By following the navigation prompt, you'll be directed to the Manage topics screen where you can see all topics available to your organisation and those provided by EvaluAgent.
To create a new Text Analytics-based line item topic, open the topic builder by clicking on the +New topic button.
From here, options for technology are presented, allowing you to choose between Text Analytics and GenAI;
- Text Analytics: Uses Evaluagent’s rules engine to determine whether queries (words and phrases) are present or absent in a conversation.
- GenAI: Uses a large language model (AI) to judge whether or not context or intent is present or absent in a conversation.
Select Text Analytics.
Then, options for the type of topic are presented, allowing you to choose between Line Item and Insight topic;
- Line Item: Line Items are added to a scorecard and then run during the evaluation process, presenting the results to the evaluator for review.
- Insight topic: Insight topics are run across all conversations as they are imported, and the results are immediately available to the evaluator to filter and organise conversations before evaluating.
Select Line Item.
The line item topic builder will slide into view, ready for you to begin populating it with details that make the line item.
The image below is an example of a Text Analytics-based Line Item topic configuration.
Name: You will use the name to identify the line item in your list of topics, and when creating scorecards, keep it brief and descriptive.
Description: Expand the purpose of the line item in the description. Evaluators can read this to gain an understanding of whether or not they want this line item on the scorecards they create.
Theme: The theme is used to group data on reports. Use the same theme name if you want all Text Analytics line items displayed together on the report. Alternatively, find a theme that best fits where you want it on the report.
Configure queries: The configure queries section defines how the line item will be scored when evaluating a conversation. We will define the words and phrases we want EvaluAgent's rules engine to seek out in the conversation and return results.
Manually add query terms: You will add a list of keywords and phrases you targeting within conversations between your agents and customers. To ensure you are targeting the right conversations in the right context, you should try to explore every possible word or phrase whilst being conscious of how words are grouped, how agents structure their conversations, and who said what.
Upload terms: You can also upload a .csv file containing your terms. Your terms must be in the first column. If the number of terms exceeds the maximum number of words allowed (100), the excess terms will not be added.
Once you’ve added all your terms, you can start to configure each query ‘pill’ (word/phrase) individually. Click on the pill to view and set conditions for your query. These can include:
- Speaker – defines who said the word or phrase: the agent, customer or either.
- Slop – the maximum number of words that can exist between the start and end of a phrase or sentence (this is not presented when singular words are used).
- Searched in order – defines the definition for a ‘found’ word or phrase either in the order they are set out using the THEN operator or in any order using the AND operator throughout the whole conversation (this is not presented when singular words are used).
- Search across utterances – groups utterances into one text string either spoken by the agent, customer or both, depending on the speaker conditions set.
- Search entities – whether or not entities are to be included in the search. These are explained in Entity & Redaction help guides.
- Query profiler – This adjusts how the search is executed and what results are returned. We recommend using the ‘unified’ or ‘default’ drivers in most instances.
Apply default speaker to all terms: You can set all terms to the same speaker rather than clicking on the speaker icon next to each term.
Pass conditions: Define how many of the terms should return a positive result before the line item is considered a pass result.
To add this line item to a scorecard, see the following help articles.
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