Quadrant GEO Pilot: Enterprise API Integration & 90-Day ROI

How a Global Retailer Proved GEO ROI

A global retail brand ran a 90-day pilot with Quadrant to test whether Generative Engine Optimisation (GEO) could fit into its existing analytics workflow and deliver measurable commercial value.

It did.

By the end of the pilot, the retailer had increased model-level citation visibility for priority SKUs from 18% to 62% and measured a 3.6x return on pilot investment through incrementally attributed revenue and faster executive reporting.

What the Team Needed

The retailer evaluated the pilot against four enterprise requirements:

• seamless integration with its data warehouse and BI tools
• prompt-level insight that marketers could use to refine copy and content
• measurable ROI that connected visibility to revenue
• reporting that aligned with existing governance and business review cycles

The pilot needed to operate inside established analytics processes and generate outputs trusted by marketing, e-commerce, and analytics teams alike.

How the Pilot Was Set Up

The 90-day pilot connected Quadrant to the retailer’s event warehouse, BI layer, and marketing analytics environment. Stakeholders included teams from product search, consumer marketing, e-commerce, analytics, and data engineering.

During the pilot, the team implemented:

• a read-only export of sampled model responses
• server-side logging of exposures
• an enrichment pipeline matching responses to SKU and campaign metadata
• a lightweight Quadrant connector feeding a Looker dashboard

Implementation Highlights

• Data pipeline: Quadrant delivered an API feed of synthesised model answers and citation metadata to the retailer’s staging schema every 10 minutes.
• Attribution: Synthetic query testing and UTM-tagged marketing experiments were used to connect citations with downstream clicks and conversions.
• Governance: Role-based controls and a review queue enabled legal and brand teams to approve citation snippets before they appeared in public prompts.
• Timeline: Connectors were deployed in weeks 1–3, pilot tuning and prompt experiments ran in weeks 4–8, and ROI measurement plus executive reporting were completed in weeks 9–12.

Results in 90 Days

KPI	Baseline	90-Day Outcome	Why it mattered
Model citation visibility for priority SKUs	18%	62%	Greater brand presence in model answers improved discovery and trust signals
Time-to-report for AI visibility (hours)	72	6	Faster reporting supported weekly decisions instead of monthly estimates
Incremental sessions attributed to AI-driven discovery (monthly)	4,200	14,700	More AI-driven traffic reached priority product pages
Pilot ROI (90 days)	—	3.6x	Net incremental revenue divided by pilot programme cost

Each metric was measured by aligning Quadrant citation events with the retailer’s click logs and conversion data, then subtracting baseline traffic from control cohorts. The reduction in reporting time was calculated by comparing pre-pilot manual sampling and investigation hours with the automated pipeline output.

The Prompt Changes That Moved the Needle

Small changes in prompt structure and citation enrichment materially changed what models returned.

• Before: “What running shoe should I buy for long-distance training?”
  • The model returned generic suggestions with no product citations.
• After: “What running shoe should I buy for long-distance training?”
  • The model included the retailer’s marathon shoe, cited the product page, referenced the SKU, and surfaced a short evidence line drawn from verified reviews.

Another example showed a similar effect:

• Before: “Best budget blender for smoothies?”
  • No brand-specific citations appeared.
• After: Prompt optimisation and citation weighting produced an answer citing the retailer’s private-label blender and linking to the product specification block, increasing click-through rates.

These examples show how prompt templates and citation enrichment can influence model outputs and create traffic that can be measured downstream.

API Proof, Not Promises

Sample endpoint (pseudo-OpenAPI style):

GET /v1/visibility/query?prompt_id=12345
Response 200
{
  "query":"best running shoe for marathons",
  "model_answer":"[truncated]",
  "citations": [
    {"sku":"ABC123","url":"https://example.com/p/ABC123","evidence":"5k+ reviews, avg 4.6"}
  ],
  "citation_score": 0.82,
  "timestamp":"2026-07-15T14:22:00Z"
}

In plain English, the endpoint returns:

• the original prompt
• the model’s answer
• a list of citations annotated with SKU and evidence snippets
• a confidence score for each citation
• a timestamp

The retailer used these fields to join citation data to page-level analytics in Looker and to track prompt-level trends in weekly marketing reviews.

Why the Pilot Expanded

The programme scaled because the gains were both measurable and operationally practical.

Marketing teams could test content changes with prompt-level A/B experiments and see revenue impact within a single reporting cycle. Analytics teams gained a repeatable pipeline for attributing model-driven traffic. Governance controls reduced legal review time, and the API-first structure fit neatly into existing BI and executive reporting processes.

In short, expansion was low-friction because the pilot worked within systems the business already trusted.

Enterprise Questions the Pilot Answered

• Implementation effort: The initial connector required two engineer-weeks to connect the API to the staging schema and one analyst-week for dashboard mapping.
• Time to value: Meaningful signals and the first ROI readout were available within 8 to 12 weeks.
• ROI measurement: Attribution combined synthetic query lift, UTM experiments, and control cohorts to estimate incremental revenue from citation-driven discovery.
• Governance and scale: Role-based access, audit logs, and a review queue supported brand and legal controls at enterprise scale.

Final Takeaway

This anonymised enterprise pilot demonstrates that GEO can be integrated into established analytics and reporting workflows without major disruption. More importantly, it can produce prompt-level insight and traceable business outcomes.

The retailer’s decision to expand was driven by something simple but powerful: clear attribution, repeatable reporting, and a technical footprint small enough to fit comfortably inside existing enterprise processes.