Businesses are constantly seeking innovative ways to enhance customer experience and satisfaction. As a differentiator, customer experience contributes to sales, engagement and loyalty. Recently, the most impactful trend that has emerged to improve customer experience has been making use of contextual data in customer service. It’s one of the most untapped resources any business has today. Customer data is collected across the customer journey, and it needs to be put to work to pay dividends within your business.
Incorporating personalized and responsive service strategies based on this contextual data not only meets but often exceeds customer expectations. Such practices are crucial because they directly influence customer satisfaction. Higher customer satisfaction, in turn, fosters stronger brand advocacy and loyalty. It’s important to note that a significant portion of businesses—60%, to be precise—rely on these non-financial metrics (brand advocacy and loyalty) to gauge the ROI of their customer experience strategies. This statistic highlights the substantial role that customer satisfaction plays in achieving business outcomes that are vital for long-term success.”
Data analytics to improve customer experience is an incredibly powerful tool within an organization.
Specifically, contextual data analytics for customer support enables companies to provide exceptional, personalized services. With the use of data-driven strategies and analytics, organizations can gain valuable insights into customers’ preferences, behaviors, and needs, allowing them to tailor their services to meet and exceed customer expectations. The advancements of data collection, AI analytics, and generative AI have made these powerful and engaging concepts more accessible to the market. Let’s explore how to use data to provide personalized experiences and higher-quality products and services.
Personalize experiences with data
For many companies today, data collection is happening all the time. Data, such as customer demographics, past support interactions, purchase history, location, product usage, and preferences is likely found across your tech stack. It is also likely that that data is not being put to good use. With good tooling and integration, companies can gain a comprehensive understanding of their customers’ needs and provide a personalized experience at every touch point.
Leveraging contextual data in customer service means taking the data you already have associated with a customer, and exposing it in a useful way to your support agents. This facilitates more intelligent conversations.
For example, you might connect to tools like the following inside your tech stack:
- Order system: Recent order numbers and current status
- Digital adoption tool: Recent actions in the product
- Shipping tool: Current shipping address or tracking numbers
- CRM: Customer location, renewal information, loyalty status, tenure
- Survey tool: Recent comments or scores
By providing these types of data to an agent, they will ask fewer triage questions, have more informed conversations, and often can provide the customer a resolution with little or no friction. Putting this data you already have to use within customer service enables organizations to create tailored experiences, fostering a sense of loyalty and increasing customer satisfaction.
Contextual Self-service
Contextual data in customer service can enhance the overall effectiveness of support interactions. If a ticket type is repetitive or manually time-consuming, an AI-enabled agent assist tool or chat bot trained on your data can transform your business
You are able to transition from a support model of asking a customer to manually solve their problem using a KB article to automating contextual actions on their behalf.
Your processes become truly self-service by connecting your tech stack to provide a personalized experience. These automations act to reduce the number of agent-handled tickets and they are often quicker and more convenient for the customer.
This is powerful for your customers and it also reduces the training needs for your agents. With your workflows being automated, your agents no longer have to train on all processes in advance. They are automatically presented with them in real time. Leveraging contextual data in customer service reduces manual process errors and provides more consistent customer experiences. Agents will never be stuck for next steps or having to look up how to complete tasks in other applications.
Data-driven customer experiences
Contextual data analytics for customer support enables proactive resolutions and smarter business decisions. By continuously monitoring and analyzing customer data, businesses can identify patterns and trends that may indicate potential issues or challenges. Data-driven customer service means taking proactive measures to address these concerns before they become major problems. For example, if a company notices a pattern of customer complaints about a particular product feature, the priority of the fix can be increased to prevent further frustration.
Analyzing aggregated contextual data can identify broader trends and patterns which inform strategic decision-making. For instance, by analyzing customer feedback and behavior data, companies can identify common pain points, preferences, and emerging needs
Contextual data can guide the development of new products, services, or features, ensuring that businesses stay ahead of the curve and continue to meet evolving customer expectations.
It also guides you to be more prescriptive when it comes to solving performance issues. For example, not only could you tell that Average Handle Time (AHT) has increased or CSAT has trended lower, but with contextual data, you are able to see more granular patterns. Perhaps AHT only increased for one team or on tickets of a certain type. CSAT may have only dropped in a particular region or with customers of a certain experience. Understanding the deeper contextual data in customer service will help you correct the course faster by focusing your attention.
Contextual Data is Power
There are three main powers of contextual data in customer service:
- Better conversations
- Contextual Self-service
- Deeper Analytics
When put to use, these powers will create an environment where your customers feel like you know them better, you anticipate their needs more accurately, and are constantly improving their experiences. Your customers will be able to solve their problems quickly, have more informed conversations with your agents, see fewer manual errors, and, when problems do start to occur, they are solved quicker. Data analytics to enhance customer experience is a trend which all companies need to embrace.
