Product finder
 |
Product finders are information systems that help consumers to identify products within a large palette of similar alternative products. Product finders differ in complexity, the more complex among them being a special case of decision support systems. Conventional decision support systems, however, aim at specialized user groups, e.g. marketing managers, whereas product finders focus on consumers.
Area of application
Usually, product finders are part of an e-shop or an online presentation of a product-line. Being part of an e-shop, a product finder ideally leads to an online buy, while conventional distribution channels are involved in product finders that are part of an online presentation (e.g. shops, order by phone).
Product finders are best suited for product groups whose individual products are comparable by specific criteria. This is true, in most cases, with technical products such as notebooks: their features (e.g. clock rate, size of harddisk, price, screen size) may influence the consumer's decision.
Beside technical products such as notebooks, cars, dish washers, cell phones or GPS devices, non-technical products such as wine, socks, toothbrushes or nails may be supported by product finders as well, as comparison by features takes place.
On the other hand, the application of product finders is limited when it comes to individualized products such as books, jewelry or compact discs as consumers do not select such products along specific, comparable features.
Furthermore, product finders are used not only for products sensu stricto, but for services as well, e.g. account types of a bank, health insurance, or communication providers. In these cases, the term service finder is used sometimes.
Product finders are used both by manufacturers, dealers (comprising several manufacturers), and web portals (comprising several dealers).
There is a move to integrate Product finders with social networking, allowing users to add and rate products and locations.
Technical implementation
Technical implementations differ in their benefit for the consumers. The following list displays the main approaches, from simple ones to more complex ones, each with a typical example:
- String search
- Comparison table
- Menu trees
- Dialog systems
- Filtering systems
- Scoring systems
- Tagging clouds
- Neural Networks
Examples
- Amazon.com (online retailer, includes product recommendations)
- PriceGrabber (price comparison shopping site)
- ProCompare (recommendation engine for business technology -- software, hardware and webware)