Gauge marketer trust in MTA results and detect channel bias. Use this template to benchmark data quality and decision confidence.
What's Included
AI-Powered Questions
Intelligent follow-up questions based on responses
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Detailed Reports
Comprehensive insights and recommendations
Sample Survey Items
Q1
Chat Message
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.
Q2
Multiple Choice
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
Q3
Multiple Choice
How is MTA delivered in your organization?
Vendor product
In-house model
Agency-provided
Not applicable—no MTA in use
Not sure
Q4
Rating
How much do you trust the outputs and documentation from your MTA solution?
Scale: 10 (star)
Min: Very low trustMax: Very high trust
Q5
Opinion Scale
Over the past 3 months, how much did you trust the MTA results you used?
Range: 1 – 10
Min: Not at allMid: NeutralMax: Completely
Q6
Multiple Choice
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
Q7
Opinion Scale
Overall, how much do you trust MTA results in today’s environment?
Range: 1 – 10
Min: Not at allMid: NeutralMax: Completely
Q8
Matrix
How concerning are the following potential biases for MTA in your context?
Rows
Not a concern
Slight concern
Moderate
High
Major concern
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
•
•
•
•
•
Q9
Multiple Choice
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
Q10
Long Text
Please share a brief example of how this bias showed up and its impact.
Max 600 chars
Q11
Multiple Choice
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
Q12
Multiple Choice
What would most increase your confidence in MTA? Select all that apply.