Magnify Insights helps you understand and predict your revenue trajectory by using AI/ML models trained on your historical data. The onboarding process ensures that Magnify has accurate, aligned data from your systems of record before generating forecasts and drivers. Below is a step-by-step overview of what to expect.
In summary, the starting steps for your team are:
- Identify your system of record (CRM, financial tool, or data warehouse).
- Set up access and permissions for Magnify.
- Complete and return the Historical Revenue Template with supporting queries/screenshots from that system.
Once these steps are complete, we can begin building your tailored revenue forecast and driver insights!
1. Understand Your Source of Truth
Magnify relies on your historical revenue and GTM data to forecast revenue two quarters into the future and identify the top drivers of churn and expansion.
- The first step is determining which integration will serve as your source of truth.
- Common sources:
- CRM (e.g., Salesforce)
- Financial reporting tools (e.g., NetSuite)
- Data warehouses (e.g., Snowflake)
- Action required: Your team should prioritize creating access and permissions for Magnify to connect to this system.
- Reference: See our required object permissions article for a suggested list of objects and fields (note: this list is not exhaustive).
2. Complete the Historical Revenue Template
While your team sets up integrations, you will also need to fill out the Magnify Historical Revenue Template (which is downloadable at the bottom of this article).
This template captures 12 months of historical data on:
- Churn
- Expansion
- Renewal
Data must be provided at both:
- Aggregate level
- Account level
Requirements:
- Data must come from the same system of record Magnify will use for long-term forecasting.
- Every submission must include either:
- A SQL query used to generate the report, OR
- A screenshot of report filters/logic (for non-SQL systems such as Salesforce reports).
Template contents:
- Instructions tab
- Aggregate numbers tab
- Account-level numbers tab
- Query screenshots tab
- Auxiliary questions about logic and fields
3. Align on Revenue Schedule
Once Magnify ingests your source-of-truth data and completed template:
- We will apply your query logic to the ingested data.
- Magnify will generate a native revenue schedule.
- This schedule is then compared against your original historical data.
- Goal: Achieve within 10% variance in aggregate.
4. Iteration & Alignment
If the initial pass does not meet the 10% variance threshold:
- It usually indicates missing filters or logic.
- We will schedule an additional working session to refine.
- Multiple iterations may be required until alignment is reached.
5. Model Training
Once alignment is achieved:
- Magnify will begin training your AI/ML model.
- Timeline: Approximately 1 week from alignment.
- Deliverables:
- Your first Revenue Retention forecast
- Initial churn and expansion drivers
- We will host a tuning session to review and incorporate feedback.
6. Ongoing Model Refresh
After onboarding:
- Weekly refreshes: Your revenue forecasts and drivers automatically update.
- Quarterly tuning sessions: Dedicated reviews to refine and align drivers.
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