According to Forrester, 71% of organizations don’t have the right analytics tools or expertise to transform their contact centers. As a result, many problems remain unsolved - problems that could be overcome with advanced customer engagement analytics driven by artificial intelligence (AI). It’s probably safe to assume that those organizations that lack the proper tools continue to face these four challenges:
- Too much data (volume) in too many different formats to be used in contact center analytics. As a result, data is selectively sampled which means you never get a truly accurate picture of what’s happening.
- It’s difficult and time-consuming to understand customer sentiment.
- Most contact center analytics aren’t robust enough to spot real-time changes and alert agents. Real-time insight can solve many problems.
- Unless you track a topic or query, gaps or issues in performance aren’t readily visible.
What percent of contact centers use a Customer Relationship Management (CRM) system? Nearly all of them have some sort of system to manage customer information. What percent of contact centers feel they are reaping measurable benefits from their CRM system? That’s the million dollar question.
Initially, call centers invest in a CRM system to assist Sales. The benefits to the sales effort are clear as sales and marketing personnel leverage their CRM information to manage deal pipelines, generate and nurture leads, create better targeted marketing campaigns, manage teams, and analyze customer service reports—all to increase revenues. But the benefits don’t have to stop with sales.
When CRM information is used as one of the data feeds into a Customer Engagement Analytics program, call centers can take customer relationship management to a whole new level as they analyze historical interactions and customer sentiments to determine who will likely buy and who will churn. And there’s more.
When CRM information becomes one of the channels in an Omnichannel Analytics program, companies can better analyze and quantify the customer journeys and preferences that lead to customer satisfaction and make that information accessible to everyone.
All You Need to Know About Measuring Customer Satisfaction Surveys that produce CSAT (Customer Satisfaction) score, NPS (Net Promoter Score), and CES (Customer Effort Score) are widely used today as part of a strategic approach to measuring customer satisfaction.
To measure customer satisfaction, companies spend significant time and effort conducting CSAT, NPS, and CES surveys, managing and analyzing the data, and taking new actions to move customers up the scale to become satisfied customers and loyal promoters.
The trouble with CSAT, NPS, and CES is that they are all based on surveys which means that companies who rely on customer surveys may have yet to learn what 85-90% of their customers think. They may be looking at the tip of the customer satisfaction iceberg and missing the unseen mass below the surface.
To truly understand what each customer wants and expects from your business, and how satisfied each customer is, you need to see and measure every interaction of every customer journey. This insight requires intelligent automation technologies to collect, aggregate, and store 100% of customer interaction data across all channels.
If you’re serious about understanding and delivering what each customer wants, you need to go “all in” to measure customer satisfaction. Read more.
CSAT functions across industries as a key metric reflecting customers. Let's dive in and look at the traditional and modern methods and how to take actions to improve the score over time.
Customer satisfaction is one of the most important metrics by which contact center performance is judged. Customer satisfaction assesses how happy or unhappy customers are after interacting with a business and it can be affected by any point in the customer journey – from information gathering and purchase to customer support and other post-sale interaction.
Tapping into this customer interaction data is not trivial. The sheer volume of data plus the number and variety of data sources – including IVR, brick-and-mortar stores, billing, agent notes, calls, emails, voice, web, mobile, etc. – are difficult and extremely time-consuming for employees to collect, unify, and analyze manually.
It requires powerful customer engagement analytics driven by artificial intelligence (AI) that can crunch the data in seconds and pick out the behavioral patterns, trends and anomalies affecting customer satisfaction. To get the most from customer engagement analytics, we recommend adopting three strategies:
- Understand customer sentiment to increase satisfaction
- Single out the behaviors that enable rapid customer satisfaction improvements
- Operationalize insights to strengthen customer satisfaction
Customer loyalty doesn’t happen overnight; it builds up over time through positive interactions with an organization or brand. A poor experience with a product or service doesn’t necessarily strike a blow to loyalty; how the contact center handles the problem is the critical factor in ensuring enduring customer loyalty.
But in exchange for their loyalty, customers today demand more than ever. With the explosion of digital channels and heightened expectations set by industry disruptors like Amazon, customers want “anticipatory, personalized experiences across the entire customer journey.”
Keep reading to learn why customer loyalty matters and how to enable it across channels.
Harvard Business Review said that “the #1 most important factor in customer loyalty is the reduction of customer effort.” Other studies and surveys reinforce this sentiment with data that show high-effort experiences reduce customer loyalty to a brand, while low-effort experiences make customers more likely to remain loyal to a brand and purchase again.
It seems THE key, or at least a very important key to customer satisfaction is making every interaction with your company simple and easy – the less effort required of the customer, the better. How do you find out whether customers interactions are difficult or easy?
Customer surveys may be used to elicit this information, but customers are notoriously averse to answering surveys so the sampling is often small and unrepresentative.
A very effective way to measure customer effort is to think of every customer interaction as a journey, and to analyze each journey over all the channels it uses. It’s passive and does not require customer participation. But it does require a Customer Journey Analytics expertise.
Customer churn is an ongoing challenge for every business. No matter how hard you try, some customers will stop using your brand during a specific period. The nagging question is “Why?” The reasons may be as varied as your customers, but since churn directly impacts the bottom line, businesses can’t ignore it. On average, 65% of an organization’s sales come from current customers who are loyal brand buyers.
To reduce churn, most companies turn to experts who build statistical models to analyze and predict churn. These models tend to look at historical data. By the time you see an increase in your churn rate, you’re six months down the line and the customer is long gone.
Today innovative technologies such as AI and machine learning are being applied to the task of reducing customer churn. These advances enable call centers to analyze 100% of real-time as well as historical interactions, giving organizations a much more accurate and timely picture of customer satisfaction and customer churn. Businesses can identify the early signs of customer dissatisfaction and the propensity to churn, so they can intervene proactively to improve the customer experience and retain the relationship.
Over and over again, studies show that retaining an existing customer is far less expensive and much more lucrative than acquiring a new customer. It’s practically a business axiom. That’s why organizations have invested billions in specialized systems to analyze and predict customer churn. They look at historical data and try to find patterns in the interactions with customers who churned and those who didn’t.
To remain competitive, it is not enough to react to churn. Organizations need to be able to predict potential churners and head them off at the pass. One way to accomplish this is by looking at the entire customer journey as a whole, instead of separate touch points. Perhaps the point at which the customer churned was caused by an earlier interaction that left a bad feeling. In such a case, blaming the churn on the current touch point will result in a misleading view of the situation.
Even though companies are providing new contact channels such as web portals, chatbots, and mobile apps, the self-service IVR channel still accounts for more than 70% of contact center traffic! Unfortunately, 85% of customers still find self-service IVR systems hard to navigate and prefer to speak to a live agent.
According to recent data from Forrester, a live agent costs $6-12 dollars per interaction whereas an automated interaction costs about 25 cents. When we consider the significant cost savings and efficiency benefits that self-service systems IVR systems can achieve, it’s well worth the investment and effort to make the IVR customer journey as effective and satisfying as it can be.
Achieving exceptional customer experience each and every time is tough. Self-service has the potential to shift customer behavior and transform the contact center workforce. But organizations need a game plan. One that involves both strategy and customer engagement analytics to optimize IVR processes and enable satisfactory self-service IVR journeys for their customers.
Part of the strategy involves applying self-service to the right situation…
COVID-19 restrictions have more people working from home. That includes contact center agents. This shift may continue long after the virus is gone. But no matter where your agents are working, you still need to assure the quality of their interactions and transactions with customers. While COVID-19 may have thrown a temporary monkey-wrench into the process, the goals of your quality assurance program remain the same:
- Improve business processes
- Improve agent performance.
- Monitor for compliance purposes
Quality assurance programs rely heavily on training and coaching. Now that agents are working from home, a tightly coordinated effort between the quality team and the coaching team has become more important than ever. One of the keys to success is to turn quality assurance into a continuous cycle of joint activity that keeps the focus on critical KPIs and the indicators that influence them. From monitoring and evaluating interactions to coaching and measuring performance, the cycle continues and QA becomes second nature.
Building such a QA Program is easier than you think.
Try to recall a recent interaction you had with customer service. Was it a better, more enjoyable experience than previous interactions? That’s the real crux of the question: what makes certain experiences better than others?
For many, it’s having their issues or questions resolved quickly and hassle-free, and it’s easy to understand why. After all, nobody likes to unnecessarily waste time or become frustrated with the process of receiving an answer. So, if you’re measuring the success of customer interaction, the ease of the experience may be more telling than the customer’s overall satisfaction. Customer satisfaction experts have addressed this idea with a new metric for customer satisfaction surveys: the customer effort score.
Read more about Enlighten AI.