Discovery Merchandising Optimizer

Analyzes ecommerce search and browsing behavior to reveal navigation friction, poor search experiences, and merchandising opportunities so merchants can improve product discovery and conversion.

The Problem

“Search and Discovery Merchandising Optimization for Ecommerce”

Organizations face these key challenges:

1

Limited visibility into why customers fail to find products

2

Manual analysis of search logs and clickstream data is time-consuming

3

Zero-result searches and poor facet design go unnoticed too long

4

Merchandising decisions rely on intuition instead of behavioral evidence

Impact When Solved

Reduce zero-result and low-engagement search experiencesIncrease conversion from search and category browsing sessionsSurface high-impact synonym, facet, ranking, and collection fixesShorten merchandising analysis cycles from weeks to days or hours

The Shift

Before AI~85% Manual

Human Does

  • •Export search, browsing, and conversion reports from analytics tools
  • •Review zero-result queries, low-performing categories, and facet usage in spreadsheets
  • •Interpret customer discovery issues and decide merchandising changes based on manual analysis
  • •Update search rules, collections, and navigation settings, then monitor results later

Automation

    With AI~75% Automated

    Human Does

    • •Approve prioritized merchandising actions for search, facets, ranking, and collections
    • •Review AI-flagged exceptions, unusual behavior shifts, and high-risk recommendations
    • •Set business goals, guardrails, and approval policies for automated optimization

    AI Handles

    • •Continuously monitor search and browsing behavior to detect discovery friction and underperforming journeys
    • •Cluster query and navigation patterns, explain likely causes, and summarize key issues
    • •Recommend and prioritize synonym, ranking, facet, collection, and landing page changes by expected impact
    • •Track post-change performance, surface alerts, and automate low-risk optimizations within policy

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence90%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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