Why Contact Centers Struggle with Customer Service Analytics


  • Most customer support organizations are data rich and information poor. They understand the value of data but are unable to transform it into actionable intelligence. Instead of data being their most valuable asset, poor data transformation leaves data idle and worthless.
  • Most contact and call centers have information reporting that goes unused. That's because the reports lack insights and present findings that are not actionable.
  • Getting analytics right delivers continuous process improvements, better customer interactions and improved decision making. That's important as customer affinity and better decision making are two of only four sustainable competitive advantages.
Johnny Grow Revenue Growth Consulting

There's no doubt business intelligence is essential to drive call center improvements. As Peter Sondergaard, Senior VP at Gartner states, "Information is the oil of the 21st century, and analytics is the combustion engine."

However, despite the promise of business intelligence to drive contact center improvements, too many times that so called intelligence fails to deliver. It's not that the technologies fail but that the implementation of the technologies fail to recognize the surrounding factors needed to be successful.

Business Intelligence Roadblocks and Resolutions

Here are the top five factors that challenge customer service analytics and the proactive remedies to succeed.


Poor or missing analytics strategy

McKinsey reports that business intelligence tools are standard practice in most contact centers, but only 37 percent are using decision support tools to create customer value. An analytics strategy is the prerequisite to avoid being one of the majority with technology but without tangible business value.

A contact center analytics strategy shows how to connect data, insights, actions and outcomes. Unless this four-sequence link is understood, the reporting technology can be implemented perfectly and produce absolutely no value for customers, agents or managers. Most strategies also define the technology budget and forecasted ROI so they can identify what outcomes matter most and need to be pursued first.


Poor decision-making culture

Using business intelligence to make faster and better decisions is as much about culture as technology. In fact, to be successful, the starting point is a culture that shifts decision-making by discouraging intuitive, subjective and gut-based decisions in favor of data-driven, fact-based and objective decisions.

About 100 years ago, a smart guy named William Edwards Deming instructed his team, "In God we trust, all others must bring data." That perfectly describes a data driven culture.

A very helpful aid to achieving this type of culture is a Data Driven Operating Model (DDOM), which is a clear framework that shows how to operationalize data and insist upon insight driven decision making.

It provides help so that decisions that impact the customer experience or contact center objectives are made with insights, and not intuition or educated guesswork. It provides help to ensure all decision making is based on data, facts and insights.

Data Driven Operating Model

Poor data = poor analytics

According to a Gartner report titled, Measuring the Business Value of Data Quality, "Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits."

In our own experience when working with clients that have questionable data, we've found that a 10 percent increase in data quality accomplishes more than a hundred percent increase in decision support technologies.

Contact center customer and case data regularly incurs data entry errors. Even correctly entered customer data depreciates two percent per month. A data quality program is needed to ensure data is accurate and complete, secured and available to those who need it, and in compliance with regulatory (especially privacy) requirements.

Data quality is more than data integrity. Data integrity measures the accuracy and reliability of data while data quality considers additional aspects such as completeness and value. Customer and case data quality is a prerequisite to effectively using data for customer interaction and decision making.

What gets measured gets managed. To achieve and maintain data quality we normally use a few exception-based CRM reports and a data quality dashboard.

Customer Data Quality Dashboard

Fragmented IT systems

Contact and call centers have a notorious reputation for fragmented business systems and data siloes. They have an average of 9.2 business applications used by agents in their daily case resolution responsibilities.

Business intelligence programs become challenged when systems are not integrated and data resides in multiple places. It becomes even more problematic when duplicate data exists. When agents or managers get different results from the same questions or queries, depending on which data silo they access, they rightly lose confidence in information reporting.

There are multiple solutions for this common problem. Sometimes a data strategy will define the systems of record and governance for each type of data. Other times system integration will fix the problem, or other times purpose-built applications such as Master Data Management (MDM) are the best fix. The best answer will depend on your information systems platform, data volumes and analytics strategy.


Analytics fail when they don't measure what matters

Customer service dashboards are an effective delivery tool to get the right information to the right person at the right time. However, they too often fail to gain adoption because they don't focus on the measures that most matter. Instead, they either limit the dashboards to the measures that are easy to display or bury key performance indicators in a sea of less essential information.

Failing to track key performance indicators such as customer lifetime value (CLV), account health score or customer retention rate because they are not available in your CRM software or case management system doesn't make those metrics any less valuable.

And too many metrics bury the signals among the noise and make your customer support dashboard unproductive. Decision support works when it focuses the users' attention to the highest priorities.

Customer Service Dashboard

I'm often asked how many metrics should be included on a customer service dashboard. The answer is always the same, as many as will be acted upon. The takeaways here are to keep your eye on the prize and recognize that that less is more when it comes to customer service dashboards.

See the 5 toughest challenges to customer service analytics and the proactive remedies to succeed.

Click to Tweet