Transforming Contact Centers with Advanced Analytics

Organizations that lack advanced analytics miss out on significant opportunities to improve customer service. To fully leverage the benefits of advanced analytics, it's crucial for businesses to establish the appropriate groundwork to maximize the potential of their data.

While basic data tools are now common in call centers, most organizations miss out on advanced analytics that generate actionable insights for internal and customer-facing applications. According to Mckinsey, only 37% of organizations use advanced analytics to create value, revealing a significant missed opportunity.

Mistakes Organizations Make

Many companies are missing out on the benefits of advanced analytics due to challenges such as entrenched organizational structures, legacy IT systems, and other obstacles. Slow adoption of advanced analytics can be attributed to two root causes.

Companies face challenges with integrating data across channels, with many call centers operating in silos, generating data without a clear system for aggregating it into a single source of truth. Instead of adopting a strategic approach based on a single integrated platform, some organizations opt for ad hoc solutions to address individual issues. In addition, teams such as quality, workforce management, and digital often lack communication and access to the same data.

Some companies struggle to translate analytical insights into action, rendering their data collection efforts futile. Organizations fail to capitalize on them fully or do not take action at all. For instance, most companies use voice-of-the-customer analytics to calculate first-call resolution and customer satisfaction metrics but fail to redesign processes or take transformative measures based on that feedback.

The common thread here is that operations managers lack the knowledge of how to use analytics effectively.

Using Advanced Analytics Effectively: 4 Use Cases

We have outlined four primary use cases for businesses to enhance call-center performance proactively.

Decreased Average Handle Time

Unstructured data, particularly text (including speech), constitutes the largest portion of data in most call center operations, presenting the greatest potential impact. Companies can collect text data from various sources such as social media channels, chats with customer-service agents, surveys, feedback forms, warranty claims, etc. Analyzing this data requires extracting it from all available channels, converting call-center recordings to text, and removing irrelevant words, punctuation, and special characters. With the cleaned data, organizations can uncover valuable insights to reduce the average handle time.

Decreased Call Volume

With advanced analytics, companies can analyze large volumes of customer data, including text and call flow, to identify potential areas for improvement. Using this information, companies can develop a solution that prioritizes individual measures based on their impact, feasibility, and required investment to improve the customer experience. An agile approach can be used to test and iterate minimum viable products in two-week sprints, with the help of IVR rapid simulations to speed up the testing process. Additionally, an interactive dashboard can measure the impact of these improvements by call type and customer type.

Enhanced Network Resilience Through Proactive Measures

Companies often overlook the challenge of network resilience and its impact on customer satisfaction. On average, a company experiences up to five major outages and 25 to 30 site or queue disruptions each year. A two-day outage in a financial-services firm, for instance, can take up to a week to recover from, and result in abandonment rates increasing by 10 to 30 percent. Unfortunately, most workforce-management teams in contact centers don’t proactively model the outcomes of outages on service levels. Advanced analytics can help organizations anticipate potential disruptions and model their impact on service levels, enabling them to take proactive measures to enhance network resilience.

Enhanced Conversion of Service-to-Sales

Advanced analytics tools can also unlock new revenue streams proactively. By utilizing a virtual sales coach, companies can assess customer factors such as demographic and behavioral profiles, purchase history, and real-time data from ongoing service calls. With this information, the coach can predict the next product that the customer is most likely to purchase and provide the sales agent with specific language and scripts designed to improve conversion rates for that customer.

Bottom Line

Despite the availability of analytics, businesses may fail to implement them for growth due to the overwhelming amount of information or lack of consensus within the organization about implementation.

It is advisable to create a business plan outlining the data to be collected and metrics to be monitored before generating insights and initiating implementation. Even with consensus on implementation, it is recommended to start small with just two or three metrics and gradually scale up from there.