How to Make Customer Service Analytics a Competitive Advantage


  • Most contact and call centers are data rich and information poor. They understand the value of data but struggle to transform it into actionable intelligence. Data is a valuable but under-utilized asset.
  • Customer service reporting and analytics drive improvements to customer engagement and contact center decision making. Even small improvements are shown to deliver significant and sustained financial benefits.
  • Improved decision making is one of only four sustainable competitive advantages because better decision making never loses value, is not easily copied by competitors and is not displaced by new technologies.
Johnny Grow Revenue Growth Consulting

Customer service analytics, sometimes referred to as business intelligence, are extremely influential in improving business performance.

In a research report titled, Customer Service Analytics: Exploit Data to Improve the Customer Experience, Aberdeen Group delivered results that compared customer support performance gains among contact centers with and without analytics.

Customer Service Analytics Improvements

The differences were significant. For instance, analytics users achieved a 72 percent higher annual improvement to customer retention (6.2 percent compared to 3.6 percent). That single benefit delivers a significant and sustained financial uplift to the company.

A research study, titled Customer Service Analytics: Exploit Data to Improve the Customer Experience, found that analytics drove a 72% improvement to customer retention.

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The goal of business intelligence is to get the right information to the right people at the right time, so they deliver improved customer experiences or make more informed and better decisions.

Analytics identify the combinations of channels, knowledgebase artifacts, agents and more that fail to meet client expectations and need improvements. They surface the agent and case factors that increase labor costs and drive prolonged resolutions. They identify the customers at risk of churn.

They can attribute the cost of support to each customer and identify customer profitability, or more specifically which 5-10 percent of customers are unprofitable, and which 20 percent of customers contribute 60-70 percent of margins and profits.

They can identify the high volume of low complexity calls well suited for customer self-service channels. They can aid proactive customer support by resolving issues before they happen. They use data patterns to detect the correlation between new incidents and existing products, extrapolate to other customers with those products and proactively outreach with a solution before they incur the failure.

Timely and detailed variance notifications permit course corrections before performance problems exacerbate. And understanding the causes and linkage among performance problems accelerates their fixes. On the sales side, analytics can uncover revenue opportunities, such as which clients, services, SLA, entitlements or premium services can generate added revenue.

To achieve these types of answers at scale, a mix of technologies are needed.

Customer Service Analytics Tools

Below are insights for each of these technologies.

Analytics Strategy

A contact or call center analytics strategy shows how to connect data, insights, actions and outcomes. It identifies what outcomes matter most and need to be pursued first. It calculates your budget and expected ROI from the investment.

Every good analytics strategy and program starts with a common question. Most call centers and even many CRM consultants think the question is "how do I best implement an analytics strategy or program?" But that's not right. The first question isn't how, it's why.

Before implementing analytics, you need to first answer the question, 'why should I implement analytics?' Why invest the time and money? How will this change agent behaviors, customer relationships and company profitability? If adoption doesn't do these things you don't need to consider it further.

But for those seeking contact center transformation, the short answer to the essential question is that analytics provide the data-driven, fact-based information to make timely decisions that aid customer objectives and performance improvements.

Data Transformation

Here's the interesting thing about data, a data paradox really. Virtually every customer support decision, agent action and customer interaction benefits from more data. But most call centers have large, vast amounts of data, that normally languishes, goes unused and dies on the vine. They are data rich but information poor.

Data offers a use it or lose it proposition. It is a powerful asset if converted to information and made actionable, or it is a cost without benefit if left idle.

Simple information reporting can be created with queries and reports accessing CRM software tables and columns. However, for more powerful insights such as self-service analytics with natural language processing (NLP), or predictive analytics you will need to create a data model.

You will also need to define the data extract, transform and load (ETL) process and supporting tools. Data extraction retrieves data from defined source locations. Data transformation filters, cleans, modifies, normalizes, appends, formats or otherwise processes data. Data load inserts transformed data to a destination, normally in a presentation-ready format, so it can be acted upon.

Customer Service Data Transformation Pipeline

To engage agents or customer support representatives, you need to evolve from broad information to meaningful insights and advance reporting from being merely interesting to inducing action.

The point of creating Insights is key. Insights are not data, facts or statistics, these are all knowledge. Insights are the reasons, behaviors or learning behind the data, facts or statistics. The dictionary defines the word Insight as "seeing below the surface." It's new learning, something that teaches and induces action.

The three best ways to make data actionable are to make it highly visual in role-based CRM software dashboards, link the data findings to recommended actions such as a Playbook (so the findings advance from descriptive reporting to prescriptive recommendations), and allow the data to be interrogated, manipulated and used for predictive analytics. For contact centers that achieve this, data will become their most valuable asset.

Performance Benchmarks

Contact center benchmarks show us what good looks like. For example, if your average inbound call cost is $3.90, is that good? Well, not if the average call is $2.70 for your industry. And multiplied by the volume of calls this could be a significant deviation that would otherwise go unnoticed and unresolved.

Inbound Call Cost Benchmark

Customer service leaders use benchmarking to create a performance baseline, identify the highest impact opportunities and apply best practices to achieve attainable targets. Benchmarking turns competitive knowledge into competitive advantage.

When managers have both visibility and measurability to the most significant gaps between their current state and where they want to be, they can employ an effective case for change and plot the most direct route to improvements.

And rather than guesswork or estimated ROI figures with questionable assumptions, customer service predictive analytics built on benchmarks use real data to provide confidence in calculating pro forma business outcomes and set realistic goals. The knowledge of what has worked for similar support operations lowers risk and accelerates time to value.

360˚ Customer View

You cannot have customer support analytics without customer analytics.

Customer analytics are needed to understand, engage and predict customer interactions. They allow agents to deliver relevant, personalized, contextual and predictive customer experiences that delight customers.

The best starting point for customer analytics is a 360-degree customer view. This view identifies what's most important to each customer. It tells us which channels customers prefer, when they like to engage, who they prefer to talk to, the issues they consider important and what content or products they are most interested. This view is essential to using data to deliver differentiated customer experiences and knowing what actions do not contribute to an elevated customer experience. Only with data can companies deliver differentiated customer experiences at scale.

360 Degree Customer View

According to a McKinsey research report, "The impact of customer analytics on corporate performance is significant—and clearly underestimated" as "Companies that make extensive use of customer analytics are more likely to report outperforming their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment. For example, companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not."


Driving improvements to customer satisfaction, agent performance, operational efficiency and financial results starts by bringing real-time visibility to the most influential metrics.

Customer Service Dashboard

The contact center dashboard is the top delivery tool to get the right information to the right person at the right time.

To make key performance indicators (KPIs) objective, realistic and meaningful, show them alongside industry benchmarks.

Predictive Analytics

Data becomes much more actionable when it advances from historical to predictive. In fact, without predictive analytics, the view for every person in your support organization is entirely backward looking.

Predictive analytics convert data to forward looking information such as next best answer, next best action or guided resolution recommendations. Giving agents new information or delivering insights which aid their productivity shifts their behaviors and improves outcomes.

Predictive analytics are the tools to increase customer retention at scale. Analytics can examine customer history to identify events, transactions, occurrences or patterns of behaviors that most typically occur just before customers churn.

These patterns can then be monitored so when they exceed a threshold score, an alert can be dispatched to an agent, account manager or other person to take action. For those customers unable to be saved, the analytics can then identify the most successful win-back or reactivation campaigns or programs. Customer churn prediction works. I know because I've implemented it with clients for many years. And for many companies, improving customer retention is their top business growth strategy.

Customer Retention Predictive Analytics

Artificial Intelligence

Contact center leaders were early adopters of Artificial Intelligence (AI) and gained both competitive advantages and remarkable ROI. However, AI for customer service is no longer in the early days. We crossed that chasm and any manager not using AI today is clearly substituting labor for technology.

The question for managers standing the sidelines is not if AI will impact their business, but how and when.

AI creates insights based on its algorithms that sift through large volumes of case data, identify important information and make recommendations that achieve outcomes important to the agent and the customer.

Business leaders use AI technologies to personalize customer engagement, deliver faster resolutions, lower cost to serve, improve customer experiences, increase customer satisfaction and scale customer support operations. It doesn't just benefit customers; it delivers an improved agent experience that increases productivity and decreases turnover.

AI is frequently used as an umbrella term that may include several other cognitive technologies. Intelligent call routing, sentiment analysis, text and speech analytics, voice of the customer and chatbots are some of the AI-driven capabilities transforming contact centers.

Market leading CRM software systems such as Microsoft Dynamics 365 and Salesforce have removed technical analytics barriers with simpler tools, such as Azure Machine Learning and Salesforce Einstein, to put AI applications in the hands of business analysts and power users.

Data is the fuel, AI is the engine, and insights are the destination.