Can't track full customer journey from trial to expansion: You know someone signed up for a trial but can't connect that to which ad they clicked, content they consumed, or sales touch that converted them. Product analytics shows usage but doesn't connect to revenue. Marketing claims credit for signups, product team claims credit for activation, sales claims credit for upgrades - nobody actually knows what drives conversions at each stage.

CAC keeps rising but don't know why: Cost to acquire new customers has doubled in 18 months. Could be worse ad performance, lower conversion rates, longer sales cycles, or acquiring lower-quality customers who churn faster - but you don't have data to diagnose which. Platform dashboards show rising CPMs and CPCs but can't connect spend to LTV by channel or cohort.

High churn with no early warning system: Customers cancel and you find out when they hit the cancellation button - not 30-60 days earlier when you could have saved them. No visibility into which usage patterns predict churn, which customer segments have retention problems, or what interventions actually work. Retention is reactive because you don't have proactive engagement data.

Product usage data doesn't connect to revenue outcomes: You track feature adoption, session frequency, and user actions but can't tie usage patterns to expansion revenue, referrals, or long-term retention. Product team ships features based on customer requests or gut feel instead of data showing what actually drives business outcomes. No idea which customer behaviors predict upgrades or expansions.

Can't tell which features drive conversion and expansion: Trial users activate different features but you don't know which combinations predict conversion to paid. Paying customers use various features but can't identify which drive upgrades, seat expansion, or retention. Building roadmap based on loudest customers or internal opinions instead of data on what actually moves revenue metrics.

Attribution is broken across marketing, product, and sales: Marketing multi-touch models end at signup. Product analytics start at activation. Sales CRM tracks pipeline but doesn't connect back to original acquisition source or product usage. Three different "sources of truth" that don't agree on which channels work, what conversion rates are, or where to invest. Decisions happen in silos because data is fragmented.

Expansion revenue is inconsistent and unpredictable: Some customers grow from $500/month to $5k/month organically while others stay flat for years. No systematic approach to identifying expansion opportunities, triggering sales conversations, or driving product-led upgrades. Account expansion feels random instead of being a repeatable growth motion with clear triggers and playbooks.

Can't forecast MRR accurately beyond 30 days: Cohort analysis is manual and outdated by the time it's done. Can't predict churn rates by segment, expansion timing by customer type, or seasonal patterns in new bookings. Board meetings involve explaining why actuals missed forecast instead of confidently projecting next quarter based on leading indicators you actually trust.

End-to-end customer journey tracking and attribution: Connect the complete path from first ad impression through trial signup, product activation, conversion to paid, expansion purchases, and renewal or churn. Implement unified customer identity that follows users across marketing touchpoints, product usage, sales conversations, and support interactions. See which acquisition channels drive not just signups but actual high-LTV customers who expand and retain.

Product analytics integrated with revenue outcomes: Track which features, usage patterns, and engagement sequences predict conversion from trial to paid, upgrades to higher tiers, seat expansions, and long-term retention. Build data models that connect product behavior to business outcomes so roadmap decisions are based on what actually drives revenue, not what customers request loudest. Identify power user behaviors and systematically drive more customers toward those patterns.

Churn prediction and proactive retention systems: Implement engagement scoring that identifies at-risk customers 30-60 days before cancellation based on usage decline, support ticket patterns, and behavioral signals. Automated workflows trigger customer success outreach, targeted feature education, or intervention offers when risk scores cross thresholds. Move from reactive cancellation damage control to proactive retention that saves customers before they mentally check out.

CAC and LTV analysis by channel, cohort, and segment: Calculate true customer acquisition cost by channel accounting for full sales cycle, not just initial conversion. Track cohort-level LTV over 12-24 months to see which acquisition sources drive profitable customers versus high-churn segments. Identify which customer attributes (company size, industry, use case, acquisition source) predict highest lifetime value so marketing can target lookalikes and sales can prioritize the right opportunities.

Expansion revenue identification and automation: Build models that identify which customers are ready for upsells based on usage patterns, team growth, feature adoption, and engagement signals. Trigger sales outreach or product-led upgrade prompts at optimal timing. Track expansion ARR by cohort and original acquisition channel to understand which customer types have highest expansion potential. Make account growth a systematic motion instead of random opportunistic selling.

Real-time MRR forecasting and cohort reporting: Implement dashboards that project next quarter's MRR based on current cohort retention curves, pipeline-weighted new bookings, and historical expansion rates by segment. Automated cohort analysis showing retention, expansion, and contraction by month, channel, and customer attributes. Replace manual spreadsheet forecasting with real-time data-driven projections that board and leadership actually trust.