Aligning Information to Improve Customer Engagement

In today’s business environment, leading organizations realize that transforming the customer’s experience of their organization is fundamental to differentiation and remaining competitive. A major challenge to improving the customer experience is the growing variety of relevant information from new and different channels, for example social media, as well as the sheer volume of information: the phenomena broadly referred to as “Big Data”.

Transforming the customer experience is seen as one of the most significant potential benefits of better exploitation of Big Data. No matter from which channels customer data are gathered, or used to interact with customers, the underlying information processes that fuel all customer exchanges need to be aligned – be consistent, accurate, and timely.

Growing Customer Expectations even as Business Data Explodes

Growing customer expectations even as Big Data Explodes

Empowered with ubiquitous, powerful search tools and ever-growing amounts of data available via the Internet, customers have come to expect better and faster access to information – the right information in the right format at the right time. Consumers are increasingly information-savvy, and can easily and quickly find your competition online.  They have come to expect the same information agility and responsiveness from the organizations with which they do business. Also note, prospects and customers are equally adept at online sharing of “thumbs up” or “thumbs down” experiences; and negative word of mouth experiences can hurt your top line in a very short order.

At the same time, businesses are trying to manage rapidly increasing volumes of information, structured and unstructured, electronic and hardcopy, and from a growing diversity of sources.  According to the annual EMC Digital Universe report, 2.8 ZBs of data will have been created and replicated in 2012. From now until 2020, the digital universe will about double every two years.1

The sheer volume and dynamic nature of “Big Data” complicate the effective management of information to optimize the customer engagement, namely the extraction of business intelligence that helps us enhance the individual customer experience.

Locate and retrieve unique information from a Bid Data superset

“The ‘art’ of an experience is based on the premise that every customer is a unique individual. Therefore, a contact center must tailor their interactions “creatively” instead of engaging all customers in a uniform way”.2

It is critical to be able to quickly locate and retrieve unique information from the Big Data superset that map to a customer inquiry or need, gleaned from widely dispersed information in both structured format (e.g., business transactions and customer information) as well as unstructured (e.g., in hard copy documents, text posted on the Web, blogs and social media, etc.)  And bear in mind, it is estimated 80% of the world’s data is unstructured.3

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Highlights from the Full Paper

  • Customers have come to expect better and faster access to information – the right information in the right format at the right time.
  • Document and information processes should be optimized so that customer-facing staff are on the same page when it comes to information.
  • Ensuring customer-facing staff have access to data (via any platform) is equally critical to effectively engage customers.
  • Consider outsourcing specific business information processes to an experienced service provider to leverage their expertise, knowledge and scalability.
  • The benefits are improved customer retention, more productive customer interactions, positive referrals to new customers and better intelligence about what customers need and want.

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1 Gantz, John and David Reinsel, “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East,” IDC iView, December, 2012.
2 “The Art and Science of Delivering Exceptional Customer Experiences: 5 Best Practice Principals,” VoltDelta, September, 2012.
3 P. Zikopoulousa and C. Eaton, “Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data”, (ed: McGraw - Hill, 2011)