The goal is to apply analytics methods that move beyond customer satisfaction to nurturing customer loyalty by more deeply understanding the customer’s total experience.
At a recent industry conference, global payments processor Elavon demonstrated the power of Big Data in the contact center environment. Retaining high-value customers has always been a top priority for Elavon, which serves more than 1 million merchants in the United States, Europe, Canada and Puerto Rico. When warning signs of potential customer defections appeared, the company was determined to take proactive steps to identify dissatisfied merchants and find ways to help them. Elavon’s biggest challenge was figuring out a practical way to do this across its large customer base.
Elavon found the answer at its Knoxville contact center where 300 agents handle approximately 10,000 calls a day, all of which are recorded and transcribed. The company used speech analytics software to automatically analyze content in the recorded chain of calls. The application helped Elavon’s business review unit determine words and phrases in their call transcripts that hinted that a customer could be on the path to switching to a competitor.
“Of the 10,000 calls our Knoxville center receives each day, about 2,000 could be construed as being ‘at risk’ in some way,” explains Roman Trebon, Elavon’s business review manager, speech analytics, who led a team of four analysts that reviewed the data. By immediately reaching out to those at-risk merchants, the company estimated it saved nearly 600 accounts worth $1.7 million, over a three-month period.
The implementation of speech analytics in Elavon’s Knoxville call center represents one of the best available cases for Big Data analysis—the advanced process of examining large amounts of different data, or Big Data, in an effort to uncover hidden patterns. By harnessing Big Data into a more useful form, its contact center can become proactive rather than reactive in solving customer service problems and finding opportunities to encourage customer loyalty.
Recent customer relationship management trends indicate that over the next few years, more contact centers will begin looking at their business intelligence as a meaningful resource to help differentiate their brand in the marketplace. A new generation of tools and technology are now available to extract that meaningful information from Big Data to help retain clients, reduce costs and improve the customer experience. In 2013, spending on business-intelligence software is expected to reach $13.8 billion, up 7 percent from 2012, according to analyst firm Gartner, which projects spending will hit $17.1 billion by 2016.
The challenge for contact center managers will be keeping their focus on organizational alignment so that team members are well versed on how to use Big Data to accomplish collective goals. Investment in data management tools, like Big Data systems, will enable center personnel to better understand how that information and technology will improve their customer service efforts. As a result, the contact center will ensure actionable business intelligence gets to the right person or department at the right time in order to make it usable.
Opportunities and Challenges
Have you ever wanted to know exactly how customers were using your product and whether they were likely to stay customers? The more you know about your customers over time, the better your chances of keeping them from going to a competitor. Big Data analysis technology is a springboard to solutions that support real-time metrics and leverage predictive analytics to simulate and forecast consumer behavior.
Predictive analysis is being used to generate vast amounts of data about customers’ buying habits, attitudes, preferences and pet peeves. In a practical way, figuring out how to make use of this data could mean all kinds of improvements to the customer support experience. Ultimately, the goal is to apply Big Data analytics methods that move beyond customer satisfaction to nurturing customer loyalty by more deeply understanding the customer’s total experience.
For example, through Big Data analysis, a company might discover that a certain demographic group of customers prefers shopping online instead of buying products at brick-and-mortar shops, and prefers text messages over emails. As a result, a retailer might launch a text message marketing campaign to promote online products and e-commerce options to customers in that demographic group to increase sales.
Additionally, knowledge derived from Big Data analytics technology has the potential to save costs, foster ideas for new and improved products, identify cross-sell/up-sell opportunities and determine the effectiveness of marketing campaigns.
With more companies leveraging consumer intelligence to stay competitive in the market, there is the danger that they will become mesmerized by Big Data, which could pose a potential risk. Any time an organization uses Big Data to amass customers’ private, sensitive information, there is the chance that it could be misused or used ineffectively. External auditors and risk professionals should play a key role in the Big Data process to address policies related to privacy, security, intellectual property and even liability.
External auditors and risk professionals should play a key role to address policies related to privacy, security, intellectual property and even liability.
Making Sense of Big Data
It’s not just the quantity of data that can offer value, it’s also the speed with which all this data is generated, as well as how it is used. When it comes to describing Big Data, much of the technology industry commonly uses the “three Vs” model—volume, velocity and variety—to characterize different aspects of the data.
- Volume refers to the massive amount of data being collected. By some estimates we create 2.5 quintillion bytes of data every day (a quintillion is 1 followed by 18 zeros)—so much that 90 percent of the data in the world today has been generated in the last two years.
- Velocity refers to the frequency of data generation or frequency of data delivery that needs to be analyzed as it comes—all in real time.
- Variety refers to the different types of data such as structured and unstructured data (i.e., images, videos and text from contact center conversations).
Before starting a Big Data initiative, contact centers must first determine exactly what they want to achieve by collecting and analyzing data: Are they looking to retain customers? Do they want to predict future trends regarding consumer purchasing patterns? Are they looking to drive maximum sales of products and services? Each objective will influence how data is collected, organized and used. Determining a strategy will help contact centers establish a clear understanding of what data is actually valuable to them.
Data quality management is something that’s often overlooked when it comes to Big Data analysis. While accessing and analyzing large data sets may be important, it’s even more critical that the information being evaluated is based on “quality” data. For example, it’s likely that call agents could enter data in inappropriate fields in a CRM system. Data that is not high quality, or riddled with errors and inconsistencies, diminishes the analysis process, and the value of the contact center’s mission, goals and objectives is jeopardized. When managed correctly, data quality minimizes risk.
Big Data also requires big leadership. It takes high-level support and call center managers with deep analytical skills to make effective decisions. Contact centers need to employ the right people who know how to apply advanced analytical tools to generate predictive insights into customer activities as a direct result of the data.
Determining a strategy will help contact centers establish a clear understanding of what data is actually valuable to them.
Five Ways Analytics Create Value
Big Data provides an opportunity for business enterprises to find insight in new and emerging types of data that will make operations more responsive. Its usage opens up new avenues for productivity, growth and customer interaction. Here are five broad ways in which Big Data analytics can create value for a contact center:
- Contact centers can unlock significant value from usable information to greatly improve their customer support value by using Big Data analytics tools. Companies with access to this level of intelligence obtain a greater understanding of customers through behavior and preference, which ultimately drives customer retention and brand loyalty.
- The valuable data that’s mined from contact center logs is of particular interest to chief marketing officers. By leveraging the massive amounts of data extracted from recorded customer transactions, marketing departments can tailor specific marketing campaigns and product offerings to drive additional revenue. Big data analytics gives marketers the capacity to identify, measure and manage the factors that are positively impacting their brand.
- Big Data analysis can help contact centers achieve costs savings, especially through first contact resolution (FCR). Research has shown that solving customers’ problems on the first call is linked to lower costs, higher customer satisfaction and other benefits. Big Data analytics enables contact centers to truly measure FCR patterns from all cross channels and data sources at any given moment.
- Successful cross-selling and up-selling opportunities are available based on what customer intelligence you find through Big Data. The right data analysis technology can help a contact center minimize the level of resources required to identify new products or service upgrades that call agents can offer customers. You’ll likely need to involve call agents in the analysis process, which requires providing them with considerably more insight about which specific products customers are most interested in.
- Identifying why customers are leaving is just one type of data that needs to be analyzed and managed. Data analytics helps contact centers understand the metrics that are impacting customer satisfaction and loyalty, and gives them the opportunity to make the changes necessary to keep customers coming back.
The right technology can help minimize the level of resources required to identify new products or service upgrades that agents can offer customers.
A Slow, Steady Shift
The move to embrace Big Data solutions won’t happen overnight for many contact centers. For years, customer care organizations have struggled to recognize the benefits of transforming the structured and unstructured data they collected from billions of customer interactions each year.
Despite their long history of collecting raw consumer data, contact centers are making slow but steady progress toward moving beyond traditional key performance indicator measurements—such as average talk time and average speed of answers—and implementing Big Data analytics to meet their customer service objectives.
The abundance of information gathered from recording calls between agents and customers, makes it the most valuable collection of custom intelligence. And yet, call recordings are a prime example of data sources that are often underexploited for business purposes. Although hundreds of millions of calls are recorded in contact centers throughout the year, some experts agree that a small percentage of these recordings are ever played back and listened by managers.
However, over the last year or so, a growing number of customer care executives are having a greater appreciation for how Big Data is being used to revolutionize the way contact centers process information. As a result, the spectrum of analytics tools that vendors are making available to contact centers has grown exponentially.
Currently, speech analytics software and technology from companies such as Avaya, CallMiner, Nexidia, NICE Systems, Utopy and Verint Systems offer contact centers a variety of ways to automate the mining of customer transaction information from call recordings and transcripts and turn that content into usable business information.
Rather than just periodically listening to a call recording to make sure agents are sticking to the script, contact center supervisors can take further steps to leverage that unstructured data. Big Data analysis enables managers to identify conversations where the customer says, “never again,” or “doesn’t work” or any such keywords that signal their dissatisfaction. Strategic analysis of Big Data extracted from recordings will go far in improving customer retention policies.
Telephone call recordings are not the only valuable data source. Customer interactions can take place over a multiple of different channels, including email, instant messaging, computer screen recordings and Web forms. The good news is that today there are huge growth opportunities in Big Data analysis, and it is proving to have a very attractive return on investment for contact centers investing in the process.
One thing that is certain in this new phenomenon of data-driven technology: It is pushing the bounds of systems used to judge customer sentiments and behaviors. Data analytics is doing a remarkable job of linking disparate data silos to patterns of customer behavior and trends that are predictive. Big Data is motivating leading contact centers to move away from pure cost reduction strategies and invest in analytic tools that effectively improve customer satisfaction and loyalty and provide real-time results.