Big Data: Getting Smarter with Customer Engagement


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Picture this: You call customer service at your phone company to fix an issue with your phone. The agent answers and greets you by name before you have an opportunity to say hello. After asking how your day has been, he gets down to business. He knows you tweeted about your problem on Twitter yesterday and apologizes that nobody got back to you right away. He is also aware that you recently chatted with a customer representative two days ago when setting up your phone, and that you are experiencing dropped calls. Lastly, he states what he perceives to be the issue and asks you to confirm. Before long he gets to the root of your problem and transfers you to the person who can help. Within a few minutes, the call is over, your problem is fixed and you hang up satisfied. This seamless service interaction is a moment that lives on in a customer’s mind, and sets the expectation for how all experiences should go. It’s the result of a smarter customer engagement strategy, and the magic wand that makes it happen is big data.

The proliferation of the multi-device, omnichannel culture has made it more difficult than ever for organizations to manage the people and processes required to respond to consumer expectations in a consistent, personalized and contextual manner. The quantity and speed at which data is generated through these various channels, as well as the diversity of that data—much of which is unstructured and difficult to analyze—creates a major challenge for organizations. Very few companies have been able to effectively correlate and assess the data to drive an action-oriented strategy for their businesses. Nuggets of invaluable insight lie within that data—customer needs, wants and issues—information that can facilitate a special connection with the brand. But harnessing that actionable intelligence is tricky. Most organizations can get to the “what” of customer behavior through transactional data; it’s harder to get to the “why.”

To foster smarter customer engagement using big data analytics, organizations should consider several technology-driven strategies. These include:

Enhanced Employee Engagement

The first effective strategy to explore when striving to enrich the customer experience is through enhanced employee engagement. It’s hard to argue the obvious—that an engaged employee will likely contribute to a more engaged customer. By looking closely at the employee’s performance and development journey, in a similar manner to that of ‘customer journey mapping,’ companies can gain a better understanding of each employee and what motivates and drives them, thereby improving their engagement with the organization. For example, personalized performance plans make target achievement realistic and motivating. When created with a single click from a concerning KPI, they are simple enough to make, manage and be utilized in the most under-resourced environments. Instead of focusing exclusively on point-in-time quality checks and measures for employee productivity, businesses with easily accessed personal development history can connect spot performance with the employee’s longer journey and growth curve within the company. In this way, the employee obtains better insight into their performance, gets credit for achievements and takes ownership of their successes. Where possible, it’s beneficial for employees to participate in their own quality evaluation process, making them feel more involved and connected to the results of their evaluations. Using technology available today, employees and evaluators can become more productive in their jobs by providing feedback that’s relevant, contextual and customized.

Automated Agent Guidance

Using real-time agent guidance, agents can enhance their interactions with customers during a contact center call through automated reminders based on the nature of the transaction, the sentiment that arises during the conversation, or even previous customer communications. With the help of big data analytics like speech and text analytics and desktop analytics, connecting the different points of the customer journey becomes easier, so that customers feel like the organization knows them. Moreover, big data can surface certain themes that the organization didn’t know existed, or didn’t know to look for in the vast sea of available data. Proactively addressing issues like a new agent struggling to provide answers or a fraud threat with a customer in real-time can go a long way to building a healthy relationship. And, gaining ongoing insights that can be turned into action, such as recognizing an emerging pattern of a customer’s product problem and offering proactive assistance can contribute to long-term brand loyalty and increased revenue potential.

Customer Engagement

Customers today seek and expect extremely personalized experiences from all interactions. From the organization’s perspective, creating points of connection and memorable moments to develop an ongoing relationship with the customer serves to facilitate repeat occurrences. But customers now expect anticipatory relationships, where a particular business knows the type of engagement that customers are looking for from the companies with which they interact. For example, something as simple as verifying the customer’s identity via a voiceprint from previous calls, without the need for an excessive amount of tedious security questions, goes a long way in making the customer feel known and appreciated. By providing acknowledgement with action that the respective customer much prefers an SMS with his/her confirmation number as opposed to waiting on the call and writing it down takes the relationship to the level of anticipatory—the real glue in long-term consumer/company relationships. The use of big data analytics enables organizations to achieve this level of engagement by helping them understand customer sentiment, and the decision-making process, in an omnichannel fashion.

By tying these three approaches together into one strategy, organizations can move away from the challenge of big data to the opportunity of developing a positive overall customer experience that creates a strategic differentiator for the business. Organizations can break away from the competition by creating real connections between their customers and the brand—by mining and analyzing the right data, at the right time and at the right interaction points to achieve smarter customer engagement.

Kristyn Emenecker
Kristyn Emenecker is Global Vice President Enterprise Workforce Optimization at Verint. She has 20 years of experience in the contact center and customer experience space, serving in a variety of operational, consultant and senior leadership roles. She is active in a number of industry groups, published in multiple trade journals and a regular on the industry speaking circuit.


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