Top 5 Approach Call Center Analytics Can Upgrade CX

Many businesses are based on call centers, also known as contact centers. The call center technology of today is sophisticated and powerful. It uses a wide range of technologies for IVR, web, and other channels to provide seamless customer journeys.

The interaction mediums of interaction are evolving at an alarming rate and it is likely to continue. More interactions are happening online via voice and video chat than in person.

The pandemic has changed the way customers shop and the expectations of businesses to respond to their customers' needs. Retail businesses have always been focused on customer experience (CX) but consumers today are more comfortable communicating with brands through new channels. Agents are required to provide services for customers in more complex and holistic ways, thanks to a greater focus on digital sales and remote workforces.

The rapidly expanding call center market:

The call center market was worth USD 18.14 billion in 2018, and it is projected to grow to USD 53.65 billion by 2026. It will increase at a CAGR of 14.7% between 2019 and 2026.

The expectation for customer service on demand is higher than ever in today's business world. Customers should meet or exceed customer expectations and tolerate sub-par service. Otherwise, they could lose their competitive edge in the market.

The call center analytics market is growing because of dynamic customer service, which allows companies to gain crucial insight into their CX. Here are some key drivers:

  • Customers adopting social media platforms
  • Remote working
  • A rise in multi-channel communication
  • Technology advancements in the areas of AI and ML
  • Advanced analytics

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What's call center analytics?

Call center analytics' primary purpose is to collect and analyze customer data to uncover valuable insights about company performance and transform it into actionable insight. These include Customer Satisfaction, Revenue, Customer effort score, customer retention, and service-level agreements (SLA) performance. Customer analytics examines multiple customer-related data sources to identify trends and potential interaction opportunities that can be used as a source of modeling. Analytics can be historical or predictive. Data sources include customer feedback, purchase data, and demographic data.

How is call center analytics changing customer experience?

Call center analytics has transformed the role of call centers from a primary service offering into a strategic differentiation that can drive powerful improvements in CX as well as financial performance.

Analytics can reduce customer handling time by as much as 40%. Call center analytics can also help to improve self-service containment by 5-20%, reduce staff costs by up to $ 5 million, and increase the conversion rate for service-to-calls by almost 50%.

Although call center analytics is just one of many enhancements that companies can implement, they are a powerful tool.

Five types of call center analytics

Call center analytics is the future of call center operations.

Companies can build capabilities to analyze financial records, survey data, and transactional insights. However, those who analyze unstructured social media data and voice data, as well as big data from apps or websites, can build a complete customer profile that allows for personalized customer interaction.

Call center analytics allows businesses to personalize conversations about CX. This helps them build customer loyalty, increase customer engagement and maximize conversions.

Analytics in call center calls can also be used to help small and large enterprises measure their performance and determine how they can improve.

Let's take a look at five types of call center analytics

  1. Speech Analytics: The primary source of speech analytics data is the call center. It and customer interactions are the primary data sources for speech analytics. They focus on identifying common problems customers are having through their voice tone and intonation. The software automatically recognizes emotions and tags them.
  2. Although this area of analytics for the contact centers is still relatively new, speech analysis is seeing a lot of interest as users have had great success with its implementation. Businesses can use speech analytics to identify weaknesses in their scripts and replace them with more efficient ones. They can also develop new systems that improve CX and achieve desired results.
  3. Desktop Analytics: Desktop analysis is extremely useful in real-time monitoring of calls to identify inefficiencies and provide valuable feedback opportunities for agent performances. They also improve security. The optimization of customer and agent experience can be achieved through call center desktop analytics. Automating repetitive and simple tasks can be tracked and automated, freeing up staff to perform more complex and cognitive tasks.
  4. Predictive analytics: Data and analytics tools are becoming more common in most well-respected call centers. Many companies are not taking full advantage of this technology. According to McKinsey's report, 37% of companies do not believe they are using advanced analytics for creating value. Call centers can use predictive analytics to monitor and record customer satisfaction, call volume and wait times, as well as service level. Predictive analytics can also be used to help customer service departments resolve current problems with historical data and turn them into actionable insights.
    Predictive analytics, for example, can be used to forecast staffing needs and allow managers to determine how many staff they need on holidays based on call volume. It can track and record the impact of new products on call volume and demand. Call center predictive analytics allows businesses to better plan for the future. They can look at past results and determine the best intervention methods to address issues.
  5. Self-Service Analytics. Although self-service is not something that many customers are comfortable with, particularly those in older age groups, they soon realize the benefits. Self-service analytics is being used by many tech-savvy businesses to optimize specific tasks. A self-service option allows customers to do their business online.
    Self-service analytics for call centers can reduce human error and increase the volume of calls that are received by the center. Self-service means lower overhead costs, happier customers, and more engaged agents. Once self-service analytics is established within an organization's technological infrastructure, it requires little human intervention.
  6. Call Center Text Analysis: This mainly involves focusing on written communication (emails, web chats, documents, and social media comments). The use of social media has increased dramatically over the past few years. This makes text analysis of comments on social media extremely informative. Social media is now a primary form of communication for many online businesses.
    Text analytics tools allow you to monitor and assign specific values to words or phrases. Data mining functions can identify patterns and relationships in the data sets. These data can be used to conclude the text messages sent by organizations and customers, as well as highlight any problems in the customer's mind.

The Bottom Line

The performance of a call center representative can make a significant difference in CRM and customer service. However, it can also prove to be a major obstacle. It is crucial to use call center analytics to track agent performance in real-time.

Advanced performance analytics software that is data-driven takes the guesswork out of creating the best customer/agent relationship experience. Companies can use a combination of these types of analytics to determine which language and behavior are most effective in helping agents reach their goals and key performance indicators (KPIs).

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