Product Knowledge comes in various forms

Product Knowledge

Introduction

Product Knowledge comes in various forms - a product's features for its intended purpose, what goes with what - a product's associations, and how a product works. A sales representative would be expected to know the product's features and sufficient of how it is used to convince the customer of its worth. When a sales contact is made, there is usually already understanding on the buyer's side of what the product broadly does, the sales rep only needing to fill in the holes - anticipate the customer's requirements, suggest support products and services, show how product features relate to the buyer's use of the product. Online selling cuts out the human representative, so some other means must be found to deliver the product knowledge.

Gathering Product Knowledge

Only part of product knowledge resides in printed catalogs, and almost none except a few values in a product database. Often the database will have information about each product crammed into a string. Much more knowledge about the product can be stored and delivered in the form of an XML message, but there being a human at the other end to read the message is problematic.

Product Features

Free form information about the product is often held in a description or string of keywords. This information can be "mined" to produce dimensions for the Product Map. Information that would not conflict dimensionally can be held as a string in one dimension, while information that would conflict can be teased out into separate dimensions - ride quality and rugged suspension, for example. A lexical analyzer primed with rules that relate to the particular product family can be used to automate most of the deconstruction of the free form information. The logic handling the segmentation of products into product families controls both the rules and the information the lexer sees, allowing a nominally context-free lexer using regular expressions to function in a context-aware manner.

Product Associations

If the supplier sells more than a single product range, associations with other products can be found from what customers buy. These can be stripped from transaction data and used to build links between products. For example, the customer buys some wooden wall studs. They are of a size for internal walls, so if the customer asks about cladding, we have used associative links to already identify plaster as probable, with timber sheeting as less probable. If the customer opts for plaster, we have already identified and can suggest a quantity of trumpet head woodscrews and suitable gap fillers.

Other Product Knowledge

Much product knowledge will not have been formalized before. The product objects, or classes of objects, can be linked to models of behavior, using textual or graphical or data mining means to build the logic of the models. The models can include analytic and experiential knowledge and automatically employ many forms of reasoning. Simulation or constraint models can be delivered electronically to sophisticated customers for fitting into models of their technical requirements.

Conclusion

The combination of linkable objects in a multi-dimensional container with multiple orderings allows dense and complex product knowledge about specific products and between products to be efficiently represented and deployed. The product knowledge is in a form that allows translation of much of it into an XML message which can be read and understood by a human or automated recipient. The form of the knowledge is also suitable for automatic optimisation.