Round 97
Marketing Attribution Model Selection
A mid-size e-commerce company spends across paid search, social ads, affiliate links, email, and offline events, and wants to reallocate a $4M annual budget more efficiently. The analytics team has clickstream and CRM data but inconsistent cross-device tracking, partial view-through data, and no reliable holdout regions for causal testing. Leadership wants an attribution methodology that is defensible to finance, actionable for the marketing team within one quarter, and not prohibitively expensive to build or maintain. The team must pick one primary model to drive budget decisions, knowing each approach trades off interpretability, statistical rigor, implementation cost, and vulnerability to data gaps.
Status
DECIDEDWinner
A · Invest instead in aggregate media mix modeling (MMM) using historical spend and outcome time series, sacrificing individual-level granularity for robustness to tracking gaps and privacy changes.Majority
A · Invest instead in aggregate media mix modeling (MMM) using historical spend and outcome time series, sacrificing individual-level granularity for robustness to tracking gaps and privacy changes.Duration
7309 msTopic
Options
Checks
Baselines
PPV
Top Influences
Samples
Answer Distribution
PPV
Influences
| Label | Role | Score |
|---|---|---|
| Claude Sonnet 5 | delegate | 1.000 |
| GPT 5.4 mini | delegate | 1.000 |
| Gemini 3 Flash Preview | delegate | 1.000 |
Voting matrix
Constructed Delegation Matrix
| To / From | Claude Sonnet 5 | GPT 5.4 mini | Gemini 3 Flash Preview |
|---|---|---|---|
| Claude Sonnet 5 | 0.000 | 0.000 | 0.000 |
| GPT 5.4 mini | 0.000 | 0.000 | 0.000 |
| Gemini 3 Flash Preview | 0.000 | 0.000 | 0.000 |
| A | 1.000 | 1.000 | 1.000 |
| B | 0.000 | 0.000 | 0.000 |
| C | 0.000 | 0.000 | 0.000 |
| D | 0.000 | 0.000 | 0.000 |
| E | 0.000 | 0.000 | 0.000 |
| F | 0.000 | 0.000 | 0.000 |
Embeddings
Sample Embeddings
Samples
Agent Responses
Given inconsistent cross-device tracking and no reliable holdout regions, methods requiring granular individual-level accuracy (multi-touch algorithmic, Shapley) or causal experiments (geo-lift) are undermined by the data gaps, while single-touch heuristics (f...
Given inconsistent cross-device tracking and no holdout regions, individual-level multi-touch models (B, C, D, F) would be built on unreliable data, and true causal experiments (E) aren't feasible within the timeline. MMM (A) works with aggregate spend/outcome...
Given the inconsistent cross-device tracking and partial view-through data, individual-level multi-touch or Shapley approaches would be built on unreliable touchpoint data, undermining their statistical rigor benefits. Since no holdout regions exist, true caus...
Given inconsistent cross-device tracking, partial view-through data, and no holdout regions, individual-level multi-touch or Shapley models would be built on unreliable identity data, undermining their statistical rigor, while single-touch heuristics (first/la...
Given the inconsistent cross-device tracking, partial view-through data, and lack of holdout regions, individual-level multi-touch or experimental models are undermined by data gaps, making them less defensible and harder to implement within a quarter. Aggrega...
Given inconsistent cross-device tracking, partial view-through data, and no holdout regions, individual-level multi-touch or Shapley models would be undermined by data gaps, while last/first-touch models are too simplistic and biased for a $4M reallocation dec...
Given inconsistent cross-device tracking and no reliable holdout regions, individual-level multi-touch and experimental approaches (B, C, E) are undermined by data gaps, while single-touch heuristics (D, F) are too simplistic and biased for a $4M reallocation ...
Given inconsistent cross-device tracking and no holdout regions, individual-level multi-touch or Shapley models would be unreliable and hard to defend, while last/first-touch models are too simplistic and biased for a $4M reallocation decision. MMM using aggre...
The company has incomplete tracking across devices, partial view-through data, and no reliable holdout regions, so a user-level multi-touch or experiment-first approach is hard to defend or execute quickly. MMM is the best fit because it is robust to tracking ...
The company has mixed data quality across devices and channels, making user-level attribution methods less reliable, while also needing something finance can trust and the team can deploy quickly. MMM is designed for exactly this situation: it works from aggre...
The company has inconsistent cross-device tracking, partial view-through data, and no reliable holdout regions, so a user-level or experimental approach would be hard to trust or slow to deploy. They need something defensible to finance, practical within one q...
Given inconsistent cross-device tracking, partial view-through data, and no reliable holdout regions, a user-level multi-touch model or experimentation is hard to trust or execute quickly. Media mix modeling is the most defensible choice for finance because it...
Given the inconsistent cross-device tracking, partial view-through data, and lack of reliable holdout regions, a user-level multi-touch model or experiment-first approach would be harder to trust or execute quickly. MMM is specifically designed to work with ag...
The team needs a model that works despite cross-device gaps, partial view-through data, and no reliable holdout regions, while still being defensible and practical within one quarter. MMM is specifically designed to be robust to missing user-level tracking and...
The company needs a model that is practical with imperfect cross-device and partial view-through data, can be built within a quarter, and is defensible to finance without expensive experimentation. MMM is specifically designed to work from aggregated historica...
The company has inconsistent cross-device tracking, partial view-through data, and no reliable holdout regions, so highly granular user-level attribution or experiments would be hard to trust or implement quickly. Media mix modeling is the best fit because it ...
Marketing Mix Modeling (MMM) is the most defensible choice for finance and leadership because it relies on aggregate historical data, bypassing the company's significant issues with cross-device tracking and partial view-through data. Unlike granular algorithm...
The company requires a model that is defensible to finance, actionable within one quarter, and robust against significant data gaps such as inconsistent cross-device tracking and missing view-through data. Media Mix Modeling (MMM) is the most viable solution b...
Media Mix Modeling (MMM) is the most defensible choice for finance because it relies on aggregate revenue and spend data, bypassing the company's significant issues with inconsistent cross-device tracking and missing view-through data. Unlike granular algorith...
Given the constraints of inconsistent cross-device tracking, partial view-through data, and a one-quarter deadline, individual-level algorithmic models (B, C) or experimental holdouts (E) are either data-compromised or too slow. Media Mix Modeling (A) is the m...
The company requires a methodology that is defensible to finance, actionable within one quarter, and robust against significant data gaps such as inconsistent cross-device tracking and lack of holdout regions. Media Mix Modeling (MMM) is the most suitable choi...
Marketing Mix Modeling (A) is the most defensible choice for finance and leadership because it uses aggregate data, bypassing the critical issues of inconsistent cross-device tracking and missing view-through data. Unlike granular algorithmic models (B or C), ...
Media Mix Modeling (MMM) is the most defensible choice for finance because it relies on aggregate spend and revenue data, bypassing the company's significant cross-device tracking and view-through data gaps. Unlike user-level algorithmic models (B and C), MMM ...
Media Mix Modeling (MMM) is the most defensible choice for finance because it relies on aggregate spend and revenue data, bypassing the company's significant issues with cross-device tracking and missing view-through data. Unlike algorithmic multi-touch models...
Timings
Stages
Errors