SaaS & Tech

The Head of Product pulls up the weekly retention dashboard. It shows 92% monthly retention. The CFO's board deck shows 87%. Both are technically correct — they just define "active user" differently, measure over slightly different windows, and exclude different cohorts. Nobody's lying. The numbers simply grew up in different systems with different assumptions, and now the leadership team spends 30 minutes of every board prep meeting debating which version to present.

This is the SaaS metrics problem at its core. Not a lack of data — an excess of inconsistent data, scattered across tools that were each best-in-class when adopted and now form an archipelago of conflicting truths.

The churn problem is a data problem first. Every SaaS company says they want to reduce churn. Few have a churn definition that holds up to scrutiny. Is it logo churn or revenue churn? Gross or net? Measured from contract end or last activity? We built a predictive churn model at eBay Classifieds that delivered a 15% retention lift — the full methodology is in our Customer & Marketing Insights practice. For SaaS product teams, the takeaway is that churn modeling is only as good as the signal unification underneath it. Listing activity in Hadoop, traffic quality in Google Analytics, ad spend in campaign platforms, and support interactions in your ticketing system — each contains a piece of the churn story, but none contains the whole picture. The companies that reduce churn effectively aren't the ones with the fanciest model. They're the ones that invested in stitching those signals into a single behavioral dataset before building the model on top.

Product-led retention signals. The behavioral patterns that predict churn in SaaS are often product-specific and non-obvious. A user who logs in daily but only uses one feature may be more at risk than a user who logs in weekly but engages deeply across the platform. We build the instrumentation layer that captures these nuances — feature adoption sequences, time-to-value milestones, expansion triggers — and surfaces them to CS teams 2-6 weeks before cancellation, when intervention still works.

The attribution gap. Marketing says the campaign worked. Finance says revenue didn't move. They're both looking at different time horizons through different lenses. We built cross-channel marketing attribution for eBay across five emerging markets — the approach and 10-18% ROAS uplift are detailed in our Customer & Marketing Insights practice. For SaaS companies specifically, the attribution challenge is compounded by long sales cycles and multi-touch journeys. A prospect might engage with a blog post, attend a webinar, start a free trial, and convert to paid over 60-90 days. Giving credit to the last touch (or the first) misallocates budget. We build attribution models that weight the full journey and connect marketing spend to the metrics finance actually cares about: net new ARR, expansion revenue, and payback period.

The platform migration trap. SaaS companies accumulate analytics debt faster than most industries because they ship fast and instrument later. At a cloud security platform, we found 377 legacy BI objects — 14,652 lines of SQL with zero automated tests — all feeding dashboards that took 60+ seconds to load. The migration to Snowflake + dbt + Sigma took 45 days, reduced complexity by 86% (377 objects to 51 tested models), and improved dashboard load times to under 3 seconds. Deployments went from 3-4 hours to 5-10 minutes. Year 1 ROI: 606%.

Revenue accuracy at scale. For SaaS companies with complex pricing — usage-based, tiered, multi-product — revenue calculations quietly drift as the product evolves. We rebuilt the revenue analytics for the same cloud security platform — the full $84M validation story and 0.002% accuracy results are in our Financial Analytics practice. For subscription businesses, the specific risk is that pricing logic encoded in engineering systems becomes invisible to the finance team. Usage-based billing introduces edge cases — prorations, overages, mid-cycle plan changes — that multiply with each new pricing tier. The companies that catch drift early are the ones where Finance owns the rate tables and validation runs continuously, not annually.

AI readiness. Most SaaS companies want to embed AI into their product or operations. Few have the data infrastructure to support it. We've built AI pipelines that process unstructured data — survey responses to actionable insights in minutes — and modern data foundations that support ML model deployment. The pattern is consistent: AI projects succeed when the underlying data is clean, timely, and well-governed. They stall when teams try to skip the foundation work.

What the engagement delivers. A metrics layer where churn, CAC, LTV, and ARR are defined once, calculated consistently, and trusted across product, marketing, and finance. Dashboards that load in seconds, not minutes. Attribution models that connect marketing spend to revenue outcomes. And a data platform built to support AI features — not retrofit them.

Diagnostic questions. If your Head of Product and your CFO each pulled a retention number right now, would they match? How long does a dashboard deployment take in your current stack? When marketing reports campaign ROAS, can finance verify it against actual revenue movement?