Experimental design in journey research
Observation shows what customers do, but not why. By combining observation with experimental manipulation — changing price, channel experience or information availability — we can isolate what actually drives decisions from what merely correlates.
Customer journey research has traditionally been descriptive. We map the journey, identify touchpoints and measure satisfaction at each point. That gives a picture of what happens, but not what causes what.
Correlation is not causation — everyone knows this. Yet most customer journey maps are built on precisely correlations. Customers who visit a physical store convert better. Does that mean the store visit drives conversion? Or that customers who have already decided seek out the store?
Reflect's approach combines observation with experimentation. We observe real purchase behavior through Journey Decision Engine and then manipulate specific variables: price, product information, availability, channel experience. By comparing outcomes between experimental groups we can isolate causal effects.
In practice this means we can answer questions like: Does conversion increase if we show price earlier in the process? Do channel switches decrease if product comparison is improved? Does free shipping drive decisions or is it a hygiene factor? The answers come from data, not assumptions.
Key takeaways
- Observation shows what happens, experiments show why
- Most journey maps are built on correlations, not causality
- Experimental manipulation isolates causal drivers
- Variables like price, channel and information can be tested in controlled settings
- Results give actionable insights, not just descriptions