Data and AI Insights in Contact Center as a Service

Data and AI Insights in Contact Center as a Service

Data and AI Insights in Contact Center as a Service

CCaaS represents a range of potential benefits to companies in the modern landscape.

More than just a tool for big business, the contact center has emerged as a must-have investment for businesses of all sizes. It’s the front-line of any brand loyalty strategy, and an essential aspect of customer experience. CCaaS opens the door to contact center innovation like never before. With a contact centre in the cloud, you can better understand and serve your clients.

With hosted solutions in the cloud, companies of any industry can access the technology they need in an affordable, managed package, without the need for on-site expertise. CCaaS can align all of the data of business interactions in one, convenient, cloud environment. From there, accessing in-depth insights with the help of AI analytics is easier than ever.

Why Bring AI and Data Insights into the Contact Center?

Reports by McKinsey show that companies adopting AI for sales and human resources already achieved a revenue increase of 66% in 2019. Following the pandemic, companies are now interacting with customers on a wider range of channels. This means more conversations to keep track of, and more data to leverage when making crucial decisions.

A complex omnichannel contact center would be impossible to fully track and analyze manually. That’s why leading CCaaS innovators have begun to package analytics with AI algorithms and machine learnings into the contact center landscape. Through these tools, companies can easily pinpoint potential roadblocks in the path to better CX, as well as gathering valuable insights about their target audience.

Delivering truly unforgettable experiences in an age where CX is king means collecting and understanding as much vital information as possible in every conversation. AI strengthens our ability to harness data by not only collecting and managing the information we gather but refining it too. AI solutions can immediately check for replicated data, inaccurate reports, and damaged insights, ensuring that the insights you leverage are as accurate as possible.

The Growing Role of AI and Insights in CCaaS

A CCaaS environment is naturally well-equipped for a better quality of contact center analytics. The cloud environment can easily bring multiple channels together in the same environment, from social media messaging tools to voice calls. When data is collected and analyzed in the same environment, rather than having to be pulled from various tools, it’s less likely to have gaps.

Many CCaaS solutions today even come with API options and integrations so you can connect your CRM platforms, ERP technology, and other programs with your contact center. This allows you to analyze information from contact center interactions, and immediately load that data into your CRM environment for use when interacting with customers.

Some of the most recent trends to emerge in CCaaS AI include:

Natural language Processing and Understanding: Artificial Intelligence tools can now pull insights directly from the language spoken in the audio or video communication space. NLP technology and Natural Language Understanding can help businesses to get a better understanding of their customers, by tracking everything from trending topics asked about during customer service conversations and more.

These insights can offer a historical overview of trends in the business, or could even apply to contact center agent technology, providing employees with next-step suggestions based on an NLP analysis.

Predictive behavior analytics: Increasingly, to offer the best customer service, you need to do more than respond quickly to customer problems. Today’s businesses need to understand the issues their audience might face before they ever encounter them.

Because AI in a CCaaS environment can collect huge amounts of information and analyze it at speed, it’s also well equipped to make predictions about customer behavior based on previous actions. AI and predictive behavior analytics work together to help companies build stronger relationships with their clients. You can even add context to predictions with CRM data.

Sentiment analysis:

Increasingly, we’re learning that the emotions of buyers, consumers, and other people we communicate with can have a huge impact on their behavior. Sentiment analysis uses natural language processing to better understand the feeling behind what people say in a CCaaS environment.

This technology could be linked with automated alerts which informs supervisors and business leaders when an employee needs help dealing with an unhappy customer. The same solution might be effective in helping companies to understand which words and phrases are most likely to encourage or dissuade a sale.

Improved self-service:

Self-service is becoming a must-have feature of the current contact center environment. However, to empower people to serve themselves properly, today’s companies need to ensure they understand what their customers need.

Using chatbots and IVR tools to collect information about commonly asked questions and discover how we can answer customer queries faster could be crucial to future self-service solutions. Combined with predictive analytics, companies could even use their AI solution to reach out to customers and offer a self-service way to perform an action (like renewing a subscription) at just the right time.

Hyper personalization:

Customers are looking for personalized and relevant experiences in a modern world. Hyper-personalization combines real-time data and AI to deliver content that is specifically relevant to the consumer. In the age of conversational AI, companies are rapidly collecting more data from NLP and machine learning tools which ensure the stronger personalization of various sales, marketing, and service efforts.

The right AI solutions in the contact center can also support agents by automatically surfacing information about a customer when they answer a call, taking details from the CRM about their previous purchases or preferences.

Combining UCaaS with CCaaS

Increasingly, companies are looking for opportunities to connect all of their communication tools in the same environment, from internal collaboration solutions to external tools for customer contact. Tools like a Microsoft Teams contact center can help organizations to access the best of both worlds within a single environment.

Using a Microsoft Teams Contact Center allows business leaders to offer their agents access to essential contact center tools, while still supporting back-end communication between specialists and agents. This allows for better resource utilization and improved knowledge sharing among agents, thereby enhancing customer experience.

Using AI and Analytics in CCaaS

The Contact Center as a Service environment isn’t just an answer to the growing need for flexible and scalable contact center environments. Used correctly, this technology can help you to better understand your customers and drive the kind of meaningful experiences that encourage long-term customer loyalty. As customer service continues to thrive as the number one tool for differentiation, AI and analytics in the contact center will soon be table-stakes investments.

Use Cases for AI and Data in CCaaS

Data is the most valuable tool any business can access for today’s customer-experience focused world. The more information you have about your company, your customers, and your everyday processes, the more likely it is that you can build a business that really speaks to your audience. Here are some of the industries most likely to benefit from AI in CCaaS.

Finance

Artificial Intelligence and data analytics in CCaaS have a lot of benefits to offer in the right circumstances. Accessing and analyzing data within the CCaaS environment in the banking landscape could help to reduce the risk of fraud. Personal data like biometric information and voice prints are much more effective at securing a person’s details than passwords and usernames.

AI solutions can even evaluate potential market threats on a massive scale, determining where security issues are most likely, so companies can act quickly. AI and data collection in the banking and finance environment also has a powerful role to play in credit solutions. Around 77% of consumers today prefer paying with credit or with debit cards over cash, but financial institutions are often subject to complex processes they need to consider before they can offer credit to each consumer. With so many of life’s requirements hanging on credit, AI could be a crucial tool.

Artificial Intelligence solutions in finance are already helping credit lenders and banks make better decisions from an underwriting perspective by utilising a variety of factors that accurately assess and understand traditionally underserved borrowers, like younger consumers. Auto lenders with access to machine-learning-based underwriting tools can cut losses by 23% annually and even predict risk too.

Government

Government sectors are increasingly using more data and AI in their activities on a massive scale. Examples of AI analytics and data appear most frequently in executive organizations throughout the governmental space, including tax offices, financial bodies, enforcement groups, and so on. For instance, the data collected when talking to citizens through a CCaaS solution about crime in a certain area can be collected and stored in the cloud.

This information, stored without personal identifiers to protect customer details, can be used to assess the criminal trends emerging in certain parts of a country or city, helping government groups to determine where the most problematic areas are. This could lead to the better use of resources and budget in the areas that need it most for future government operations.

Other examples of governments using data to make their community a better place could include the introduction of new citizen services based on information discovered about citizen complaints and issues. If citizens are constantly struggling to access public transport, further investment could be shifted to this area the next time funding is available. Governments can even use AI and data management to make more accurate decisions about where personnel should be distributed in each government sector.

Healthcare

Data and AI have some of the biggest impacts in the healthcare environment already. The way we collect information and analyze it with machine learning tools can make it easier to determine potential treatments for chronic illnesses and diseases. AI innovations are even becoming more effective at collecting and recognising information for the faster diagnosis of various conditions in people around the world. In the healthcare CCaaS environment, there’s also an opportunity for data analytics to provide a better level of patient service.

Accessing insights on the number of patient calls and appointment requests taken for a certain time period can show a hospital or care center when they’re most likely to have the greatest demand for skills. This could make it easier to determine when extra support needs to be accessed in the form of things like freelance doctors and consultants.

Information onto how many patients arrange appointments through different contact center avenues, like SMS, email, calling, and so on can also make it easier for administrators to determine how to devote future funding for the management of hospital environments. Data analytics can provide insights into not just how many requests and conversations move through each channel, but how often those conversations lead to a positive resolution.

Education

Today’s educational environment represents one of the fastest moving sectors in terms of digital transformation and innovation. Since the pandemic, remote and distance learning has skyrocketed, and educational facilities have begun to invest more frequently into tools that would allow them to manage their students wherever they are.

Using AI and data analytics in the CCaaS environment for education could be the key to teams learning more about the kind of educational experiences their students really want. This could significantly improve the chances of educational facilities overcoming high drop out rates, coming from an ineffective one-size-fits-all approach.

CCaaS solutions in education can also collect more information about the kind of service that today’s students need to thrive, particularly if they’re learning at a distance. Additional resources may need to be put in place for those dealing with mental health issues. Self-service solutions may need to be available for people dealing with software and hardware issues outside of the school environment.

The correct collection of data will even play a role in helping higher education institutions to determine where their students come from, and what kind of characteristics identify these individuals, for better marketing in the future.

Retail

Data and AI in retail are gaining more attention all the time. In the retail landscape, AI insights collected from CCaaS interactions can tell companies everything they need to know to convert one-time customers into repeat purchase advocates. Retail companies can use CCaaS data to learn more about the kind of products their customers are most interested in at any time, helping with better inventory forecasting and storing.

In the CCaaS environment, data and AI can come together to provide insights into individual customer preferences too. An agent in a retail environment could use a CCaaS system with a real-time virtual assistant, capable of pulling up information about a customer’s previous purchases the moment a call is answered, or a conversation begins. This paves the way to a much greater level of personalization, and a consistent experience on every channel.

All the while, the collection of data from the CCaaS environment gives us a better understanding of the consumer journey, and the steps the average buyer has to go through to make the right purchasing decisions.