As per Twilio's 2023 report, about 92% of companies are leveraging AI-powered personalization. Even amid the economic downturn, 69% of business leaders plan to increase their investments in personalization initiatives.
This inclination towards tailoring user experiences isn't merely a byproduct of technological maturity. It's, in fact, deeply intertwined with what users prefer and how costly it can be for businesses not to suffice these preferences.
For example, McKinsey outlines that 71% of consumers expect personalization, and 76% get frustrated when the experiences are not tailored to them. The consulting giant further stresses that "loyalty is up for grabs" and that non-personalized communications are a risky endeavor "in a low-loyalty environment."
It's here that AI's inclusion makes a whole lot of sense. From putting forth product/service recommendations to personalizing communication with users, AI-powered personalization initiatives can accrue a sense of belonging for the users. Brands can well and truly:
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Be where the user is
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Comprehend user's tastes
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Offer something super specific to the user
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Keep the communication active post-transaction
But how does this all transpire? How exactly does AI personalization work?
How Does AI Personalization Work?
Personalization is the process of tailoring a product or service to the specific needs and preferences of an individual. For brands, this entails capturing the nuances and habits of users, particularly when it comes to their likes and dislikes.
For example, Netflix uses AI to recommend TV shows based on your viewing history, while Amazon offers tailored recommendations on what best suits you when it comes to products. In fact, you can also walk into a QSR today and expect personalized offers in line with your transactional loyalty to the restaurant.
The question that begs to be answered, of course, is how does this all work in the background? For the purpose of this discussion, let's take an example centered around AI-driven personalization in the streaming space. Netflix-esque streaming services apps build on user preferences and display the most pertinent content. They accommodate that via the following procedure:
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Aggregating User Data
The very first step is to collect data that can subsequently help map user behavior. Facets like the user's viewing history, genre preferences, viewing patterns (time and day), language preferences, interaction duration, location data, demographic data, etc., come in handy to kick-start the process of understanding the user.
Streaming services platforms often employ frameworks and tools such as Amazon Kinesis, Google BigQuery, Apache Kafka, etc., for data collection. Amazon Kinesis, for instance, allows enterprises to leverage a serverless infrastructure with low latencies to process real-time streaming data. The data ingestion and storage supported by Kinesis can help businesses build custom applications and stream processing frameworks. They can then use BI tools to analyze data and make informed decisions.
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Processing Data Once the data is collected, streaming services apps bring in machine learning (ML) algorithms to predict patterns in data or user behavior, so to speak. For example, users belonging to a certain demographic may prefer watching psychological thrillers more. ML frameworks like TensorFlow, PyTorch, and Scikit-learn come in really handy here.
On the technical front, a lot goes into discerning these patterns and eventually leveraging that knowledge for informed decision-making associated with the personalization initiatives to undertake.
Consider this:
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The collected data is transformed and prepared into data points or features that can be fed to the ML model.
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After feature engineering, vector representations are created for users based on their interactions.
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Then, the streaming apps go forward with collaborative filtering. This process, via the use of a technique called matrix factorization, helps unearth any similarities in viewing patterns and the associated content items.
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It's here that Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) come to the fore for modeling sequential and visual data.
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Algorithms like Alternating Least Squares (ALS) are further used for laying the foundation for the recommendation system.
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Generating Personalized Recommendations The overarching idea of the above process is to give way to a robust recommendation system. The aforementioned recommendation algorithms and frameworks like ALS and collaborative filtering allow streaming services apps to create hyper-personalized recommendations for users.
Netflix, Amazon Prime Video, Spotify, Sony Liv, etc., are excellent examples of how experiences are hyper-personalized for users based on their presumed preferences. Netflix, for instance, has a separate section labeled "Top Picks for [Username]" dedicated to users' preferences. There are location-specific categories as well, alongside general thematic recommendations based on viewing history.
What Role Can Generative AI Play in Personalizing Experiences?
"If you're seeking to rent or purchase a car, future generative AI assistants will possess knowledge about your preferences, family, driving style, and destination. They may even provide weather updates for the area you're heading to," said Treasure Data's CMO, Mark Tack, in a recent interview with VentureBeat.
Tack's arguments are definitely not far-fetched, considering the pervasiveness of generative AI solutions. Of course, much of that can be attributed to the democratized AI access that the likes of OpenAI and Microsoft have ushered in over the last few months. However, ChatGPT-esque solutions are well and truly commencing the "hyper-personalization" era where:
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Manual interventions are minimal.
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Personalized touchpoints extend beyond product/service purchase.
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Chances of errors creeping up are relatively subdued
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Changes of bias seeping in w abare significantly reduced
But how come? For the purpose of better understanding, let's take a look at the impact these solutions can have across industries.
The Marketing Function
Recently, Salesforce announced Marketing GPT and Commerce GPT for marketing campaign personalization. These solutions very well elucidate and reflect upon the potential of generative AI for hyper-personalization. For example, with Marketing GPT, marketers can:
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Quickly create market segments by leveraging prompt engineering and AI-driven recommendations
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Automate the creation of super-personalized campaign emails
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Automate the creation of visual assets based on their required usage on different advertising channels
Likewise, Commerce GPT allows enterprises to automate the creation of product descriptions, fill in the requisite catalog data, and help shoppers effectively navigate storefronts.
The Customer Service Function
While we all know about the influence of conversational AI in streamlining customer support, there are more aspects to the entire customer service function when we consider sophisticated enterprise operations — insurance, financial services, healthcare, etc., are some examples.
In insurance, for example, there's a lot to consider — case management, broker or employee productivity, policy servicing operation, omnichannel support, document processing, complaint management, etc. Insurance businesses often struggle to answer users' queries on time, let alone personalize their experiences.
However, generative AI solutions solve this problem by:
- Extending textual and audio support on the user-agent interaction front
- Literally orchestrating customer experiences (CX) via automated content generation around user queries, even those that are sophisticated and were earlier routed to human agents.
- Assisting the agents in summarizing the cases and providing them with recommendations to better tailor the conversations per the user's needs and demands
- Weaving in personalized recommendations across the interactions in a tone and voice that's true to the brand's style guide
The Development Function
When we talk about personalization, we mostly think about what's transpiring on the user front, and understandably so. However, there's a lot to consider on the development front, especially for highly dynamic and living-breathing ecosystems like eCommerce sites. These platforms have to tweak their personalization initiatives based on seasons, traffic, sales, conversions, etc.
Approaches like headless and composable commerce do contribute and support such dynamism, but generative AI takes the idea of facilitating through and through personalization up a notch. Microsoft Power Platform is a great example.
Microsoft recently announced the inclusion of Copilot in the Power Platform applications for amplified capabilities. Copilot, an AI assistant, leverages large language models (LLMs) to automate content generation. It empowers developers, especially those using Power Apps (a Power Platform application for developing low-code applications) to:
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Instantly convert unstructured data into structured information
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Make granular changes to the application to accommodate pertinent transformations on the user end
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Leverage natural language to create multi-screen applications
Such a solution can prove immensely viable for food delivery apps, business listing sites, or even eCommerce apps to quickly add screens and customize experiences across them. This can translate into well-formulated loyalty programs, timely product recommendations and discount offers, and more.
What's the Way Forward?
In McKinsey's 2023 global survey, 40% of the respondents affirmed that their organizations will increase AI investments. For the personalization narrative to gain even more momentum, enterprises have to employ AI assistance across the board — from sales to support, marketing, and product development. What's more, the adoption of generative AI technologies will be pivotal to augmenting the user experience and helping improve customer loyalty, turnover, and monetization.