IMI Say/Do™ Gap Framework

This skill teaches the AI to diagnose and close the gap between what consumers SAY they

will do and what they ACTUALLY do — one of the most commercially valuable problems in

market research.

The Say/Do™ Problem

In every category, there is a gap between stated intention and actual behaviour:

"I intend to eat healthier"              → Still buys the same snacks
"I would definitely try that product"    → Never purchases it
"I prefer Brand A"                       → Buys Brand B on promotion
"I want to switch to sustainable options"→ Picks the cheaper conventional option
"I'll enter that competition"            → Never bothers

IMI's core insight: The Say/Do gap is not lying. Consumers genuinely intend to act.

The gap exists because of friction, habit, context, and cognitive biases that research

(asking in a survey) cannot observe but behaviour reveals.

The three types of Say/Do gap:

The Say/Do™ Diagnostic Framework

Step 1: Quantify the Gap

-- Compare stated intent with actual behaviour
WITH intent AS (
    SELECT
        respondent_id,
        CASE WHEN response_value IN (4, 5) THEN 1 ELSE 0 END AS stated_intent
    FROM survey_responses
    WHERE question_id = 'PURCHASE_INTENT'
        AND study_id = :study_id
),
behaviour AS (
    SELECT
        respondent_id,
        CASE WHEN purchased = TRUE THEN 1 ELSE 0 END AS actual_purchase
    FROM purchase_tracking
    WHERE category_id = :category_id
        AND purchase_date BETWEEN :start_date AND :end_date
)
SELECT
    COUNT(*) AS total_respondents,
    SUM(i.stated_intent) AS stated_intenders,
    SUM(b.actual_purchase) AS actual_purchasers,
    SUM(CASE WHEN i.stated_intent = 1 AND b.actual_purchase = 1 THEN 1 ELSE 0 END)
        AS said_and_did,
    SUM(CASE WHEN i.stated_intent = 1 AND b.actual_purchase = 0 THEN 1 ELSE 0 END)
        AS said_but_didnt,
    ROUND(100.0 *
        SUM(CASE WHEN i.stated_intent = 1 AND b.actual_purchase = 1 THEN 1 ELSE 0 END) /
        NULLIF(SUM(i.stated_intent), 0),
    1) AS conversion_rate,
    ROUND(100.0 *
        SUM(CASE WHEN i.stated_intent = 1 AND b.actual_purchase = 0 THEN 1 ELSE 0 END) /
        NULLIF(SUM(i.stated_intent), 0),
    1) AS gap_rate
FROM intent i
LEFT JOIN behaviour b ON i.respondent_id = b.respondent_id;

Interpreting the gap:

• Gap rate 0-20% = Low gap (intentions are predictive — rare)
• Gap rate 20-40% = Moderate gap (typical for established categories)
• Gap rate 40-60% = High gap (significant friction or context barriers)
• Gap rate 60%+ = Extreme gap (intentions are almost meaningless — deep structural barriers)

Step 2: Identify Friction Drivers

Friction is anything that adds effort, uncertainty, or cost between intention and action.

The IMI Friction Taxonomy:

COGNITIVE FRICTION
├── Information overload (too many options, too complex)
├── Uncertainty (don't know enough to decide)
├── Risk perception (fear of making wrong choice)
└── Decision fatigue (too many decisions in sequence)

PHYSICAL FRICTION
├── Availability (can't find it)
├── Accessibility (too far, inconvenient)
├── Process friction (too many steps to purchase)
└── Time cost (takes too long)

ECONOMIC FRICTION
├── Price barrier (too expensive)
├── Switching cost (loss of current benefits)
├── Sunk cost (already invested in alternative)
└── Price uncertainty (don't know if it's good value)

SOCIAL FRICTION
├── Social norms (nobody I know does this)
├── Identity conflict (this doesn't fit who I am)
├── Social risk (what will others think?)
└── Lack of social proof (no reviews/recommendations)

HABITUAL FRICTION
├── Existing habit (autopilot behaviour overrides intent)
├── Default bias (current choice is the path of least resistance)
├── Status quo preference (change feels risky even when objectively better)
└── Routine disruption (new behaviour doesn't fit existing routine)

Step 3: Profile the Gap Segments

import pandas as pd

def profile_say_do_segments(df, intent_col, behaviour_col, profile_cols):
    """
    Profile four Say/Do segments:
    - Said & Did (converted intenders)
    - Said but Didn't (gap — lost intenders)
    - Didn't Say but Did (unexpected buyers)
    - Didn't Say, Didn't Do (non-market)
    """
    df['say_do_segment'] = 'Non-Market'
    df.loc[(df[intent_col] == 1) & (df[behaviour_col] == 1), 'say_do_segment'] = 'Said & Did'
    df.loc[(df[intent_col] == 1) & (df[behaviour_col] == 0), 'say_do_segment'] = 'Said but Didnt'
    df.loc[(df[intent_col] == 0) & (df[behaviour_col] == 1), 'say_do_segment'] = 'Didnt Say but Did'

    profiles = df.groupby('say_do_segment')[profile_cols].mean()
    sizes = df['say_do_segment'].value_counts(normalize=True) * 100

    return profiles, sizes

The key comparison: What distinguishes "Said & Did" from "Said but Didn't"?

The difference reveals the friction drivers — what the converters had that the

non-converters lacked.

Step 4: Design Interventions

Each friction type requires a different intervention:

Step 5: Measure Intervention Impact

Gap Closure Rate = (Gap Rate Before - Gap Rate After) / Gap Rate Before × 100

If gap rate was 55% and after intervention it's 40%:
  Gap Closure Rate = (55 - 40) / 55 × 100 = 27% gap closure

Promotion Mechanics: Closing the Gap Through Incentives

Promotions are a direct intervention on economic friction. IMI evaluates promotion

mechanics on their ability to close the Say/Do gap:

Promotion Effectiveness Scoring

Promotion types ranked by Say/Do gap closure potential:

Habit Formation Framework

When the goal is to convert a one-time behaviour into a sustained habit:

IMI's Habit Loop:

    CUE ──→ ROUTINE ──→ REWARD ──→ REPETITION ──→ HABIT

CUE: What triggers the behaviour? (time, place, emotional state, preceding action)
ROUTINE: What is the behaviour itself?
REWARD: What reinforcement does the consumer get? (functional, emotional, social)
REPETITION: How many times must the loop run before it becomes automatic?

IMI's rule of thumb: Most consumer habits require 15-25 repetitions to become automatic.

A marketing programme that drives trial but not repeat is wasting the trial investment.

def habit_formation_programme(target_repetitions=20, trial_conversion=0.15,
                               repeat_rate_per_cycle=0.60, cycles_per_month=4):
    """
    Model a habit formation programme.
    How many months of sustained engagement to reach habit threshold?
    """
    months = 0
    repetitions = 0
    active_users = 1.0  # normalised

    while repetitions < target_repetitions and months < 24:
        months += 1
        monthly_reps = active_users * cycles_per_month
        repetitions += monthly_reps
        active_users *= repeat_rate_per_cycle  # attrition each month

    return {
        'months_to_habit': months if repetitions >= target_repetitions else 'Not achieved in 24 months',
        'repetitions_achieved': round(repetitions, 1),
        'retention_at_habit': round(active_users * 100, 1)
    }

Output Templates

Template: Say/Do™ Gap Diagnostic

## Say/Do™ Gap Analysis: [Category/Brand]
**Study:** [name] | **Base:** n=[n] | **Gap measurement period:** [dates]

### The Gap
| Metric | Value |
|---|---|
| Stated intenders (T2B PI) | X% |
| Actual purchasers | X% |
| Conversion rate (said & did) | X% |
| Gap rate (said but didn't) | X% |
| Gap severity | [Low / Moderate / High / Extreme] |

### Gap Segments
| Segment | Size | Key Characteristics |
|---|---|---|
| Said & Did | X% | [profile] |
| Said but Didn't | X% | [profile] |
| Didn't Say but Did | X% | [profile] |

### Friction Diagnosis
| Friction Type | Evidence | Severity |
|---|---|---|
| [Cognitive/Physical/Economic/Social/Habitual] | [data point] | HIGH/MED/LOW |

### Recommended Interventions
| Friction | Intervention | Expected Gap Closure | Priority |
|---|---|---|---|
| [type] | [specific action] | [X%] | HIGH/MED/LOW |

### Essential Insight
[One sentence: the primary reason consumers say but don't do, and the minimum intervention to close the gap]

Common Pitfalls

• Assuming the gap is irrational. The gap exists for real reasons — friction, context,

habit. Never dismiss it as "consumers lying."

• Treating all gaps the same. An economic gap (too expensive) requires a different

intervention than a habitual gap (autopilot behaviour). Diagnose before intervening.

• Using discounts to close every gap. Discounts close economic friction but create

new problems (price sensitivity, brand equity erosion). Match the intervention to the friction.

• Ignoring the "Didn't Say but Did" segment. These unexpected buyers reveal unobserved

purchase triggers that surveys miss. They are often the key to understanding the category.

• Measuring intent without measuring behaviour. A concept test that measures PI but

never validates against in-market behaviour is measuring the SAY, not the DO.

Cross-Skill References

• For campaign evaluation that tests say/do conversion → imi-campaign-evaluation
• For audience segmentation of gap segments → imi-segmentation-engine
• For brand strategy when preference doesn't convert → imi-brand-strategy
• For Pulse™ profiling of gap segments → imi-pulse-intelligence
• For writing up Say/Do findings → imi-client-deliverable

*Built for IMI International's Local AI — grounded in IMI's Say/Do™ methodology,

bridging the gap between what consumers say and what they do.*

*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
)