Digital transformation and experience have rapidly evolved from cutting edge business philosophies to absolute necessities for any brand that is looking to compete in the digital world. Customers have become more demanding in their expectations of a brand’s entire digital experience and product discovery lifecycle. From content to search and the concept of “best next” (offer, experience, action, etc.), delivering digital experiences is equally challenging and lucrative. Similarly, employees have their own demands for navigating internal systems and staying productive in the hybrid landscape of in-office and remote work. The culminating result is a need for a digital experience platform (DXP) that is capable of delivering the entire brand experience on any channel at any time.

How does an effective DXP make this happen? With so many elements to consider in the digital experience, the answer has to be algorithmic. Luckily, this is something that artificial intelligence (AI) is capable of handling and expanding. While today’s AI may not look quite like “science fiction” as many might imagine, DXP management represents a prime example of AI’s practical application. In this context, AI supports DXPs in five key ways.

Digital experience delivery and presentation

The variety of digital experiences is wider than it has ever been. Immersive applications like augmented and virtual reality are becoming commonplace, and IoT devices and conversational interfaces are standard. All of these devices require content to be presented in a format specific to them and still be able to be relevant to the end user.

Businessman sitting at his desk talking to smart speaker. Male professional asking digital assistant a question at office.

This level of demand easily outweighs what typical website and mobile templates were designed to accomplish. AI, however, is capable of not only organizing the depth of content, but continually iterating on it and devising how best to present it — making the overall digital experience adaptable to the specific delivery. This is especially helpful in scenarios where text content may need to be adapted to non-text, as with display applications like AR and VR.

Content curation and experience orchestration

This element has historically been the most time consuming piece of everyday DXP operations. With AI functionality like machine learning, natural language processing, and natural language generation, the curation of content can be massively sped up while improving the quality of the overall digital experience.

AI is capable of supporting DXPs in numerous content automation and creation areas. These include auto classification, tagging, protection, and compliance. The technology can also process video and audio transcription, as well as content translation. AI can handle content sourcing and discovery from multiple sources. Finally, AI can use data for Natural Language Generation of new content. AI can also guide the creation of content — intelligently suggesting new content based on discovery and analysis. These are all incredibly important factors in composing a fulfilling digital experience.

Intuitive search

Search plays a fundamental role in any sort of content-focused system, be it internal or external. Tailoring search to the user requires dozens of quick, tailored decisions that AI is capable of calculating. Search engines tend to apply AI in three key ways:

  • Query: This involves parsing natural language using natural language processing (NLP). Essentially, this is meant to move beyond keyword searching and instead determine user intent (semantic vector search). Once intent is established, you can map that intent to your catalog’s terminology. By understanding intent, it’s also possible to ensure that a user never reaches a zero-results query and is always given something relevant. Using intent-based search, you can move away from rules or rewrites and towards a smarter, less manually curated experience.
  • Indexing: This involves extracting content from rich media (such as speed to text, sentiment, or image recognition) through machine learning. It can also involve classifying and labeling content, or representing and interrelating content.
  • Display: This involves returning information in the form of natural language, and selecting and adapting content to fit the touchpoint through machine learning. The same technology can also be used for converting text to speech, presenting search recommendations, and allowing for predictive, visual, and personalized search.

Personalization and optimization

Personalization requires a combination of analytical techniques to get the right message to the right user at the right time. True personalization enables much more compelling experiences than traditional segmentation based on factors like age, gender, location, or profession via a user profile. The major blind spot of segmentation is behavior and intent. As a result, segmentation has the tendency to make customers feel like one of a million, as opposed to one in a million.

Shot of a young woman using a smartphone while shopping.

AI is able to automate and optimize deep behavioral insight that goes beyond what segmentation can parse. This includes the entire insight lifecycle at a much larger scale with higher velocity than has been previously possible. This augmented form of user analysis is capable of turning around “best-next-action” analysis at a much more personal level that can then be optimized to make much more meaningful recommendations and suggestions for users.

Effective analytics

Analytics is an integral source of data for delivering relevant digital experiences. AI-enhancement of analytics is necessary for any business that manages an extremely large and diverse amount of content that is both structured and unstructured. AI is capable of working through the dense amount of content and data to pull together analytics that are reflective of all channels and touchpoints across the DXP.

Traditionally, organizations have had to rely on descriptive and diagnostic analytics. AI can give a massive lift in mining unstructured content for consumer insight, competitive intelligence, and sentiment. The ability to manage and act on this depth of data drives incredible business value.

At the end of the day, a powerful digital experience meets the customer or employee where they are and as they are. AI is a valuable technology for parsing the heavy amount of data that comes from a fully integrated digital experience platform. Through all of the above methods, AI enables DXPs to give a truly relevant, engaging, and meaningful digital experience.