Will Call Center Quality Assurance Jobs Be Replaced By Technology?

Will Call Center Quality Assurance Jobs Be Replaced By Technology?

 Technology is changing the way we assess quality.


Call center quality assurance jobs are being revolutionized by innovative technology. At the forefront of this innovation is the development of speech analytics, which has the potential to totally transform the way contact centers analyze calls and mine data.


This technology can now determine what is said during a call, who said it, and even speculate the emotional state of each person by analyzing vocal inflection. What’s more, analytics software programs designed for call center QA can compile this data on a massive scale, revealing trends, problems, agent competence, and customer satisfaction to an extent that was once unimaginable. This has profound implications for agent training and development, and for the fate of call center quality assurance jobs themselves.

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Emerging quality assurance technology cannot replace the human workforce

Researchers and analysts have begun to speculate whether this technology has the potential to make the human workforce of call centers and quality assurance obsolete. Although most of the operational tasks of quality assurance in call centers may eventually be taken over by machine, a human workforce will still be needed to put this wealth of data and analysis to productive use for teams of human agents.


Despite speculation by researchers and market analysts that call centers are the new “factory floor,” what makes a call center different than a fully-automated factory is the central role of the customer.

Customer interaction is not a product that can be poured into a mould, dropped onto a conveyor belt, boxed, shipped, and sold. Human capital cannot be digitized, compartmentalized, and modernized like a saleable commodity.

Present in any human interaction is the need to listen intently, think critically, and respond appropriately. Call center quality assurance that is fully computerized cannot possibly pick up on the subtle nuances of human interaction that characterize meaningful communication

**How to train agents to personalize customer service**.


A quick search of call center quality assurance jobs reveals that the skills and tasks required for this position extend beyond the capabilities of even the most advanced machine.


A representative sample of such a job lists responsibilities such as “develop and implement improvement strategies,” “manage communication,” and “lead calibration meetings”. As sophisticated as modern technology may be, actions like “develop,” “implement,” “manage,” and “lead” can only be executed in a meaningful way by a complex and competent human being.


The term ‘specialist’ in itself reveals the complex nature of this role. Leadership cannot be fabricated by an algorithm, prepackaged, and sold to a team of agents. QA specialists will still be in demand despite advances in technology.


What technology cannot capture

Speech analytics technology can offer a wealth of information about interactions between agents and customers.

**Speech analysis: The future of QA monitoring**


For example, this technology can transcribe entire conversations and log them into a searchable database. It can detect and scalably report incidents in conversations such as agents and customers talking over one another (an indication of a dissatisfied customer) or long pauses in the conversation (indicating a knowledge deficiency on behalf of the agent).  It can even assess calls in real-time and suggest best-next-steps for agents, increasing the efficiency and effectiveness of even the greenest of agents.


But despite its amazing capabilities, QA technology cannot entirely replace a specialized human ear. For example, although a computerized QA system may classify an agent’s rote, on-script interaction as successful, there may be more to the story.

Customers today are turned off by standardized interaction. Thus, whether or not the agent followed the script to a T may not be a good indicator of the customer’s overall impression of this interaction.

According to Help Scout, the most requested improvement in customer service is better human service. Customers want to feel they are being spoken to as individuals. As sophisticated as speech analysis technology may be, capturing the empathy, enthusiasm, sensitivity, well-timed and appropriate humor, or responsiveness of a human interaction is a task suited best to humans themselves.


Technology is an ally to call center quality assurance jobs

Instead of viewing innovative quality assurance technology as an enemy to call center quality assurance jobs, contact centers should embrace this technology as a valuable tool to add to their QA arsenal.


The scaleable potential of this technology is by far the biggest boon to QA specialists.

Rather than spend valuable time monitoring calls, spot-checking recordings, or compiling scorecard data, QA specialists can let the technology do this operational work for them.

This frees up more time for digging meaningfully into this data and developing, implementing, and managing team initiatives for improvement. Essentially, innovative QA technology has the potential to take the grunt work out of quality assurance, clearing the way for more substantial work to be conducted by QA specialists.


It’s not time to worry

Contact center jobs in the U.S. are growing at an increasing rate, with a net growth of nearly 8,000 jobs in the first quarter of 2015 alone. During this period and beyond, automation software, CRM systems, and QA monitoring software including speech analytics have been steadily developing, but the demand for high quality, onshore customer service continues to grow as well.

Innovative quality assurance technology has the potential to empower management and agents to make meaningful improvements to their overall operations. Most importantly, any improvement in contact center quality means a better experience for the customer, a result that everyone can agree is worth any growing pains along the way.

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