Automated Call Auditing for InsurTech Contact Centre

Good customer rapport is the most important tenet for any organization, but it has even more deeper implications in Contact and Call Centres. To understand the wants and needs of the customer in line with their company’s regulations, is a very hard process for the call centre agents. Also a massive amount of data is generated every working day, which makes it practically untenable for manual data analysis. With all these problems in mind, creating an automated system for customer analysis creates a more sound environment for boosting agent productivity and customer relations via proper communication.

India

Client

InsurTech Call Centre

 

CLIENT

Our client is an automotive insurance firm based in India, which also has a con- tact centre support regarding customer FAQs and other services. They handle nearly thousands of calls each week, so they contacted us to create a model that will boost their customer satisfaction rates and also for streamlining their targeted coaching measures for agent training primarily.

There is also support given for analyzing performance metrics for both the customer-side and client- side of the company. They also need a system of audit, in the structure of a scorecard for easy performance resolutions on the agent side. All of these components work in tandem to boost both the regulatory and market-oriented objectives of the firm.

 

CHALLENGE

With these goals in mind, we had to focus on identifying the fields of challenges that occur with regards to the creation of our model.

  • Identification of Speech Patterns - The model should be able to understand and have coherence towards the various syntax that occurs in human speech.

  • Caller Data Storage - The data that’s being perused needs to be safe- guarded and properly stored, because of it’s immense size there definitely might be some problems with it.

  • Phrasal Analysis - The expanse of phrases used between customer-agent interactions has to streamlined and condensed for analytical purposes.

  • Audio Processing - The quality of the sound data used for analysis has to be maintained even varying quality of recordings are present on the customer’s end.

 

SOLUTION

Solution summary

  • Audio analytics is done to derived the core structure of the conversation between the customers and the agents.

  • Discursive/Conversational analysis is used to make sense of the dialogue patterns used by both parties.

  • Conversational analysis further underlines the hidden emotions and other reaction-based content within interactions.

  • Peformance analysis is utilized for organizing the systems of audit used by the model; process, compliance audit, and customer experience.

  • Finally, a field of depth analysis is run. This gives us more details pertain- ing to customer security.

With the challenges in mind, we arrived at the following steps for a solution :

  1. Audio analysis is properly tuned to fit the syntax of both the manner of speeches used by the customer and agents.

  2. The criteria for call reviews are expanded, everything from agent quality to churn potential are analysed.

  3. Conversational analysis is coupled with phrasal and structural analysis for a two in one solution, leading to a concise understanding of the differing parameters.

  4. Critical parameters regarding policy and payment details can be discerned automatically.

  5. A scorecard is created to definitively list out the auditing procedures that affect performance analysis. This scorecard includes the procedures for understanding the audits done for overall processes, customer’s experience during the call, and compliance with rules and regulations. This scoring system is very concise and immensly accurate.

 

RESULT

We have, after perusing various strategies created a model that is robust and can churn data to the maximum for very concise output. The efficiency and new patterns for agent training is increased, with also an substantial effect in customer resolution. These create an environment where growth is absolute and failure rate is very minimal. Conversational analysis also pushes a very qualified and humanistic manner to process data that will reach out to every customer irregardless of their manner of expression. This model will definitely make a massive impact on not just the profits but also public and personal relations associated with the company.

  • 82% increase in customer satisfaction rates.

  • 100% increase in agent targeted training procedures.

  • 5% increase in case closing rates.

  • 18% increase in net promoter scores, which pertains to positive customer experience and business growth increment.

  • 12% increase in first and immediate call resolution rates.

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