Instead of using labels to improve the performance of Answer Bot, you can use labels to help with targeting for your trigger. Adding labels to your Answer Bot triggers is optional. Labels enable you to specify a limited subset of articles that you want to search within.
This article covers three scenarios for using labels to better target articles with your Answer Bot triggers.
Scenario 1: Targeting customer segments
The most common scenario for when to use labels is when you have different customer segments and you want to show each segment only the relevant articles. For example, suppose you are a mobile game developer and you support both Android and iOS platforms. When you get a request from a customer who's using Android, you want to show only Android articles.
To accomplish this, create an "Android" Answer Bot trigger with the condition based on your custom field "Platform = Android." Then, configure Answer Bot using labels to include only articles that contain the "android" label.
Likewise, set up an additional trigger for the iOS platform and label.
Scenario 2: Reducing the "noise" in your Help Center
Your Help Center might contain a lot of articles, most of which you never want to be used as Answer Bot recommended articles.
In this case, review your articles and add a "use_for_answer_bot" label to ~200-300 of the best articles. This will allow Answer Bot to focus and only suggest articles that make sense.
We've created an (unsupported) ruby script to help you bulk update all articles in a section, or a specific list of article IDs, to make the process of adding the labels in bulk a little easier.
Scenario 3: Conducting a limited trial for a specific type of inquiry
While not recommended (it's a slippery slope), some customers have proven the value of Answer Bot by focussing it on a specific type of inquiry, such as "password reset" requests.
By creating an Answer Bot trigger that looks for specific words in the subject / description, and then using labels to restrict the articles suggested, you can limit the pilot and get some quantitative data to help support a broader rollout.