AI-Based Hyper Personalization: A Primer

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Hyper personalization uses customer information to tailor content, products, and services to a customer’s wants and preferences. The data used includes profile and demographic data, location, browsing, and purchasing decisions. This data is analyzed to create a customer profile and inform dynamic personalization of content and offers.

Why is Hyper Personalization Important?

According to research from Accenture, 81% of customers think it’s important for brands to approach them in a timely, personalized manner. Prior to the development of hyper personalization methods, businesses customized their marketing using demographics and manual customer segmentation, based on readily available behavioral information.

But these techniques were lacking because they were not personalized enough—in many cases, they missed the mark and offered the wrong content or offer to the wrong customer. Each time a marketing campaign misjudges a client interaction, the result might be negative customer experience.

Hyper personalization solves for this, by ensuring that campaigns carefully target each customer’s individual interests and expectations. This can only be achieved through AI-driven automation, because it is not feasible to manually customize marketing campaigns for each individual at large scale.

A Framework for Hyper Personalization

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A hyper personalized approach has many obvious benefits, but it can be difficult to implement. Collecting and analyzing data efficiently is often the biggest hurdle. Additional hurdles include automating decisions at scale, achieving a real-time view of customers with full context across channels, and understanding customer behavior in context.

To create an effective hyper personalization framework, you need to incorporate analytics, typically derived from machine learning.

A hyper personalization technique or technology must perform the following key functions:

  • Data Collection: Obtain data to know customer needs and preferences. This means collecting information on all client segments. To do this, combine data from web analytics, CRM, and customer support interactions. Some of the more important factors include geolocation, brand interaction history, average spending, demographics, and satisfaction level.
  • Client Segmentation: Once your information is gathered and analyzed, you can start segmenting your client base. Segmentation helps you produce hyper personalized messaging and interactive routes for customers. Hyper personalization requires automating segmentation at some level, otherwise becomes infeasible at large scale.
  • Targeted Journeys: These are the possible paths for each hyper personalized interaction with a client. Hyper personalization can help you select an appropriate channel, timing, messaging, and offers that are exactly suited for the customer at hand.
  • Measurement and Analysis: Personalized interactions must be analyzed on an ongoing basis, collecting metrics that influence the bottom line. There must be a continuous feedback loop that collects data from interactions, to improve personalization for similar customers in the future.

In order to fully implement all these capabilities, you need a technology platform that performs automated AI-driven hyper personalization.

How AI and Hyper Personalization are Evolving the Digital Landscape

The rise of AI-based hyper personalization is improving the competitiveness of brands and is changing customer expectations.

Building Customer Relationships

Hyper personalization helps you develop deeper relationships with customers. Customers appreciate that you understand their unique needs and concerns, and knowing customers in-depth helps you deliver better experiences that lead to higher satisfaction.

Use of Feedback Loops to Refine Profiles

The use of AI in hyper personalization lets you create and adjust customer profiles in real time. AI algorithms can readjust behavioral data incrementally based on each new interaction, making marketing campaigns progressively smarter as they rollout across more customers and channels.

Standardizing Adoption of Hyper Personalization

As AI technologies become more readily available it is easier to incorporate AI tools into websites. For example, chatbots with natural language processing can integrate with customer datasets and incorporate them seamlessly into customer interactions. Email campaigns use increasingly sophisticated tools that can dynamically modify messaging, and even the timing and the type of email sent, for each individual customer.

Predicting User Engagement

Machine learning and AI can help you reliably predict user engagement, making it possible to scale and invest in campaigns based on hard data. Knowing in advance which type of customers will respond to which types of messages, at what time and with which probability of conversion or purchase, takes campaign planning to a completely different level.

Conclusion

AI is changing how personalization as we know it. Personalization used to be a complex, clumsy, and manual process. Analysts and marketers had to sift through huge amounts of data, make sense of this data, create micro-segmentation, then deploy personalized campaigns, and optimize with A/B testing. That is not the case with hyper personalization.

Hyper personalization cuts back on segmentation and optimization time, enabling you to provide each user with content that suits individual needs. In this model, users are no longer groups of micro-segments; rather, they’re individual people with unique needs. Hyper personalization AI serves your users with optimized content, thus creating a customized and positive experience.

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