Case study

Powering Klaviyo lifecycle flows with Modiva

A look at how ecommerce teams use Modiva to keep Klaviyo profiles rich and segments accurate, read flow performance, and give AI agents scoped access — without building and maintaining a custom integration.

This is a representative example of the Modiva + Klaviyo workflow. Have a story to share? Tell us and we’ll feature it here.

The challenge

For ecommerce, Klaviyo lives or dies on data quality — the right profile properties trigger the right flow. Keeping that data clean usually means:

• Klaviyo profiles held little more than an email, so lifecycle flows fired on guesses instead of real purchase and browse behavior.
• Segments were built by hand and drifted out of date, so win-back and post-purchase flows reached the wrong people.
• Flow and campaign revenue lived only in Klaviyo, so planning the next flow meant leaving the team's own dashboards.

The approach with Modiva

1. 1

   One connection, richer profiles

   The team authorizes Klaviyo in Modiva once. Modiva stores only a scoped, encrypted token and refreshes it automatically, so purchase and browse data can be written onto profiles from the store and stack — making every profile flow-ready instead of email-only.

2. 2

   Profiles and segments over a unified API

   Instead of learning Klaviyo's JSON:API and its revision headers, the team calls Modiva's typed tools to create and update profiles, add them to lists and read segments, campaigns, metrics and flows. The same request shape works across every tool Modiva supports.

3. 3

   Sync-and-report for AI agents via MCP

   An MCP key exposes the same tools to AI assistants. An agent enriches profiles from events elsewhere in the stack, keeps lists accurate and pulls flow and metric performance — with scopes the team controls and can revoke.

4. 4

   Flows designed from evidence

   Metric and flow data flows back into the team's own reporting, so revenue-per-recipient sits next to the rest of the numbers and each new flow is designed from what actually converted.

The outcome

• Lifecycle flows fire on real purchase and behavior data instead of email-only profiles.
• Segments stay accurate because they're driven by synced properties, not manual edits.
• AI agents enrich profiles and pull flow performance through scoped, revocable access.