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Product-level predictions

When your workspace is set up for multi-product analysis, FunnelStory can store prediction scores per account and per product — in addition to the overall account-level prediction you may already use.

Product-level scores help teams answer: “How is this account trending on Product A vs Product B?” especially inside hierarchy where a parent may aggregate several subsidiaries with different product mixes.

Prerequisites​

  1. Prediction / ML configuration is enabled and maintained for your workspace.
  2. Products model with stable product_id values.
  3. Accounts carry a products array listing which product ids apply to that account (see Setting up hierarchy). Ids that are not in the Products catalog are ignored. Accounts with no valid products do not get product-level predictions.
  4. Rich signals improve all predictions: Conversations, Notes, support integrations, account metrics (including product-scoped metrics where relevant), and subscriptions.

What gets stored​

For each qualifying account, FunnelStory can persist:

  • Latest per-product score and history for charts and trends.
  • Factors and recommendations attached to that product-scoped run, analogous to account-level predictions.

Container parents​

Container accounts (see Setting up hierarchy) are skipped for normal per-product scoring because they are not leaf “customer motion” rows.

After scores are computed for real accounts, FunnelStory can roll up container parents from children for account-level container behavior. Treat Container Parents as: “this row’s intelligence is mostly the sum / story of its children.”

Hierarchy tips​

  • Put products on the accounts that actually generate telemetry for that product (often child sold-to accounts). Parents then benefit from rollups and summaries without duplicating product ids across every intermediate node—unless your business truly attributes the same product to every level.
  • Keep product_id consistent across subscriptions, metrics, and products on the account.