UXCam’s first data analyst set up their entire data stack in less than 6 months using Y42 and saved up 80% of their time when building new data models.
San Francisco, USA
360-degree analytics setup for product, sales, and marketing teams in < six months.
Complete independence working with SQL on Y42 in < three months.
80% of time saved when building new models.
Picture this: You join an early-stage B2B SaaS company as a data analyst and you need to get the entire analytics setup up and running, completely from scratch.
Your company uses data to generate customer insights for other businesses, but you obviously need to understand your clients, their buying journey, and their product experience too.
That was the case for UXCam, a mobile analytics solution. They provide tools to help users understand app usage and learn how to optimize for the best user experience. Their core offering started with session replay and heatmaps, but it has expanded to include a more holistic product analytics overview as well as user flow and funnel analytics.
UXCam provides a single source of truth for mobile app creators to collect and analyze their users’ data — and that’s exactly what they needed for themselves. Because their own product only caters to mobile apps, they needed a tool to visualize all the product data their data engineers were collecting, as well as a single place to combine and analyze data from the sales and marketing teams. “We needed to enable the commercial teams to get away from Excel spreadsheets because it’s clearly not scalable,” Anna explains.
Therefore, UXCam’s biggest pain points were:
Providing an end-to-end data pipeline for the business teams.
Visualizing product analytics metrics and setting up dashboards that business users could easily explore.
"No one expects product managers or marketing managers to do their own research and set up their own dashboards, but they should be able to get simple answers and learn from data. So we were looking for a solution that would allow for that."
Anna explains that she had to review several tools before making a decision. She looked into visualization tools like Tableau, Looker, and PowerBI. However, two key elements made Anna realize that these solutions were not the right fit: ease of setup and data integrations.
She even attempted to implement Grafana (an open-source tool) to build dashboards. But this also turned out to be more of a headache than a help: “Setting up the dashboards would’ve been possible, but I would have had to basically do nothing else but maintain them. It’s not Grafana’s fault — they’re meant to be used to monitor servers — it was simply not a match,” Anna tells us.
She tried to put another workaround in place: a combination of Data Studio and Python endpoints. However, the issue here was the lack of scalability and data orchestration. After she had set up a few endpoints for specific tables, she realized the solution wouldn’t work long-term when more complexity would be required.
After much trial and error, UXCam’s data analytics needs became very clear. They decided that Y42 responded to the exact pain points they were experiencing. They could:
Integrate their database, PostgreSQL, to source the product analytics metrics they needed and then visualize them.
Integrate Y42 with all their sales and marketing tools like Hubspot, Google Analytics, Google Ads, Facebook Ads, etc.
Centralize their data operations.
Set up the tool easily with the support of Y42’s customer success team.
Have a scalable tool at their disposal.
Start monitoring their data live and set up orchestrations to fully automate the data pipeline.
Most importantly, a low barrier to entry was key for Anna, especially because she had transitioned from working as a scientist in biomedicine to working in product analytics: “My tool is Python. SQL is just something I got familiar with through courses, so I wasn’t SQL-native when I started working with data, and still wouldn’t consider it my strongest skill. In the beginning, my knowledge was limited to simple queries only.” This is why Y42’s UI model was decisive for Anna. It made it faster for her to adapt to this new framework.
She explains that the customer success team was also very responsive and supportive in her onboarding process. “My favorite part of Y42 is the customer success team; it’s a huge asset,” says Anna. “It got to the point where they would send me the queries I needed to use in my models, and I learned a lot from that. Now, I’m fully independent with writing SQL models on Y42. It took me less than three months to get here — it’s what I consider a really efficient learning process.”
When scouting for the right data tool, Anna’s biggest concerns were performance and scalability: “I was worried about performance because tools tend to get slower the more data you have. But I’m super happy to be proven wrong with Y42.”
This shift in perspective happened when Anna started working on UXCam’s first use case with Y42. They had a new feature release, a rather complex one. They wanted to track the beta test’s performance and the feature usage after the release.
Anna set out to build a layered dashboard with an initial model that would feed the top-level metrics to the visualization. This project was ongoing for months, as people would come back to the initial high-level metrics and request more detailed information. Anna added more complexity to the dashboard as the end users’ needs changed.
"I got good feedback from the dashboard’s end users; they were happy that they could find what they needed. As the dashboard grew, they could also interact with it enough to get answers to their questions independently. It was a longer project because the complexity kept increasing, but it was a good thing that we could keep adding more layers to the dashboard. Y42 is modular enough that if you need another tab you just add a few more models and you can quickly set it up."
Anna explains that now she has a good framework with her integrations and models, she can just clone specific models and adapt them to the analytics of different product features. “I now save around 80% of the time it used to take me to build new models. You have to invest time in the first setup, but after that, you can always duplicate things — and that makes my life easier.”
Also, when other team members need insights or ask Anna for specific numbers, she can now direct them to the relevant dashboards and communicate with them in a more streamlined way. She can share the documentation she prepares for each model and visualization so that the end users know what is being measured. Then, she can leave them to work with the data independently.
"We finally have one source of data in one place where we agree on the numbers, which also helps us to democratize the data across the company."
Now, Anna’s integrations, models, and dashboards are all set up and orchestrated. This means that now, she can mostly focus on maintaining the data pipeline and ensuring everything is working correctly.
She also explains how satisfying it is to have dashboards in place that allow users to spot problems and act on them immediately. For instance, if there’s an issue with the activation of a new feature, the dashboards Anna has built would reflect this, meaning she doesn’t have to run a separate research project for that specific problem.
This access to data also facilitates quicker decision-making, since the issues and the solutions required become clearer when the visualizations have been checked.
Anna also speaks about the emotional benefit of data transparency: “It’s really satisfying to tell the developer team responsible for building new features that people are using what they created. It makes me happy to tell them, ‘Look what’s going on! People really like what you did!’ And I think this is really nice to see. It boosts morale to see that people value what you put out.”
In less than six months, UXCam was able to go from being an analytics company with zero dashboards of its own to having a full overview of its product, marketing, and sales analytics.
The game changers for them were being able to implement a scalable data platform that allowed them to centralize their data sources, build models, visualize their key metrics, and automate the entire pipeline to focus on democratizing data and acting upon its insights.
For them, high performance and a low barrier to entry were key — and that’s exactly what they found in Y42.
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