The Advantages of Analytics-Enabled Quality Management

By Donna Fluss

For most of the past forty years, contact centers have performed quality management (QM) the same way. Calls are identified for evaluation from recordings, and then a QM specialist listens, assesses, and completes a monitoring form for each of them. While traditional QM applications have been enhanced over the years, the QM process remains highly manual, even in the best of cases.

QM Challenges

A major challenge facing contact centers is that there is an increasing variety of interaction types, and they are coming from a growing number of channels, including calls, emails, tweets, SMS, chat, social media, and more. While many companies perform QM on their calls, too few apply this practice to other channels. This means that companies receive an incomplete view of why people are contacting their organization, how well agents are performing their job, and whether customers or prospects are satisfied with their experience. Companies must perform QM on interactions in all channels and evaluate many of them; otherwise, they won’t know what is happening with their customers, and they may be out of compliance with regulations without knowing it.

The Future of QM

Analytics-enabled QM (AQM) is the future for QM and can address many of these issues. DMG defines AQM as “an application that leverages interaction analytics, business rules and automation to identify, classify, and score as much as 100 percent of voice and text-based interactions based on defined quality criteria.” At the same time, AQA measures critical interaction components to evaluate agent performance and to assess their impact on the customer experience and customer effort. 

An important feature of AQM is that it “understands” what customers are saying; it identifies customer needs and wants, automatically surfaces trends and operational opportunities, and spots problematic interactions in which agents do not comply with an established script, policies, procedures, or other requirements.

Artificial intelligence (AI) and machine learning technology are being incorporated into AQM modules to further enhance their effectiveness by enabling them to automatically evaluate interactions and to identify new and emerging trends and opportunities. Additionally, sentiment analysis is being applied to interactions handled by the AQM module, which enriches these solutions with a new level of analytics and helps companies to better understand their customers’ reactions to their policies and procedures.

Why AQM Is Better Than Traditional QM

There are many reasons why AQM is substantially better than the traditional method of performing QM:

  • It can automatically review up to 100 percent of calls and text-based communications.
  • It can provide feedback on a timely basis.
  • It can identify and assess the importance of each issue, regardless of the channel in which it is received.
  • It can surface emerging issues and opportunities not previously known.
  • It can identify coaching opportunities at an employee and group level.
  • It can automatically schedule and deliver individualized coaching.
  • It realizes all the benefits of traditional QM, including improving the effectiveness of the contact center, reducing operating expenses, and improving the customer experience.

Why AQM Adoption Is Slow

The adoption of AQM applications is very slow, despite its many benefits. The reason for the market’s reticence to purchase this solution is that it is expensive. AQM is an application provided by speech analytics vendors. To acquire AQM, end users typically must buy a complete speech analytics solution and then spend more to purchase the AQM package. The sad story is that a high percentage of end-user organizations that purchase a full speech analytics solution do not have the budget to add on AQM.

How to Get Enterprises to Invest in AQM

If vendors want to open the large addressable market for AQM, they need to reduce the price for this add-on module, include it as a standard component of a speech/text analytics application, or improve its business case and return on investment. Most prospects for AQM, including the thousands of companies that have already invested in speech or text analytics, have made it clear that they are not willing to pay a large incremental fee for an AQM add-on module.

Final Thoughts

The benefits of using AQM are far-reaching and include valuable contributions to the enterprise, contact center, its agents, and customers. With the addition of AI, ML, and sentiment analysis, the benefits are increasing, and interest in these solutions is growing, particularly as contact center leaders question the efficacy of their traditional QM applications and practices. As soon as the vendors come up with more appealing and attractive pricing for their AQM solutions, this market is expected to take off.

Donna Fluss is president of DMG Consulting LLC. For more than two decades she has helped emerging and established companies develop and deliver outstanding customer experiences. A recognized visionary, author, and speaker, Donna drives strategic transformation and innovation throughout the services industry. She provides strategic and practical counsel for enterprises, solution providers, and the investment community. 

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