This recipe explains how to link your product catalog to real customer behavior to make cross-sell more meaningful.
Cross-sell works best when it matches customers’ existing workflows. If suggestions feel random, customers ignore them. A cross-sell advisor bases its ideas on which modules customers already use and where they encounter gaps.
Create Audiences based on which lan an end user currently has, so you know which products they have access to. Then, create an AI Skill that that outputs offers for adjacent_products with a cross_sell_rationale. Under the hood, you can define a simple mapping (a playbook) that says, for example, “If module A and workflow X are used, suggest module B.”
Ground the skill using flat Topics such as Product-map, Cross-sell-playbooks, and Workflow-patterns. These Topics describe which modules complement one another and for which workflows, so the AI’s rationales align with product strategy. This also limits the chance of suggesting unsupported combinations.
For internal consumption, the skill can create tasks for account managers when adjacent_products are detected, with a one-paragraph rationale and links to relevant documentation. A weekly notification can list accounts with strong cross-sell signals so teams can plan outreach.
On the customer side, in-app notifications can present cross-sell hints inside the portal for specific segments. For instance, when users frequently run a workflow that would be easier with another module, a small inline suggestion can appear, linking to a short explanation. Use audience segmentation based on active_modules and segment to avoid over-communicating.
Conclusion
A cross-sell advisor for adjacent products builds on what customers already use and where they struggle. With an AI Skill, flat Topics that encode your product relationships, and a mix of internal tasks and segmented in-app hints, it supports thoughtful growth without overwhelming users.
