Overview

Skill Level: Any Skill Level

Hyper personalization is a strategy used in marketing that leverages customer data to improve customer experience. It enables you to deliver timely communications, curated recommendations, and custom content to your customers.

Ingredients

Personalization enables site owners and admins to serve users with customized content. You create segments, classifying users into groupings of customers, and then serve each group with relevant content. Hyper personalization enables you to do away with group segmentation. Instead of groups, you have individual users, each served with unique and different content. 


In this article, you will learn what is hyper-personalization, how it works, where Artificial Intelligence (AI) comes into the equation, and what it does to improve hyper-personalization.

Step-by-step

  1. What Is Hyper Personalization?

    Hyper personalization is a strategy used in marketing that leverages customer data to improve customer experience. It enables you to deliver timely communications, curated recommendations, and custom content to your customers. 

    When you use hyper-personalization you can better engage your current customers and convert new customers. Personalized content and communications show investment in customers and can help strengthen your relationships. 

    You can implement hyper-personalization using custom tools or with the help of marketing platforms designed for personalization. The former allows you to fine-tune your personalizations and granularly control your customer analysis and targeting but requires significant expertise. The latter may not offer as much control but requires little technical expertise and typically offers pre-built templates to get you started.

  2. How Does AI-Based Personalization Work?

    AI-based personalization leverages analyses of customer data as well as real-time data to determine what personalizations are relevant for a given customer and when. To accomplish this requires the following:

    Analyzing historical and real-time data

    Machine learning algorithms are supplied with and trained on vast customer data sets to learn how you want personalization to be performed. Known factors and trends are identified to help teach the algorithm which can then be used to more thoroughly correlate and analyze data. This helps you develop more robust customer profiles and to better identify successful vs unsuccessful marketing campaigns.

    Integration of technologies and data sources

    AI should be integrated with all of the technologies that you want to apply insights to. This means being able to ingest data from a variety of sources as well as implement automated actions. Integrated systems should include content delivery networks, communications platforms, customer management platforms, web servers, and social media accounts. 

    You should also consider whether new technologies should be added to facilitate AI-recommendations. For example, integrating chatbots into your website.

    Monitoring and measurement

    Although AI-based systems are designed to do much of the work for you, you cannot rely on these systems blindly. If you mistrain your algorithm or your system becomes unavailable without you knowing it, it can completely sabotage your efforts. 

    It is important to monitor your tools to ensure that everything is working as expected and to intervene if it is not. Track effectiveness metrics for your implementation and make sure to tweak your system as needed. In the beginning, you may need to make more manual adjustments but in time your system will learn and improvements will come automatically. 

  3. How Artificial Intelligence Can Enhance Your Personalization Strategy

    While it is possible to implement personalization with traditional technologies, it is just not as effective as implementing AI. In particular, the following aspects of personalization can be drastically enhanced by AI.

    Analysis of critical customer variables

    AI enables you to incorporate customer data in real-time to determine the best personalization. For example, if a customer visits your site from a mobile device and there is a physical store nearby, AI can use that information to send a timely notification. Or, if a customer has flipped back and forth between two products, you can use AI to highlight differences between the two.

    It is impractical and near impossible to accomplish either of the above personalizations manually, or even with basic programmatic tools. To accomplish these feats, you need a system that can directly harness customer variables and immediately evaluate and apply insights from those variables. 

    The complexity of data needed to create profiles for this type of hyper-personalization is not possible with traditional marketing tools. For example, customer relationship management (CRM) solutions can only capture limited data and are not designed for extensive analysis and correlation between customers. 

    Eliminate data paralysis

    As more customers shop online and use a wider variety of devices, customer data is not hard to come by. There are many sources you can collect from but many marketing teams run into problems when it comes time to process and analyze that data. 

    Traditional methods simply cannot process data fast enough and are not powerful enough to derive the highly complex insights needed to excel at hyper-personalization. AI tools, however, can often work much more efficiently to process and correlate data. These tools can also help marketers sift through analyses to identify the highest value and most actionable insights.

    Additionally, using AI-based tools enables you to start with personalization immediately. Since your tools will only get ‘smarter’ with time, there is less demand on perfecting implementations before beginning personalization. This is because there is less risk of ‘choosing the wrong data’.

    Create unique customer profiles 

    Traditional personalization tools are designed to use a limited set of customer profiles or personas. Each of these profiles represents a category of customer traits and which isn’t always based on fact. This means that even when personalizations are applied it is done with the assumption that a customer fits a pre-defined profile. 

    When AI-based tools are used, however, each customer can be connected to an individual profile. While these profiles may be initially based on templates, each can be expanded and customized with individual customer data. This means that when profiles are applied, each more realistically represents the customer, and personalizations are more likely to match real needs. 

    Additionally, since AI evolves with more data, customer profiles are constantly adapted to customer behavior. This helps prevent issues such as sending anniversary reminders after a customer has just purchased a t-shirt emblazoned with ‘Just Divorced’ or similar situations.

  4. Conclusion

    AI-based hyper-personalization leverages user data. You feed the algorithm with historical and real-time data and then let the AI perform the analysis. You do this for each user, and once the AI learns the needs of this user, the AI serves the user hyper-personalized content. 

    Then, for example, if the AI identifies indecision, the software highlights the differences between the two viewed products. As part of the website, the AI acts as an invisible salesperson, helping customers make choices and find the products and services they are most likely to buy.

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