As traditional contact centers increasingly adopt more advanced technologies, the power of artificial intelligence (AI) is helping to modernize and improve call centers around the world. According to Global Industry Analysts, the value of AI is expected to increase by 120% year-over-year and over 13x during the next eight years, per Grand View Research. The customer service industry is well-positioned to benefit from this growth — empowering contact centers to elevate their customer experience strategies by utilizing AI.
In the last decade, the business world experienced a significant shift from on-premises to the cloud. In a similarly important way, contact centers around the world are now experiencing an important step change from no-tech or low-tech processes to AI-powered solutions that pair the best of technology with human talent.
Using AI technology, contact centers now have the ability to evaluate 100% of agent conversations across:
- Voice calls
- Video calls
- Live chat
- Social media
Today, there is now the ability to combine contact center tools such as performance evaluation, autonomous coaching, and semantic intelligence with the capabilities of AI. By harnessing these resources together, contact centers can make more impactful decisions for improving team performance and customer satisfaction across the board.
Trends in Call Center Quality Monitoring
There are several significant trends in call center quality monitoring and AI. Artificial intelligence is altering the way we know what’s monitored, how we interpret data, the systems we use, and the approaches we take to the data.
1. Broader, more accurate call center quality monitoring
The average number of calls monitored in the contact center is an abysmal 1-2%. The integration of AI into the contact center monitoring process is helping managers cover the entirety of that gap. Using AI, contact centers can track and evaluate 100% of agent interactions across all channels available.
2. Faster case selection & reviews
The power of AI in call center quality monitoring is due to the availability and utility of centralized data. Contact centers that have upgraded from outdated tools to AI technology are seeing faster results.
QA teams can automatically:
- Surface the most critical agent-to-customer interactions
- Highlight key moments to focus on in every conversation
- Tag different parts of conversations for key behaviors
- Filter conversations by category
The agility of AI helps contact centers learn the most valuable monitoring insights and apply them to arrive at informed decisions.
3. Conversational intelligence
Call center quality monitoring is only as valuable as the insights teams can extract from it. Keyword-based systems that use natural language processing (NLP) lack the ability to interpret customer intent. As a result, NLP systems tend to be inaccurate and produce many false positives and negatives.
A semantic intelligence engine using natural language understanding (NLU) truly comprehends customer intent. Semantic intelligence is more accurate than NLP. QA teams no longer need to manage giant keyword lists with hard-coded responses triggered by a customer using a keyword.
Human beings can use a wide range of words, sounds, tones, combinations of words, and sentences to convey a similar meaning. Legacy NLP engines required an entire team to manage and maintain a complex list of keywords to categorize conversation topics: a thankless effort that always led to inaccurate conversation topic categorizations. Until the advent of new generation NLU models, true speech understanding was not attainable. Level AI, leveraging NLU, is changing the game for contact centers.
4. Centralization of contact center data
Let’s say your company has high-quality monitoring, accurate data, and the ability to categorize the data effectively. The next important question to ask is: How much good can the data do if the teams that need it most can’t access it?
That’s where the power of centralization and report-centric systems comes in. New analytics tools now allow for the building of customized reports for QA teams, individual agents, their managers, and executive reports.
For example, if Marketing wants to know what customers are saying on the phone, it is possible to generate a report for them at a set cadence. If Product Development wants to know which features are working well, they can get a custom report with that data sent to them. For enterprise-level companies, data is paramount to their competitive advantage. The centralization of that data is a game-changer.
5. Omnichannel approach to data
Along with centralizing your data, it’s also essential that teams can view and analyze that data in the most optimal ways. No data point exists in a vacuum — every point impacts others, and teams must see how the data interacts.
Using a multi-channel approach, data is connected only to each individual channel. So, you’ll have one report for voice calls, one for chat, and so on. AI technology helps teams mix and match data to get a complete view of the contact center’s performance across all channels. Agents and QA teams also have individual dashboards to track important metrics.
Agent Observation, Evaluation, and Coaching
Let’s take a look at how our team at Level AI sees AI call center quality monitoring improving the agent experience.
1. Agent Screen Recording
Contact center agents work in complex environments and have to juggle many tools to perform their work effectively. Some of the most common day-to-day issues facing contact center leaders include:
- Tool proliferation
- Knowledge availability
- Tracking compliant behavior
- Agent engagement
To that end, having agent desktop monitoring is a critical capability.
Agent screen recording is exactly as it sounds; it records on-screen behavior during customer interactions. The software captures the screen from the moment a support ticket is assigned until the moment it’s resolved. These case recordings offer a treasure trove of information to QA auditors and contact center leaders.
2. Agent Performance Scorecard
Contact center agent scorecards measure an agent’s compliance with its internal business standards. The data includes agent performance, customer feedback, and communication preferences. Companies can see where their agents are performing well and also the areas where they can improve.
3. Agent Coaching
AI enables autonomous learning for agents by checking every interaction across voice calls, email, and chat. The ability to coach agents using AI is important and something that we do at Level AI. Without the help of 1:1 human coaching, agents can:
- Review their scores
- Keep track of their most important metrics
- Learn how to improve performance
Agents get helpful tips, both in real-time and also post-call, so that during and after conversations there are opportunities to improve their performance. They also receive strength and weakness totals based on the data gathered from all conversations.
For all the reasons we’ve discussed here, and more, AI is a game-changer for contact centers. To learn more from the Level AI team about the power of AI for call center quality monitoring and other benefits, request a demo with Level AI today.
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