How ServiceTitan Saves Over $350K per Year in Analyst Time with Coefficient and Snowflake
From Manual Exports to $350K in Annual Savings
- Every analyst lost a full workday each month to manual Snowflake exports - writing SQL, downloading CSVs, and pasting into Google Sheets. Across the organization, nearly 5,000 hours per year consumed by maintenance.
- Dashboard complexity was artificially capped at 20 KPIs per segment because each new data source added manual update time. The team avoided adding depth to avoid 40-minute refresh cycles.
- Half of all data team tickets - 10 to 15 per week - were manual data update requests from stakeholders who couldn't access Snowflake or write SQL themselves.
- Coefficient automated all data refreshes, reclaiming over $350K per year in analyst capacity. Every hour previously spent on CSV exports and copy-paste now goes toward strategic analysis, cross-functional collaboration, and building new dashboards.
- With manual refresh eliminated, dashboards expanded from 20 KPIs to 80-90 per segment - pulling from Snowflake, Salesforce, and Gong data in a single sheet with hourly auto-refresh.
- Data requests dropped 75%. Every remaining request is for new analysis, not maintenance. After seeing the impact on RevOps, ServiceTitan expanded Coefficient to finance and product teams.
ServiceTitan’s revenue operations team was spending thousands of hours a year manually exporting data from Snowflake – writing SQL, downloading CSVs, and pasting into Google Sheets just to keep dashboards running.
To serve their stakeholders across sales, marketing, product, and finance, the data team needed a way to deliver live Snowflake data directly into the spreadsheets where analysis actually happens.
That’s when they discovered Coefficient.
How ServiceTitan Saves Over $350K per Year in Analyst Time with Coefficient and Snowflake
ServiceTitan centralized all their data in Snowflake – Salesforce pipeline, product usage metrics, consolidated data marts. One source of truth.
But extracting that data for analysis was entirely manual. The workflow: write SQL in Snowflake, run the query, download a CSV, copy and paste it into Google Sheets. Each table took 2-3 minutes. Each dashboard had 5-10 tables. Each update cycle took 15-20 minutes. And the team had dozens of dashboards to maintain.
We used to have dashboards with pages that said 'here's the SQL code to copy and paste into Snowflake to pull up the CSV.' That was our standard operating procedure.– Hunter Kaufman, Revenue Operations & Strategy, ServiceTitan
Analysts were trapped in maintenance loops – each person losing upwards of 10 hours per month just refreshing existing dashboards. The team rationed refresh frequency to Tuesdays and Thursdays because daily was too time-consuming. Dashboard depth was artificially capped at 20 KPIs per segment because each new data source added more manual refresh time. And half of all tickets – 10-15 per week – were just manual data update requests from stakeholders who couldn’t access Snowflake themselves.
Across the growing analytics organization, thousands of hours per year were being consumed by work that added zero analytical value just moving data from one place to another.
The Solution: Automating the Most Expensive Manual Work in the Stack
Coefficient connects Snowflake directly to the Google Sheets where ServiceTitan’s teams build dashboards and run analysis – replacing manual CSV exports with live, auto-refreshing data pulls.
Snowflake remains ServiceTitan’s single source of truth. Coefficient extends that investment to the last mile – delivering live warehouse data into the spreadsheets where work actually happens. Together, they eliminate the manual labor between where data lives and where it gets used.
This is exactly what we need to solve probably the most tedious pain point we have right now. After that, it spread like wildfire throughout the department.– Hunter Kaufman, Revenue Operations, ServiceTitan
The Results: $350K+ in Reclaimed Analyst Capacity
Every analyst on the team got a full workday back each month – time that had been consumed entirely by manual data refreshes. Across the analytics organization, that adds up to nearly 5,000 hours per year and over $350K in reclaimed capacity, all redirected from copy-paste work into strategy and cross-functional collaboration.
Our team got back thousands of hours a year. But the real impact is what we did with that time – we went from maintaining dashboards to building entirely new analysis that drives strategy across sales, marketing, and product.– Hunter Kaufman, Revenue Operations, ServiceTitan
The ticket queue tells the story most clearly. Before Coefficient, the team handled roughly 20 requests per week – 10-15 were manual data refreshes. Now those are gone entirely. About 5 tickets a week, and every one is for new work.
Our data requests reduced by 75%. The requests we get now are to set up new dashboards, new pieces of analysis – which is so much better than maintaining old ones.– Hunter Kaufman, Revenue Operations & Strategy, ServiceTitan
When Kaufman asked his team to quantify the impact, one number came back consistently: 25% more productive. Reports that used to take 30 minutes to update now take 30 minutes of one-time setup and then run automatically. Dashboards that refreshed twice a week now refresh every hour.
The strongest evidence is how fast adoption spread. What started in revenue operations quickly expanded into finance, product, and even the team managing ServiceTitan’s Tableau infrastructure – all driven by word of mouth, not a mandate.
The Return You Can’t Quantify
The $350K in time savings is the measurable part. But Kaufman says the real impact is the work his team can now attempt that was never on the table before.
Instead of maintaining dashboards, analysts now spend their days collaborating with sales and marketing leaders – identifying what’s working, game-planning new approaches to campaigns and rep productivity. The kind of strategic work that actually moves revenue.
There's tons of stuff I wouldn't even have considered attempting in the past. But now I can because I know it's just going to be automatically done. It almost does a disservice to quantify it as a before and after – it's more like a phase change.– Hunter Kaufman, Revenue Operations & Strategy, ServiceTitan
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