Industries
SaaS analytics for growth and retention.
SaaS companies generate more data than most industries — product usage, conversion funnels, churn signals, revenue cohorts. The challenge isn't collecting it. It's turning it into decisions that product, sales, and customer success teams can act on this quarter.
Common challenges for SaaS data teams.
SaaS companies generate more behavioral data than almost any other industry — product usage events, conversion funnels, subscription metrics, support tickets, NPS scores. The problem is rarely a lack of data. It's that product, sales, and customer success teams are all looking at different slices of the same customer, often in different tools, with different definitions of the same metrics.
Pain points we see most often
- ✓Churn signals buried in product usage data that nobody monitors — customers quietly disengage weeks before canceling.
- ✓Revenue reporting that doesn't match between finance, sales, and product because each team defines MRR and expansion differently.
- ✓Embedded analytics customers want but engineering can't prioritize — leaving a gap between what users expect and what the product delivers.
- ✓A/B test results that take weeks to analyze because the experimentation data isn't connected to revenue outcomes.
What we've delivered for SaaS companies
For Cvent, we built an embedded analytics platform that turned fragmented event data into a revenue driver — giving their enterprise customers self-service dashboards that increased platform stickiness.
For CareerPlug, we delivered embedded analytics and a churn prediction model that helped their team identify at-risk accounts before they canceled.
SaaS data services for visibility and profits.
There's no such thing as a one-size-fits-all data solution, so we tailor everything to your business needs and goals.
Data Analytics & BI
SaaS data analysis, custom dashboards and reports, and SaaS business intelligence (BI) tools result in smarter decisions about pricing, customer conversions, and more.
Learn moreAI & ML
Artificial intelligence (AI) and machine learning (ML) can predict consumer behavior, learn from platform usage, spot trends and inefficiencies, target prospects, and automate efficiently.
Learn moreData Strategy
Establish an actionable strategy to understand where you're at and how you'll get where you want to go using SaaS data strategies and business intelligence to grow.
Learn moreData Engineering
Gather, process, integrate, and act on software and platform data more efficiently through robust data architectures and seamless SaaS data pipelines.
Learn moreWe've helped SaaS companies seize opportunities.
“VisionWrights serves a variety of needs. They have a great team that can understand complex problems and work in a way that is integrated with internal teams to drive sustainable solutions.”
Christine Watts
Chief of Staff | Ninety
Frequently Asked Questions
What SaaS metrics should we be tracking?
Core metrics include product usage patterns, conversion funnels, churn signals, revenue cohorts (MRR/ARR), customer health scores, Net Revenue Retention, and expansion revenue indicators. The specific metrics depend on your growth model — PLG companies prioritize different signals than sales-led organizations.
How do you help reduce SaaS churn?
We build predictive churn models using product usage data, support ticket patterns, and engagement signals to identify at-risk accounts before they cancel. This gives your customer success team time to intervene with targeted outreach rather than reacting to cancellation requests.
Can you integrate product analytics with revenue data?
Yes. We connect product usage tools (Amplitude, Mixpanel, Segment) with CRM and billing data (Stripe, Salesforce, HubSpot) to create end-to-end visibility from first touch through expansion revenue. This unified view is essential for understanding what drives conversion and retention.
What does a SaaS analytics engagement look like?
We typically start with a data audit to assess your current analytics stack and identify gaps. Then we build a unified data model connecting product, revenue, and customer success data — usually in a cloud warehouse like Snowflake or BigQuery — with dashboards for each team.
Do you work with product-led growth (PLG) companies?
Yes. PLG companies generate rich product usage data that's especially valuable for conversion and retention analytics. We help PLG teams understand which features drive activation, where users get stuck, and what behaviors predict long-term retention.
Success Stories in SaaS
Transforming Solar Sales with AI-Driven Tools
How VisionWrights built a machine learning API that generates optimized solar panel layouts in seconds, trained on 30,000+ historical quotes.
Turning Fragmented Data into a Revenue Driver for Cvent
How VisionWrights architected embedded analytics for the world's largest event technology company — transforming disparate post-acquisition data into a paid product feature.
How an Online Ticket Reseller Streamlined Data and Scaled Success
How VisionWrights helped a secondary ticket marketplace redefine customer segments, reduce over-budget transactions, and build a data strategy for smarter pricing.
Insights for SaaS
Analytics & BI
Embedded Analytics for SaaS: What Product Teams Need to Know
SaaS customers expect analytics inside your product. Here's what product teams need to know about building embedded analytics that drive retention and revenue.
Data Strategy
Data Strategy for SaaS: Unifying Product, Revenue, and Growth Metrics
SaaS companies are data-rich but often data-fragmented. Product analytics, revenue metrics, and go-to-market data live in separate systems. A practical guide to building the unified data strategy that boards and operators need.
Related Industries
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