lab-summary

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# Future Lab Summary Generator

## Lab Reference Data

### CAP (Consortium for AI Personalization)
- **What:** AI-driven ad personalization at scale
- **Key metric:** +160% average lift vs BAU
- **Scale:** 29 experiments, 18 brands, 4 countries
- **Top results:** Kroger (259% lift), AT&T (222%), GM, Home Depot, Uber, MLB
- **Current testing:** Video/CTV, brand-lift, retail foot-traffic, D2C, Amazon/Twitch
- **Partner:** Claritas / Rex Briggs
- **Cost:** $35k + media spend
- **Maturity:** Most mature lab, strongest evidence base

### A3 (Agentic AI Advertising)
- **What:** End-to-end AI video ad production (research → storyboard → final assets)
- **Key metric:** Dramatic time/cost reduction in creative production
- **Scale:** 2 pilots completed, recruiting waves 2-3
- **Partner:** Monks (Monks.flow)
- **Cost:** Free to participate
- **Maturity:** Early stage, high interest

### ACE (AI Creative Effectiveness)
- **What:** Pre-launch creative scoring using historical performance patterns
- **Key features:** Brand/platform compliance checking, edit suggestions
- **Partners:** Vidmob, Celtra
- **Cost:** Free to participate
- **Maturity:** Active testing phase

### SIFT (Semantic & Intent Fusion Targeting)
- **What:** Contextual targeting using pre-computed LLM embeddings
- **Key metric:** ~4B URL index, refreshed hourly; vector similarity at auction time
- **Partner:** Cognitiv
- **Cost:** Free to participate
- **Maturity:** Technology proven, scaling

### AURA (AI Uncovering Responsive Audiences)
- **What:** AI-powered audience discovery
- **Key metrics:** 300 campaigns analyzed, avg +24% lift, 90% showed incremental conversion improvement
- **Partner:** Claritas / Rex Briggs
- **Cost:** Free to participate
- **Maturity:** Strong evidence, scaling

### RAIL (Responsible AI Innovation Lab)
- **What:** Governance frameworks and practical guides for responsible AI in marketing
- **Deliverables:** 8 published guides (Implementation Checklist, Governance Framework, Risk Evaluation, Principles Template, Policy Template, Vendor Evaluation, Regulatory Landscape, Measuring AI Success)
- **Scale:** 60+ leaders, 45+ companies
- **Maturity:** Published, ongoing

### ARC (AEO via Reddit Communities)
- **What:** Tests how Reddit communities influence Answer Engine Optimization and branded LLM responses
- **Partner:** Reddit
- **Status:** Work in progress

## Summary Formats

### Executive Summary (for board, CMOs)
**Length:** 1 page max
**Structure:**
- Headline finding (lead with the number)
- 3-bullet summary of methodology, scale, and result
- Business implication in 1 sentence
- Next steps / opportunity to participate

### Member Brief (for newsletter, member comms)
**Length:** 300-500 words
**Structure:**
- Hook: why this matters for marketers
- What was tested and with whom
- Key findings with data
- How members can participate or apply learnings
- Link to full report

### Marketing Material (for sales, recruitment)
**Length:** Varies
**Structure:**
- Bold headline stat
- Visual-friendly data points
- Testimonial or brand quote if available
- CTA to join the lab

### Detailed Research Summary (for research team, think tank)
**Length:** 1000-2000 words
**Structure:**
- Methodology
- Sample details
- Full results with statistical context
- Limitations
- Implications
- Next steps

## Process
1. Identify which lab(s) to summarize
2. Clarify the audience and format
3. Gather latest data (check SharePoint Research site, recent communications)
4. Draft summary in appropriate format
5. Ensure all data points are accurate — flag any numbers that seem outdated
6. Present for review

From alectivism/maven-template, MIT.

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