has transformed parts of our product, marketing and metrics,” said Cassandra Rowe, Pinterest’s head of product research. 

What was the goal of the project? 

Enabling inspiration for its users is the core of Pinterest’s mission and it is actively exploring new and innovative ways to measure this mandate. Last year Pinterest approached Firefish with a unique challenge; to understand inspiration. The goal was to create a working model of what inspiration means, to guide internal development, support content creators and build a compelling narrative to take to market. Crucially, we needed to bring that knowledge to life through a visual lens – allowing teams across the business to see the opportunities for action.

Why did you decide to do this project?

Inspiration is largely intangible and very individual, yet we needed to understand the common human experience as well as the nuance of how it shows up across the offline and online world for people around the globe. This was a really squishy challenge to unpick the abstract, and the Firefish and Pinterest teams absolutely embraced tackling this via a completely new approach blending qual, quant and semiotics all with human understanding at its heart. 

How hard was it to analyze all the data points? 

The volume of data inputs was huge, but it was crucial that the outputs be visual and digestible. Key to this was utilizing the efficiency of machine learning to generate semiotic themes. The human layer was then overlaid by taking response to imagery via survey and the visual stimulus used at both stages allowed us to distill the insights, taking us from data to visual storytelling.