Contact center analytics

Everything you Need to Know About Contact Center Analytics

With cross-channel customer experiences (CX) becoming the norm, customers have grown more accustomed to innovative ways of shopping. The increasing flexibility between digital and offline experiences means meeting customer requirements quickly and with more accuracy, no matter the channel, in-store, online, or both. The need to deliver a dynamic CX has propelled companies to generate critical insights into customer journeys, purchase behaviors and patterns, and other crucial aspects, ultimately driving the growth of analytics in a contact center ecosystem.

What is contact center analytics?

Contact center analytics refers to the process of gaining business insights with the key purpose of helping management track, analyze, and improve various services and performance metrics and convert them into actionable insights. It is crucial for both large and growing contact center ecosystems.

Processing unstructured data from disparate sources and converting them into useful reports can help a business formulate more customer-centric strategies. With the implementation of analytics, it is easy to identify various customer trends, engagement opportunities, demographics, purchase patterns, and more.

Benefits of Contact Center Analytics

By introducing dramatic improvements in CX and financial performance, contact center analytics can be the strategic differentiator for contact centers. Companies that apply analytics are able to significantly:

  • Reduce Average Handle Time (AHT)
  • Cut employee costs
  • Increase self-service containment rates
  • Boost the conversion rate on service-to-sales calls
  • Improve CX
  • Enhance employee engagement

While analytics is only one of the broader set of improvements that can be introduced to a contact center ecosystem, it is a powerful tool that should be implemented, nonetheless.

Key Performance Metrics Impacted by Contact Center Analytics

  1. Customer Satisfaction – For any business, customer satisfaction is the ultimate goal. While many rely on surveys to gather customer satisfaction feedback, it is difficult to get the complete picture due to certain limitations. Therefore, connecting with customers across multiple channels from a single platform make it easier to determine the problem areas and prevent issues before they start.
  2. Customer Retention – Customer retention rate is the percentage of existing customers who remain customers after a given period. It indicates what retains customers with a business and the opportunities to improve customer service. Analytics can calculate the customer retention rate with the help of the following key data points:
      • Customers at the start of a specific period
      • Customers at the end of that period
      • New customers acquired during that time
  1. Customer effort score – A customer effort score is quantifiable as the effort a customer puts in to acquire the information they require or a solution to their problem. This Key Performance Indicator (KPI) is directly tied to customer loyalty and is measured using analytics.

Types of Contact Center Analytics

  1. Speech Analytics – The data harvested from recorded calls is the primary source for speech analytics. Through the tone and intonation of the customer’s voice, it can help identify some of the common customer problems, which are then, automatically tagged by the software. These insights are used to develop new and improved systems and processes to achieve optimized performance and results.
  2. Desktop Analytics – Desktop analytics is extremely useful for real-time call monitoring. It helps optimize both the customer and agent experience by recording inefficiencies, providing valuable feedback on agent performance, as well as providing critical insights into security.
  3. Predictive Analytics – In a contact center ecosystem, the ability to stay one step ahead of customer concerns solves half the problem. Predictive analytics helps achieve this using Artificial Intelligence (AI) to analyze and apply the logic from historical data to solve current problems. This vital tool can review call volumes, first-contact resolution, SLA performance, call handle times, customer satisfaction, and more. Predictions can range from staffing forecasts to churn risks.
  4. Self-service Analytics – Many businesses optimize specific tasks with self-service analytics as it translates to a drop in overhead costs, more engaged agents, and satisfied customers. Not only do self-services like chatbots reduce the chance for human error, but they also reduce the incoming call volumes and minimize human interference once set up within the contact center ecosystem. Analytics comes into play when any new trends in customer requests and common searches need to be identified and any bottlenecks or issues in the CX need to be reviewed.
  5. Text Analytics – Text analytics focuses on written communication, such as social media interactions, web chats, emails, and documents, that are exceptionally informative. Words and phrases are analyzed and assigned specific values by text analytics tools, and any issues that are present are highlighted through pattern and relationship identification in the datasets with the help of data mining.
  6. Omnichannel Analytics – Customers today prefer to make use of multiple channels during their purchase journey or when they seek advice depending on the context. In the contact center ecosystem, keeping track of all these interactions is only efficient when agents have a complete view. Omnichannel analytics solves the disconnection of solutions through its single dashboard thus improving the overall contact center productivity. 
  7. Voice Analytics – Voice analytics enable contact center supervisors to monitor calls in real-time or review automated transcripts. It is especially helpful if the team is working remotely. Managers can immediately step in to help the agents when needed or suggest suitable training opportunities to improve customer satisfaction, or escalate cases if required.

Making Contact Center Operations more Data-Driven

Collecting customer data is relatively easy but transforming it into something valuable and meaningful is significantly more challenging. Making sense of analytics in a contact center ecosystem depends on:

  1. Data collection –Storing data from contact centers for more accuracy in performance measurements
  2. Data analysis –Generating understandable reports for the team by making use of standard templates
  3. Action –Using the data-based insights to optimize contact center performances

The rich repositories of customer-centric insights and feedback on products and services enable businesses to unlock data from contact centers and facilitate enterprise-wide accessibility to improve their CX strategy. However, to maximize the benefits of analytics, organizations must start with the right foundations in order to make the most of their data.

Essential Features in a Contact Center Analytics Tool

Having a monitoring tool for each application adds unnecessary and unmanageable complexity to the contact center infrastructure. The ideal analytics solution packs everything into one dashboard with easily accessible data.

Here are the top five must-have features in a contact center solution:

  • Data integrations with CRM, team chat, and email
  • Instant access to critical business metrics
  • Real-time analytics for agents and supervisors
  • Actionable insights into the customer journey
  • Customer 360 view for tracking omnichannel performance

Key Attributes of an Analytics-Driven Contact Center

It is not necessary to have all the elements in place for businesses to implement analytics in their contact centers. As with most fast-paced technologies, an effective approach is identifying use cases with the existing data, developing them fully, launching pilot programs, and then iterating.

So, why are more businesses not taking advantage of this opportunity? The reason is deep-set organizational structures and processes, legacy IT systems, and other challenges not creating the right foundation for analytics to thrive. Its slow adoption can be attributed to the lack of integrated data across channels and the inability to link analytical insights to actions. The most commonly observed theme is that managers simply do not know how to leverage analytics.

Despite the growing amount of data and the large pool of analytics vendors and tools, contact centers struggle to make sense of all that data. To build the right foundation and reap the most benefits, contact centers should have:

  1. A coherent, enterprise-wide vision for analytics and a road map for implementing specific use cases
  2. A strong in-house talent in analytics and agile mechanisms to capitalize on the insights generated
  3. A comprehensive data strategy and ecosystem to support the broader analytics strategy that includes data governance, IT or data architecture, and data and infrastructure security frameworks
  4. An ecosystem of expert partners to outsource to when required
  5. A culture of data-based decision-making rather than instinctive

Contact center analytics

HGS Agent X’s ready-to-use analytics dashboards have the capability to monitor agent-level, team-lead-level, and organization-level performances. Its real-time updates help optimize operational efficiency, quality, and workforce management.

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