AI decisioning for B2B SaaS growth & CRM.
For growth, marketing ops, and customer success teams at B2B SaaS companies. EkamFlow predicts next best action, lead score, churn risk, and expansion propensity per account — so lifecycle motions ship from one API instead of hundreds of segment rules.
Why B2B SaaS marketing teams use EkamFlow
B2B SaaS growth teams accumulate a specific kind of complexity over time: dozens of lifecycle motions (onboarding, activation, feature discovery, expansion, renewal, win-back), each with its own segment rules and its own timing logic. Marketing ops ends up owning a rule library that no single person fully understands, and each new motion requires re-authoring routing rules across email, in-app, and CS-outreach systems.
EkamFlow collapses that decisioning layer into a single API. Next best action decides which of your six lifecycle motions is right for each account this week; lead scoring ranks trials by conversion probability; churn prediction drives CS outreach queues; expansion propensity feeds the sales team's account prioritization. Marketing ops maintains creative and campaign catalogs; the model handles allocation.
The largest single win for B2B SaaS is usually retiring the rule library — teams that were maintaining 400+ segment rules find themselves shipping a single API integration and reallocating ops time to actual campaign work. Product-usage signal (feature adoption, seat utilization, session depth) becomes first-class input alongside CRM fields, so the model sees the whole account picture the marketing ops rules could only approximate.
Priority use cases for B2B SaaS
Next 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 morePredictive 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 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 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 Product & Recommendations
Predict the next best product, content, or offer for every customer. EkamFlow's recommendation engine — sometimes called next best product — ranks your catalog per customer to drive conversion and engagement.
Learn moreHow B2B SaaS teams put it to work
Lifecycle motion routing from one API
Instead of maintaining a rule library that routes accounts across onboarding, activation, expansion, renewal, and win-back, NBA picks the single best motion per account per week. Marketing ops owns creative and campaign catalogs; the model handles allocation. Adding a new lifecycle motion means adding it to the campaign catalog — not authoring another 40 rules.
Trial and PLG lead scoring
Product-usage signal (feature adoption, session depth, seat utilization, invitation-acceptance rate) becomes first-class input alongside CRM fields. Trial-conversion prediction identifies which self-serve signups will convert, which need a sales touch, and which should be nurtured longer — the classic PLG triage problem, automated.
Expansion propensity as a sales input
Expansion propensity ranks paid accounts by likelihood of upgrade, add-on adoption, or seat expansion. Sales-team account prioritization stops depending on stale scoring rules and starts consuming a live per-account probability. Same model outputs net-revenue-retention drivers so CS teams work the same list.
Feature adoption recommendations in-app
In-app 'you should try this' surfaces are driven by per-user feature-recommendation ranking — pointing each user toward the specific module or workflow they're most likely to adopt next based on their usage pattern. Adoption uplift on recommended features is typically 3× vs. blanket feature-education campaigns.
Signals from B2B SaaS data
- Feature adoption breadth and depth per user
- Session count, session length, and weekly active users per account
- Seat utilization and team-level roll-up patterns
- Onboarding milestone completion (activation events)
- Support ticket volume, category, and sentiment
- NPS, CSAT, and in-product survey responses
- Contract renewal timing and value trajectory
- Multi-product usage and integration adoption
- Invitation-send and invitation-accept rates
- Billing events (upgrades, downgrades, payment failures)
Outcomes B2B SaaS teams track
- Trial-to-paid conversion rate
- Time-to-first-value and activation rate
- Feature adoption per module
- Net revenue retention (NRR) and gross revenue retention (GRR)
- Expansion ARR per account
- Churn rate per cohort and per pricing plan
What it looks like in B2B SaaS
Subscription analytics platform: 400+ segment rules → one API
A subscription analytics platform's marketing ops team maintained 400+ segment rules routing accounts across six lifecycle motions (feature-onboarding email, in-app tooltip, CS outreach, expansion offer, renewal reminder, and 'do nothing'). NBA replaced the entire routing layer with a single API integration. Marketing ops now maintains creative and campaign catalogs, not routing logic.
B2B SaaS growth: engineering-buyer send-timing
A B2B SaaS company noticed engineering-buyer accounts opened emails around 10pm — well after their outbound team's polite 10am send window. Next best time routed engineering-heavy accounts to late-evening sends while keeping executive accounts on business hours. Reply rate on prospecting emails doubled without changing creative.
SaaS in-app: feature-discovery recommendations
A subscription analytics platform surfaces next-best-feature recommendations in-app, pointing power users toward the specific module they're most likely to adopt next based on their usage patterns. Feature-adoption uplift on recommended modules is 3× vs. control. Product-adoption metrics moved from a quarterly initiative to a daily surface.
Frequently asked about EkamFlow in B2B SaaS
Customer-success platforms like Gainsight and Totango centralize account data and provide health-scoring and playbook execution — they're the workflow layer. EkamFlow is the decisioning layer: it produces per-account predictions (churn risk, expansion propensity, next best motion) that health-scoring and playbooks can consume. Most B2B SaaS customers keep their CS platform and use EkamFlow to replace the hand-authored health-score formulas with continuously learned per-account predictions.
Yes — PLG is one of the strongest fits. Product-usage signal is first-class input alongside CRM fields, so the model can predict trial conversion, activation, expansion, and churn directly from usage patterns. Marketing ops stops writing rules like 'if user completes 3 activation events, send email X.' The model learns which combinations of activation events actually predict conversion, and NBA picks the right nudge per user.
Yes. Expansion propensity, lead score, and churn risk all return via API and can be pushed into your CRM (Salesforce, HubSpot) as scored fields on the account or opportunity record. AE and CSM teams work the same list as marketing ops — no separate scoring system to reconcile.
Multi-product usage is a first-class signal. An account with strong adoption of product A but no touch of product B is a different expansion-propensity profile than an account with equal light usage across both. The model learns cross-product adoption patterns and can predict which second product an account is most likely to adopt next.
EkamFlow reads from your warehouse (Snowflake, BigQuery, Databricks, Redshift) and returns predictions via REST API. Predictions can be pushed back into your CDP (Segment, Rudderstack, Hightouch), CRM (Salesforce, HubSpot), or marketing automation (Braze, Iterable, Customer.io) via reverse-ETL or direct API integration. The model doesn't replace any of these — it augments them with per-account predictions they can't produce themselves.
EkamFlow is SOC 2 Type II certified, GDPR-compliant, and supports data residency in US and EU regions. Every customer gets a private model trained only on their own data; nothing is shared across tenants. For enterprise procurement, we support standard security questionnaires and provide a DPA. Talk to sales for the full security package.
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