Leading cross-border e-commerce platform Superbuy has implemented a sophisticated multichannel attribution model
The Data That Changed Everything
Superbuy's data science team analyzed over 214,000 customer journeys across:
- Social media advertisements
- Influencer partnerships
- Search engine marketing
- Video content (including unboxing videos)
- Email campaigns
The mathematical model revealed fashion influencers' unboxing videos contributed 45% of high-value customers
Dynamic Budget Shifting Based on Channel Performance
Using their proprietary Superbuy spreadsheet, the marketing team visualized:
| Content Type | Previous Budget % | New Budget % | ROI Impact |
|---|---|---|---|
| Fashion Unboxing Videos | 18% | 42% | +217% |
| Static Social Posts | 35% | 22% | -12% |
The reshuffled budget allocation led to remarkable improvements:
How Markov Modeling Changed Attribution Accuracy
Unlike traditional last-click models, Markov chain analysis:
- Maps all touchpoints in customer journeys
- Calculates each channel's "removal effect" on conversions
- Assigns probabilistic value to assisted interactions
The Superbuy spreadsheet system continuously updates these weights through machine learning algorithms that track:
- Customer lifetime value by acquisition source
- Content fatigue rates
- Real-time performance trends
Automated Content Performance Monitoring
Superbuy's system flags declining content effectiveness using multiple indicators:
Engagement Decay Rate
When video watch-through rates drop below 35%, budgets are automatically redistributed to newer content.
Conversion Probability Scores
Any channel falling below 0.2 probability score enters 7-day probation before automatic budget reallocation.
This data-driven approach has enabled Superbuy