Feature
The Gist
- Data dominance. Accurate personalization requires significant, high-quality data for AI-driven insights.
- Precision personalization. AI and data analytics allow micro-segmentation for hyper-personalized experiences.
- Privacy paramount. Brands must balance personalization efforts with respect for customer data privacy.
Brands today are using artificial intelligence (AI) and data analytics to create compelling, personalized experiences that enhance their relationships with customers. By leveraging advanced data handling, customer segmentation and dynamic content generation, AI and data analytics are redefining personalization across industries. This article will explore the ways that AI and data analytics are driving personalization strategies, improving the customer experience and enhancing customer loyalty.
Data: The Bedrock of Personalization
When it comes to personalization, data is king. The more data that is used, the better the insights that can be discovered. When used together, AI and data analytics enable brands to aggregate data from a variety of channels including website interactions, social media activity, customer service chat history, email, mobile apps and more.
Data analytics solutions are able to parse through the sea of information to obtain a deeper understanding of customer tendencies, behaviors and inclinations. AI achieves this process through the application of machine learning algorithms, which enable it to discern trends and forecast outcomes. This paves the way for the type of real-time personalization that was once deemed unobtainable.
Cameron Turner, VP of data science for digital transformation consultancy Kin+Carta, told CMSWire that advancements in AI have fundamentally changed the personalization landscape, and with it the expectations of customers. "Adaptive experiences tailored to an individual are no longer a novelty, but rather a necessity."
As Turner suggested, real-time personalization is no longer just a nice touch, it is expected by today’s consumers, especially if they are sharing their personal data with a brand. EY's recent Future Consumer Index 12 revealed that although 53% of consumers are very concerned about data security and data breaches, 60% are willing to share data for a personalized online experience.
Aron Ezra, chairman at Plan A Technologies, a software engineering company, told CMSWire that although data analysis is key, you need good data to analyze in the first place. “AI is great at plowing through oceans of data to come up with patterns and insights, but it's next to useless until it gets enough good data,” said Ezra.
Ezra explained that companies that are looking to get into AI should consider the example of AI art. “If you ask DALL-E or similar generative AI to create an image of a human being, you may notice that it does an uncanny job of imagining human faces, hair, bodies, and realistic environments. But then, if you look more closely, you notice that the people in the AI image all have 16 fingers. No matter how many times you try, you'll likely find that AI images of humans just can't get hands right nine times out of 10,” said Ezra.
The reason for this is that most photos we take of each other don't really include great shots of our hands. “Our faces and hair? Sure. But we humans ignore each other's hands most of the time in favor of faces, so there just aren't enough great photos of hands at the volumes AI platforms need to ingest,” said Ezra. “And that means there just isn't enough good hand data for image-creating AI to do human hands justice (yet).”
The point of Ezra’s example is that the same kind of thing happens when a retailer asks its shiny new AI to come up with a personalized marketing strategy for its customers. “If all you have to feed it is a dozen receipts from a single storefront, it's going give you some truly wonky advice.”
Related Article: Midjourney vs. DALL-E 2 vs. Stable Diffusion. Which AI Image Generator Is Best for Marketers?
Crafting Unique Experiences With Customer Segmentation
One of the fundamental elements of personalization is customer segmentation. Data analytics assist in segmenting customers based on various parameters including demographics, purchase history or location. AI breaks it down further with micro-segmentation, creating more nuanced categories and personas.
For example, suppose an ecommerce brand sells a wide variety of outdoor equipment and clothing. The company may have a customer base that includes people with diverse outdoor interests, such as hiking, camping, fishing, birdwatching and rock climbing. Traditional segmentation may classify these customers into larger, broad groups, such as "camping enthusiasts," "fishing enthusiasts" and "hiking enthusiasts." However, this approach might overlook key differences within these groups.
With AI-driven micro-segmentation, the brand could dive deeper. For instance, within the "camping enthusiasts" segment, there might be sub-groups such as "family campers," "solo wilderness campers," "weekend campers," or "luxury campers." Each of these micro-segments has different needs and preferences, which could influence their buying behavior.
“Beyond predicting customer behavior, we now use the technology to prescribe actions," said Turner. This empowers brands to engage in more personalized conversations with customers, while gathering valuable data that can be used to address broader questions about their customer base.” Turner explained that over time, this data can be used to answer macro-level questions about a customer base in addition to micro-level recommendations for an individual. “A key attribute of this approach is its ability to adapt, enabling experiences that account for individual and market changes in real-time."
Related Article: Personalization and Segmentation: How They're Different and Why It Matters
Deliver the Right Message With Dynamic Content Generation
AI-driven algorithms are great at understanding the type of content that resonates with different customer segments. AI is able to analyze user data such as search history, browsing patterns, social media interactions and purchase history to understand their interests, preferences and needs. A customer who often reads articles about renewable energy is likely to be interested in content that is generated on that topic. Additionally, an ecommerce store’s homepage may change based on the visitor’s preferences and past purchases, highlighting items they’re more likely to buy.
Sharad Varshney, CEO of OvalEdge, a data governance consultancy and end-to-end data catalog solutions provider, told CMSWire that the use of machine learning models that analyze past searches, buying patterns and demographic details, allows brands to target personalized content to individual shoppers. “In addition, when displaying a product description to the potential shopper, product content can be adapted to match demographic and psychographic understanding in terms of language and culture, the nuances of which will greatly enhance the level of personalization and the ultimate experience of the shopper,” said Varshney.
Powering Choices With Recommendation Engines
Most of us are used to brands making suggestions for us to watch, read, listen or buy. Netflix, Spotify, YouTube, Amazon and many other brands have been using recommendation engines to provide customers with suggestions that they are likely to be interested in based on their viewing, listening and buying histories. AI and data analytics are the technologies that are used to power these recommendation engines. By analyzing customer behavior, item attributes, and historical data to make these recommendations, they are able to provide a personalized browsing experience.
“For every potential shopper, these recommendations can be different, leading to trillions of suggested options and combinations,” said Varshney. “These simply cannot be created efficiently and dynamically without the power of AI and machine learning. Online retailers such as Amazon have been honing this technology for years and have now provided services for other businesses to easily use this power, creating more of a competitive landscape for smaller retailers.”
Jure Leskovec is an AI and machine learning expert who worked at Pinterest as chief scientist prior to co-founding Kumo AI, a predictive ML solutions provider. Leskovec spoke with CMSWire about the goals of personalization: providing relevant content and simplifying product discovery. “We want to bring content that is interesting to the user right in front of their eyes. At the same time, personalization is especially important when the inventory of possible content to recommend is large and there is a big 'discovery' problem. In that case, personalization helps users discover the content/items they love."
Tailored Marketing Campaigns
Similar to the way brands are using recommendation engines, AI and data analytics facilitate the creation of hyper-personalized marketing campaigns. By using customer data, brands can create personalized emails and ad campaigns that are more relevant to the customer. For example, a clothing brand can send personalized discount codes to customers on their birthdays or anniversaries. Similarly, AI algorithms can optimize the timing of emails or social media posts to maximize engagement.
“This method of hyper-personalization fundamentally changes the entire paradigm of content curation for retailers and merchandising teams, who historically have commissioned survey after survey and deployed significant manual effort to understand how to target potential shoppers for their products,” suggested Varshney, who noted that in the last year, a typical shopper, via search and rich product imagery, could come very close to finding products matching their tastes and preferences.
“But in today’s competitive, fast-paced world of shopping, attention spans are at a premium. Retailers now can display exactly the kind of product the shopper would like to buy on the very top of the results,” said Varshney, who suggested that while they have a product open to view its specifications and description, they can also see product recommendations they are much more likely to buy based on the insights gleaned from their past shopping history.
Challenges and Ethical Considerations
While AI and data analytics are transforming personalization, there are challenges and ethical considerations. Privacy is a significant concern. With the amount of data being collected, brands must ensure that they comply with data protection regulations and maintain transparency with their customers regarding how their data is used. Additionally, biases inherent in data can lead AI algorithms to make biased decisions, which is especially concerning in sensitive areas like healthcare and finance.
There’s also the risk of over-personalization, where customers may feel that the targeting is too invasive, leading to a negative experience. Most consumers have had the unpleasant experience of feeling as though they are being followed from site to site, with ads for a product they previously viewed showing up on consecutive pages of an altogether different website. As this article on CMSWire pointed out, there’s a fine line when “hyper-personalization becomes hyper-personal, it becomes downright creepy.”
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"This does get into a tricky area, and retailers need to protect their customers’ privacy and also ensure company-wide data security practices,” said Varshney. “Data security is nothing new, but AI and machine learning do offer additional new challenges. Unlike traditional software engineering, typically one can’t use ‘dummy data’ for testing/pre-production stages in AI & ML."
"One way to safeguard sensitive data is to have a good data governance structure in place,” explained Varshney. “Ownership and accountability should be clear for various stakeholders as data changes hands at different stages of each workflow.” Varshney said that this is particularly important given the wide circulation of data that will be inevitable in ML & AI projects — especially when it comes to consumer data collection for hyper-personalization.
Final Thoughts on AI, Data Analytics & Personalization
AI and data analytics have drastically impacted the concept of personalization. From customer segmentation to dynamic content generation to recommendation engines, these technologies have equipped brands with the tools needed to create tailored experiences that enhance customer loyalty and satisfaction while continuing to drive revenue.
