How DataSnipper replaced 5 analytics tools with Optimizely

Analytics tools replaced

Non-analysts creating dashboards

15+15+

Dashboards across teams

DataSnipper's team was managing five analytics tools, inconsistent metrics, and a growing trust problem. With Optimizely Analytics connected directly to their Snowflake warehouse, they built a single source of truth that turned a reactive support function into a strategic partner.

DataSnipper replaced Power BI, Amplitude, Mixpanel, Vitally, and HubSpot Reporting with Optimizely Analytics, creating one governed source of truth connected directly to their Snowflake warehouse
Advanced product analytics like retention, cohort, and funnel analysis are now easy to set up. Previously, answering a question like “how many users performed one action but not another?” required manual data pulls and custom scripting
A purpose-built Customer Success dashboard quantifies time saved and ROI per customer, giving CSMs data-backed proof of value for upsell conversations
Teams now build their own dashboards and analyses without documentation or analyst support, reducing requests and allowing the data team to be more strategic

Industry

Software

Company

DataSnipper

Products used

AnalyticsFeature ExperimentationOptimizely OnePersonalizationWeb Experimentation

When every team had their own version of the truth

DataSnipper builds agentic automation software used by over 600,000 audit and finance professionals in over 175 countries, serving the likes of the Big Four accounting firms, Fortune 500 companies, government agencies, and global enterprises. The company is growing fast, and last year, its leadership decided the data infrastructure needed to keep pace.

Zack Porach, Senior Product Manager of the Data Platform team, joined DataSnipper last year with a specific mission: build a centralized data platform from scratch. He relocated from Ohio to Amsterdam for the role.

“When I was brought over, the team didn’t really exist yet,” Zack explains. “I was brought over to start a new team — still growing.” Working alongside him is Nicole Liao, Product Analyst, who has spent two years embedded in DataSnipper’s data team. Together, they sit within the product engineering org, reporting to the VP of Product Engineering.

Before they could build anything new, Zack and Nicole had to understand the full scope of what they were inheriting. Teams across DataSnipper were making decisions using Power BI, Amplitude, Mixpanel, a customer health tool called Vitally, and HubSpot reporting. Depending on which part of the organization you sat in, you used a different tool with a different calculation for the same metric.

Zack saw the pattern immediately. “Siloed data, inconsistent data, manual ad hoc reporting — all of these things were used to make decisions,” he says. There was no centralized definition for something as foundational as “active users.” Different teams had set up their own formulas in their own tools, and the numbers didn’t match.

You’d have one tool that says this many active users, and another tool says something different. Then you find out it’s because they manually set up formulas differently. Even something as foundational as active users wasn’t consistent.
Zack Porach|Senior Product Manager, DataSnipper

For Nicole, the daily reality was a constant loop of cross-checking. “I used to just check multiple sources to ensure it’s correct,” she says. “That’s it — just me checking it.” Hours that should have been spent on strategic analysis were going toward validating whether a number was even right. Nicole was building dashboards in Power BI, which Zack describes as “too complicated for end users.” Time-series analysis was particularly difficult, and quick product explorations that should have taken minutes stretched into days.

The fragmentation didn’t just slow the team down. It eroded trust. When different tools produced different numbers, stakeholders started questioning the data itself — every conversation becomes about the data rather than what the data is telling you.

Starts with Snowflake

DataSnipper’s leadership recognized the problem and started investing. The first major decision was to move to Snowflake as the company’s centralized data warehouse. With that foundation in place, the team began building proper data models using dbt, bringing structure and governance to what had previously been a free-for-all.

That investment in Snowflake turned out to unlock something bigger. Optimizely Analytics (formerly NetSpring) connects directly to Snowflake — reading from the warehouse without duplicating or moving data. For a team that had spent years managing a patchwork of opinionated tools, each storing and processing data in its own way, this was a turning point.

“Mixpanel does its own thing, same with Amplitude, same with HubSpot — they’re all really opinionated about how things should be,” Zack explains. The appeal of Optimizely Analytics was the opposite: it lets the data team own the modeling while the platform handles visualization and exploration.

Being able to get a tool that just reads directly from your warehouse — we can do all the modeling on our own because we’re the experts of data, and then just have the tool access the database directly. That was huge.
Zack Porach|Senior Product Manager

Nicole felt the difference immediately. “There is no connection issue between Snowflake and Optimizely Analytics,” she says. “This saved quite some time.” No more wrestling with data source refreshes. No more broken connections. The data was just there. Zack puts the decision simply: “The ultimate goal was easier self-service, and Optimizely Analytics was picked because it was easier than Power BI.”

Proving customer value with data

With the platform in place, Zack and Nicole’s team shifted from fixing infrastructure to building things that drive the business forward. The first major project connected product analytics directly to revenue: Nicole developed a Customer Success dashboard that shows, in concrete terms, how much value DataSnipper delivers to each individual customer.

For every key product event within DataSnipper’s platform, the team defined a standardized “time saved” metric, measured in seconds. Those time savings are aggregated at the customer level to quantify total efficiency gained. The dashboard also calculates ROI percentage — a single number that tells a Customer Success Manager exactly what to say in a quarterly business review or upsell conversation.

We developed a dashboard for CSMs to demonstrate the value our product delivers to each customer based on their actual usage. The dashboard shows ROI percentage, enabling CSMs to clearly communicate product impact and value during customer upsell calls.
Nicole Liao|Product Analyst

Before Optimizely Analytics, this kind of dashboard wasn’t feasible. The data existed in fragments — usage events in one system, customer data in another, sales information somewhere else. Now, because everything flows through the centralized warehouse, Nicole can blend usage data with business metrics in a single view.

Zack has watched the impact firsthand. “Customer Success — the usage we’ve seen has been growing,” he says. “They’re using it every month. This is the most successful dashboard we built for another team.”

From bottleneck to enabler

Before Optimizely Analytics, every data question landed on Nicole’s desk. She was the bottleneck — not because she wanted to be, but because the tools demanded it. That dynamic has fundamentally changed.

Three engineers and one product manager have already created their own analyses and dashboards without needing documentation or hand-holding. They duplicate an existing dashboard, adjust it, and run their own deep dives. “It’s really cool if you can set up a platform and let people just do things with it,” Zack says. “Optimizely Analytics is good for that.”

Engineers and product managers are able to copy analysts’ dashboards and create their own dashboards to perform deeper analysis on their own, without needing documentation. It has helped us reduce the time to fulfill requests.
Nicole Liao|Product Analyst

The customizability has been key. Being able to add descriptions alongside visualizations, set up the semantic layer with clear metric definitions, and let users click through to understand exactly what “active users” means builds understanding. “The way you’re able to add more information than just throw a chart at someone — that’s been really helpful at building understanding,” Zack says.

For Nicole, the shift goes deeper than time savings. She now runs retention analysis, cohort analysis, and funnel analysis — advanced product analytics workflows that were previously difficult or impossible with Power BI. “I used to build dashboards with Power BI,” she says. “Now I spend much less time on querying and doing analysis. I also get to do advanced product analytics which I didn’t do before. And the advanced analysis is easy to set up.”

Having it so people can just trust in it — versus debating whether or not why this number looks this way versus this tool looks that way — that part sucks. I hate that part. But being like, ‘hey, we set this up, we have this data model, you can just use it’ — that’s awesome. I like that.
Zack Porach|Senior Product Manager

Fewer requests landing on the data team’s desk. More time for strategic work. And a different kind of conversation happening across the organization. “Now we’re talking about new metrics to track, new data to provide, new data sources to integrate — versus just ‘answer this question for me,’” Zack says. “We’re getting more of the former, which is cool.”

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Single source of truth

Scaling data-informed decisions across the business

With five product teams already running their own dashboards and Customer Success using analytics to drive commercial conversations, the playbook is proven. Now the focus is on extending it further.

Finance data has already been integrated. Go-to-market teams are next. Solutions engineering is starting to receive product usage insights earlier in the sales funnel — giving them data to help them do their job better, as Zack puts it, “earlier in the funnel.”

For Nicole, the ambition is about depth — uncovering patterns in how DataSnipper’s 600,000+ users engage with the platform and turning those patterns into product decisions. For Zack, it’s about reach: every team at DataSnipper should be able to see the data they need, trust it, and act on it without waiting for a data team to pull it for them.

Zack captures the shift in a single phrase. “Enabling people to build cool things,” he says, “is the part I like personally.” In an industry built on trust and precision, DataSnipper is applying those same principles to how it understands its own product and serves its customers. Optimizely Analytics is the platform making that possible.