Getting Started With AI at Your Business

It seems like artificial intelligence is getting a lot of attention lately. You read articles about it, see presentations that cover AI and you hear about a range of products and services that now have “smart” features. 

For today’s businesses, there are a lot of options for ways AI can be used. If you are looking for the most beneficial applications, AI applied to business intelligence has a lot of potential. Using an AI-powered BI tool, businesses can streamline their analytics operations and get deeper insights.

With AI data analytics having so much to offer businesses of all sizes, more companies are looking to introduce this technology to their teams. However, many people still do now fully understand what AI can do for their data and what they might need to do to start using AI.

What can AI do for analytics?

You probably already know what AI is: it is the science of making smart machines. With powerful AI systems, machines can be taught or programmed to perform complex tasks that used to require the intelligence of a human. Data analysis happens to be one of the tasks that AI performs well at.

With enough data, an AI algorithm can find meaningful patterns and relationships in the data. In many cases, AI systems can even learn as it is exposed to more data. This means that it can get smarter and better at its job the more it works.

As it concerns business applications, you could feed all of your business data to the AI-powered analytics tool. With automated analysis, it can find insights that might benefit a business in a number of ways. It might find waste in your operation, a missed revenue opportunity, a trend in the market, a group of customers that could be valuable or any number of things.

Even beyond insights about the past and present, AI can work well for forecasting. Using predictive analytics, the AI builds models based on historical data. It then runs data about the current condition to predict what may happen in the future. Many of these systems can even run various solutions or actions a business could take to respond to the prediction through the model to help leaders find the right path forward.

In the past, all of these functions required the skills of a data scientist. Not only that, it would often take an analytics team weeks to perform these functions. With AI applied to analytics, it can make data science teams more efficient and it can also offer some of these insights to people who do not have the skills of a data scientist.

The Implementation of AI 

Introducing AI to your business is not as simple as buying an AI analytics platform and feeding it some data. You need to take some time to assess the needs of your business and determine the goals you are trying to achieve.

Depending on the type of business you run and the goals you have, different analytics tools might work better for helping you reach your goals. You will need to find the right analytics tools and determine the types of resources your business will need to support those tools. 

Once you have the tools, it is not as simple as turning them loose on your company and its data. You are going to need to train employees on using the tools and make sure they understand how you expect them to use the tools. Make sure employees have the training they need and brief them on the types of goals you want them to achieve when they use them.

You might also need to spend a little time promoting the use of the tools. Some employees might not take to the tools as quickly as others. Teach employees about the benefits of using analytics tools and the ways AI can help them perform better at their jobs. Employees will be more likely to embrace the use of AI when they understand the benefits and have the training they need.

AI can be a valuable tool when businesses implement it in the right way. Making data-driven decisions can be a way to put your business ahead of the competition, and with features like predictive analytics, your business can be prepared for the future. For many businesses, their ability to adopt AI and integrate it with their operations will be the difference between success and failure.

Taking A Proactive Approach Prepares a Contact Center for Any Eventualities

We all want to believe we are taking the right measures to stay one step ahead of whatever life might throw at us next. We put blankets, tire inflators and glass cutters in our cars to be ready for emergencies. We purchase insurance policies to cover our homes, our cars and even our day-to-day care as we age. Some people even go so far as to sleep with a firearm at hand

But unlike individuals who feel the need to plan ahead, many contact centers operate reactively. Customers call, e-mail, and send social media messages with inquiries that are responded to on an as-needed basis. In an evolving environment where challenges keep coming faster and issues are more complex, this may no longer be the optimal strategy for long-term success.

Forward-thinking contact center executives are discovering the potential of providing proactive customer service. This can be defined as the process of pinpointing specific customer issues and acting on them before they become problems. In essence, the paradigm shifts to reaching out to the customer instead of waiting for them to take the first step. Taking this kind of initiative offers businesses the unique opportunity to meet and go beyond customer expectations, while strengthening customer relationships, increasing business volume, and building advocacy.

Some companies might be wary that customers might be reticent to receive their overtures, but an inContact study quoted in a recent SuperOffice blog reveals that 87 percent of U.S. adults would be happy to be contacted proactively by an organization or company. Sixty seven percent would be pleased to be contacted about fraudulent activity and a majority is fine with being contacted about appointments, reminders and being asked questions about an order. A full 77 percent who had a good incoming call experience reported feeling more positive about the company that delivered it.

Not only can proactive customer service help reinforce customer loyalty, this growing practice can also result in gaining new customers via advocacy. In addition, it gives businesses a leg up in limiting escalation while staving off the brand negativity that often comes when issues fester.

Taking a more preemptive approach gives companies the luxury of knowing what was coming ahead of time and being able to do something about it. Establishing proactive contact center enables organizations to know when… and how …, to reach out to customers and act early to manage situations instead of simply putting out fires.

Managers and executives can learn how to set up an effective Early Warning system in an upcoming complimentary CrmXchange webcast entitled “The Case for a Proactive Contact Center” on January 14, 2021 at 1:00 pm ET. Shawna Malecki, Senior Product Marketing Manager for NICE InContact, will share her expertise in helping businesses use technological solutions and common-sense best practices to build a greater rapport with customers.

Among the topics to be explored in creating a proactive contact center environment are:

• Be Prepared. Communicate the need for adaptability that enable a business and its front-line team are to be less reactive to sudden, unexpected change.
• Spot Early Warnings. Quickly act on early warnings before they impact customer service.
• Take Advantage. Understand the effects of analytics on cost, revenue, quality, and insight and improving the customer experience.

Register now to find out how your company can not only keep existing customers happy but turn them into brand advocates that can bring in both new customers and increased revenue. If you can’t attend the live webinar, a link to it will posted 24 hours after it is presented.

Agent churn: It’s not you, it’s your employee engagement strategy

Jeff Gallino, CTO and founder of CallMiner

It’s no secret that contact centers are infamous for their high turnover rates, which average 45 percent year-over-year—more than double the average for all U.S. occupations. What most companies don’t realize, however, is that this doesn’t have to be the status quo. Identifying the signs an agent is about to check out and having solutions in place to change the outcome can dramatically reduce agent churn well before they decide to give their notice.

If retention isn’t motivation enough, research shows that an astonishing 77 percent of employees worldwide are not engaged, which, according to Gallup, can cost upwards of $605 billion in lost productivity per year. There’s incredible value in spotting non-engagement signs and addressing the lack of productivity that often lead to agent turnover early. This can ensure strong employee engagement and stop the turnover cycle. Not only will it save billions in lost revenue, it will promote better customer experiences through an organization’s No. 1 advocate—its employees.

Warning Sign 1: They go into silent mode

One of the primary indicators of an unengaged employee is silence. Silence is commonly caused by a lack of agent training, but this isn’t only applicable immediately after onboarding. Agents require extensive knowledge of your company’s products and services; however, many employees miss out on new product information because organizations neglect to offer continual education programs.

Employee silence can also happen outside of customer interactions, as managers of unengaged agents tend to notice an increase in the amount of time between each call. Although this doesn’t usually stem from a lack of company knowledge, it’s a telltale sign an employee is experiencing a lack of motivation. Distant employees are comfortable with doing the bare minimum to get by and will likely keep their heads down, and calls quietly recording, to purposely limit the number of customers they interact with.

Warning Sign 2: Under (but not terrible) performance

Decreased performance in areas such as average handle time (AHT), call volume, and following a script could all point to a lack of engagement that, if not fairly addressed, can lead to lower NPS scores, turnover, and even compliance risks.

Sometimes, however, quantifiable performance metrics aren’t the sole indicator of an agent’s performance—as agents aren’t at-fault for many of the disruptions that happen during the call. Companies need to take training, tools and technical factors into consideration when it comes to gaps in an agent’s performance and use contextualized scoring methods to accurately and thoroughly understand where performance issues are occurring and the root cause.
Warning Sign 3: Inconsistent feedback on their work

According to research by Gallup, less than 21 percent of employees strongly agree their employee implements fair evaluation processes. Contact center agents handle dozens of calls per day, but many outbound surveys and manual quality management reviews only account for three to five percent of an agent’s interactions—leading to ill-informed assessments of their overall performance.

In addition to being inaccurate and irregular, many legacy feedback systems are impersonal. Call center feedback usually only involves reprimanding, despite the employee’s desire to be recognized for exceptional service. The lack of effort put into celebrating successes usually causes agents to feel unappreciated and less likely to advocate for the business.

How to Stop the Cycle

Proper training—during and after onboarding: Before sending your agent out on their own, how do you know you’ve given them the proper training to handle the influx of problems they’ll face out on the floor? To keep up with the fast-paced environment of the contact center, they need to stay informed, especially if your products and services are constantly evolving. Each one of your employees is unique and despite what’s suggested by legacy employee education programs—their training processes should be as well. Speech analytics data can help managers offer personalized training programs in accordance with agents’ specific needs, even after onboarding.

Tools to optimize performance: Aside from training, contact center operators need to ask themselves whether their agents have the resources they need to succeed. While two-thirds of customers dial in with a problem, some caused by lack of self-serving options on other channels, they expect your agent to be able to solve, lack of resources is one of the biggest factors leading to job-related stress. It’s impossible to guide each of your representatives through every single interaction—but tools and customer engagement analytics software can take information in real time and apply historical data to provide your agent with better insight and guide them through the call based on the context of the conversation.

Real-time feedback: Agents should always know where they stand when it comes to their performance. A discussion a week, a month, or a year later about a specific interaction with a customer will not help anyone succeed. Having an analytics tool removes any sense of unfairness that’s usually associated with random selection by providing an inclusive and holistic view of your caller engagement data, ensuring a stronger voice of your employee. It also helps with coaching by automatically scoring 100 percent of your agents’ customer interactions to pinpoint the exact areas they need to both improve customer experiences in real-time and add business value in the long run.

While employee turnover is one of the biggest problems companies face today, employee engagement is just as impactful to your business’s bottom line, as those with highly-engaged workforces outperform their peers by nearly 150 percent. All problems associated with the warning signs of an unengaged employee point to a similar source—the company’s inability to fully understand the needs of their employees from both a personal and professional perspective. Similar to how analytics and artificial intelligence work to strengthen customer loyalty, these tools and concepts can help personalize your organization’s approach to agent management—offering a fully-developed employee engagement strategy that involves critical coaching and feedback procedures. In doing so, companies can foster a positive work culture and keep employees from feeling as though they are ‘just another number’.

Important Business Intelligence Tools for the Contact Center

In order for the contact center to meet (and exceed) performance goals, the right business intelligence (BI) tools and technologies have to be in place. BI tools help contact centers improve and optimize their processes in order to heighten their success rate. BI digs deeper into the data you’re already collecting to find ways to relate your findings to the customer experience and, in turn, improve customer retention.

Aggregate Analytics

Aggregate analytics, also referred to as big data, deliver information about the contact center’s overall performance. Both structured and unstructured data is organized and delivered via an “at a glance” dashboard, or something similar. The key is to have only relevant data included so that supervisors don’t have to wade through inapplicable data.

Call Recording Tools

Speech analytics tools – a long-time staple of the modern contact center – accurately transcribe conversation audio to ensure that agents are adhering to a script or guidelines when speaking with a customer. In industries that have strict rules about what can and cannot be mentioned (like collections, for example), speech analytics are even more important than usual. Certain advanced call recording tools will allow for Voice of Customer considerations, too. Real-time feedback, call coaching and customer surveys enable supervisors to make decisions that can improve the customer experience in-the-moment.

Predictive Voice Analytics

Predictive voice programs not only record agent-customer conversations, but also use them to make predictions about how both parties will respond. Emotional changes in the agent’s vocal features can determine if the agent is speaking in an appropriate way to the customer, while emotional changes in the customer’s voice can determine a variety of outcomes, like if they’re likely to become a regular customer.

Selling Recommendations

BI tools can increase revenue by recommending up-sell and cross-sell opportunities to the agent in real-time. By considering the customer’s purchase history and buyer persona, combined with predictive voice analytics, the dashboard can alert the agent when a selling opportunity presents itself. Not only is it easier to sell to customers with a strong purchase history, but BI tools can also determine which products and services will be of most interest to the customer.

The contact center is generating helpful information every single minute. Data is regularly being collected, sometimes passively. With BI tools, supervisors can gather and review all pertinent data to see where improvements can be made.

5 Tips for Root Cause Analysis in the Contact Center

The best way to solve a problem is to dig deep and find out where it started in the first place. Often, what you see of a problem is a symptom, not the cause. Here are five steps you can take to improve your contact center’s root cause analysis.

1. Consider acoustic issues.

Root-cause analysis should take acoustic factors into account. For example, if the call has long periods of silence, this could point to a problem with the system. If the contact center agent can’t access data quickly enough or if there are problems with IVR, a slow system may be the problem.

2. Flag conversations that are abnormally long.

Speech analytics will let you sort through calls based on parameters like duration and repeated calls. You can also find calls where specific keywords are mentioned, like those that are normally associated with a complaint. This will let you know which calls need the most attention.

3. Monitor data in real time.

Accessing real time data can help you spot and stop issues early. If a new sales or marketing strategy launches and then phone calls start coming in within an hour or two, you’ll know that there’s a problem with the launch that must be fixed. Real time data lets you identify trends as they emerge, giving you the opportunity to stop a problem in its tracks.

4. Sort problems into categories.

As you start to uncover the main problems customers are having, you can segment them into categories, such as product defects, customer education and marketing communication. Then, you can meet with specific teams to come up with targeted strategies to solve the problems.

5. Understand the context of the situation.

Relying on word count frequency isn’t enough – the terms and phrases that are being used have to be understood contextually, too. Knowing the context of a problem instead of just the hard data will allow you to pinpoint the situation that caused or contributed to it.

Knowing the average number of complaints your contact center receives on a weekly basis is just a start. You have to figure out the root cause of the complaints in order to effectively tackle them and prevent them in the future. Root cause analysis is a way to solve prominent issues instead of merely putting a Band Aid on them.

4 Important Categories of Contact Center Analytics

 

Contact centers collect a lot of data. They can find out what their customers are doing on a daily basis. They can determine what time a customer contacted support and how long their contact lasted. They can listen carefully to conversations and decide if a customer is happy or angry based on certain keywords. All of this data helps the contact center do things like reduce call times and examine agent performance. To be competitive, contact centers have to stay focused on the customer. By keeping track of customer service metrics, contact centers can make decisions based on reliable data.

1. Speech Analytics

Speech analytics help contact centers improve a customer’s phone call experience. Customer service agents are monitored to ensure they’re adhering to scripts and following regulations. This can also pinpoint the areas in which an agent needs additional training. Speech analytics will also segment hard-to-handle calls so that they can be dealt with by a supervisor or an agent with more experience. Furthermore, speech analytics can determine the reason for the customer’s call, what they hope to get out of the call, and if they are happy, upset, stressed, satisfied, etc.

2. Interactive Voice Response (IVR) Analytics

Intuitive IVR systems improve the customer experience. Insights that can be gleaned from IVR analytics include the percentage of callers who want to speak with a live agent and their reason for doing so; the reason for the transfer of calls between departments; the percentage of callers who were not identified accurately; and the number of calls that were handled from start to finish by IVR.

3. Overall Customer Satisfaction

Gauging overall customer satisfaction will give you an idea of how well you’re delivering the entire customer experience. In order to measure customer satisfaction, the CSAT score is often used. The contact center will ask the customer to rate their satisfaction with a specific experience, like an interaction with the company or a transaction. For example, the customer may be asked to rate their satisfaction on a scale of one to ten. Any answer that’s a six or above means the customer is satisfied. To figure out the percentage of satisfied customers, the number of customers who responded with a satisfied rating is divided by the total number of customers who were surveyed.

4. Predictive Analytics

Tracking analytics isn’t worth much if you’re not going to take the information and figure out how to improve the contact center. Predictive analytics show the changes that will most impact the performance of the contact center. Management can then figure out the best way to communicate with customers, retain happy customers and resolve problems with dissatisfied customers.

One single metric will not give you a useful view of customer service quality. Instead, several metrics that are carefully chosen based on your customer service goals have to be followed. Tracking analytics allows the contact center to improve, update and revamp their programs on a regular basis.