Product Adoption Prediction
“Which features will this account adopt next?”
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A real-world example
Which features will this account adopt next?
Accounts that adopt 3+ features in the first 90 days retain at 95%; those adopting 1 feature retain at 55%. A SaaS product with 30 features where 70% of accounts use fewer than 5 features has $40M in untapped expansion potential. Generic onboarding sequences push every account through the same feature tour, overwhelming some and boring others. The adoption path depends on the account's industry, team structure, and integration stack.
How KumoRFM solves this
Graph-learned product intelligence across your entire account base
Kumo connects accounts, users, feature events, and onboarding steps into a graph where adoption patterns propagate through similar-account clusters. It learns that manufacturing accounts that adopt the API integration first then adopt custom reports at 4x the base rate, while retail accounts follow a dashboard-first path. The model captures the sequential dependency between features and the role-specific adoption signals (when an admin enables SSO, end-user feature adoption accelerates 2x).
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
ACCOUNTS
| account_id | industry | plan | onboarded_date | csm |
|---|---|---|---|---|
| ACC201 | Manufacturing | Growth | 2025-01-15 | Sarah K. |
| ACC202 | Retail | Enterprise | 2025-02-01 | Mike R. |
| ACC203 | Technology | Growth | 2025-02-15 | Sarah K. |
USERS
| user_id | account_id | role | department | active_days_30d |
|---|---|---|---|---|
| U201 | ACC201 | Admin | IT | 22 |
| U202 | ACC201 | User | Operations | 15 |
| U203 | ACC202 | Admin | Engineering | 28 |
FEATURE_EVENTS
| event_id | account_id | feature | first_used | events_30d |
|---|---|---|---|---|
| FE01 | ACC201 | API v2 | 2025-01-20 | 4500 |
| FE02 | ACC201 | Dashboard | 2025-01-15 | 680 |
| FE03 | ACC202 | Dashboard | 2025-02-01 | 1200 |
ONBOARDING_STEPS
| step_id | account_id | step_name | completed | completed_date |
|---|---|---|---|---|
| OS01 | ACC201 | Data connection | Y | 2025-01-16 |
| OS02 | ACC201 | First dashboard | Y | 2025-01-18 |
| OS03 | ACC201 | Team invite | Y | 2025-01-20 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
RANK TOP 3 FEATURE_EVENTS.FEATURE FOR EACH ACCOUNTS.ACCOUNT_ID PREDICT BOOL(FEATURE_EVENTS.*, 0, 30, days)
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | RANK | NEXT_FEATURE | ADOPTION_PROB_30D |
|---|---|---|---|
| ACC201 | 1 | Custom reports | 0.78 |
| ACC201 | 2 | SSO | 0.52 |
| ACC201 | 3 | Webhooks | 0.34 |
| ACC202 | 1 | API v2 | 0.65 |
Understand why
Every prediction includes feature attributions — no black boxes
Account ACC201 -- Manufacturing, Growth plan
Predicted: 78% probability of adopting Custom reports in 30 days
Top contributing features
API v2 adoption (prerequisite)
Active, 4500 events/mo
30% attribution
Similar-industry adoption path
API > Reports (82% of mfg)
24% attribution
Operations user activity
15 active days
19% attribution
Onboarding completion rate
100% of steps
15% attribution
CSM engagement cadence
Bi-weekly calls
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A SaaS company that personalizes onboarding to guide each account toward their most-likely-to-adopt features reaches 3+ feature adoption 40% faster, lifting 90-day retention from 55% to 85%. Kumo learns industry-specific adoption sequences and feature dependencies that generic onboarding cannot adapt to.
Related use cases
Explore more B2B SaaS use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
