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Big Data Applications in the Contact Center:  Opportunities and Challenges

  • January 2014
  • 12 pages
  • Frost & Sullivan
Report ID: 2000247

Summary

Table of Contents

Untapped Sources of Data Promise Hyper Intelligence for Customer Service

Big Data, a hot business topic these days, is emphasized on a myriad of web sites of solution providers in the contact center space, claiming the ability to glean customer intelligence and improve the Customer Experience. But what are the realities of this emerging trend? How much is Big Data really being used in the contact center? Is Big Data an emerging contact center trend or just so much “market-speak”? This market insight explores the challenges and opportunities inherent to Big Data when considered as information overlay to Customer Interaction Analytics (CIA).

Definition

Frost & Sullivan's Stratecast Growth Partnership Program, Big Data & Analytics, helps companies of all types navigate this key topic. The practice defines the Big Data phenomenon in the following manner:
Volumes of data so large and moving at such a high velocity that it is difficult or impossible to work with using traditional database management tools. Big Data represents the collective and exponential growth of both structured and unstructured data, from every imaginable digital source, including data logs, pictures, audio, and video. The size and scope of the data being generated each day has surpassed the capabilities of traditional enterprise systems to capture and process.

Frost & Sullivan also notes that by its very nature, Big Data defies the efficient data processing and management of relational databases. Instead, "parallel processing," in which hundreds or even thousands of individual CPUs simultaneously process a portion of the dataset, is used to quickly and effectively turn this unruly mass of data into usable information. While Big Data was originally directed at scientific endeavors such as genome sequencing and meteorological forecasting, recently similar approaches have been applied to other realms as well, including improving the Customer Experience in markets such as retail and telecommunications.

Frost & Sullivan's Stratecast group also points out that:
Big Data is also about data complexity—and how an organization mitigates and manages that complexity. Stratecast defines Big Data as a series of capabilities and processes that centralize the management of diverse datasets across a distributed and large-scale data system. The term "Big Data" refers to the complexity of the data and the queries designed to extract information from it.

Big Data can be aggregated from a plethora of sources, including:
• Social networks, such as Facebook, Google, LinkedIn, YouTube, and others
• Corporate documents
• Sensors, such as Radio Frequency Identification (RFID) tags on physical assets, or motion sensors to detect movement on toll bridges or turnstiles, or GPS on smartphones, or location-based technologies
• Instrumented machinery
• Population Census data
• Web interactions, such as clickstream data, web page hits, and search indices
• Customer data within vertical markets, such as financial or medical records
• Video, such as surveillance footage or in-store cameras
• Geographic, psychographic, or demographic data

In essence, these categories represent just some of the possible sources of data currently being tapped to gain information about customers. And while businesses such as Communications Services Providers (CSPs) and large retailers are using Big Data to gain more intelligence about customers—and honing supply chains and marketing programs as a result—the contact center also is paying attention to the benefits that Big Data can bring when it comes to the Customer Experience.

The Promise of Big Data as it Applies to Customer Contact

Big Data projects cut across all industries and are being used to do myriad things including enhance forecasting models, contribute to product design, or determine consumer buying patterns, and it's now being looked at as a way to enhance the customer service industry as well. Big Data—in all its variety of sources, volume, and velocity at which it arrives and must be acted on—can augment the transaction history of a customer journey.

But handling Big Data also demands a different set of rules than more traditional contact center data sets. Some data, such as trending topics on social networks, has fleeting value. Dynamic and unpredictable occurrences, such as a flare-up of activity about a company, product or service, means that running structured, planned reports as with other contact center data requires a different level of data storage and tools. However, when combined with more traditional data sources used in the contact center (such as CRM, WFM, IVR, and ACD statistics) Big Data can bring a new level of customer insight, and help drive real-time decisions on customer handling and workflow.

Over the past decade the focus has been on the customer journey and how to improve it. Traditional contact center metrics provide visibility from a macro customer journey level down to individual input on a single channel (events). Contact center analysts and supervisors can drill down and see which DTMF inputs are done in a single transaction in an IVR system, or go a level higher to see at which point the customer got frustrated and "zeroed out" to an agent (transaction). They can see what happened between agents and the customer (interaction) and tie those interactions together to see how the whole issue was resolved (engagement). This type of data allows companies to gain intelligence on a number of fronts, including:
• Understanding what the customer wants
• Finding out where there are breakpoints between systems and contact channels (for instance, did the customer opt out to an agent because the IVR is poorly implemented, or because they needed something more than self-service could provide?)
• Understanding which agents are the best performing and which need training
• Determining which contacts are being handled the first time, and if not, what needs to be improved

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