INSIGHTS

How purchase decisions are actually made — not how customers say

Traditional methods ask customers how they buy. The problem is that people don't remember, rationalize after the fact and omit key steps. Reflect observes actual online purchase behavior in real time, classifies decision narratives with AI and uses experimental design to identify what actually drives decisions.

How online purchase decisions actually work

Online purchase decisions are not the linear funnel most models assume. They are iterative, chaotic processes where customers jump between channels, compare, abandon and return — often without being aware of the pattern themselves.

Why funnel models fail

The funnel model assumes customers move linearly from awareness to purchase. In reality they jump back and forth, leave the funnel, return via a different channel and make decisions based on factors the funnel doesn't capture.

AI classification of purchase narratives — how and why

Open-ended questions about purchase decisions yield rich data but are time-consuming to analyze manually. AI classification makes it possible to identify decision themes, motivation and barriers in thousands of narratives with consistency that manual coding cannot match.

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.

Reflect's journey analysis framework

Reflect's customer journey framework combines three methods: real-time observation of online behavior, AI classification of decision narratives and experimental design. Together they give a picture of how customers actually buy — not how they say they do.