Attribution Model Comparator
See how different models tell different stories about the same revenue
The Insight
eCom brands using platform-reported last-click data overcount conversions by 30-50%. Different attribution models credit channels differently — making budget decisions unreliable without model comparison. KLAR already offers 7 models, but brands rarely understand why the numbers diverge.
The Opportunity
A side-by-side model comparison view answers the #1 question from new KLAR customers: 'Why don't your numbers match Facebook?' Showing exactly how credit shifts between models builds trust, reduces churn from attribution confusion, and makes the case for KLAR's advanced Data-Driven and MMM models.
The Proposal
- 1Build a model comparison view where brands select 2-3 attribution models and see revenue credit shift across channels in real time
- 2Highlight the biggest disagreement between models with a 'conflict zone' callout — this is where budget decisions are most vulnerable
- 3Add a 'Model Recommendation Engine' that suggests the best model based on brand's customer journey length, channel mix, and data maturity
- 4Create educational tooltips explaining *why* each model credits differently — turning confusion into understanding
Skill Mapping
- Developed iOS SKAN Reporting Pack at Google — navigating attribution model transitions at scale
- Built KPI scorecards comparing attribution methodologies across 25+ advertiser accounts
- Deep expertise in multi-touch attribution from managing $XXXmm+ quarterly ad spend
Working MVP Prototype
Attribution Model Comparator
Select up to 3 models to compare how they credit the same €480K revenue
Click a channel to drill down into all 6 models
Conflict Zone: TikTok — €47,000 disagreement between models. Budget decisions for this channel are most vulnerable to model choice.
Model Agreement by Channel
Implied Budget Allocation (%)
| Channel | Last Click | Data-Driven | MMM | Spread |
|---|---|---|---|---|
| Meta Ads | 38.5% | 30.8% | 33.8% | 7.7pp |
| Google Ads | 29.6% | 27.5% | 22.5% | 7.1pp |
| TikTok | 10% | 15% | 19.8% | 9.8pp |
| Email/SMS | 15% | 12.1% | 8.8% | 6.2pp |
| Direct | 6.9% | 14.6% | 15.2% | 8.3pp |
TikTok has the largest allocation spread (9.8pp) -- models fundamentally disagree on this channel's contribution.
Insight
Meta Ads shows the highest model agreement (±9% variance) -- safe to optimize. Direct shows dangerous disagreement (±31% variance) -- run an incrementality test before reallocating budget.
Synthetic data demonstrating how the same €480K revenue is credited differently by each model. In production, this would use KLAR's actual attribution engine across all 7 supported models.