In this article: Data Integrations, Revenue Data, Product & Behavioral Data, Recommended Additional Data
Magnify uses AI agents to generate on-demand insights, revenue forecasts, and key drivers behind churn, expansion, and renewal outcomes across your customer base. To power these insights, Magnify requires access to your customer data through integrations with your existing systems of record.
This article covers the data prerequisites and requirements for enabling Magnify Insights and Forecasts, including the integrations and data types that feed the AI assistant.
Data Integrations
Magnify Insights is powered by data from your connected integrations. During onboarding, your implementation team will work with Magnify to configure the integrations that supply the data listed below. Magnify integrates with popular CRM platforms (such as Salesforce), customer success tools (such as Gainsight), data warehouses (such as Snowflake), and other systems your organization uses to track customer activity and revenue.
For details on available integrations and setup instructions, refer to the Integration Guides section of this help center.
Revenue Data (Required)
Revenue data is the foundation of Magnify Insights. The AI uses your historical and current revenue records to generate forecasts for the current and next quarter, broken down by renewals, churn, and expansion.
Magnify requires the following revenue record data, typically sourced from your CRM (e.g., Salesforce Opportunities or Contracts):
- Amount — The dollar value of the revenue record
- Stage — Current status of the record (e.g., closed-won, closed-lost, or open)
- Close Date / Attribution Date — The date the revenue should be attributed to on the dashboard
- Is Renewal — Whether the record represents a renewal
- Available to Renew (ATR) — The expected renewal amount, used to calculate churn and expansion
- Source ID and Source Table — References back to the originating record in your system (e.g., Opportunity ID)
The AI uses this data to compute revenue metrics across three time periods: Current Quarter QTD (actuals based on closed records), Current Quarter Remainder (forecast for open records), and Next Quarter (full forecast). Revenue is categorized into renewals, churn, and expansion based on the fields above.
Account Data (Required)
Magnify requires account-level data to associate revenue records and behavioral signals with individual customers. At minimum, this includes:
- Account ID — A canonical identifier that links across your systems
- Account Name — The display name for the account
The AI assistant uses account data to generate brief account summaries visible in the dashboard, providing context such as health trends, expansion opportunities, and upcoming touchpoints.
Product & Behavioral Data (Preferred)
Magnify strongly recommends product telemetry and behavioral data to identify the drivers that most strongly predict churn and expansion. This data feeds the AI-powered drivers that appear on your dashboard, each with an Impact Severity rating (High, Medium, or Low).
Examples of product and behavioral data include:
- Login frequency and recency
- Feature usage metrics (e.g., key feature adoption, time spent in-product)
- User-level or account-level activity, depending on your data model
- Timestamps for all activity data
Depending on the nature of your product and customer base, Magnify may also require mappings linking revenue-owning accounts to product entitlements, as well as clear product mapping for telemetry data.
Recommended Additional Data
The following data types are not required but significantly enhance the insights generated by the AI assistant. Providing these allows Magnify to produce richer driver analysis and more actionable account summaries:
- Firmographic data — Examples: company size, industry, segment
- Support data — Examples: ticket volume, severity, resolution time, ticket type
- Marketing data — Examples: webinar attendance, email engagement
- Customer Success data — Examples: CS sentiment scores, meeting history with CSMs, CS notes
- Other account or user data — Examples: community engagement, LMS completion, NPS scores, call recording transcripts, emails
Implementation Team Recommendations
While not required, Magnify recommends including a data science or BI analyst resource on your implementation team. This person can help ensure that your data integrations are configured correctly and that the right data fields are mapped to power Magnify Insights effectively.
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