For decades, the traditional purchase funnel model was proposed as means to capture how consumers chose products, mainly on the consumer packaged goods category. The typical purchase process suggested that consumes advance through the funnel from awareness of the products to developing interest and consideration, to choice and purchase. The model was also extended by including a loop from the purchase to incorporate experience from consumption which builds loyalty leading to repeat purchases. For customers in typical offline retail settings, the decision process was straight forward: they placed themselves in front of the shelf, examined the brands, attributes and prices, selected a few products to consider and made a purchase decision.
Nowadays, with the rise of online retailers and marketplaces, the assortment available for the consumer has increased dramatically, offering consumers a large variety of products targeted to fulfill heterogeneous needs, and high variation in pricing from the same products in different channels. Additionally, the ease of search and product comparison online has led to a reduction in search cost. This change has led consumers to engage more actively in the process of searching for a product. At the same time, customers are using new technologies that allow them to compare prices, sort products, filter results by features, and return to previously seen products to make the decision. Thus, the purchase process has moved from a mostly linear funnel to a complex (back and forth) journey in which consumers go iteratively between the steps of awareness, search, and consideration, gaining awareness for new alternatives and product features, adding and removing products from the consideration set, and revisiting previously considered products throughout the process, making decisions along the way, making the purchase funnel complex and highly non-linear.
Although complex, unlike the possibly simpler and linear offline journey, the online journey is mostly observed to the researcher and the firm, making it possible to better map the journey and aim to understand the deliberate process consumers go through in choosing products or services. Customers compare products based on (mostly) observable features, making observable decisions along the journey such as searching, clicking on products, and analyzing products information.
The purpose of this research is to investigate the online customer journey. First, we aim to map the non-linearity of the online customer journey. Although the journey is complex, different customers follow similar patterns of journeys, leading to predictive patterns of the new purchase journey. Second, we show that firms can use the new customer journey to predict how likely a journey is to finish with a purchase. For example, is a customer who searches for a flight and returns from the flights option page to make another search query more or less likely to eventually purchase a flight than a customer who went linearly search to flight options? On the one hand, the customer signals that they did not find the fight they looked for in the flights option page, but on the other hand, the customer indicates that they are engaged in the process. Finally, we investigate which steps or paths of the customer journey are most indicative that the journey is likely to end in purchase. In doing so we aim to identify the moment of truth, which is step in the journey that increases the likelihood of purchase the most.
As an empirical setting to investigate the non-linear customer journey we use search and purchase data from one of the largest online travel agencies, analyzing data from flight search of over 106,245 customers, making 650,317 purchase ,journeys. We identify that customers indeed tend to go through a highly non-linear purchase funnel. On average a customer went through 5.6 steps in their customer journey, revisiting the query for a flight, changing the parameters of the search and clicking on multiple options in a highly non-linear fashion.