Leaders Need to Pay Attention to AI-Powered Analytics

Shawna Malecki shares insights on why contact centre leaders need to pay attention to their AI-powered analytics.

Today’s contact centre analytics can give leaders unprecedented insights into operations and identify if they’re delivering the experiences customers expect and value.

Artificial intelligence (AI) can take standard contact centre data and transform it into actionable information about customer behaviour, agent performance, and operational efficiency – key information that can’t be ignored for the modern contact centre.

Contact centres have always been rich with data, but it can be difficult to piece together a holistic view of performance based on different data points on multiple, separate reports. And consolidated dashboards are great, but they often don’t provide the “why” behind the metrics.

In today’s “experience economy,” organizations need to maximize the value of contact centre data. Artificial intelligence, which is ideally suited to analyse massive quantities of data, is the right tool to help leaders identify where to focus their attention and resources.

The Many Benefits of Contact Centre AI

Artificial intelligence can now be found all over the contact centre. AI can be leveraged by core contact centre applications to make processes and experiences better, more consistent, and more accurate.

AI can help you anticipate and predict customer needs so you can provide the more personalized service that customers are craving.

And it helps save customers time by getting them the information they need faster. AI can even make self-service easier to use by allowing the customer to tell the IVR or chatbot what they want, instead of having to press the keypad or say specific phrases.

Customers aren’t the only ones who benefit from AI – artificial intelligence can also improve the agent experience (AX). AI can be used to handle repetitive, routine transactions, which frees up agents to handle more complex, value-added interactions.

You can also use it to coach your agents in real time, on every call so they’re constantly improving their performance, skills, and customer experience.

By using AI to deliver faster, easier experiences, more of your customers will use your self-service more often, which gives your business a better return on investment. And if you can increase your adoption and containment, your per-interaction costs will go down.

According to Gartner, interactions that require a live agent cost an average of $8 per contact, while self-service channels can cost about 10 cents per contact – that’s a huge savings!

Artificial Intelligence can also make forecasts and schedules more accurate, enhance interaction routing, and flag potential compliance issues. It’s truly transforming the way contact centres operate.

Keeping Tabs on Customer Behaviour

We all know the importance of customer experience as a differentiator in today’s economy. When you consider proactively improving the customer experience, you want to be able to spot and drive continuous improvement.

But customer behaviour is constantly evolving. Preferences and perceptions change, budgets shift, and formerly loyal customers begin to show signs of churning.

AI-powered interaction analytics can help contact centres keep their finger on the pulse of what their dynamic customer bases are thinking and feeling.

Interaction analytics tools are able to zero in on keywords and phrases to identify contact drivers and potential problems. For example, if the phrase “couldn’t check out” is suddenly used more frequently, that can alert the contact centre that there may be a problem with the website’s check-out functionality.

Leaders can then inform the web team about the issue and provide special instructions to agents. Proactively addressing emerging issues reduces the number of impacted customers and also enables organizations to avoid issue-related contacts.

Interaction analytics’ capabilities aren’t just limited to problem identification. Products and services also play an important role in CX. Product quality is important, but so is addressing an unmet need.

AI analytics can also help identify gaps in the product portfolio, which is useful intel for product development teams. Additionally, analytics tools can inform agents about up-sell and cross-sell opportunities.

When handled well, introducing customers to products they want or need can enhance the customer experience.

Businesses can also use interaction analytics to determine customer sentiment. This AI-powered tool is able to analyse every interaction from every channel and identify how customers are feeling and what they’re thinking.

For voice interactions, it considers factors like voice pitch and volume, length of pauses, and whether agents and customers interrupt each other to determine if a customer is frustrated, happy, angry, etc.

Because sentiment scores can be calculated at the individual customer level, supervisors can close the loop with unsatisfied customers by calling them and trying to make things right.

Sentiment scores can also be tracked by an agent to identify top performers and development opportunities. Expivia has found success by tying a portion of agent compensation to sentiment scores.

Agent Assistance

In the process of analysing the insights provided by contact centre analytics tools, leaders may determine their agents need additional support for demand swings.

AI and bot co-pilots provide continuous assistance during voice and digital interactions, turning every agent into a specialist with all the information they need at their fingertips. Additionally, these solutions provide personalized coaching without needing to add additional supervisors.

No matter how good your self-service channels are, many of your customers will want agent assistance. Phone is still a very popular and widely used channel.

AI can help optimize these voice interactions by providing agents with real-time information, workflows, and turn-by-turn guidance. This will help eliminate those long pauses when agents are searching for information.

And AI-powered interaction guidance can coach agents on their soft skills while interactions are unfolding. Coaching agents on every interaction may be every supervisor’s dream, but it’s impossible to do without AI.

Artificial intelligence tools can guide agents on the best responses to provide, how to adjust behaviours to build better rapport and improve CSAT, and even remind them if they are talking too quickly.

And this is done in real time, not a few days later when it’s too late for agents to change the outcomes of the interactions.

AI-enabled guidance and coaching can yield several meaningful benefits, including:

• Lower handle times
• Higher first contact resolution rates
• Improved customer sentiment and satisfaction
• Increased agent engagement and satisfaction
• Improved customer experience

How will you know if you’re realizing these benefits?

Some of the data will come from standard operational reports, and your analytics tool can fill in the gaps by providing insights about customer sentiment and CX. Contact centre analytics software doesn’t just help leaders identify improvement initiatives – it also helps measure the effectiveness of the improvements following implementation.

AI Analytics Can Identify Operational Efficiency Opportunities

While reviewing analytics insights, leaders might also identify opportunities to create efficiencies by improving self-service and streamlining agent processes Once again, AI-infused solutions can provide the capabilities needed to make contact centres run like well-oiled machines.

Improving Efficiency With Virtual Agents

Natural language processing (NLP), a form of artificial intelligence, is transforming self-service experiences, resulting in higher success and containment rates. NLP enables “machines” to understand human language and reply to people conversationally, creating a natural interaction experience.

Your average chatbot doesn’t use NLP. Rather, it’s rules-based and uses decision tree logic to recognize inputs and determine responses.

These types of chatbots are great for executing narrowly defined transactions, like providing account balances and order statuses, but they stumble when a customer uses a word they aren’t programmed to recognize.

By contrast, a chatbot with conversational AI (enabled by NLP) can understand what a customer says, rather than limiting their input to specific words or phrases. This reduces customer friction – they just speak naturally, and the bot knows what to do.

Conversational chatbots, known as virtual agents, can be used in voice and chat channels. A virtual agent complements an agent and, depending on what you need, they can handle certain steps or the entire interaction. They typically act in the role of an “agent” and perform at least some parts of an interaction.

For example, rather than having a customer wait on hold until a live agent is available, the virtual agent bot can start the call right away.

They can greet customers when they call, identify and authenticate them, collect information, and then hand the call and any information they collected over to a live agent who can handle the more complicated elements of the interaction.

When DSW implemented a virtual agent to authenticate callers, they slashed handle times by two minutes while increasing customer satisfaction.

Virtual agents can also collect caller inputs, copy data from one application to another, or do simple data entry to eliminate after-call work. For example, they can document the interaction in the CRM system so agents can skip that step and start on the next interaction.

This type of assistance reduces handle times, increases agent capacity, and allows agents to focus on strengthening relationships with customers rather than worrying about data entry.

But the benefits don’t end there. Virtual agents also improve data integrity because the potential for human error that comes with manually cutting and pasting information from one system to another is removed.

Virtual agents don’t make typos! Increasing data accuracy means staff doesn’t need to spend valuable time correcting mistakes and customers won’t call about transaction errors.

Improving Efficiency With Conversational IVR

IVRs don’t need to be integrated with virtual agents to provide more natural and effective self-service. Conversational IVRs recognize and process what callers say via natural language understanding.

Like the conversational AI for chatbots, conversational IVR lets a caller speak naturally to the IVR. This means callers don’t have to navigate long menus, “press 1 for sales,” or only say certain words or phrases, which removes friction from the transaction.

Conversational IVRs can also remove blind transfers. Half of customers who attempt self-service eventually transfer to an agent to finish the transaction.

These customers may be frustrated with their failed self-service experience and having to completely start over with an agent can make a tense situation worse.

With conversational IVRs, if the customer does opt to speak to an agent, that agent will have the full history of the interaction up to that point.

They’ll see all the information the IVR gathered so they don’t have to annoy the customer or waste their time collecting all of that information again. This capability can reduce handle times, increases productivity, and, best of all, improves CX.

Final Thoughts

Contact centre analytics can provide the insights contact centre leaders need to remain competitive in the experience economy. AI analytics highlights new issues, identifies product gaps, and provides insights about what customers are thinking and feeling.

These capabilities can also highlight opportunities to provide agents with more assistance or enhance self-service solutions. And on the back end, analytics tools can tell you if your initiatives to improve CX, agent effectiveness, and operational efficiency are working.

This blog post has been re-published by kind permission of NICE CXone – View the Original Article

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