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Customer interaction: Is your contact center compliant enough?

Mohan Raj Manager, Customer Service Analytics Practice Team
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January 31, 2017 - In our rapidly evolving business environment, the volume of customer interactions with organizations keeps growing. The number of reasons why customers are reaching out to the organization is also increasing. While phone continues to be the most preferred channel, contact volumes across other digital channels are also steadily increasing. During this vigorous phase, it's critical to ask if we are taking sufficient measures to check for non-compliance.

There are industry-specific compliance standards, like PCI for card payments, or FDCPA for collections which details the measures a customer facing organization should take to ensure compliance . Process-level changes are made to contact centers to uphold these standards, but how can we make sure 100% adherence during interactions with the customer? In order to fully appreciate the scale of repercussions related to non-compliance, consider this fact:

In 2014, top 20 large US and EU banks combined together paid $4.4 billion in settlement and fines. This is 25% higher than the previous year. Quite recently an US bank was ordered to pay more than $100 million in fines due to compliance related issues1.

Is the quality audit (QA) process of listening to a small fraction of calls good enough to capture non-compliance on this scale? The answer may not be an easy one. While the QA process captures a lot of parameters around agent performance and compliance, its range of coverage is narrow. At the same time widening this range will result in additional cost. However, now we can break this status quo. Thanks to Speech Analytics. A significant percentage of these activities can be automated; the quality audit and compliance process can be made more robust.

Customer service organizations generally record nearly 100% of customer interactions. But they only use a small percentage (<10%) of the recordings to measure agent performance and compliance. Speech Analytics framework helps you break this status quo. Now you can utilize up to 100% of your call recordings to monitor your agents.

Figure 1: Traditional call quality audit process

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Speech analytics frameworks can be customized to achieve a whole range of objectives. For example, in the healthcare industry, listening to customer interactions and reporting adverse events may be a key business objective. Compare this with the banking industry, where monitoring regulatory compliance may be paramount. To be sure, the underlying solution cannot take a one-size-fits-all approach, but rather must be customized on a case-by-case basis.

CFPB (Consumer Finance Protection Bureau) outlines the process that a collection organization should follow while interacting with consumers notified under Fair Debt Collection Practices Act (FDCPA)2

Figure 2: Emerging areas in compliance

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Any new solution should work in tandem with existing KPIs and reporting solutions. This is fundamental for the insights from the solution to be actionable, and for the solution to gain wider acceptance. Consider the below case in which we use speech analytics to measure the agent's communication compliance. The agent highlighted has the highest score. However, if you look at the AHT, it is one of the highest. The key takeaway is that we need to look at the data holistically to make the insights relevant. 

Sample analysis

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In summary, speech and text analytics in due course will emerge as a de facto standard for measuring interaction compliance in a contact center. The QA process will move from the manual mode to a robust automated process powered by a speech engine. In adopting such a solution, organizations need to be cognizant of a few recommended practices to ensure success:

  • The solution framework should be customized to achieve specific business imperatives
  • The process should be automated and underlying statistical models must be tuned periodically
  • The insights from the solution must be aligned with existing performance metrics
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