The retail industry provides customers with goods or services. Analyzingthe purchase behavior of customers is critical for expanding thebusiness. Therefore, managing the repurchase intention of customersis crucial. A sequence of purchase behaviors by each customer constitutesa set of purchase customer journeys (purchase CJs), whichdetail purchase pathways and repurchase behaviors. Purchase CJs arethe actual retail transaction data. This study investigated purchaseCJs and proposed a purchase funnel called CJ graph (CJG) bymeasure theory and knowledge space theory with actual retail transactiondata. To achieve this objective, a customer journey block (CJB),denoting “touchpoint,” is defined as a series of purchase behaviors duringa period and used as the base of this method. Each touchpointis allotted a measurable function, named purchase measure (PM).By integrating all the CJBs of purchase CJs, the purchase measuregraph (PMG) can be constructed as the primary structure ofthe PM knowledge structure. Finally, when all CJs are coordinatizedwith CJ codes, knowledge space theory is used to develop the customerjourney graph (CJG). In this method, knowledge bases are usedas the spanning framework of the CJ knowledge space, and the filteringproperty of the purchase funnel is illustrated. Furthermore, to validate the feasibility of the proposed method, a set of two-year andone million transactions retail data generated from more than thirty-sixthousand customers was segmented by recency, frequency, and monetary(RFM) model. Thus, the corresponding PMGs and CJGs of various(12, here) customer segments are detailed, and the resultant knowledgebases of the primary purchases and secondary re-purchases are appliedto analyze and predict the customer purchase behaviors accurately.