Apr 2023, Pritish Udgata
Data-driven web personalization has been reshaping the internet at a staggering pace. The progression of artificial intelligence and machine learning has played a crucial role in pushing the boundaries of this field, and with the advent of GenAI (generative AI) technologies, we can anticipate the adoption of even more advanced personalization techniques becoming widespread. This article will explore the essential components of web personalization and underscore how GenAI can elevate each of the components in the web personalization process.
Essential components of web personalization
Web personalization refers to the practice of tailoring a user’s experience on a website based on their preferences, behaviors, and other relevant data. It allows organizations to curate a website with content that matters to users, it can be a list of relevant product recommendations, targeted ads, and other elements. This customization aims to make users more engaged and satisfied, and ultimately drive desired actions such as conversions, purchases, or other key performance indicators.
Let’s establish a foundation by understanding the essential components of web personalization. We’ll maintain a broad overview without delving into all the intricate details of these components to avoid making this article overly lengthy.
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The above components are worth defining:
1. User Data Analysis: It’s the process of collecting and analyzing data about users, including their browsing history, preferences, demographics, product usage, website interactions, etc. As part of this process, users can also be grouped into segments based on common characteristics or behaviors. This segmentation helps in delivering more targeted and relevant content.
2. Content Generation: Creating content that is going to be placed on websites. This is primarily achieved in the content management systems (CMS) that are used to generate different types of digital content i.e., images, video, text, etc. Please note that the content will have its own metadata that is also stored in the CMS.
3. Recommendation Engine: Implementing recommendation systems that can adapt content and presentation on websites based on the user’s past behavior, preferences, or the behavior of similar users. This also involves changing the content of a webpage in real-time based on user interactions or profile data, ensuring that the user sees the most relevant information.
4. Journey Orchestration: Creating personalized pathways or journeys through the website based on user preferences and behavior. Conduct A/B testing to try various personalized experiences and identify which variations result in improved user engagement or conversion rates.
Now let’s see how each of the key components is connected to enable web personalization –
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Note – Components like Web SDK (which I didn’t mention earlier) is also a crucial component for web personalization, but our primary focus will be on the other four components (highlighted in grey) due to the predominant role of GenAI in these areas. This simplifies our overall discussion. The Web SDK functions as a bridge between a website and server-side systems, performing essential tasks such as signal collections, API calls, caching, and business logic.
Where and how GenAI can be leveraged
Generative AI is a category of artificial intelligence technology capable of generating diverse forms of content, encompassing text, images, audio, and even software code. This makes it a very powerful tool for personalizing the web experience as it empowers marketers and practitioners to unleash creativity while also saving time.
Let’s delve into each of the components and examine the possibilities for incorporating GenAI.
1. User Data Analysis
a) Text to SQL: Make it easier to generate SQL queries from natural language, even if you don’t know SQL. This would empower users without technical expertise to conduct spontaneous analyses of user data and actively participate in the overall data analysis procedures. There are commercial and open-source tools and APIs available that offer text-to-SQL capabilities- text2sql.ai, sqlchat.ai are a few examples.
b) Rapid Analysis: It can efficiently analyze and interpret vast amounts of data and create easy-to-consume visuals empowering data experts and organizations to derive valuable insights more rapidly. Tools like DataGPT can enable iterative querying necessary for in-depth data exploration and uncovering valuable insights.
c) Improved User Categorization: Using GenAl, analysts and business users can segment the user base more effectively. Instead of relying on static rules, GenAl uses large language models to analyze user data and place users into dynamic, contextual, and up-to-date segments.
2. Content Generation
a) Ideation and Concept Generation: Generating new ideas is crucial for grabbing customer attention. GenAI models like ChatGPT can facilitate brainstorming by providing creative suggestions and different viewpoints. Marketers can use these AI-generated ideas to inspire innovative conceptualization and develop unique content strategies.
b) Streamlining Content Creation: Generative AI aids marketers in automating content creation, speeding up time-to-market, and saving resources. It can generate drafts for various content, such as social media posts, blog articles, banner texts, and email campaigns. Human marketers can subsequently refine and personalize this content to align with the unique tone of each individual user. Tools like ChatGPT, Jasper, Copy.ai, and Writesonic are examples of AI writing tools that assist marketers in quickly producing high-quality copy.
c) Generating Visual Elements: Our brains are wired to process images much faster than text. Generative AI models can produce impressive visuals such as graphics, images, art, and videos. Marketers can use these AI-generated visuals to elevate their storytelling, craft attention-grabbing social media posts, and develop visually appealing presentations.
Midjourney and DALL.E2 are excellent real-world examples of AI image generators. They can generate stunning, hyper-realistic visuals of humans, animals, and real-world objects.
d) Optimizing Current Content: Generative AI can enhance current content by offering valuable insights and improvement suggestions. Through the analysis of data patterns and user feedback, AI models can pinpoint areas in content, such as marketing copy, ad creative, and customer messaging, that can be optimized. An example of this is Phrasee, an AI-driven marketing copywriting tool that utilizes generative AI to enhance and optimize email subject lines and ad copy.
3. Recommendation Engine
a) Store and retrieve unstructured information: The recommendation engine must handle both structured data (such as application logs, tables, and graphs) and unstructured data (including text documents, rich media, and audio). When dealing with unstructured information, it can utilize embedding models to transform this data into vectors that encapsulate the meaning and context of an asset. For example, the sentence-transformer model can be used to transform thousands of documents into high-dimensional vectors and store them in vector DB. This information can be used during the online inference.
b) Hyperpersonalization: Generative AI makes hyperpersonalization possible by analyzing extensive data and customizing content based on individual preferences and behaviors in real-time. For example, LLaMa-2 and FLAN-T5, are generative AI models that can be used to provide highly personalized conversational experiences on a website by understanding the user’s intentions and the context of the conversation.
4. Journey Orchestration
The initial three components center around the analysis of user data, content creation, and content recommendation. Journey Orchestration integrates with all other key components to deliver the right content on a website to the right user at the right time (as highlighted in the connected view above). Its pivotal role lies in ensuring a cohesive user experience for a specific user segment throughout the entire campaign lifecycle.
a) AI-driven Journey: The Journey Orchestration tool has access to a wide range of campaigns, users’ actions, and associated timing details. It can analyze and suggest the best possible journey (relevant channel, frequency of activation, next best action, etc.) for a user segment based on real-time customer behavior, historical customer data, and business-specific data sets. Marketers can take advantage of this suggestion to determine the right content, channel, or offer for users.
b) Real-time Affinity Profiling: Gen AI can synthesize in-the-moment intent data and past business engagements to identify customer affinities, interests, and preferences. This can help marketers develop nuanced perspectives on customers based on individual preferences and they can use these insights to deliver custom messaging and offers across websites. Where the AI-driven Journey focuses on the best journey path for users, Real-time Affinity Profiling focuses on what users want. Let me illustrate it with a use case, for an organization like Airbnb, Real-time Affinity can help identify to determine a user’s travel interests (national park, theme park, historical sites) and then they can leverage AI-driven Journey to suggest a suitable tailored itinerary plan for each user when they visit Airbnb website.
Conclusion
Gathering high-quality user data, turning it into actionable insights, adopting a suitable content strategy, and delivering experiences at the right place and time will continue to be crucial for any web personalization strategy. Before applying GenAI, organizations should note that it is a “product” by itself and requires the same attention as any other commercial product. The impact of GenAI technologies on web personalization is contingent on how effectively and swiftly an organization develops, implements, and refines them at each stage over time.