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? At eBay Classifieds, paying customers — real estate agents and auto dealers — were churning despite significant ad spend. The issue wasn't that nobody cared about retention. It was that churn signals were scattered across listing data (Hadoop), traffic data (Google Analytics), quality scores (internal tools), and campaign data (marketing platforms). We compiled these into a unified dataset, built a predictive churn model using R, and deployed it through Databricks with Tableau dashboards for ongoing monitoring. The model delivered a 15% retention lift by identifying the behavioral patterns that preceded churn — weeks before cancellation — and routing those accounts to targeted interventions.

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. During COVID, we built cross-channel marketing dashboards for eBay across five emerging markets — connecting Meta, Google Ads, product feeds, and GA attribution into a unified view. The result: 10-18% ROAS uplift, not from spending more, but from shifting budget toward the channels and campaigns that actually moved the metrics finance cared about. The key was giving marketing and finance the same dashboard with the same definitions.

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 validated $84M in revenue at a cloud security platform and found 15 silent bugs, including a $472K undocumented rate anomaly. The rebuild gave Finance direct ownership of pricing tables, eliminating the engineering-ticket bottleneck that had allowed five years of pricing debt to accumulate.

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?