Engineering Quantum Leaps
The unprecedented explosion of data that has taken place since the Internet's coming of age is finally matched by the quality of tools and methods that we have at our disposal, which allow us to explore, visualize, and mine this data. Observers estimate that 90 percent of all the digital data in the world has been created in the last two years alone.
Big data is transforming industries and business processes irrevocably. Customer service, however, has yet to unlock the transformative potential of this phenomenon. This is surprising, given that customers themselves are major drivers behind the creation of this data. Customers are connecting with each other and with companies, and generating data at a massive scale through these connections. A vast potential exists to creatively mine all of this data for insights, and to manufacture differentiated and intuitive experiences for customers. These experiences would embed intelligence about the customer derived from data to increase customer satisfaction, loyalty, retention, and advocacy. Businesses that fail to do so will risk gradually losing customers and revenues to competition.
Let's take a closer look at the transformative power of big data and how you can leverage it in your enterprise to make the next quantum leap in customer care.
The 90-10 Equation
Big data, put simply, refers to data sets that are so large and diverse that traditional data management and analytics tools are incapable of handling them. As a deluge of data flows into the enterprise from countless sources, conventional methods of analysis, such as relational databases, desktop statistics, and visualization tools, break down.
Big data can be structured or unstructured. While this statement sounds like a truism, the fact is that only about 10 percent of all enterprise data is structured and formatted to cleanly fit into databases and logical organization schema. The vast majority of enterprise data is unstructured. Unstructured data includes emails, Facebook posts, tweets, chat transcripts, call center interactions, Web site activity, and support forum conversations—all gathered from multiple channels, such as your Web site, mobile applications, contact centers, emails, and social media interactions. These form more than 90 percent of the data deluge that businesses routinely collect. Arguably, this 90 percent contains the more interesting and valuable data about customers, since these conversations are a direct expression of customer likes and dislikes. These conversations offer a window into customer preferences and opinions on your products, brands, and the effectiveness of the customer experiences that you offer. This vast majority of customer data goes untapped, unmined, and unseen.
This is increasingly important, because the change in data is accompanied by a change in consumer behavior. Customers want intelligent interactions with companies, and they expect these experiences to be seamless and consistent across channels. The key to delivering these experiences lies in the data. To deal with data at this scale, certain emergent technologies are gaining prevalence. These include distributed databases and file systems, and programming tools such as Hadoop, which allow easy manipulation of data at scale. Other sets of technologies, such as machine learning, predictive modeling, and pattern recognition, leverage big data tools to enable us to identify trends and learn at scale.
When put together, these new tools and technologies allow us to use big data to transform customer service, engineering a quantum leap from the old, reactive way of doing things to new, differentiated models of customer experience.
Big Data Technologies at Work
Unifying platforms must combine a number of big data technologies to drive differentiated customer experiences and outcomes. These include:
- Distributed computing across huge data sets: required to collect, store, and process detailed user interaction data across all customer touchpoints
- Text mining: different techniques (e.g., categorization/classification, clustering, feature extraction, sentiment analysis) used to derive patterns from unstructured human communications (e.g., chats)
- Data integration and orchestration: used to provide access to and combine data from a variety of enterprise data sources
- Data fusion: integration of structured and unstructured data from different sources, e.g., integrating Web journey and chat data to better predict intent
- Speech recognition and processing: required to match user utterances to language models (grammars)
- Natural language understanding: used to construct language models that reflect the large number of possible user responses to a question
- Cloud-based application delivery: used to deliver large-scale applications at high levels of performance and availability
The Rise of Consumer Experience
Consumer expectations of customer service are increasingly shaped by access to connected technologies and services that anticipate needs and personalize experiences. When faced with a poor experience while interacting with a company or brand, consumers are more likely to give up—down-vote, dislike, abandon, or switch—than before. This has a critical impact on customer loyalty, retention, and revenues.
Big data is already a part of your customers' everyday lives. Google leverages big data for its users every time it runs a search, to return results that are relevant and personalized. Anyone with an Amazon account and associated browsing history with the company is greeted with astonishingly relevant product recommendations based on the patterns and clickstreams of thousands of other people who have behaved in a similar fashion. The focus of these experiences is to reduce consumers' net effort by making each interaction personalized and relevant.
Influenced by these technologies, customers today expect experiences (enabled by data) that simplify their tasks and reduce effort. This expectation is belied for them almost every time they reach out to a company to seek service or support. Voice, chat, or Web interaction still starts with a clueless agent or an opaque Web site that seems to ask the customer: "Who are you and why do you want to talk to us?" Seeking customer service from the enterprise continues to be an inherently painful exercise for most customers.
A big gap exists between the actual and potential quality of each and every customer interaction. Businesses have data. Deploying it to gain insights into the consumer and applying these insights back to every interaction—that's the issue.
While many enterprises are becoming increasingly adept at using analytical tools and big data to increase transactional sales, customer service and support interactions have not received an equal share of attention. Moreover, customer data has yet to make the leap from a tool to enhance some transactions to a real strategic asset.
Your customer data can tell you who your customers are, their history with you, their past interaction data, or how their current journey on your Web site or at your toll-free number is going. It can also tell you if your customers are vocal about you on social media.
In fact, big data is already telling businesses all that and more about their consumers. Enterprises collect, report, and store vast amounts of data that consumers generate, including:
- Transaction history and customer data from ERP and CRM systems
- CRM data on billing history, past purchases, loyalty programs, etc.
- Location data from smartphone or tablet apps
- Clickstreams and Web behavior data from Web analytics software
- Chat or voice interaction transcripts from contact centers
- Product reviews and recommendation data from the Web site
- Data from social connections and friend networks, available from social media tools
Aggregated together, with the right tools and methods, this data across thousands and millions of customers reveals patterns, trends, and insights.
Ravi Vijayaraghavan leads the analytics and data sciences organization at 7. His team builds data-driven solutions and predictive systems that enable superior customer acquisition and customer service. Previously, he served in diverse leadership roles, from scientific research to IT and strategy, at Ford Motor Company. He was also a vice president and part of the executive leadership team of Mu Sigma Inc., a Chicago based pure-play analytical services company, where he was responsible for client management.