Personalization allows admins and site owners to provide their users with customized content. You can develop segments, categorizing your users into different customers, thereby providing each group with specific and important content. Hyper personalization allows you to easily carry out group segmentation. Instead of the typical groups, you have individual users provided with specific and different content.
What is Hyper Personalization?
Hyper personalization is an ideal strategy used in marketing, which exploits customers’ data to enhance their experience. It allows you to provide customized content, seasonal communications, and management recommendations for your customers.
Why is Hyper Personalization Important?
Implementing hyper-personalization helps you properly engage your customers and convert more customers. Personalizing content and communications show you’re investing in your customers, which can strengthen your relationships.
Hyper-personalization can be implemented with custom tools or with marketing platforms that are created for personalization.
Based on Accenture’s research, 81% of customers think it’s vital for brands to approach them in a personalized and timely way. Before developing hyper-personalization methods, businesses customize their marketing by manual customer segmentation or demographics based on available behavioral information.
These methods were still deficient because they aren’t accurately personalized. In most cases, they missed the mark and gave the wrong customer the wrong content. Anytime a marketing campaign wrongly judges a client interaction, it might lead to a negative customer experience.
Hyper-personalization provides solutions for this, making sure campaigns accurately target each customer’s interests and expectations. And it can be done with AI-driven automation because it isn’t achievable to customize marketing campaigns manually for everyone on a large scale.
How does AI-based Hyper Personalization work?
AI-based personalization uses customer data analyses and real-time data to know what personalization is specific for a customer and when. Achieving this requires:
Analyzing historical and real-time data
Machine learning algorithms are filled with and trained on large customer data sets to learn how you desire to want the personalization to be executed. Factors and trends are identified to aid the teaching of the algorithm, which will be used more efficiently to analyze and correlate data. Also, it helps you create vast customer profiles and correctly and know successful and unsuccessful marketing campaigns.
Integration of technologies and data sources
AI must be integrated with all the technologies you want to apply insights to. It’s implied that you have to collect data from various sources and implement automated actions. Integrated systems include communication platforms, content delivery networks, social media accounts, web servers, and customer management platforms.
It’s essential you consider if new technologies should be included to bring about recommendations. For instance, when integrating chatbots into your website.
Monitoring and measurement
AI-based systems are created to do most of your work, but you can’t depend on the system. If your algorithm or system is misdirected, it will be unavailable without your knowledge, damaging your efforts.
You must monitor your tools to ensure that everything works perfectly and attend to if it’s not. Monitor effectiveness metrics for your implementation and modify your system when needed. At first, you may have to make several manual adjustments, and with time your system will learn and automatically improve itself.
How can AI enhance your personalization strategy ?
While it is possible to implement personalization with manual technologies, it isn’t as effective as implementing AI. In particular. Written below are aspects of personalization that AI can improve:
Analysis of critical customer variables
AI allows you to incorporate customer data in real-time to get the best personalization. For instance, if a client visits your site with a mobile device and there is a physical store close by, AI can use that information to send timely notifications. Or, if a customer flipped back and forth between two products, AI can be used to highlight their differences.
It is almost impossible to accomplish any of the above personalizations manually or with essential programmatic tools. To achieve these feats, you must have a system that can directly support customer variables and instantly examine and apply insights from those variables.
The complexity of data required to develop profiles for this type of hyper-personalization can’t be achieved with traditional marketing tools. For instance, customer relationship management (CRM) solutions can capture limited data and aren’t developed for extensive analysis and correlation between customers.
Eliminate data paralysis
As many customers shop online and use different devices, customer data is not difficult to come by. You can get several sources, but various marketing teams experience troubles whenever it’s time to process and analyze the data.
Traditional methods can’t rapidly process data fast and aren’t powerful enough to obtain the high-complex insights required to exceed hyper-personalization. AI tools usually work more effectively to process and correlate data. Also, the tools assist marketers in examining through analyses to identify the most valuable and actionable insights.
Also, AI-based tools allow you to start with personalization instantly. Since your tools will get ‘smarter’ with time, there’s little demand for perfecting implementations before starting personalization. It’s due to the lesser risk of ‘selecting the wrong data.’
Design unique customer profiles
Traditional personalization tools are created to use a limited set of customer profiles or personas. Each profile shows a category of customer character, and it isn’t based on fact. This implies that even when personalizations are implemented, it is performed with the assumption that a customer suits a pre-defined profile.
Whenever AI-based tools are used, each customer can be connected to an individual profile. At first, these profiles may be based on templates, and each profile can be customized with individual customer data. This implies that when profiles are implemented, they represent the customer, and personalizations will suit real needs.
Also, since AI is improving with more data, customer profiles are regularly adapted to customer behavior. It helps prevent issues like sending wedding anniversary reminders after a customer purchases a t-shirt inscribed with ‘Just Divorced’ or similar situations.
Examples of AI-based hyper-personalization across industries
Behind-the-scenes machine learning workflow management
Netflix started with the use of machine learning technology to allow personalized user experiences. To the point that their user expectations increased to the state where personalized experiences aren’t more surprising but expected.
Netflix provides a behind-the-scenes look at how it manages its machine learning technology, and it was called Meson by a system. The system serves as a traffic cop for the company’s multiple ML pipelines that “develop, train, and verifies personalization algorithms that enhance video recommendations.”
Meson is a remarkable example of how a media brand can successfully build upon its early experiments with machine learning personalization to allow elegant new abilities.
Personalized AI-powered chatbots
Collecting detailed and accurate data on your users is the initial step for a successful personalization. Unluckily traditional website forms aren’t usually capable. Personalized AI chatbots can gain more profound and accurate insights from users.
AI-enabled avatars, robots, and greeters
Most brands understand the value of including robots in their company. For instance, Hilton Hotels uses a robot concierge called Connie to ensure guests have personal and pleasant experiences. Connie stays in the lobby to greet guests and answer questions. Being aware of which guests are coming into the hotel, these robots can learn over time to offer more specific greetings and services based on that user’s personal preferences.
Personalized content
Currently, Brands understand that all users aren’t the same, and the content isn’t a one-size-fits-all approach. To solve this problem, organizations must design their content specifically for individuals. For instance, if the customer uses mobile technology, brands can automatically enhance personalized content and make provisions based on the customer’s location.
Conclusion
AI is transforming personalization. Personalization was formerly a complicated, awkward, and manual process. Analysts and marketers had to examine through large amounts of data, make sense of this data, develop micro-segmentation, implement personalized campaigns, and later optimize with A/B testing. This isn’t the same case with hyper-personalization.
Hyper personalization reduces segmentation and optimization time, allowing you to offer each user content that fits individual needs. With this model, users aren’t groups of micro-segments; instead, they’re individual people with specific needs. Hyper personalization AI provides your users with optimized content, thereby designing a customized and positive experience.
