The First Benefit of Contact Center Automation Isn’t What You Think

Customer contact is a critical component of any business. A winning approach will minimize churn, increase sales, and reduce costs.

Automation can and will do all these things if implemented correctly. But these big benefits aren’t the first benefit you’ll see. Collecting and analyzing data is a prerequisite to automation, and it will tell the story of your contact center and provide a roadmap for immediate improvements.

You don’t know what you don’t know, as the old saying goes. Implementing data systems in your contact centers will reveal exactly what you don’t know.

Data is the key to making better decisions for your contact center, and automation can drive data acquisition in more thorough and timely ways. It is tempting to simply implement AI tools right away, but you’ll get far more value out of these upgrades if you step back and understand the data fundamentals. You’ll also avoid poor implementations that will prove to be costly later.

Even prior to deploying automation, there is real and immediate value in analyzing existing data. Here’s how data analytics from your contact center will open up a ton of value and insights to increase your ROI in short order.

CX data reduces churn

Keeping your customers happy ensures they remain your customers. When you use automation technologies to harvest data, you’ll gain a much better understanding of why your company might be experiencing churn and what you can do to reduce it.

Data analytics from automated processes translate insights into actions. If, for example, a customer says key words like “you’re not helping me” or “I want to cancel my service,” AI tools can collect that data immediately and alert supervisors or provide live agents with nearly instant information to address the customer’s needs.

Examples of data analytics that can make an immediate impact to reduce churn include:

• Speech and text analytics
• Performance metrics and reporting
• Root cause analysis

Instead of doing a postmortem on a call with an unhappy customer, automated systems do the analysis in real time to help live agents or bots pivot immediately to provide better service. You can analyze customer calls, transcripts, emails, and more to identify issues and opportunities.

Performance and metrics reporting, as well as root cause analysis, can help your team develop guidelines for improvements after a call. An analysis of all calls from customers can illuminate what your customers truly want and need from your call centers. From there, you can analyze your current CX and make relevant adjustments. For longer-term strategy, all that data can help define a targeted plan and a roadmap for further implementations of automation in the future.

Your customers want personalization, and they want their needs addressed immediately. If you stay on top of those concerns, you’re guaranteed to reduce churn—and the data analytics gleaned from automated processes will help you get there.

CX data increases sales

Better customer experience equals higher revenue. A happy customer is more likely to spend more money, and the data proves it. Customers who reported the most positive past experiences with a brand’s customer service spent 140% more compared to those who reported negative interactions, according to a study published in Harvard Business Review.

But you already know that happy customers spend more. You still need to identify which parts of your CX to improve to keep them happy. That’s where data analytics come in.

Omnichannel analytics reveal insights into all of your customers’ interactions across the business. Emails, texts, chatbots, and live calls all factor into omnichannel success, particularly because most customers will use more than one communication method depending on what their particular issue is. Omnichannel analytics data can reveal where your live agents succeed and where they need improvement.

Predictive analytics use machine learning to anticipate and plan for future events. Based on those predictions, you’ll be able to make better decisions about budget and personnel allocations or other improvement strategies. If, for example, machine learning anticipates higher call volumes around certain holidays, you can create a CX plan in advance of that rush to engage customers more efficiently, leading to an increase in customer satisfaction and, in turn, sales.

That data will help tremendously when you do a customer journey analysis, which gives you a complete understanding of where your customers are coming from, what touch points they used to contact an agent, potential pain points and bottlenecks, and opportunities for improving that flow.

That means there’s real and immediate value in analyzing existing data, even if you haven’t deployed your new automation yet. Data analytics will also drive your decisions about what automations make the most sense for your business.

CX data reduces costs

Quality assurance (QA) teams that analyze calls require significant resources to sort through information that may or may not matter to your CX. A human QA agent needs to listen to a call recording, fill out a scorecard, and follow up with customer service agents on training.

Automation and AI tools make it possible to analyze all calls and fill out scorecards automatically. The analysis happens far more quickly, and your QA agents can analyze data rather than listen to calls to seek out valuable information in a sea of non-vital info.

From there, QA agents can spend more time driving training to improve the CX journey. Shorter timelines save you money and dramatically increase efficiency.

Analyzing existing data can also provide a roadmap for reducing handle time. Culling through your existing data to find out why your handle time is high, what roadblocks both customers and agents ran into while on a call, and even when a call that reached a live agent could have been handled by a chatbot will all help you understand what automations will prove useful in the future.

For example, using AI chatbots that can answer a customer’s questions without a live agent’s intervention already cuts costs. But when a customer does need to speak with a live agent, automated processes can deliver crucial information to your live agents more quickly and give them more personalized information about the customer.

This means the live agent spends less time searching for solutions, which lowers handle time and consequently improves the customer’s overall experience. These two outcomes combined will reduce operation costs by streamlining the CX journey and allowing your company to allot resources more efficiently.

You can also streamline training processes using data analytics from customer calls and interactions with AI bots. With a massive amount of information on your customer’s needs, habits, interactions, and pain points at your disposal, you can create training plans that get live agents prepared for calls more quickly and efficiently.

This, too, saves you money by simplifying training, reducing the amount of time needed to create training processes, and even reducing the amount of training necessary overall.

How to start capitalizing on data analytics

Once you decide this is the right approach to streamlining your contact center, the next step is determining which data to collect and analyze—even before you begin to implement automations. Given the sheer volume of data your contact center generates, sorting through all of it can be overwhelming. And that simply isn’t a good use of resources.

Choosing the relevant key performance indicators (KPIs) for your contact center needs starts with drawing up a strategic plan and a detailed plan for implementation of new automations. At that point, the relevant data will become apparent.

Looking for more recommendations on how to transform your contact center experience? Download our latest e-book “Unlocking efficiency with automation: Optimizing CX with holistic solution stack.”