If you operate a contact center and if you are wondering what contextual data is, what it’s for and how to measure it, here are some answers.

Contextual data is information that provides perspective into an event, person or thing. It provides a broader understanding by showing how disparate pieces of data relate to each other, placing them into a larger picture.

Contextual data is all around you. If you walk into a jewelry store, for example, and tell the salesperson that you want to buy a ring, the rep will immediately start asking you questions to establish the context of your purchase. Is the ring for a man or a woman? Is the ring a gift to celebrate a special occasion? How much do you want to spend? Your answers to these questions give the sales rep data points that put your visit to the store into context. Your answers help the store serve you better.

Or, imagine that you visit the doctor with pain in your chest. The doctor aims to diagnose you and prescribe a cure, and to do that the doctor needs to gain some context into the cause of your chest pain, and why you are visiting the doctor today, in particular.

So the doctor asks you questions to establish some context for your visit. Where in your chest does it hurt, exactly? Is the pain constant, or does it come and go? Have you lifted anything heavy, or exerted yourself physically, recently? Do you have a history of heart disease in your family? And so on.

As you can see, the key to understanding a given situation is context. Contextualization is crucial to turning disparate data points into actionable insights.

How (and why) are call centers embracing contextual data?

For call centers, contextual data is information that helps agents understand why a customer is calling, and how to help them achieve their desired outcome.

To be useful and effective, contextual data must combine multiple data points from multiple touchpoints. These data points include:

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

Data points can come from any channel that customers interact with your business. They include:

  •   Phone call
  •   Email
  •   Social media activity
  •   Chat session
  •   Instant message
  •   Website visit
  •   In-store visit

Contextual data is being used in multiple industries. Universities and colleges, for example, are using contextual data to identify candidates with the highest chances of succeeding in their academic endeavors, regardless of their ethnicity or socioeconomic background. Contextual data is also being used in marketing because it yields highly accurate predictions about buyer behavior.

What is the value of contextual data in the contact center?

But context is crucial in contact centers because it gives agents the data they need to have personalized conversations with callers. With contextual data, agents have a complete view of their caller. Historical data, for example, indicates how the customer has already interacted with the business. Real-time data provides a continuous experience for the consumer, whether this data is from a mobile device, the company website or the retail store.

Context isn’t just valuable for call centers, either. It is valuable for callers as well, making their calls easier to make and less painful to endure. With contextual data, callers don’t have to re-describe their problem or ask their question multiple times.

Contextual data, of course, is vital to accurate and timely call routing. It puts customers through to the right agents quickly. Combined with predictive analytics, contextual data helps to anticipate the questions and concerns that customers are likely to have.

Contact centers are also using contextual data to make customer service chatbots more intelligent. Static chatbots are becoming obsolete because they are limited in their effectiveness and because they frustrate customers.

But chatbots that understand conversational context respond better to prompts and deliver a better customer experience. Advancements in machine learning have improved natural language processing. The result is contextual data obtained in real-time that helps chatbots solve customer challenges.

What is driving the demand for contextual data?

To be frank, today’s consumers are fed up: with calls to customer service that are painful and difficult. Of telling IVR systems what they want, only to have to repeat this information once they are put through to a live agent. With repeating themselves as they are passed from one agent to another. Of telling agents about their order history, their past calls to customer service and other things that the agent should already know.

Contact centers are embracing contextual data because it helps them deliver a better customer experience. The goal is to remove barriers to good service. The goal is to give callers a personalized experience, one in which the agent has the context of their previous interactions with the business and understands the reason for their call. 

What are the benefits of contextual data?

Contextual data offers several benefits to contact centers. It helps call centers:

Improve first-call resolution rates. Contextual data helps agents deliver a better customer experience. Agents that understand the context of a call are quicker to answer a caller’s questions, solve their problems, offer advice, and resolve all issues during the first call.

Reduce agent turnover. Happier customers translate into happier agents. Agents who have the contextual data at their fingertips to understand caller intent and resolve calls quickly are happier on the job—and more likely to stay. Contextual data reduces employee attrition.

Shorten average handle times. Contextual data helps agents shorten their average handle times by achieving positive outcomes sooner.

Reduce average time in queue. Shorter average handle times translate into lower average times in queue for waiting callers.

How long will it take to measure contextual data?

You can begin to track your contextual data rather quickly. But doing the analysis of that data is something that requires more time, and a good understanding of your data.

Many businesses are able to turn on reporting and spot valuable insights almost instantaneously. But in many cases, they realize they are missing visibility. For example, if you use the HubSpot CRM but you have never looked at the data in HubSpot, you may discover (as many businesses have) that you are not capturing data in a given field. You realize that you’re not taking advantage of a field that helps you understand your customers better.

How does contextual data help call centers meet the top-two KPIs?

The two most important KPIs for call centers are caller intent and call outcome.

Caller intent

Caller intent answers the question, “How may I help you?” This question has traditionally been answered by IVR phone systems that prompt callers to identify the reason for their call by selecting from a menu of options.

Today, caller intent is being identified with the help of contextual data and artificial intelligence. Some of today’s solutions capture contextual data from a customer’s in-app or website activity and present it to the agent. This helps the agent quickly identify and authenticate the caller, while delivering insights into what that customer is likely calling about.


Contextual data helps you answer the question, “Did we resolve the issue the customer called about, or was the problem something else entirely?”

Contextual data is helping contact centers measure the level of success of their outcomes.

For example, if the majority of the calls you receive are from customers who have a dispute with their bill, then your reporting tells you that you may have a problem with your billing system.

But if some of your calls are from customers who can’t find the charge on their bill, reporting tells you that you either have a customer education problem, or a bill design problem. You don’t have a problem with your billing system or your billing process.

What’s a good case study of a call center using contextual data to improve customer experience?

The State of Customer Service in 2019 describes how HubSpot’s support team used contextual data to reduce their annual customer support costs by $7 million.

The HubSpot team analyzed their call data and discovered that agents resolved 41% of customer cases with a documented solution, at a cost of $7 million. This discovery prompted the company to realize that they could solve these issues with a self-help system.

The team realized that, if they made their support documentation customer-facing, they could reduce case volume and solve problems much more quickly. So, they reported their discovery to upper management. Then, they persuaded their operations leadership to invest in self-service resources for their customers, such as a knowledge base.

The result? HubSpot saved $7 million in call center costs by helping their customers resolve the issues that used to take a live agent to solve.

How do we get started with contextual data?

If you want to start capitalizing on contextual data to improve your data-based decision making, take a page out of the book of Jeff Pruitt, Chairman & CEO of Tallwave, on INC.com. Pruitt maps out the three steps you must take to put the power of contextual data to work for your call center.

Step 1: Pick your data points

Conduct an audit and create two lists. In list one, determine the data points that you are already collecting today. In list two, write out the metrics that you are not collecting, but want to. Use these two lists to determine the data points you want to track in your contact center.

Step 2: Visualize your data

Data without context is useless. And data that’s invisible is doubly useless. So, build dashboards that are tailored to your agents or your outcomes. If needed, map your data to the customer journey. Your goal is to gain understanding of what’s causing pain and frustration for both your callers and your agents. 

Your customer service agents cannot provide frictionless service if their access to customer information is limited. If your agents can’t see when a customer is showing signs of defection, they can’t take steps to repair the relationship. And if your agents can’t see order histories, they can’t recommend relevant products.

Step 3: Look for optimization opportunities

After you have collected your data, put it into context, and visualized it, look for opportunities to improve your processes. Start by articulating the questions that your data elicits.

For example, are your agents escalating too many calls? Hunt for patterns among those calls and those agents. Are the calls being routed to the wrong agents? Or, if the calls are being routed to the correct agents, are there gaps in their training that you need to close?

How can we gather contextual data when we have data silos?

Your contact center uses data to make decisions. Which means your data must be readily available to anyone who needs it. The problem is, data silos hinder availability, since data silos (and the people who manage them) don’t share information easily with each other.

You can eliminate these data silos with technology. The right data reporting tools provide the transparency and collaboration you need to knock down silos. The short-term cost of implementing these solutions is nothing compared with the long-term benefits of capturing clean, contextualized data and making it readily available.

The latest contact center technology delivers context by integrating your data silos, helping your analytics team identify relationships between separate pieces of customer information. The data of greatest value to a contact center includes purchases, brand interaction and attitudes about your brand. Integrating these data points gives you context. It gives you deeper insights into your customers’ behavior, such as why they buy from you and whether they would recommend you.

How Aceyus Provides Solutions You Need

Aceyus understands the importance of contextual data. Our data reporting tools make your multi-channel data easily accessible to anyone who needs it, providing your team with valuable, contextual data insights at their fingertips. Schedule your personalized demo today to see how our solutions include the experience of your customers and employees. Or contact us to find out more on how we can improve operations in your contact center.

Michelle Hernandez

Michelle Hernandez

Related Posts

Blog News

Five9 to Acquire Aceyus

Five9 to Acquire Aceyus extending the Five9 platform to streamline the migration of large enterprise customers …