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.
Quick answer
Feature adoption prediction works best when you connect account profiles, user activity, feature event sequences, and onboarding progress in a relational model. Adoption follows industry-specific sequences: manufacturing accounts that adopt the API first adopt custom reports at 4x the base rate, while retail accounts follow a dashboard-first path. Graph ML accelerates time-to-value by 40% by guiding each account down their optimal adoption path.
Approaches compared
4 ways to solve this problem
1. Generic onboarding sequences
Walk every account through the same feature tour: connect data, build a dashboard, invite your team, explore advanced features.
Best for
Simple to build and maintain. Works for products with a clear, universal workflow that most accounts follow.
Watch out for
Treats every account the same. A manufacturing company that needs APIs first gets stuck in a dashboard tutorial, while a retail company that wants dashboards gets pushed toward API docs. Overwhelming some and boring others.
2. Segment-based adoption paths
Create 3-5 onboarding paths based on account segments (industry, size, plan tier) and route accounts into the matching path.
Best for
Better than one-size-fits-all. Captures the broadest industry-specific patterns with minimal complexity.
Watch out for
Segments are coarse. Two manufacturing accounts in the same segment might have completely different adoption needs based on their team structure, integration stack, and which features they have already adopted.
3. Collaborative filtering on adoption sequences
Recommend the next feature based on what similar accounts adopted after the same set of features, using sequential pattern mining.
Best for
Data-driven adoption paths that adapt as your customer base grows. Captures real adoption patterns rather than assumed ones.
Watch out for
Ignores the account's current context: which users are active, what departments are involved, and whether prerequisites are in place. Recommending SSO to an account with no admin users active does not work.
4. KumoRFM (relational graph ML)
Connect accounts, users, feature events, and onboarding steps into a graph. The GNN learns industry-specific adoption sequences, feature prerequisites, and role-based adoption triggers.
Best for
Highest accuracy for next-feature prediction. Captures that when an admin enables SSO, end-user adoption accelerates 2x, and that API adoption is a prerequisite for custom report adoption in manufacturing.
Watch out for
Requires granular feature-level event tracking. If your product only logs logins without feature-level detail, the adoption sequence signal is unavailable.
Key metric: Personalized adoption paths powered by graph ML accelerate 3+ feature adoption by 40%, lifting 90-day retention from 55% to 85%.
Why relational data changes the answer
Feature adoption is a sequential, context-dependent process. The next feature an account should adopt depends on what they have already adopted (feature events), who is using the product (user roles and departments), how they are using it (event volumes and patterns), and what similar accounts did at this stage (cross-account patterns). A flat recommendation based on 'accounts that adopted Feature A also adopted Feature B' misses the context: Feature B only works after Feature A reaches a minimum usage threshold, and only if an admin user is active.
Relational models connect feature events to users to accounts and learn the sequential dependencies. They discover that manufacturing accounts follow an API-first path while retail accounts follow a dashboard-first path, that admin-enabled SSO unlocks a 2x acceleration in end-user adoption, and that 100% onboarding step completion predicts the next feature adoption with 78% accuracy. Accounts that adopt 3+ features in the first 90 days retain at 95% vs 55% for single-feature accounts. Getting the adoption sequence right is the highest-leverage retention intervention available.
Generic onboarding is like giving every tourist the same guided tour of a city. The history buff wants museums, the foodie wants restaurants, and the architecture enthusiast wants walking tours. A great concierge asks what you have already seen, what you enjoyed, and what similar visitors loved, then builds a personalized itinerary. Graph ML is that concierge for product adoption.
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.
Frequently asked questions
Common questions about product adoption prediction
How do you predict which features a B2B account will adopt next?
Connect account profiles, user activity data, feature event sequences, and onboarding progress into a relational model. The model learns industry-specific adoption sequences and feature prerequisites. Manufacturing accounts that adopted APIs first adopt custom reports at 4x the base rate. The model ranks the next 3-5 features by adoption probability for each account.
Why does feature adoption order matter for SaaS retention?
Accounts that adopt 3+ features in the first 90 days retain at 95%, while those adopting only 1 feature retain at 55%. The adoption order matters because features build on each other: adopting the wrong feature first creates friction and abandonment. Guiding accounts to their highest-probability next feature accelerates time-to-value by 40%.
How do you personalize SaaS onboarding with ML?
Replace generic onboarding sequences with model-driven recommendations. For each account, predict the top 3 features they are most likely to adopt next based on their industry, current usage, user roles, and what similar accounts did at this stage. Surface these features prominently in the product and in CSM conversations.
What data do you need for product adoption prediction?
Four tables: account profiles (industry, plan, onboard date), users (roles, departments, activity levels), feature events (which features each account has used and how intensely), and onboarding steps (completion status). The feature event data needs to be granular: not just 'used API' but 'made 4,500 API calls this month.' Volume and recency matter for adoption prediction.
How does industry affect feature adoption in SaaS?
Industry is one of the strongest predictors of adoption sequence. Manufacturing accounts typically follow API > custom reports > webhooks. Retail accounts follow dashboard > custom reports > alerts. Technology accounts often adopt SSO and API simultaneously. Relational models learn these industry-specific paths automatically from historical adoption data.
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. Infinite Predictions.
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 Research Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: research agent, fine-tune capabilities, and forward-deployed engineer support.
