How AI self-service is transforming customer portals

5-minute read

Quick summary: Today’s customers demand quality self-service solutions within customer portals, and AI delivers the capabilities that can help businesses deliver.

Driven by expectations of zero-wait convenience, 24/7 availability, and fast response times, today’s customers are demanding high-performance self-service capabilities within customer portals. Yet it seems that not all businesses are getting the picture. In a recent survey, 81 percent of customers affirmed their desire for more self-service options, while businesses estimated this number at closer to 60 percent. At the same time, a mere 15 percent of consumers said they are “very satisfied” with the self-service tools available to them today, a far cry from the 53 percent that business respondents estimated.

The benefits of AI in enhancing self-service capabilities are significant, improving efficiency, customer satisfaction, and reducing operational costs across various industries, including retail and customer service.

The message is clear: To meet (and possibly exceed) the escalating expectations of today’s customer, businesses are being challenged to increase the quantity as well as the quality of self-service capabilities within their customer portals. This new fact of business life applies to B2B organizations as well as their more highly visible B2C counterparts.

Fortunately, AI delivers the capabilities organizations need to take their customer portals to the next level while keeping costs in line. In this article, we’ll explore three ways AI helps service organizations optimize their portal experience and look at some industry use cases that are changing the customer portal game.

What is AI self-service?

AI-powered self-service refers to a platform that uses artificial intelligence technologies to automate customer interactions. This innovative approach enables customers to resolve their issues quickly and efficiently, without the need for human intervention. By leveraging AI, businesses can provide 24/7 support, reduce wait times, and improve overall customer satisfaction. The call deflection rate is a crucial metric in this context, as it measures the reduction in incoming calls to contact centers by encouraging self-service through AI, thereby assessing the effectiveness of these systems in improving overall customer interaction efficiency. 

The applications of AI in self-service are vast, including use cases like automated customer support, personalized user experiences, and predictive assistance. Successful implementations have shown how AI can transform customer support processes, making them more efficient and user-friendly.

Improved accessibility

According to the World Health Organization, “an estimated 1.3 billion people—or 1 in 6—experience significant disability.” In addition, regulations such as Title III of the Americans with Disabilities Act (ADA) in the United States and the European Accessibility Act in the EU call on businesses to offer accessible experiences for people with disabilities.

Regarding customer portals, AI technology offers businesses an array of capabilities for improving accessibility:

Screen reader compatibility

Screen readers enable users who are blind or have low vision to access customer portal services—but only if the site is set up to accommodate them.

AI models can be trained to identify and label elements on a screen by shape, color, and semantic meaning and feed this data into the user’s screen-reading platform (e.g. “red rectangular button labeled submit“). AI makes it straightforward for users of screen readers to easily access services. Computer vision and natural language processing (NLP) can also be leveraged to support screen readers by generating alternative text for photos, charts, and other images appearing on the screen.

Voice recognition

For users with motor or cognitive disabilities, voice-activated virtual assistants can assist with portal navigation. For those with hearing impairments, speech-to-text plus NLP technology can deliver real-time captioning of audio and video content. This shift from interactions with a live agent to AI-driven solutions also highlights the growing preference among all users for quick, efficient support.

Automated testing

AI enables automated testing tools and accessibility scanners to evaluate the accessibility of online resources, identify potential issues, and offer recommendations for fixing them. Another AI-supported option is human-in-the-loop testing, in which a human tester interacts with the website or app while an AI tool monitors the interaction to uncover potential accessibility problems.

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Personalization and revenue generation

Thanks to B2C trailblazers such as Amazon, Netflix, and Spotify, AI-driven personalized recommendations have become table stakes for any business hoping to retain its customers. AI-powered systems analyze consumers’ purchase history to provide personalized product recommendations, enhancing the overall shopping experience. In addition to improving customer satisfaction by serving up products or services that are likely to interest them, recommendation engines can serve as powerful revenue generators by creating targeted cross-selling and upselling opportunities.

Thanks to machine learning, recommendation engines can continuously improve by analyzing patterns in user behavior and preferences. For example, if a logged-in customer consistently clicks on articles about a certain topic, machine learning enables the recommendation engine to suggest products or services related to that topic.

Semantic search with artificial intelligence

AI self-service portals utilize relevant information to effectively respond to customer queries, such as checking account balances, by extracting and delivering accurate data in real-time to enhance user experience. By leveraging a set of AI-based features known collectively as semantic search, businesses can improve the quality of responses to text-based queries within their customer portals. These features include the following:

• NLP: Enables more accurate understanding of the user intent behind search inputs.
• Semantic re-ranking: Prioritizes search results based on semantic relevance to the query.
• Semantic captions and highlights: Extracts text from documents that most closely summarize the content.
• Semantic answers: Provides direct answers to queries formulated as questions, without the need to click through to results pages.

Businesses can also leverage generative AI to offer chat interfaces that enable a conversational approach to search (think ChatGPT), in addition to delivering results that incorporate content from websites, documents, etc.—not simply a list of links. Both Glean and Bing are incorporating generative AI into their search capabilities, and we expect more businesses to capitalize on this trend in their customer portals as the technology continues to influence user expectations.

Industry use cases for deeper insights

As we mentioned, leveraging AI in customer portals can benefit organizations across the B2C/B2B spectrum. Here are just a few examples of industry-specific use cases.

Utilities

If a customer with a smart meter typically charges her electric vehicle (EV) between midnight and 6am, the next time she logs in to the utility’s customer portal, a virtual assistant can serve up a recommendation to shift her charging window to 10pm–4am, when electricity is cheaper.

Financial services

Banks and other financial institutions are experimenting with AI-driven “virtual financial advisors” to help customers with tasks such as choosing investment options based on their preferences and risk tolerance.

Telecom

If a customer logs in to the portal when their most recent payment is past due, an AI-powered virtual assistant can offer a payment plan and walk them through the setup process. AI can manage customer interactions across various channels, including phone calls, providing a comprehensive telecom customer experience.

Pharmaceuticals

As customers browse a pharma provider’s catalog, AI can generate tailored product recommendations based on their previous purchases or items bought by other customers with similar needs. Balancing AI self-service with human support is essential in the pharmaceuticals industry to create a seamless customer experience.

Health insurance

AI-powered virtual assistants can help members quickly access the information they need, such as details on their current coverage or the status of claims or prior authorization requests.

Healthcare providers

AI tools can guide patients through processes such as onboarding and eligibility verification, walk them through online forms, answer their questions, direct them to test results, and more.

Building a smarter customer portal for customer satisfaction

In the study we referenced in the introduction, the No. 1 improvement in self-service portals requested by customers (34 percent of respondents) “is that customer service will be smarter … having the ability to digitally address more complex tasks than are currently being handled.” However, resistance to change poses significant challenges during the adoption of AI self-service solutions. Addressing this resistance through comprehensive employee training and customer education is crucial to facilitate a smoother transition and successful deployment.

By leveraging artificial intelligence and its subsidiary technologies such as NLP, computer vision, speech-to-text, and machine learning, organizations across the spectrum—both B2C and B2B—can deliver the “smarter” services customers demand today, while maintaining the flexibility and scalability to accommodate the future demands of a rapidly changing business ecosystem.

Mary Beth Gibson is Director of Consulting at Logic20/20. She works with clients to meet complex business and technology needs, delivering strategic solutions that save time, improve productivity, and enhance accuracy.

Lionel Bodin is the Senior Director of Digital Transformation at Logic20/20. He manages highly complex, multi-faceted digital programs related to CRM systems, cloud and on-prem implementations, big data, and more.