Welcome! This brief survey asks about your experience with MTA trust and potential biases. Most people finish in 5–7 minutes. Please answer based on your recent work.
Which attribution approaches has your organization used in the past 12 months? Select all that apply.
- Multi-touch attribution (rules-based)
- Multi-touch attribution (algorithmic/data-driven)
- Marketing mix modeling (MMM)
- Last-click attribution
- First-touch attribution
- Position-based/heuristic models
- None of the above
- Not sure
How is MTA delivered in your organization?
- Vendor product
- In-house model
- Agency-provided
- Not applicable—no MTA in use
- Not sure
How much do you trust the outputs and documentation from your MTA solution?
Over the past 3 months, how much did you trust the MTA results you used?
In the past 3 months, how often did MTA disagree with other measurement (e.g., MMM, experiments)?
- Never
- Rarely (less than monthly)
- Sometimes (about monthly)
- Often (weekly or more)
- Not applicable—did not compare
Overall, how much do you trust MTA results in today’s environment?
How concerning are the following potential biases for MTA in your context?
Which single bias is your biggest concern today?
- Over-crediting branded search or direct traffic
- Self-attribution by walled gardens
- Incomplete tracking due to privacy/consent gaps
- Recency bias toward last touches
- Touchpoint inflation from ad stacking/high frequency
- Model overfitting or instability
- Selection bias in conversion data
- None of the above
Please share a brief example of how this bias showed up and its impact.
Max 600 chars
Which steps have you taken to reduce bias in MTA outputs? Select all that apply.
- Apply lookback windows/decay
- Exclude brand search or direct from credit
- Deduplicate conversions across platforms
- Calibrate with MMM or causal lift
- Run holdouts/geo experiments
- Independent or vendor audit
- Review model transparency and features
- Data quality checks (consent, IDs, events)
- Other
What would most increase your confidence in MTA? Select all that apply.
- Transparent methodology and assumptions
- Third-party audit/validation
- Alignment with MMM or causal lift
- Regular back-testing/out-of-sample validation
- Access to raw signals and feature importances
- Better identity resolution/clean-room integrations
- Clear conversion deduplication policy
- Run geo/cell experiments
- Other
Attention check: To confirm you are paying attention, please select “Often” below.
- Never
- Rarely
- Sometimes
- Often
- Always
Rank the evidence sources that most increase your trust in MTA (top = most trust-building).
Allocate 100 points to where MTA most influences your decisions.
What minimum confidence level (%) do you require to act on MTA findings?
Which best describes your role?
- Marketing leadership
- Performance marketing
- Growth/Acquisition
- Data science/Analytics
- Media/Activation
- Product/MarTech
- Consultant/Agency
- Other
How many years have you worked with attribution or MTA?
Company size (employees)
- 1–49
- 50–249
- 250–999
- 1,000–4,999
- 5,000+
Approximate annual paid media spend
- <$1M
- $1M–$4.9M
- $5M–$19.9M
- $20M–$99.9M
- $100M+
- Prefer not to say
Primary region
- North America
- Latin America
- Europe
- Middle East & Africa
- Asia-Pacific
- Other
Primary industry
- Retail/ecommerce
- Consumer services
- B2B/Enterprise
- Technology/Software
- Media/Entertainment
- Financial services
- Travel/Hospitality
- Healthcare/Pharma
- Other
- Prefer not to say
Your involvement in decisions based on MTA
- I make final decisions
- I influence decisions
- I consume results but don’t decide
- I implement/operate MTA
- Not involved
Anything else we should know about trust or bias in MTA at your organization?
Max 600 chars
AI Interview: 2 Follow-up Questions on Trust and Bias in MTA
Thank you for your time—your input is greatly appreciated!