DIRECT READINESS VERDICT The dataset is NOT ready for production Robyn modeling in its current form. Three issues are blocking: 1. Twelve missing weekly dates 2. Seven missing dependent-variable values 3. Forty weeks with positive Meta impressions but zero recorded Meta spend The dataset may become conditionally ready after the calendar is rebuilt, missing revenue is resolved, media scopes are reconciled, and variable definitions are stabilized. No Robyn or R execution was performed for this assessment. KEY ASSUMPTIONS - The intended frequency is one observation per consecutive week. - Missing dates are absent rows, not intentionally excluded periods. - Positive Meta impressions represent real paid delivery unless proven otherwise. - Weekly net revenue is the dependent variable used for model estimation. - Discount rate is finalized after weekly trading performance is observed. - Affiliate spend is primarily recognized when a transaction occurs. - The current analysis is based only on the supplied description, not the raw dataset. FINDINGS | Finding | Severity | Evidence | Modeling Impact | Recommended Fix | |---|---|---|---|---| | Missing weekly dates | Critical | 12 expected weeks are absent | Adstock, lag, trend, seasonality, and chronological validation assume consecutive periods. Missing rows distort carryover and split boundaries. | Build a complete weekly calendar and left-join every source onto it. Distinguish true zero activity from missing data. | | Missing revenue | Critical | 7 dependent-variable values are missing | A Gaussian revenue model cannot be estimated reliably when the target is unknown inside the modeling window. | Recover revenue from source systems. If impossible, shorten the window or document a separate imputation sensitivity analysis. | | Meta spend-exposure inconsistency | Critical | Positive impressions with zero spend in 40 weeks | Exposure may come from a different account, currency, market, billing scope, bonus inventory, or missing cost. Budget allocation will be unreliable without a valid spend-to-exposure relationship. | Reconcile campaign IDs, account scope, currency, invoice timing, and market definitions. Split paid and free delivery if both are real. | | Channel naming changed | High | Naming conventions changed in year two | A single variable may represent different campaign scopes over time, creating a structural break and false variation. | Create a stable channel taxonomy and back-map all periods before modeling. | | Discount rate may be post-treatment | High | Finalized after the week closes | It may encode realized demand or management reaction to performance, causing leakage or biased media coefficients. | Reconstruct the discount decision known before or during the week. Run models with and without the variable. | | Affiliate reverse causality | High | Commission is recorded when transactions occur | Affiliate cost can be partly caused by sales rather than independently causing sales. | Prefer a pre-conversion exposure metric where possible. Otherwise interpret cautiously and run exclusion sensitivity. | | Brand-search endogeneity | High | Brand search rises during TV and promotions | Brand search may capture demand created by upper-funnel media and organic interest. | Keep brand and generic search separate, test alternative specifications, and use calibration when available. | | TV and OOH overlap | Medium–High | Campaigns frequently run together | Multicollinearity may make individual channel effects unstable even if total media prediction is good. | Review flight calendars, aggregate where necessary, and compare models with alternative channel groupings. | | Long zero-spend periods | Medium | Several media variables contain extended zero blocks | Genuine on/off variation may help identification, but missing-data zeros can severely bias the model. | Validate operational explanations for every long zero block. | | No incrementality calibration | Medium | No experiments available | Prediction and decomposition will not be strongly anchored to causal evidence. | Create a future geo-lift or holdout test plan for high-risk channels. | | Sample-size pressure | Medium | 156 expected observations with many regressors | A large structural specification or flexible Weibull search may be weakly identified. | Start with aggregated channels, geometric adstock, and a conservative control set. | BLOCKING ISSUES 1. Reconstruct all missing weekly rows. 2. Resolve or remove the seven missing revenue observations. 3. Reconcile Meta spend and impressions. 4. Standardize channel definitions across the full period. 5. Confirm whether discount rate is available before outcome realization. NON-BLOCKING BUT MATERIAL ISSUES - TV and OOH overlap - Search-brand demand capture - Retargeting audience dependence - Affiliate reverse causality - No experimental calibration - Limited sample size relative to possible regressors CAUSAL AND IDENTIFICATION RISKS - Prediction is not causality. - Ridge regularization can stabilize correlated coefficients but cannot remove endogeneity. - Positive media constraints do not prove incrementality. - Post-treatment discount controls may suppress or reassign media effects. - Affiliate and retargeting may be jointly determined with demand. - Brand search may absorb effects generated by TV, OOH, promotions, and organic activity. - Structural breaks from changing channel definitions may look like response variation. RECOMMENDED DATA-REPAIR SEQUENCE 1. Create a canonical weekly date spine. 2. Reconcile and join all sources using the same week definition. 3. Recover missing net revenue. 4. Audit all spend, exposure, and currency scopes. 5. Build a stable channel dictionary across all years. 6. Classify context variables as pre-treatment, contemporaneous, or post-treatment. 7. Plot revenue, media, price, discount, availability, and events. 8. Review zero blocks and structural breaks. 9. Reduce the initial variable set to a defensible baseline. 10. Document unresolved risks before modeling. PROPOSED BASELINE AFTER REPAIRS - Weekly geometric adstock - ts_validation = TRUE - Fixed train_size = 0.70 for comparable validation windows - Separate brand and generic search - Separate Meta prospecting and retargeting only if definitions are stable - Conservative context-variable set - add_penalty_factor = FALSE - Multiple trials and seeds - Sensitivity runs excluding discount and affiliate - No allocator recommendation until spend-exposure mapping is validated VALIDATION CHECKLIST [ ] All expected weeks exist exactly once [ ] Net revenue has no unresolved missing values [ ] Meta spend and impressions share the same scope [ ] Channel names are stable across years [ ] Discount timing is causally defensible [ ] Affiliate treatment is documented [ ] Long zero blocks are confirmed as genuine [ ] Structural breaks are reviewed [ ] Regressor count is appropriate for 156 observations [ ] Calibration experiments are planned MOST IMPORTANT NEXT ACTION Rebuild the complete weekly calendar and reconcile Meta spend against impressions before creating robyn_inputs().