Predictive support: A guide on boosting customer satisfaction using AI

Customer interactions are complex, especially in B2B. Just when you think you’ve fully understood your target audience, you’ll run into scenarios that remind you how varied customers really are. Their challenges, expectations, and the way they engage with a product could vary largely based on factors such as industry, geography, business size, etc.

In scenarios like this, it is challenging to design (and nail) those delightful experiences to consistently meet customer expectations. But in an AI-everything era, no excuse is ever good enough for delivering less than optimal customer experience.

Using AI-centered thinking to optimize customer interactions through data and automation, along with a design-first approach to enhance user engagement, businesses can create products and experiences that are efficient, user-friendly, and more importantly, predictive.

In this blog, we explore how integrating AI and design can propel businesses from reactive support to proactive anticipation of customer needs and ultimately transform customer interactions.

What is predictive support?

When you imagine "customer support," what comes to mind? If you're picturing dull chatbots, digging through knowledge base articles, or submitting tickets to support teams, you're not alone. These are all examples of reactive support—the industry norm, but far from inspiring.

What truly sets businesses apart is using context, data, and analytics to forecast potential issues or needs of customers before they even occur.

Predictive support is a proactive approach to customer service that anticipates and addresses potential issues or needs before they arise. Instead of waiting for customers to reach out with problems or inquiries, predictive support uses data analytics, AI, and a robust knowledge graph to forecast customer behavior, identify patterns, and predict future needs or issues.

Being predictive is not just about fewer tickets or being able to resolve them faster. It's about having no tickets at all. In other words, the best support is when customers don’t need support at all. This mindset and culture, powered by AI and design fundamentals, starts from the very foundation of an organization.

Design as a core element of product strategy

AI-powered analytics, in combination with design-first thinking, are considered integral to the company's strategy, where effectiveness of one is enhanced by the thoughtful application of the other. They are seen as two sides of the same coin, where:

• Design inputs enhance user engagement
• AI optimizes these interactions through better understanding and automation.

The combination of both enables businesses to take preemptive actions, such as identifying user flows where users tend to struggle, take wrong turns, or run into errors, and proactively resolve these issues without waiting for a user to file a ticket.

When businesses integrate design principles into their product development process, they inherently understand their users' needs and preferences. Though it may feel like a lot of work upfront, deep-diving into user interactions helps uncover patterns and trends in user behavior that product teams can take into account when developing new features. Taking the time to do this analysis can resolve product issues or customer needs before they arise.

The product and design teams at Aditya Birla Capital, a leading finance and Fortune 500 company in India, demonstrated their design-thinking in their mobile app. The teams initially relied on quantitative data or anecdotal feedback from customers to improve their product. They could not get a nuanced understanding of actual user interactions to identify hotspots and dead zones encountered while navigating the app.

DevRev’s PLuG observability gives the product and design teams at Aditya Birla Capital a window that shows them how real life users are engaging with their product. This allows the design team to evaluate effectiveness of the overall user experience. They are able to see how users interact with the product rather than hoping that users interact as intended.

They can make informed decisions by analyzing how users interact with the product. This enabled the team to refine the design, paving for a more intuitive, user-friendly, and optimized app interface, ultimately enhancing the user experience and keeping customers satisfied.

By prioritizing design-thinking, businesses can proactively refine their product offerings, enhancing the features that align with customer preferences while de-prioritizing or redesigning those that are underutilized or problematic.

True AI is built-in, not bolted-on

As businesses prioritize incorporating AI to cut costs and be more efficient, it’s crucial to reevaluate processes holistically, rather than implementing AI in isolation.

Adding AI functionalities onto an existing product or system without deeply integrating them into its architecture can lead to disjointed experiences, inefficiencies, and limitations in the capabilities of the AI system.

In contrast, when AI is "built-in," algorithms and functionalities are deeply ingrained within the foundational components of the product or system. This seamless integration enables AI to continuously interact with other aspects of the product. It actively crawls, indexes, and analyzes various data sources such as documents, knowledge base articles, technical documentation pages, and blog posts to build Language Model Models (LLMs).

With access to historical data, continuous inputs, and real-time insights, built-in AI systems can develop predictive models that forecast future outcomes, such as customer churn, product demand, or service interruptions.

The result? Enhanced product performance and improved user experience. This is the essential AI that businesses need to be predictive and customer-centric.

While AI is undoubtedly the powerhouse of future businesses, design should also be seen as a powerful tool for innovation within the organization. By prioritizing design and AI, businesses can not only meet but anticipate customer needs, setting new standards for proactive service and differentiation in the B2B landscape.