MMM Readiness & Data Governance Assessment

Welcome to the MMM Readiness & Data Governance Assessment. This survey explores your organization's current Marketing Mix Modeling capabilities, data infrastructure, and governance practices. Your responses will help identify strengths, gaps, and opportunities to improve MMM effectiveness. • Participation is voluntary and you may stop at any time. • There are no right or wrong answers—we are interested in your honest perspective. • All responses are confidential and will be reported in aggregate only. • Estimated time: 7–9 minutes. Please proceed to begin.

Are you currently involved in—or do you have visibility into—marketing analytics, media measurement, or budget planning at your organization?

• Yes
• No

Which best describes your organization's current stage with Marketing Mix Modeling (MMM)?

• Actively running MMM
• Ran MMM in the last 12 months
• Piloting or prototyping MMM
• Planning to start within 12 months
• Not planning MMM

Which decision areas does your organization currently use—or plan to use—MMM to inform? (Select all that apply.)

• Budget allocation across channels
• Media mix within a channel
• Campaign flighting and timing
• Geographic allocation
• Creative and messaging strategy
• Pricing and promotions
• Scenario planning and forecasting

Which data sources does your organization currently use or plan to use for MMM? (Select all that apply.)

• Paid media spend and impressions by channel
• Search data (paid and organic)
• Social platform data
• Website and app analytics
• CRM and marketing automation engagement
• Sales data by product or region
• Pricing and promotions
• Distribution and availability
• Brand tracking
• Competitor spend estimates
• Economic, weather, or other external factors
• Experiment and lift test results

Approximately how many distinct data sources are integrated (or planned) in your MMM dataset?

• 1–3
• 4–7
• 8–12
• 13–20
• More than 20
• Not sure

Which integration challenges are most significant for your MMM data pipeline? (Select all that apply.)

• Inconsistent IDs or keys across sources
• Missing or incomplete historical data
• Different time grains (daily, weekly, monthly)
• Taxonomy and naming differences
• Data silos and access constraints
• Data latency or delayed availability
• Vendor definition changes over time
• Legal or privacy restrictions
• None of the above

What is the typical refresh cadence of your MMM dataset?

• Near real-time
• Daily
• Weekly
• Monthly
• Quarterly
• Ad hoc or as needed
• Not yet established

How would you rate the completeness of the data available for your MMM (i.e., minimal missing values or gaps)?

How would you rate the consistency of your MMM data across sources (i.e., aligned definitions, taxonomies, and formats)?

How would you rate the timeliness of your MMM data (i.e., data is available when needed for modeling and decisions)?

Overall, how ready is your organization to leverage MMM for marketing decisions in the next 12 months?

Rank the biggest inhibitors to effective MMM at your organization. (Drag to reorder; most limiting at top.)

1. Data quality and coverage
2. Analytics talent and ownership
3. Budget constraints
4. Stakeholder buy-in
5. Fragmented martech and data stack
6. Time and bandwidth

Which governance practices are currently in place for your MMM-related data and models? (Select all that apply.)

• Documented data dictionary or definitions
• Data catalog or lineage tracking
• Named data owner or steward
• SLA for data refresh and issue resolution
• Formal QA or validation checklist
• Access controls and role-based permissions
• Change log or versioning for datasets and models
• Model governance committee or review board
• Documented data retention policy
• None of the above

How confident are you that your MMM workflow complies with applicable privacy regulations and internal data policies?

What best describes your current or planned MMM approach?

• In-house team
• External vendor or consultancy
• Hybrid (in-house plus partner)
• Automated cloud service
• Open-source stack built internally
• Not sure or still evaluating

Which validation methods do you use (or plan to use) to assess MMM reliability? (Select all that apply.)

• Time-based cross-validation (rolling origin)
• Holdout or out-of-time testing
• Alignment with experiments or incrementality tests
• Back-testing on historical shocks
• External benchmarks or market events sanity check
• Business stakeholder review
• None of the above

Based on your responses in this survey, what single change would most improve your organization's MMM effectiveness in the next 6–12 months?

Thank you for your survey responses. We'd like to explore a few of your answers in more depth. Please share your thoughts in the conversation below.

What is your primary role?

• Marketing leader
• Growth or performance marketer
• Media and activation
• Data science or analytics
• Finance or revenue operations
• Product or CRM
• Consultant or agency
• Other
• Prefer not to say

How many years of experience do you have in marketing, analytics, or related fields?

• Less than 1
• 1–3
• 4–6
• 7–10
• 11–15
• 16+
• Prefer not to say

Approximately how many employees does your organization have?

• 1–49
• 50–249
• 250–999
• 1,000–4,999
• 5,000+
• Prefer not to say

Which industry best describes your organization?

• Retail or ecommerce
• Consumer packaged goods (CPG)
• Technology or software
• Financial services
• Media or entertainment
• Healthcare or pharma
• Travel or hospitality
• Automotive
• Other
• Prefer not to say

What is your organization's primary region of operation?

• North America
• Latin America
• Europe
• Middle East & Africa
• Asia-Pacific
• Global
• Prefer not to say

What is your level of influence on paid media budget decisions?

• Final decision maker
• Recommender or approver
• Contributor or analyst
• No direct role
• Prefer not to say

Thank you for completing this survey. Your responses have been recorded and will be analyzed in aggregate to advance MMM best practices. If you have any questions, please contact the research team.