IMI Sponsorship Intelligence Framework

This skill teaches the AI to evaluate, design, and measure sponsorships the way IMI's

senior strategists do — with rigorous fit assessment, activation-first thinking, and

ROI measurement that goes beyond media equivalency.

IMI's "Live Will Thrive" Philosophy

IMI's foundational belief about sponsorship and experiential marketing:

Live experiences create disproportionate emotional impact. A consumer who experiences

a brand in a live setting (event, activation, sponsorship touchpoint) forms stronger

emotional connections than one who sees the brand in advertising. This is measurable.

But live only works with the right fit. A sponsorship without brand-property fit is

worse than no sponsorship — it confuses consumers and wastes budget.

The three sponsorship questions:

• FIT — Does this property align with our brand and audience?
• ACTIVATION — What will we DO with this sponsorship beyond logo placement?
• RETURN — How will we measure whether it worked?

Stage 1: Fit Assessment

The IMI Fit Framework

Brand-property fit is assessed on four dimensions:

Audience Fit (using Pulse™)

This is the most data-driven dimension. Use Pulse™ passion point alignment analysis:

from scipy.stats import pearsonr

def audience_fit_score(brand_passion_profile, property_passion_profile):
    """
    Calculate audience fit using passion point index correlation.
    brand_passion_profile: dict of {passion_point: index_score}
    property_passion_profile: dict of {passion_point: index_score}
    """
    common_passions = set(brand_passion_profile.keys()) & set(property_passion_profile.keys())
    brand_vals = [brand_passion_profile[pp] for pp in common_passions]
    property_vals = [property_passion_profile[pp] for pp in common_passions]

    correlation, pvalue = pearsonr(brand_vals, property_vals)

    # Convert to 1-5 score
    if correlation >= 0.70:
        score = 5
    elif correlation >= 0.55:
        score = 4
    elif correlation >= 0.40:
        score = 3
    elif correlation >= 0.25:
        score = 2
    else:
        score = 1

    return {
        'correlation': round(correlation, 3),
        'score': score,
        'common_passion_points': len(common_passions),
        'p_value': round(pvalue, 4)
    }

Values Fit Assessment

Scored qualitatively on a 1-5 scale:

Category Fit Assessment

Competitive Fit Assessment

Total Fit Score

def total_fit_score(audience_score, values_score, category_score, competitive_score):
    """
    Calculate weighted fit score.
    All inputs are 1-5 scores.
    """
    total = (
        0.35 * audience_score +
        0.25 * values_score +
        0.20 * category_score +
        0.20 * competitive_score
    )
    return round(total, 2)

# Interpretation:
# 4.0+ = Excellent fit — strong go
# 3.0-3.9 = Good fit — go with activation plan
# 2.0-2.9 = Marginal fit — proceed only with compelling activation strategy
# Below 2.0 = Poor fit — do not proceed

Stage 2: Activation Design

IMI's cardinal rule of sponsorship: Logo placement is not activation.

A sponsorship without activation is a brand tax. The activation is what creates the

consumer experience that drives brand metrics.

The Activation Pyramid

                    ┌──────────────────┐
                    │  ADVOCACY         │  ← Consumers share/recommend the experience
                    │  (WOM, social)    │
                    ├──────────────────┤
                    │  ENGAGEMENT       │  ← Consumers interact with the brand
                    │  (participate,    │
                    │   try, experience)│
                    ├──────────────────┤
                    │  AWARENESS        │  ← Consumers see/notice the brand
                    │  (signage, media) │
                    └──────────────────┘

A good activation strategy has all three levels. Most sponsorships only achieve awareness.

Activation Design Framework

For each sponsorship, design activations across the pyramid:

AWARENESS LAYER:
- On-site signage and branding
- Broadcast/digital media integration
- Social media presence
- Athlete/performer endorsement

ENGAGEMENT LAYER:
- Interactive brand experiences at events
- Product sampling or trial opportunities
- Competitions, challenges, games
- VIP experiences, behind-the-scenes access
- Content creation opportunities for attendees

ADVOCACY LAYER:
- Shareable moments designed for social media
- Referral mechanics (bring a friend)
- User-generated content campaigns
- Community building around shared passion
- Exclusive content for brand + property fans

Activation Scoring

Rate each proposed activation on:

Stage 3: ROI Measurement

The IMI Sponsorship ROI Model

IMI's ROI model goes beyond media equivalency to measure actual brand impact:

Sponsorship ROI Components:

1. AWARENESS IMPACT
   - Brand awareness lift (control vs. exposed)
   - Ad recall / sponsorship recall
   - Brand association with the property

2. ATTITUDINAL IMPACT
   - Brand consideration lift
   - Brand favourability lift
   - Trust / emotional connection lift

3. BEHAVIOURAL IMPACT
   - Purchase intent lift
   - Actual purchase / trial (where measurable)
   - Website / store visit attribution

4. AMPLIFICATION VALUE
   - Word-of-mouth multiplier
   - Social media earned value
   - PR/media earned value

WOM Multiplier

The Word-of-Mouth multiplier measures how many additional people are reached through

attendees sharing their experience:

def wom_multiplier(attendees, avg_conversations, reach_per_conversation,
                   brand_mention_rate, sentiment_positive_rate):
    """
    Calculate the WOM multiplier for a sponsorship activation.

    attendees: number of event attendees
    avg_conversations: average post-event conversations per attendee about the event
    reach_per_conversation: average people reached per conversation
    brand_mention_rate: % of conversations that mention the sponsor brand
    sentiment_positive_rate: % of brand mentions that are positive
    """
    total_conversations = attendees * avg_conversations
    total_reach = total_conversations * reach_per_conversation
    brand_reached = total_reach * brand_mention_rate
    positive_brand_reach = brand_reached * sentiment_positive_rate

    multiplier = positive_brand_reach / attendees

    return {
        'total_conversations': int(total_conversations),
        'total_reach': int(total_reach),
        'brand_mentions_reach': int(brand_reached),
        'positive_brand_reach': int(positive_brand_reach),
        'wom_multiplier': round(multiplier, 1),
        'interpretation': (
            f"Each attendee generates {round(multiplier, 1)} additional positive "
            f"brand impressions through word of mouth"
        )
    }

# Typical benchmarks:
# WOM multiplier 3-5x = Average (logo-only sponsorships)
# WOM multiplier 5-10x = Good (some activation)
# WOM multiplier 10-20x = Strong (excellent activation with shareable moments)
# WOM multiplier 20x+ = Exceptional (viral activation)

Naming Rights Valuation

For naming rights opportunities (stadium, event, series):

Naming Rights Value Components:
1. Media exposure value (broadcast mentions, signage impressions)
2. Brand association transfer (property equity → brand equity)
3. Hospitality / B2B value (corporate entertaining, client hosting)
4. Community goodwill value (local/fan community association)
5. Exclusivity premium (only one brand gets naming rights)

Valuation formula:
  Annual Value = Media Exposure Value × (1 + Association Premium + Hospitality Premium + Exclusivity Premium)

Where:
  Media Exposure Value = estimated annual media impressions × CPM equivalent
  Association Premium = 0.2-0.5 (depending on property strength)
  Hospitality Premium = 0.1-0.3 (depending on hospitality assets)
  Exclusivity Premium = 0.15-0.35 (depending on category competitiveness)

Output Templates

Template: Sponsorship Fit Assessment

## Sponsorship Fit Assessment: [Brand] × [Property]
**Assessment date:** [date] | **Assessor:** [name]

### Fit Scores
| Dimension | Score (1-5) | Weight | Weighted | Rationale |
|---|---|---|---|---|
| Audience Fit | X | 35% | X.XX | [explanation] |
| Values Fit | X | 25% | X.XX | [explanation] |
| Category Fit | X | 20% | X.XX | [explanation] |
| Competitive Fit | X | 20% | X.XX | [explanation] |
| **TOTAL** | | | **X.XX** | |

### Interpretation
[Excellent / Good / Marginal / Poor] fit — [recommendation]

### Key Risks
1. [risk and mitigation]

### Recommended Activation Direction
[If proceeding — activation themes]

### Essential Insight
[One sentence: should this brand sponsor this property?]

Template: Sponsorship ROI Report

## Sponsorship ROI: [Brand] × [Property] — [Period]
**Investment:** $[amount] | **Attendees/Audience:** [n]

### Impact Summary
| Metric | Control | Exposed | Lift | Sig? |
|---|---|---|---|---|
| Brand awareness | X% | X% | +Xpts | Y/N |
| Sponsorship recall | - | X% | - | - |
| Brand consideration | X% | X% | +Xpts | Y/N |
| Purchase intent | X% | X% | +Xpts | Y/N |

### WOM Multiplier
- Multiplier: [X.X]x
- Positive brand reach: [n]

### ROI Calculation
[Show the math]

### Recommendation
[Continue / Modify / Discontinue] — [rationale]

Common Pitfalls

• Relying on media equivalency alone. Impressions are not impact. A million impressions

that don't move brand metrics are worthless. Always measure attitudinal and behavioural lift.

• Sponsoring without activating. Logo placement without activation generates awareness

but not engagement or advocacy. The activation IS the sponsorship.

• Ignoring audience fit. A brand sponsoring a property whose audience doesn't match

theirs is paying to talk to the wrong people.

• Overvaluing naming rights. Naming rights are only valuable if the brand activates

the association. A name on a building that no one connects to the brand is wasted.

• Measuring only during the event. Sponsorship impact extends weeks/months beyond the

event through WOM and brand memory. Measure at multiple time points.

Cross-Skill References

• For audience fit analysis via Pulse™ → imi-pulse-intelligence
• For brand health pre/post sponsorship → imi-brand-strategy
• For segmentation of event attendees → imi-segmentation-engine
• For writing up sponsorship recommendations → imi-client-deliverable
• For sponsorship as part of a pitch → imi-pitch-intelligence

*Built for IMI International's Local AI — grounded in IMI's "Live Will Thrive" philosophy

and decades of sponsorship strategy across sports, entertainment, and cultural properties.*

*Purpose: Insight. Method: Rigour. Outcome: Profit.*

AXE MCP Server Integration

Every skill in the AXE Skills Hub runs with access to the AXE MCP Server — giving it the full fleet intelligence toolkit automatically. No setup required; tools are available in any AXE-powered session.

Core Tools Available

Quick Start

# In any AXE session, tools are pre-loaded. Example chaining:

# 1. Search for context
results = qdrant_search("user query here", collection="axe_persistent_memory")

# 2. Fetch live data if needed
content = web_fetch("https://docs.example.com/api")

# 3. Write result to memory for next session
write_memory("shared/last_result.md", output)

# 4. Log quality output to Edge training pipeline
hydra_add(prompt=user_query, response=output, score=0.9, source="skill-name")

Edge Training Integration

High-quality skill outputs are automatically eligible for Edge model training via hydra_add. When a response scores ≥0.85 in evals, pipe it to the Hydra pipeline to compound Edge's knowledge. This is how skills make Edge smarter over time.

# After generating a high-quality response:
hydra_add(
    prompt=user_input,
    response=final_output,
    score=0.9,          # eval score
    source="skill-name" # tracks provenance
)