HER is one of the largest dating apps for 2SLGBTQ+ women and queer folk. It allows users to connect with each other and their community through news, social media content, and events. By being tailored directly for queer people, HER avoids some of the downsides of mainstream dating apps.
“Narrator allows a single analyst to do the work of a data engineer”
Narrator’s unique data model, the activity schema, allows teams to maintain a full production data system, with 10s of billions of rows, without dedicated data engineering.
Before starting with Narrator, HER's data team evaluated tools to build and maintain materialized views. They built out a dbt project with a full time consultant, but realized that as the company grew it would require a team of analytics engineers to maintain.
Sydney Reeves, a Senior Data Analyst at HER, turned to Narrator to see if it could easily manage their materialized views at scale. She built Narrator's activity stream on their warehouse over the course of a month, and in three weeks had their materialized views running in production.
In less than two months a data analyst built a full production data system managing billions of rows.
“Since we started using Narrator we haven’t talked about building big queries in Metabase to extract data from the non-materialized views in BigQuery”, says Sydney.
She particularly appreciates that the ongoing maintenance is far simpler as well. “With Narrator, updating a materialized view to add a new column or something similar only takes an hour."
Victor, HER's Head of Engineering, agrees: “Maintenance time has been drastically reduced. Narrator allows a single analyst to do the work of a data engineer"
"I feel like I can do my job so much better. Having all this extra time back gives me the time to do the extra things I want to do on top of what’s expected of me in my role"
"With Narrator I was able to run 20-40 analyses in two days to understand what leads to user success"
Narrator dramatically improves a data analysts day-to-day workflow.
HER’s data team wanted to increase the conversion rate from signup to user success. They already knew which customer behaviors correlated with user success, but they wanted to analyze which ones were actionable -- what the business should actually promote to increase conversion.
Over the course of two afternoons Sydney used Narrator’s analyze button to test 10 different hypotheses, ranging from number of likes received to number of swipes within 30 minutes of signup. She explored several variants of each, ultimately creating 30 to 40 different analyses.
A data analyst was able to create dozens of full analyzes in days, without pausing for data prep
Narrator’s automated analysis took the same steps that Sydney would have done herself, without the need for data prep.
“That would’ve taken way more than two days to do that before - just getting the data and cleaning it up is where most of the work is. When you're an analyst, running the analysis is usually the fast part."
"But with Narrator even the analysis is much faster than doing it myself because I can just keep clicking on the Narrative, then go do something else and come back and run a new Narrative, and keep going."
After a couple afternoons' worth of work Sydney had 4 or 5 specific actionable ideas for the business to try.