AI decisioning that shows up in your growth funnel.
For growth leaders at DTC, B2C subscription, PLG SaaS, fintech, and marketplaces. EkamFlow gives you per-customer predictions across acquisition, activation, expansion, and retention — through one API that feeds your CDP, CRM, and ad platforms without a data-science project.
Why VP Growths use EkamFlow
As VP Growth, you own the metrics that compound: CAC, activation rate, LTV, retention, net revenue retention. Every one of them is a per-customer question — which lead is worth calling, which trial should convert, which subscriber is about to churn, which product to upsell next. Every one of them is currently answered by rules, heuristics, or scoring systems that stop working the moment your customer base or acquisition mix changes.
EkamFlow gives you per-customer predictions — lead score, activation propensity, LTV, churn risk, next best action, next best offer — from a single API that reads your warehouse and writes to your CDP, CRM, and ad platforms. No ML team required, no separate model per motion, no quarterly scoring refresh. Growth teams that have run this pattern typically see the biggest lift from three specific applications: sizing acquisition bids to predicted LTV (not first-order revenue), routing trial signups by conversion propensity (not blanket nurture), and gating retention offers by margin-aware scoring (not blanket save-offer).
For growth VPs, the operational value is that you stop waiting on the ML team's backlog. Standard growth-decisioning use cases — lead scoring, activation, expansion, retention, cross-sell — ship in weeks from one platform, with holdout support so you can defend the lift internally.
Priority use cases for VP Growths
Predictive Lead Scoring
Rank every lead by conversion probability using machine learning. EkamFlow's predictive lead scoring goes beyond rules and heuristics — your sales team focuses on the leads that will actually close.
Learn moreCustomer Lifetime Value (CLTV) Prediction
Predict how much each customer is worth over 12, 24, or 36 months. EkamFlow's CLTV prediction helps you allocate acquisition spend, prioritize high-value accounts, and forecast revenue accurately.
Learn morePropensity Modeling
Score every customer's likelihood to buy, upgrade, renew, or respond. EkamFlow's propensity models predict the probability of any business action and feed directly into your marketing automation.
Learn moreNext Best Action (NBA)
Determine the optimal action for every customer in real time. EkamFlow's next best action prediction selects the right campaign, channel, offer, and timing — personalized at the individual level.
Learn moreChurn Prediction
Score every customer's risk of leaving before they churn. EkamFlow's private AI model identifies at-risk accounts in real time so you can intervene with the right retention action at the right moment.
Learn moreNext Best Offer (NBO)
Serve the right promotion to the right customer. EkamFlow's next best offer prediction selects the optimal discount, bundle, or incentive to maximize both conversion and margin.
Learn moreHow VP Growths put it to work
LTV-bid ad-platform integration
Predicted 24-month LTV flows into Meta CAPI, Google Enhanced Conversions, and TikTok Events API as a per-customer conversion value. Bids scale to predicted lifetime revenue instead of first-order revenue — usually the single biggest lift in acquisition efficiency once ad platforms have enough signal to optimize on.
PLG / self-serve trial triage
Activation-propensity scoring identifies which trial signups warrant sales touch, which need automated activation nurture, and which are unlikely to convert regardless. AE utilization improves because outbound concentrates on the high-fit high-propensity cohort, not the broad top of funnel.
Expansion propensity as a first-class metric
Expansion propensity ranks paid customers by likelihood of upgrade, add-on adoption, or seat expansion. CS and sales teams stop guessing which accounts to invest expansion motions in and start working the same ranked list — the one the model updates every day.
Retention as a growth lever, not a defensive spend
Churn scoring + LTV together reframe retention from cost center to growth lever. Save-offer depth scales to customer LTV, so retention spend stops being blanket and starts being value-weighted. This usually moves retention budget from 'defend' to 'invest' in the P&L narrative.
Outcomes VP Growths own
- Blended CAC and payback period
- LTV per acquired customer, per channel
- Trial-to-paid conversion rate
- Activation rate and time-to-first-value
- Net revenue retention (NRR)
- Expansion ARR per account
- Retention rate per cohort
What VP Growths do with it
DTC growth VP: bid to LTV, not first-order revenue
A skincare brand's VP Growth had been optimizing Meta and Google campaigns to first-order revenue — leaving predictable second- and third-order revenue off the table. Predicted 24-month LTV per customer became the ad-platform conversion value. Bids scaled to per-customer predicted revenue; blended ROAS lifted 28% because acquisition started paying for the customers actually worth acquiring.
PLG SaaS growth VP: trial triage
A subscription analytics platform's growth VP was drowning in 3,000 weekly trial signups with 4 AEs. Activation-propensity scoring routed the top decile (~300 leads) to AE-led outbound, middle tier to automated activation nurture, and bottom to a lightweight resource sequence. AE utilization improved 40%; trial-to-paid conversion held while AE-driven outreach shifted to the leads actually worth their time.
Fintech growth VP: expansion propensity + cross-sell
A neobank's growth VP inherited a cross-sell playbook driven by a manual scoring rubric maintained in a spreadsheet. Expansion propensity + product-fit scoring per customer replaced the rubric. Cross-sell attach rate on the top decile of propensity scores lifted 35%; the growth VP retired the spreadsheet and reallocated their rev-ops analyst to expansion campaign strategy.
Frequently asked by VP Growths
Platform-native predictive scoring works from platform-collected data — usually CRM activities and email engagement. EkamFlow scores using your warehouse, which typically includes product usage, in-app behavior, and cross-channel signal your CRM doesn't see. For PLG and self-serve growth motions specifically, this is the biggest single quality gap — platform-native scoring can't see the product-usage signal that actually predicts activation and conversion.
Yes. Predicted 24-month LTV per customer becomes the conversion value in Meta CAPI, Google Enhanced Conversions, and TikTok Events API. Ad platforms then bid to predicted lifetime revenue instead of first-order revenue. This is one of the highest-ROI single integrations for consumer growth teams — typical customers see 20-40% lift in blended ROAS on retargeting once bid signal switches to LTV.
Every use case supports holdout groups by default — a small share of customers continues to receive the previous scoring so you can measure incremental lift against a real control. For paid acquisition specifically, holdouts are configured at the audience level so ad-platform lift is measurable independently of downstream lifecycle lift.
EkamFlow reads from your warehouse (Snowflake, BigQuery, Databricks, Redshift) and returns predictions via API. Predictions can be pushed to your CDP (Segment, Rudderstack, mParticle), CRM (Salesforce, HubSpot), or marketing automation via reverse-ETL (Census, Hightouch, Polytomic). It slots into the modern data stack without replacing any of it.
Cold-start customers get cohort-based predictions from their first interaction — matched to comparable existing customers by acquisition source, first-touch category, device, and geography. As personal signal accumulates (usually 3-10 interactions), predictions shift from cohort defaults to personalized scoring.
The typical pattern is: ML team keeps their bespoke work (custom research-grade models, foundation-model tuning, non-customer prediction tasks); growth team consumes EkamFlow for the standard customer-decisioning stack (lead scoring, LTV, activation, churn, cross-sell). This usually resolves the 'ML team as bottleneck' problem without displacing anyone.
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