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Case Study

+35% client CRO via autonomous AI shopping

This case study answers the five questions stakeholders ask about autonomous commerce: why switch, which agents to deploy, how to measure lift, how to stay safe, and how to communicate wins.

5 min readIntermediateUpdated December 1, 2025

1
What was the baseline challenge?

A $40M ARR personal care brand plateaued at 2.3% conversion. Merchandising and lifecycle teams were swamped with manual segmentation and promo swaps. We audited their funnel and saw that shoppers bounced because recommendations were static, incentives lagged inventory, and support could not customize carts quickly.

Quantify how much revenue sits between your current CRO and target CRO before pitching autonomous upgrades.

2
Which autonomous agents did we deploy?

We launched three agents: Discovery Pilot (reordered collections and PDP modules per visitor intent), Incentive Concierge (tested shipping, bundles, and loyalty offers in real time), and Support Bridge (turned chat sessions into prebuilt carts). Each agent had a charter, guardrails, and shared a telemetry feed.

Launch no more than three agents in phase one so operators can monitor and tune behavior daily.

3
How did we reach +35% CRO?

Discovery Pilot lifted product engagement 18%, Incentive Concierge cut promo abuse by 22%, and Support Bridge closed 31% more chats with carts. Combined, they pushed conversion to 3.1% in 45 days—a 35% relative lift. Contribution margin improved because incentives matched margin guardrails and low-stock SKUs were protected.

Share lift as both relative and absolute numbers; finance teams care about incremental dollars, not just percentages.

4
What guardrails kept things safe?

Signals flowed through a contract that limited PII, pricing changes stayed within ±12%, and every agent logged decisions to a Slack channel. If override count spiked, ops paused the agent with one click. Weekly audits replayed the top 10 autonomous actions so merchandising and legal stayed aligned.

Write a ‘kill switch’ runbook so new teammates know exactly how to freeze an agent mid-campaign.

5
How do you report results to execs?

We built a dashboard tracking conversion, AOV, incentive cost, override count, and LTV signals. Monthly reviews highlighted learnings, upcoming experiments, and ROI. Because leadership saw transparent telemetry, they greenlit expansion into post-purchase and paid media agents.

Include customer quotes or chat snippets alongside metrics—storytelling speeds up buy-in for the next phase.

Snapshot metrics

  • • Conversion rate: 2.3% → 3.1% (+35% relative lift)
  • • AOV: +11% because bundles respected margin guardrails
  • • Manual hours saved: 46 per week across merch and support
  • • Incentive cost: -9% due to targeted offers
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