This is a question that several of our customers have asked us, when they start to build their own knowledge base of answers, to enable customer web self-service.
The knowledge base search (knowledge foundation) is not a simple mechanism. That is why it is so powerful, intelligent, dynamic and self-learning.
A picture is worth a thousand words, so I decided to put together a diagram that depicts the process, and below leave you with a few definitions to be better understand the different components.
When a search is performed, each keyword and/or phrase entered by the customer is compared to the contents of the answers.
The Weight is a numerically calculated value, based on the number of occurrences, capitalisation, and location of a word. It is equal to the sum of the weights of all the matched words from the search.
The location of the word is important. It is ordered and weighed as per the diagram – e.g. words that match the Summary field will have higher weights than those that appear in the Answer field.
The Computed Score of an answer is usually the same as its Score, unless its Display Position is set to fix it at top/bottom. In that case, the Computed Score is calculated using the score of the answers located at the top or bottom of the list.
To better understand, if a new answer is created, and set with Display Position = “Fixed at the top”, once it is published, its Score will be zero, but the Computed Score will be larger than the highest score for all the published answers.
The Score is a calculated value that ranks the order of answers, and indicates the usage of the answer, as well as how helpful that answer has been to customers. It is calculated based on the Solved Counts:
- 75% of the score is based on Solved Count, linked to customer usage
- 25% of the score is based on Solved Count, linked to agent usage
An answer with a large score indicates that several customers (and/or agents) have viewed that answer and that the answer was somehow useful to them.
The Solved Count collects information about the usefulness of answers in the Knowledge Base. Two types of data is gathered:
- Implicit data – compiled by how customers select and view answers. If a customer views an answer, the solved count of the 1st answer is increased, but not as much as the 2nd viewed answer. In other words, the answer that the customer views last receives the largest solved count increase.
- Explicit data – compiled by how customers rate individual answers – from the responses to the question “Is this answer helpful?”