Use Contextual Data to Improve the Contact Center Customer Experience

Contact centers are discovering that one way to deliver a better customer experience is to gather, analyze and act upon contextual data.

Contextual data, also called contextual intelligence, is metadata, metrics, statistics, key performance indicators and other information that helps call centers understand how disparate pieces of data relate to each other, placing them into a larger picture.

Contextual data includes

  •   Caller intent (why they are calling)
  •   Personal information
  •   Date of last purchase
  •   Last item purchased
  •   Time on hold
  •   Date of last call
  •   Status of last call (resolved versus not resolved, for example)
  •   Attitudes about your brand
  •   Customer’s preferred communication method

This data on its own is not enough. It must be analyzed in context with other data. The key to understanding a given situation is context. Contextualization is crucial to turning disparate data points into actionable insights—and better customer service.

Contextual data connects customer interactions across channels

Contextual data is at the heart of omnichannel commerce. Omnichannel, of course, means “all channels.” The goal of omnichannel commerce is to enable a single customer experience across a brand by unifying sales, marketing, customer service and technical support between all channels.

These channels include:

  •   Web
  •   Phone
  •   Email
  •   Mobile app
  •   Social media
  •   Chat
  •   Instant message
  •   In-store

An omnichannel contact center allows customers to contact companies using their preferred method of communication, whether it be phone, the company website, chat, a mobile app, or social media.

Omnichannel contact centers use contextual data to make their operations seamless and to eliminate the painful customer experiences so common with so-called “mono-channel” call centers. They do this by passing interactions between channels. So, when Samantha calls customer service to upgrade her mobile phone, the call center agent already knows that she purchased her current device three years ago, she has called in twice in the past three weeks about calls being dropped, she initiated a live chat this morning on the support webpage, and she still has an outstanding case.

Today’s consumers expect an experience that is consistent across every channel, requires little effort from them, and is personalized to their needs. Contextual data makes this possible.

Contextual data helps you resolve issues more rapidly

As consumers get more comfortable with using both offline and digital channels to accomplish the same goals, so do their expectations for instantaneous responses to their questions and concerns. This is especially true for their expectations of call centers. They want their experience to be quick.

Call Centre Helper Magazine recommends that Average Handle Time should be six minutes. In other words, the total duration of one call center transaction (including hold time, talk time and follow-up tasks) should be six minutes.

Contextual data helps your call center reduce your Average Handle Times by auto-populating agent dashboards with relevant data, such as:

  •   customer information
  •   reason codes
  •   product codes
  •   next-best-action suggestions
  •   knowledge articles

So, going back to the Samantha example, when she calls customer service to upgrade her mobile phone, the contact center agent on the receiving end already knows she is having issues with her calls being dropped, and the agent also knows what was discussed in her live chat with the support team earlier that day. Armed with this information, the agent can dive right into the issues at hand when speaking with Samantha and cut down on the amount of time she has to spend re-explaining her phone issues.  

Contextual data is crucial in contact centers because it gives agents the data they need to have personalized conversations with callers. Without context, agents have a one-dimensional view of the person on the other end of the phone. But with contextual data, agents have a 360° view of their caller. They know, for example, how the customer has already interacted with the business, across all channels—and that means the agents deliver a better customer experience.

Context reduces customer effort

“I enjoy calling customer service and repeating myself to each representative,” said no customer ever.

Today’s consumers are tired of telling IVR systems what they want, only to have to repeat this exact same information word for word once they reach a live agent. Cumbersome IVR menus and lengthy customer verification protocols frustrate today’s customers, who are demanding a customer experience that is quick, seamless—and painless.

Contact centers are embracing contextual data because it reduces the amount of effort that customers have to expend to reach customer service and resolve their issue. Contextual data removes barriers to good service. It helps call centers give callers a personalized experience, one in which the agent knows the context of each customer’s previous interactions with the business, and understands the reason for their call. 

Contextual data is all about customer convenience, and providing a seamless customer experience.

Context leads to more sales and better customer retention

Contextual data helps contact centers increase sales and boost customer retention rates because it tells agents where each caller is in their buying journey.

Contextual data help businesses predict customer behavior by combining past data with future possibilities. Using artificial intelligence and machine learning algorithms, for example, businesses interpret billions of customer data points to develop customer personas. Businesses then use these customer personas to anticipate the products and services the business can offer each customer—today and tomorrow.

Contextual data includes:

  •   Previous customer calls, chats, emails and other communications with the company
  •   Geo-specific events, such as purchases made with mobile devices and in-store visits
  •   Purchase behaviors, including time and frequency of purchases, as well which products were purchased, and in what quantities
  •   Customer interactions on the company website, including pages visited, time spent on pages, and links clicked
  •   Psychographic factors, such as customer preferences, values, lifestyle and opinions
  •   Source of customer referral

Using contextual data to improve call center working processes

Contextual data helps your call center uncover problems you are facing with bad processes, bad procedures and bad actors.

You may be facing challenges uncovering these problems because you currently lack visibility into your data. And without that visibility, you’ll never catch these issues.

But once you put the systems in place to gather and analyze your contextual data, many of these issues will show up right away. Other issues will show up once you begin using thresholds and alerts to flag potential problems.

The right systems also offer reports that help you improve your call center working processes. These reports help you discover if you’ve had a good day or a bad day. And if you’ve had a bad day, the reports show you the contextual data you need to prevent these bad days from happening again.

Using contextual data to uncover fraud

Criminals use the phone to defraud businesses. And call centers are wise to use contextual data to detect these fraudsters before they are successful in carrying out their scams.

One contextual data point that you can examine for fraud is the calling number for evidence of spoofing. Spoofing consists of altering the caller ID to show a different number than the one being used to place the call. Fraudsters typically use spoofing to mislead call center agents into believing that a call is either local (when it is actually being placed from overseas), or that the call is from a trusted individual or organization (a trusted customer, for example).

An effective monitoring system features alerts and thresholds that notify agents at the desktop level or notify supervisors to either listen in on suspicious calls or intercept calls for quality review.

Contact center software solutions also help you avoid scams that involve fraudulent product returns. Some systems insert breadcrumbs into the call center service cloud and track them along the customer journey. Here’s how the solution works.

In a common fraud, a customer orders a product online but picks it up at a nearby retail store. But before they walk out of the store with the product, they use their mobile phone to call customer service and cancel the order. What fraudsters hope is that the retailer’s systems aren’t fast enough to know that the customer picked up the product but is receiving a refund while walking out the door.

Another scam has fraudsters speaking to a manager in a store, complaining about a bad in-store experience. The manager remedies the situation by giving the customer a free gift card. The customer walks out of the store and immediately calls customer service with the same scenario, hoping to receive another gift card. The fraudulent customer is hoping that the retailer’s offline and online systems aren’t connected and communicating with each other.

Conclusion

The key to improving the customer experience at your call center is data—contextual data in particular. Contextual data not only helps your agents have more-personalized and more- productive conversations with callers. It also empowers self-help tools, such as IVRs and chatbots. In the end, contextual data means a better experience for all concerned—your agents, your business, and your customers.

How we can help you

Aceyus understands the importance of contextual data. With the Visionary User Experience or VUE, our data reporting tools make your omnichannel data easily accessible to your contact center managers, providing your team with valuable, contextual data insights at their fingertips. 

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