With speech analytics, no two projects are ever the same. Which is why we approach every project as a fresh challenge, customizing our approach to create the best outcomes for our clients. Here are just some of the results we’ve achieved:
Developing a complex application with 3,000 weighted business rules that predicts customer churn better than the cable company’s propensity model
Analyzing non-linguistic prosodic speech patterns which identify specific medical conditions such as Alzheimer’s and depression
Increasing sales by 2.5% by a professional Referral Network which uses speech to identify the most successful sales techniques
Dropping refunds by 2.8% for a national shoe retailer
Cutting inbound calls by 12% by identifying numerous unnecessary customer efforts
Immediately identifying more than 50 violations from compliance agents
Reducing AHT by 16 seconds for a national retailer by identifying coaching opportunities.
Slashing repeat calls pertaining to a specific product by 47% by improving screen-sharing troubleshooting strategies
Deeper knowledge leads to better insights
Speech and text analytics are complex fields that involve a lot of moving parts. It’s the science behind the art. Part technology and part creativity. Part business and part strategy. To do it well, it requires expertise in a long list of skills, including:
Strategically understanding the current situation
Listening to recordings to interpret the true voice of the customer
Reverse engineering calls with known outcomes to create predictive models
Developing keyword and phrase combinations to ensure contextual accuracy
Interpreting linguistic patterns and associated metadata
Testing the fluency of acoustic measures like speed/tempo, sentiment, talk over, silence, and prosodics
Applying MainTrax’s business rules, variances, weights, thresholds, and scoring algorithms
The Process Employed by MainTrax to Improve Agent Performance:
Leading a discovery meeting with executive sponsors and key stakeholders
Performing a Content Audit of random audio files to benchmark performance occurrences and identify technical issues that may impact results
Reverse engineering calls of successful agents and compare those with the characteristics of bottom performers
Forming a rules-based hypothesis that reflects call outcomes
Developing sets of language patterns and complex queries that appear to have greatest impact
Evaluating and incorporating other available metrics including reason codes and customer sentiment scores
Calibrating automated outcomes with manually scored calls
Developing a champion vs. challenger approach
Designing and testing customized scoring algorithms that stratify the results
Testing and verifying complex business rules that generate automated predictors/prescriptors
Developing post-deployment utilization strategy with change agents to maximize value
Rolling out to specific teams and measuring impact
Adapting and expanding to other teams
Let’s work together to achieve your business goals.
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