THE DMMAIR FRAMEWORK EXPLAINED

1) DEFINE, set the question, scope, and success metrics

Objective: Turn business intuition into testable research questions and decide what “success” looks like.

Key activities

• Stakeholder workshop: clarify business objective, decisions to be made, constraints (time, budget, geography).

• Convert business objectives into 3–4 researchable questions / hypotheses.

• Define target segments, channels, and sample frame.

• Agree KPIs & success criteria (e.g., uplift in purchase intent, target NPS lift, acceptable margin of error).
  
  

Deliverables

• Project brief (1-page) with decisions to inform, timelines, budget.

• Research questions + hypothesis log.

• KPI & reporting dashboard spec.

India tip: Explicitly note regional / language considerations in scope (e.g., metro vs. non-metro, vernacular needs).

2) MEASURE, collect the right data efficiently

Objective: Capture reliable quantitative and qualitative data aligned to the defined questions.

Key activities

• Select methods (surveys, in-depth interviews, ethnography, passive analytics).
  
  

• Build questionnaires & discussion guides (use the Questionnaire Design Checklist).

• Sampling plan: quotas, stratification (age, income, city tier).

• Deploy data collection with quality controls (backchecks, digital timestamping, interviewer training).
  
  

Deliverables

• Clean, validated dataset (raw + cleaned).

• Fieldwork report: response rates, quotas hit, deviations.

Tools / methods: mobile surveys, online panels, CATI, in-home ethnography, web analytics.

India tip: Pilot in 1–2 key cities/towns to catch language/interpretation hazards early.

3) MODEL, convert data into structured, testable representations

Objective: Structure the data so it can be interrogated for trade-offs, segments, and predictive signals.

Key activities

• Data preparation: coding open text, creating variables, weighting.

• Statistical modelling: segmentation, conjoint, regression, propensity scoring, MaxDiff where relevant.

• Scenario modelling: revenue projections, TAM/SOM impact, price elasticity.
  
  

Deliverables

• Analytical datasets and modelling notebooks (or model summary).

• Scenario outputs (e.g., “If price = X, forecasted share = Y”).
  
  

Quality control: peer code review, holdout samples for model validation.

India tip: Use local priors and field validation to temper global model assumptions.

4) ANALYZE, extract the insight(s)

Objective: Move from numbers to meaning identify root causes and high-confidence insights.

Key activities

• Theme synthesis: cross-link quantitative results with qualitative quotes.

• Prioritise findings by business impact and confidence.

• Produce the “One Sharp Arrow” insight: the single most important action the brand should take.
  
  

Deliverables

• Insight deck (executive summary + evidence map).

• Evidence table mapping each recommendation to data points and confidence level.
  
  

Tip: Always show both the insight and the risk/uncertainty attached to it.

5) IMPLEMENT, turn insight into action and measure adoption

Objective: Ensure insights guide real business experiments and changes.

Key activities

• Convert recommendations into experiments / pilots (A/B tests, regional rollouts, price tests).

• Define success metrics for each experiment and set monitoring cadence.

• Support deployment (creative brief, product specs, channel plan).
  
  

Deliverables

• Implementation roadmap (who does what, by when).

• Experiment design and KPI tracking sheets (live dashboard).
  
  

India tip: Start with a high-impact, low-cost pilot in 1–2 states or city tiers to validate before scaling.

6) REVIEW, close the loop and institutionalise learning

Objective: Verify outcomes, capture learning, and improve the next DMMAIR cycle.

Key activities

• Post-implementation evaluation vs baseline.

• Capture learnings, failures, model recalibration.

• Update playbooks, personas, and templates.
  
  

Deliverables

• Post-mortem report (what worked, what didn’t, next steps).

• Updated knowledge repository & revised KPIs.
  
  

Tip: Hold a short stakeholder workshop to socialize learnings keep it action-focused.

Example timeline (typical 6–8 week consumer study)

1. Week 0: DEFINE, workshop & final brief (2–3 days)

2. Week 1–2: MEASURE, questionnaire finalised, pilot, fieldwork begins (7–10 days)

3. Week 3: MODEL, data cleaning & initial modelling (4–5 days)

4. Week 4: ANALYZE, insight synthesis + deck (3–4 days)

5. Week 5–6: IMPLEMENT, pilot execution & early monitoring (2 weeks)

6. Week 8: REVIEW, post-pilot evaluation & next steps (1 week)
   
   

(Adjust cadence for B2B, ethnography, or long fieldwork projects.)

Deliverable templates you should keep ready

• 1-page project brief (purpose, decisions, KPIs)

• Questionnaire checklist (already built)

• Model spec sheet (variables, weights, holdout split)

• Insight one-pager (“One Sharp Arrow” + 3 actions + success metrics)

• Implementation tracker (owner, deadline, KPI, status)

• Post-project learning log

How AnveMark uses DMMAIR (practical note)

At AnveMark we operationalise DMMAIR by pre-building these artifacts as project templates. That means: faster project kickoffs, consistent quality gates, and a single source of truth for clients — from the signed project brief right through to the post-pilot review.

Example: For a D2C brand launch we used DMMAIR to reduce time-to-decision from 10 weeks to 6 weeks while increasing confidence intervals (smaller MoE) by using layered pilots and rapid modelling.

The “One Sharp Arrow” deliverable (how to write it)

1. One sentence insight (e.g., “Price sensitivity is highest among 25–34 urban buyers; communicate value via pack size, not discount.”)

2. Three prioritized actions (A: pilot a 20% larger pack at +15% price; B: run regional messaging emphasizing value per gram; C: test distribution in 2 Tier-2 cities)

3. Two success metrics (e.g., 12% lift in repeat purchase rate within 8 weeks; CAC reduction of 10%)

This single artifact ensures research translates into a measurable business experiment.