What is Intelligent Scoring?
Clarabridge Intelligent Scoring is a patent-pending, proprietary feature that allows customers to distill many complex criteria into a customizable, unifying measurement.
Clarabridge’s Intelligent Scoring is a breakthrough in omnichannel interaction analytics that can automatically evaluate and score both conversational and non-conversational data sources.
What’s a score?
We see scores all the time, often without questioning their origin. For example, consider safety inspection ratings at restaurants; we’ve been socialized to understand that an A is better than a B, which is better than a C, and so on. This understanding helps us decide which ratings we’re willing to risk, and which we might pass on when choosing a restaurant for a quick lunch. Let’s dissect why these ratings are so useful.
Behind the scenes, over fifty different criteria—each weighted according to their likelihood of causing foodborne illness—are distilled into a single score, which then is ranked on a scale and placed in public for potential diners to see. Rather than forcing diners to sift through pages of information, the single measurement quickly displays a cohesive story about the state of the facility so restaurant-goers can make their own informed decisions about where to eat.
What counts as an Intelligent Score?
Intelligent Scoring automatically distills many complex criteria into a single unifying score, harnessing features across the entire Clarabridge product suite. You can leverage enrichments like effort and sentiment to create customer experience scores without surveys, build complex conditional logic, and deploy root cause features like Drivers and Outliers to ensure your index captures what you’re aiming to track.
In a nutshell: if you can ingest it into Clarabridge, you can score it, regardless of the data source. Intelligent scoring can be used to measure agent quality, customer experience, sales efficacy, legal risks, or even to monitor consumer reactions to current events.
Intelligent Scoring answers questions like:
- Which contact center agents are struggling to answer questions about new products?
- Which agents are better equipped for chat customer service as opposed to phone channels?
- How can I improve my agent training materials based on score discrepancies between new agents and more tenured agents?
- Which interactions have multiple important indicators of legal risk?
- Which customers are good candidates for proactive outreach based on poor experiences with company policy?
- Which customers are having poor experiences with my brand, despite having interacted with highly trained agents?
- Which current events are my customer base reacting most strongly about, and how can I future-proof my organization’s policies accordingly?
- Which agent behaviors most correlate to high customer satisfaction?
Clarabridge’s approach to unified measurements with Intelligent Scoring includes:
- User-defined, customizable criteria based on your intimate knowledge of your organization’s business rules
- Automation of time-consuming, manual scoring processes
- Objective scoring processes that remove the inherent biases of traditional scoring methodologies
- True omni-channel, unified scoring across all data sources with Clarabridge’s best-in-class natural language processing and understanding engine
- Access to industry standard category models across verticals to stand up the solution and drive value more quickly
- Transparent, explainable scoring rubrics including custom weights, relevant importance, and desired result of rubric criteria
- Targeted, well-informed rubric weights based on desired outcomes by leveraging additional root cause features like Drivers and Outliers
- Clear tracking of trends over time to drive continuous improvement
Debunking Natural Language Processing
We’re on a mission to demystify a complex term: Natural Language Processing. Natural Language Processing (NLP) is a field of study that focuses on a computer’s ability to interpret human language in order to process, analyze, and extract meaning from large volumes of natural language text data.Download Now