Your Crash Course in Hyperpersonalization

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The Gist

Hyperpersonalization pivot. Achieving it is an evolutionary journey, anchored in robust data collection and analytics.
AI advantage. Machine learning and AI are next-level tools for automating and refining your hyperpersonalization strategy.
Ethical boundaries. Be transparent and respectful of privacy when collecting data to build trust while hyperpersonalizing.

Hyperpersonalization is the Holy Grail of modern marketing, but how do we get there? Hyperpersonalization doesn’t occur by flipping a switch. There are critical building blocks that set the foundation for a successful deployment. To navigate the intricate web of customer data, analytics and technology, companies must embark on a well-planned journey. Below are some of the critical steps for companies to get started with their hyperpersonalization strategy, offering a roadmap to transform generic consumer interactions into deeply resonant experiences.

Hyperpersonalization Starts with Data

Several experts agree that the most fundamental building block is data, which provides insights into what a customer wants and needs.

There’s a number of technical and legal (privacy) requirements that makes this a complex exercise, cautioned Nicolas Garfinkel, founder of Mindful Conversion. “Marketers can start off trying to create hyperpersonalization in a single channel whether that’s touchpoints from website interactions, email engagements, social media interactions, and offline interactions. Eventually, you’ll want to find ways to tie it all together and use those signals to create a complex network of behaviors that you can derive personalization from.”

Garfinkel added, “Event-level user data can be cumbersome, but it also unlocks incredible data-powered opportunities. CRMS (customer relationship management systems) and marketing automation platforms exist to help marketers gather, consolidate and analyze this data to create detailed customer profiles.”

However, the links between the data and analytics success can be shaky, Gartner cautioned, pointing to its 2022 Marketing Data and Analytics Survey, which found 57% of organizations with linkages among customer data sources agreed that marketing analytics has not had the level of influence on marketing organizations that leaders expected.

So digital marketing leaders work to accelerate the collection, aggregation and deployment of first-party data, according to Gartner. “They must pursue customer data integration with an understanding of possible obstacles to overcome along the way to ensure they obtain value from the investment.”

Related Article: AI in Ecommerce: True One-on-One Personalization Is Coming

Pattern Recognition in Hyperpersonalization

After establishing data collection procedures and resolving any challenges, an organization should segment customers based on demographics, behavior, preferences and buying patterns to tailor relevant messaging and offers that resonate with each group, Garfinkel said. “This provides a solid framework for subsequent hyperpersonalization tactics. You can start off doing some simple heuristics. Look for common customer usage patterns and use personalization and marketing automation tools to help guide users who fall off the ‘happy path’ to those same actions.”

As you mature, you can start using ML-first solutions like Markov chains to help create more detailed, more sophisticated logical paths for customers and build marketing automation interactions for each one, according to Garfinkel. “With the emergence of OpenAI’s GPT and other LLMs it gives you the opportunity to layer in personalized one-to-one messaging in your marketing communication.”

There’s even more sophisticated AI-driven recommendation engines that do this at massive scale, Garfinkel added. These engines can do this in real-time generating tailored product messages, recommendations or experiences that should help improve the overall experience.

Related Article: Taking Hyper-Personalization to the Next Level

Predictive Analysis in Hyperpersonalization

With a consolidated data reservoir, an organization can use advanced machine learning algorithms to predict future behaviors, says Johan Holmström, Mediatool head of marketing. “It’s not just about acknowledging a user’s past behavior; it’s about anticipating their next step. If a coffee aficionado starts exploring coffee beans or milk frothers, a hyperpersonalized approach would involve not just recommending products, but perhaps suggesting a coffee recipe blog or hosting a virtual coffee brewing workshop. Predictive analysis aids in crafting these precision-tuned experiences, turning casual buyers into loyal advocates.”

The transition from simple personalization to hyperpersonalization is not merely linear but evolutionary, it compels marketers to delve deeper into the realms of data intelligence and predictive foresight, offering experiences that are not just personalized but deeply resonant, Holmström added.

Related Article: 3 Ways AI-Powered Predictive Analytics Are Transforming Ecommerce

AI for the Most Advanced Hyperpersonalization

While customer profiles can help you abundantly in hyperpersonalization, AI and machine learning can help you take it to the next level, says Aashish Ramamurthy, Lifesight head evangelist. “These technologies can help you to automate the personalization process and to deliver even more relevant messages to your customers. At the same time, you can use predictive analytics on the customer data collected in order to automatically personalize messaging based on predicted outcomes like LTV, churn, subscription, etc.”

With AI and ML, an organization can automatically add a profile to different campaigns like product recommendations, or upsell, etc. without manually interfering with the setup. Every action will have a trigger that leads to an outcome.

Additional Tips for Hyperpersonalization

Ramamurthy offers the following additional tips:

Start small and scale up. Don’t try to do too much too soon. Start by personalizing your marketing messages for a small segment of your customers. Once you have success with that, you can gradually expand your personalization efforts.
Be transparent with your customers. Let them know that you are using their data to personalize their experience. This will help to build trust and rapport.
Be respectful of your customers’ privacy. Only collect the data that you need and use it in a responsible way

Final Thoughts on Hyperpersonalization

In an era where consumer attention is fleeting, hyperpersonalization stands as the ultimate goal for marketers. From the foundational role of robust data collection to the advanced capabilities of AI and machine learning, achieving hyperpersonalization is an evolutionary journey. It requires not only understanding customer behavior but also predicting future actions to craft experiences that go beyond mere relevance to deep resonance. While the path is complex, laden with technical and ethical challenges, the payoff in customer loyalty and engagement is unparalleled. As we advance into an increasingly data-driven landscape, mastering hyperpersonalization becomes not just an option but a necessity for brands aiming for lasting impact.

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