Using Contextual AI to Facilitate Seamless Omnichannel UX

Imagine a world where your marketing tools don’t just follow instructions but truly understand your customers—anticipating their needs, recognising their preferences, and delivering exactly what they’re looking for at the perfect moment.

That’s the promise of contextual AI.

As more companies and individuals experiment with artificial intelligence, many are interested in the potential of contextual AI. What should today’s marketing professionals and business owners know about this technology, and how can they use it?

What Is Contextual AI?

Although still in the early stages, contextual AI from an omnichannel marketing perspective is artificial intelligence that can understand situations and people’s needs in the appropriate contexts and respond accordingly.

Contextual AI is already used in several ways. The appropriately named Contextual AI is a company offering end-to-end-optimized language models to give people more accurate results. There’s also LILT, which brings contextual AI to language translation, and Illuma Technology which offers content classification and monetization opportunities.

Although contextual AI tools to enhance omnichannel experiences are not yet widely available, that could change soon as the technology improves and more people experiment with it. How could businesses apply that technology to improve user experiences for individuals interacting with them across multiple channels?

Understand Users’ Needs and Pain Points

All AI applications that improve user experiences must address people’s needs and the barriers preventing them from having trouble-free experiences. Those specifics will vary depending on individuals’ interactions. Expectations differ depending on whether they have purchased a product before, or are first-time buyers. Outcomes could also change if someone follows an enterprise on social media and has developed opinions as a result.

One study found 86% of marketers cited elevated traffic and exposure as the main benefits of social media advertising. Indeed, social media content can shape how users see brands, and their interactions on the respective channels could inform AI algorithms that display different content based on individuals’ previous actions.

Many people leave social media comments to specify their likes and dislikes. Representatives could use that feedback to learn more about what frustrates those customers.

However, social media is only one of many options for determining such information. In one case, NASA developed an AI-driven tool after interviews with researchers indicated those professionals needed centralized search capabilities to help them find the necessary information faster. That is unsurprising since research teams can access 715,000 documents across 128 sources.

Thanks to a contextual AI solution that understands almost 9,000 scientific terms, researchers now format their queries in natural language and refine them based on the technology’s initial responses. Marketing professionals should use this example from science as inspiration when making navigation and other actions smoother for everyday users.

A contextual AI tool could recognize when someone searched for a specific shoe style, such as “strappy sandal for evening with 3-inch platform heel” in a general search engine. Once someone navigates to a particular footwear retailer, the artificial intelligence might send them immediately to pages featuring the products they’d searched for previously.

Use AI to Give Relevant Content Across Various Channels

Today’s business owners adapt their methods to reflect current consumer behavior trends. For example, people in their teens to mid-20s are using more channels to make purchase decisions than ever before. Marketers must stay on top of maintaining omnichannel touchpoints to reach these consumers, which will increasingly involve the use of AI. Applying AI tools to market to users across channels could increase conversion rates and overall satisfaction.

Contextual AI could also improve interactions between salespeople and consumers who are not immediately ready to buy. In one example, an automobile brand used AI to tailor the messaging, images and other content people saw. The automated technology also tweaked the user-facing material based on someone’s position in the buying funnel. That meant those learning about different models had a different user experience than a person who was ready to buy and knew precisely what they wanted.

The results from this effort showed customized web content caused twice the conversions as standard material. Additionally, the cost-per-lead metric was 64% lower than techniques that showed the same website information to every visitor.

Decision-makers should strongly consider applying this use case more broadly by accounting for aspects such as social media use, customer service interactions and other engagements that could pinpoint consumers’ intentions and the content they would find most meaningful.

Amazon excels at omnichannel marketing, improving user experiences through consistency whether people shop through their Alexa smart speakers, on an app or through the website. The company recently began interacting with people across yet another channel. Now, shoppers can also communicate with a generative AI chatbot. It can understand the context of time-related questions, such as “When did I last order moisturizer?”. The responses help shoppers decide if it is time to restock.

Design AI Solutions to Suit Different Business Needs

Contextual AI applications will naturally differ according to how and why someone interacts with a company and their relationship with it. The questions current supply chain partners have will not be the same as those of a recent graduate who wants to work in the customer service department.

However, succeeding with useful AI applications may require designing several versions to suit varying requirements. Leaders from China’s largest e-commerce enterprise took that approach when building several artificial intelligence chatbots. Those tools assist with more than 2 million chats daily, comprising more than 10 million lines of text.

Additionally, people interact with several bots depending on context. There is one for merchants with questions about selling on a particular platform and another for sellers with buyer-related challenges. A buyer-facing chatbot analyzes text and voice-based questions. The executives also created an AI tool to train customer service personnel. It simulates the conversations they might have when helping people, preparing them for real-life situations.

Only some can afford to develop numerous chatbots as this business did. The next best option is determining the similarities between most customer engagements. Could AI address some or many of them? You do not have to task artificial intelligence tools with assisting every customer. Humans will still need to handle many queries. Still, if AI can meaningfully reduce their workloads by understanding parties’ needs in the appropriate context, the results will positively impact the workforce.

Imagine if a contextual AI tool recognizes that a company’s supplier previously interacted with a customer service representative by phone but now has an additional question. Streamlining this experience might involve artificial intelligence reviewing that interaction and directing the person to a specific page in a self-service portal. Then, they receive relevant information efficiently.

Anticipate the Rise of Contextual AI

Although there are relatively few examples of contextual AI in marketing, design and omnichannel interactions today, that may not remain true for much longer. Now is an excellent time for business owners to consider how using artificial intelligence that responds to users’ needs in the proper contexts could streamline efforts across channels and organizational departments while boosting conversion rates and causing other desirable outcomes.

Additionally, they should plan specifics, such as project budgets, timelines, and whether they will buy off-the-shelf solutions or opt for custom-built ones. Figuring out those details in advance makes it easier to act once contextual AI products become more widely accessible.

Eleanor Hecks is a design and marketing writer and researcher with a particular passion for CX topics. You can find her work as Editor in Chief of Designerly Magazine and as a writer for publications such as Clutch.co, Fast Company and Webdesigner Depot. Connect with her on LinkedIn or X to view her latest work.