Customer segmentation divides customers into groups of individuals that are similar in specific ways. There are several different customer segmentations that use different criteria. In the context of Product Guidance and Guided Selling tools, the following customer segmentations are relevant:
- Product knowledge: the customer's experience with the specific product or service that he wants to select. Users with more product experience have other needs for search tools than users with less product experience
- Internet affinity and know-how for web applications: the user's knowledge of internet technologies, browsers and Rich Internet Applications as often used in product search tools
- Social, socio-demographic and individual parameter: the customer's characteristics and need dimensions, lifestyle dimensions and personal environment. See also the Sinus-Milieu.
- Buying behaviour: decision behavior during the purchase preparation und the purchase decision process. Buying patterns are influenced by social parameters and product knowledge.
Considering customer segmentation in conception of Guided Selling applications
The customer segmentation is taken into account by elements of the Guided Selling Advisor Concept:
- User Interface - design and complexity - adapted to previous user's experience.
- Themes and complexity of the Product Guide - especially adaptions to user's product knowledge, selecting the focus of the questions regarding technical / need orientation, level of detail and content of product characteristics.
- If clusters are used: Considering parameters defining a cluster (for example need dimensions, lifestyle dimensions and social dimensions) for the definition of the Product Advisor and optimising the recommendation behaviour. If clusters are used, decide how a cluster influences the recommendation behaviour. Possibilities are e.g.:
- Clusters are calculated by user responses and dominate the product recommendations (e.g. by a fix (editorial) assignment of recommendations to a certain cluster)
- Clusters are calculated by user responses and only influence the product recommendations along with other (mostly more specific) customer requirements. In this case, the advantages of the Fuzzy Matching Engine and the consideration of purchase decision attributes taken into consideration.
- Designing the interaction - especially how to guide the user through the product selection process.