Applying Automation to Improve Customer Experience

Looking for automation applications to improve your CX? Here are five areas to explore.

As we advise enterprise customer experience (CX) leaders, one fairly consistent area of deficiency is using automation to improve agent performance and/or customer satisfaction. Sure, most companies use some automation, but the customer experience is one area where the job is truly never done.

I’m not advocating a boil-the-ocean approach to artificial intelligence and automation — quite the opposite, actually. Automation strategies for CX should be a continuum, where projects build upon on another. Tackle one problem or opportunity, measure success, refine for further success, and move on to the next one.

Fortunately, there is no shortage of innovation among both startup and legacy providers to help automate manual processes in CX — an endeavor ultimately resulting in lower operational costs, better customer satisfaction, and even improving revenue. I often finish briefings with vendors and think, “Wow! That’s a really cool product!” So here and in the future, I’ll periodically provide No Jitter readers with some highlights for you to consider. Today’s piece looks at ways automation can improve operations in customer experience.

(To be clear, no vendors requested, paid, or provided any incentive whatsoever to be in this post.)

What Should You Consider Automating?

From a high level, automation projects should always address a problem or opportunity. These initiatives—particularly when they can change and benefit through the real-time learning of artificial intelligence and/or machine learning—can be truly transformative by changing processes, expectations, and results. Here are a few examples, along with technology providers that I think offer some solid solutions:

1. Quality Management – for the Customer and Agent

We’ve all called into banks, airlines, or retailers and heard: “This call will be monitored for quality assurance.” Typically, that process has been manual. The supervisors randomly (or intentionally if they are checking in on under-performing agents) listen to the calls and coach based on the outcome.

By using AI to automate this process, supervisors get a more accurate view of what’s happening globally across all agents—and AI can flag conversations that score low in sentiment analysis, for example. Or, they can see which agents are performing flawlessly and listen-in to what they’re doing better than others to replicate their behavior. Reporting can show data, trends, and analytics on the all calls over time. is one company offering such a service. The sweet spot for them is companies with 100 to 5,000 agents.

Intradiem also has an interesting approach for what I still partially consider quality management—except it’s on the agent side. Agent AHT Assistance reviews rules and conditions for average handle time (AHT) of calls and takes targeted action (i.e., supervisors proactively intervene to offer assistance). What gave me a “wow” moment is Intradiem’s upcoming release, which will evaluate agents’ current state and dynamically adjust their Key Performance Indicators (KPIs) based on pre-established factors and outliers—continuously challenging agents and improving their performance. For example, AHT for an airline may increase during a strike or storm system, so what’s considered an acceptable AHT during that time may change, so agents aren’t penalized for missing their KPIs.

Another possible approach relies on automation. uses AI to track all conversations, and its technology can identify whether agents are reading disclosures or using words (like “guarantee”) that can get the company in trouble. Similarly, Dialpad’s Ai QA Scorecards transcribe calls in real-time and provide a checklist that aligns with required tasks. A future Dialpad release will flag phrases on the QA scorecards to speed the call review process for supervisors.

Along those same lines, NICE Actimize recently introduced Compliancecentral, a cloud-based platform that monitors communications to uncover potential risk issues with financial transactions. What’s cool, though, is that it uses AI and analytics to corelate employee behavioral data with their communications patterns and activity. This combined data makes it easier (and more accurate) to monitor and investigate risk.

2. Real-time Schedule Optimization

Scheduling the right number of agents each day is a challenge in and of itself. Workforce management applications have been working on this issue for years. Amazon Connect took that to a new level by adding sophisticated algorithms that analyze the accuracy of capacity planning, making adjustments regularly to improve the accuracy (38.8% of the respondents in our Customer Engagement Transformation 2022-23 research study of 724 companies say this is a vital capability for them.)

Ensuring agents’ days are as productive as possible, based on interaction volume, is another challenge. For example, agents often get stuck on calls prior to their scheduled break times, resulting in a shortened break or a manual exception task whereby they inform their supervisor they started their breaks late and get permission to extend the scheduled time. Intradiem offers a proactive and automated capability that evaluates call volume and identifies agents with breaks coming up within the next five to 10 minutes. The system can ask the agents if they want to take their breaks early; when they respond affirmatively, the system fires off a series of rules to automate break times.

Akixi also helps companies with scheduling through out-of-the box reports that document and help improve productivity and recapture lost callers. The volume-by-time reports help ensure contact centers map the number of agents with volume. They also track the activity of employees regardless of their workplace. Its analytics identify trends to help companies (particularly SMBs) improve capacity planning.

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Image: SiberianArt – Alamy Stock Vector

Sourced from: Metrigy Blog. View the original article here.

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