Home > Press Releases

How Contact Centers can use the latest AI to improve customer outcomes and reduce call volume

FOR IMMEDIATE RELEASE

PRESS CONTACT:
Dan Somers
Warwick Analytics, 35 Kingsland Rd
London, London E2 8AA
020 7060 6990
dan.somers@warwickanalytics.com
http://www.warwickanalytics.com

How Contact Centers can use the latest AI to improve customer outcomes and reduce call volume

Understanding why outcomes vary between agents can prove difficult, requiring proper categorisation and real-time tracking of outcomes. A contact center provider, handling thousands of customer contacts on a daily basis, recently used the latest in AI text analytics to improve customer outcomes, set intelligent KPIs for their agents, and simultaneously reduce overall contact volume.

The contact center used a Machine Learning platform called PrediCX from Warwick Analytics that automatically classified their interactions for early warning signs, insight and the next-best-action to be derived.

Sentiment and sales intents models were also built to allow the reassessment of existing agent KPIs, maximising their performance without the need for CSat surveys.

New topics

New topics and models that were demonstrably more accurate and relevant were produced, with previous ‘bucket’ categories being reduced by over 50%. Newer, smaller issues (potential early indicators of problems) were identified that would previously have been overlooked, allowing agent effort to be directed towards solutions instead of categorising.

Agent performance optimised

Models were created for standard sentiment, and emotional cues or ‘intents’ such as: ‘didn’t understand’, ‘bad previous advice’, ‘previous agent disconnected’, ‘repeated problem’, and highlighted issues such as other agents not performing, excessive customer effort, repeated issues, and likely to churn or unfulfilled sales.

These issues were used to redesign elements of the customer journey as well as coach agents as one of the issues surfaced was a 50% disparity between best and worst performing agents.

Capturing and measuring these parameters for first time allowed the company to redesign its agent coaching plans and to move to real time information and templates.

Additionally, cross referencing this output with the topic model highlighted contact types and situations that were problematic and allowed the platform to advise on the next best action. This happened in real time, advising the agent what to do next if a customer expressed particular intent in a certain situation as well as allowing for partial automation.

Conclusion

Immediate operational improvements included a 20% increase in customer satisfaction, 5% reduced demand, 5% saved from improved early warnings, 25% increased efficiency in AHT and a 20% improvement in scripts and templates.

The output from the models allowed them to make changes, immediately measure the impact and also underpin a business case for automation. Customer satisfaction with agents increased markedly, and coupled with the highlighted opportunities for operational improvement, the client was able to provide their customer with a far better service.

41% of their existing contact was shown as being avoidable or able to be fully automated, significantly reducing costs and increasing scalability for the client.