When businesses become more customer-centric, customer experience (CX) becomes the focal point for contact center operations. This is a marked change from the conventional model, where customer service was largely transactional and managed query-by-query. Legacy systems supported this model well, but a change is needed to support the customer experience.

Focusing on the overall customer experience could improve businesses’ bottom lines. According to a customer experience report by PWC, customers who feel valued are willing to pay up to a 16% price premium on products and services. Furthermore, 65% of customers are more willing to share their personal data with companies where they feel valued.

Providing good CX requires understanding the entire customer journey, not just resolving an issue in the moment. This entails having visibility into what happens before, during and after a customer engagement, along with the capability to process that information effectively, allowing agents to improve CX. Not only do agents need to handle inquiries as they come, but CX requires them to optimize the entire customer journey.

Since agents operate in a desktop environment, having an integrated contact center agent desktop is central to supporting their needs. Agents cannot effectively jump in and out of siloed applications, manage multiple screens, or juggle various endpoints while engaging with customers. The key is to have all the right information in one place and at the right time. Given that every customer journey is different, and most will require a distinct set of inputs, agents cannot rely on their wits to pull everything together in real-time.

This represents a prime use case for artificial intelligence (AI) with two essential attributes. First, AI has the scale and speed to gather all the right data and information from across the organization and present them on the desktop, so agents have it all in one place. Second, AI does more than retrieve information; it provides analytics to target the most impactful pieces for the situation.

From there, AI can coach the agent to say the right things or make the right offers based on metrics from similar situations and customers. All of this can be managed from the agent desktop, providing a complete picture of the customer journey and leaving the agent free to have a more personalized conversation with the customer. To illustrate how all of this flows from an integrated desktop, consider these examples across each stage of the customer journey.

Before the Customer Interaction

There are many scenarios in which a change will trigger customer inquiries. Common examples include a new product release, a sudden price change, and new rules or requirements. When these events happen, contact centers know there will be a surge in inquiries, many of which will be similar.

With predictive analytics, AI can anticipate how customers will respond before the interaction begins and provide ready-made responses on the desktop so agents can manage call volumes more efficiently. AI can do this at a general level for basic scenarios and on a targeted basis for specific customer types. Customers could be segmented by demographics, purchase history, Customer Satisfaction (CSAT) risk, and more. Based on customer journey and history data, AI can profile highly personalized responses and offers for each type, making it much easier for agents to manage when call volumes surge.

During the Customer Interaction

Once a call is in progress, agents must focus on the moment. With today’s higher CX expectations, customers want their inquiries resolved quickly. Customers have little patience to wait for callbacks and even less when transferred to another agent taking their call cold. Agents need fast and easy access to the wealth of customer journey data that the contact center collects. Often, this data isn’t readily available, and agents don’t always have the time to look for it during a customer interaction.

Being in the moment with customers puts the onus on agents to be personal, empathetic, engaged and knowledgeable – all things that make for good CX, especially when AI can empower agents. Rather than guessing what customers want or being unable to answer their questions, agents want an informed conversation that takes the customer further along their journey. This is where the integrated omnichannel contact center desktop holds the key. When it’s all there for agents, right in the moment, that’s when they’ll do their best work.

After the Customer Interaction

The customer journey doesn’t end once an inquiry is handled–that’s the transactional part. Good CX might lead to improved customer retention or new sales; subsequent purchase data will only tell some of that story. Furthermore, while the customer may think the engagement is over after the agent interaction, the organization knows the journey must continue.

Your value proposition is validated at every point of contact with the customer–in or outside the contact center. The best way to test that validation is with some form of follow-up contact. Automated surveys are widely used, provide feedback at scale, are cost-effective, and can be done immediately. With AI, these surveys can be customized and personalized, which shows customers that you care about getting their feedback.

The tie-in to the agent desktop enables agents to quickly see the results of follow-up surveys with the customers they interacted with. From there, agents can reach out directly to customers open to being contacted again, presenting an opportunity for truly end-to-end customer service. With the help of Generative AI, agents could be coached to say all the right things for each specific follow-up based on having the entire customer journey map at their disposal. This may not be practical at scale, but it illustrates what’s possible when viewing CX as a product of the entire customer journey rather than just what happens in the contact center.