Hyperpersonalization for Omnichannel Success

Hyperpersonalization is currently a hot topic in corporate marketing. It combines artificial intelligence with customer and product data to create individual and personalized customer experiences across different channels. In our blog, we show you how you can use personalization in channel design to create digital wow moments and successfully turn prospective customers into loyal customers of your brand.

In a previous blog article we have already discussed the importance of artificial intelligence for personalization in Product Information Management (PIM) discussed. We discovered that a PIM platform offers the ideal environment for making the most of AI functions in e-commerce. By integrating external AI functionalities and services into your PIM system, you can meet the needs of your customers: AI helps to enrich product data and optimize it using image analysis or video generation, allowing you to achieve a personalized customer approach on a previously unimagined scale. The personalized product data must be played out on all relevant channels so that your customers receive the right data and information at the right time and in the right place. This is where your company's marketing can focus on hyper-personalization with the help of artificial intelligence.

Hyper-personalization is a marketing strategy for customizing the shopping experience of each individual customer - tailored to the brand's message. By intelligently analysing customer data and behaviour using AI, companies can make individual recommendations, provide tailored offers and place personalized advertising. This innovative approach not only enables a unique shopping experience for customers, but also an increase in conversion rates and sales as well as the development of long-term customer relationships for companies.

The importance of hyper-personalization in channel design is enormous! Nowadays, customers expect to be reachable around the clock on all channels, including landing pages, online stores, social media channels, and mobile apps. Today, prospective customers pass through up to 10 channels on average during their shopping trip. In omnichannel retail, companies, therefore, benefit in particular from the integration of artificial intelligence. AI supports the display of data by processing existing content in the backend, e.g. in a customer data platform (CDP) or a Customer Relationship Management (CRM)combined. Data on click paths and user behavior contribute to the ongoing personalization of channels. Therefore, the more data available, the better the design can be adapted to the interests of the channel visitor in all relevant channels through hyper-personalization.

Even if prospective customers have already become buyers and customers through personalized channel designs, the possibilities of personalization for your company have not yet been exhausted. After all, it's not just e-commerce that can benefit from hyperpersonalization. In addition to pure retail activities, digital commerce also includes advertising, reactivating existing customers and rewarding and strengthening regular customers.

We have distinguished between e-commerce and digital commerce in this blog article.

In modern digital commerce, companies must therefore place their customers at the heart of their activities - from start to finish, from product range planning to customer loyalty programs. Hyper-personalization can take place with customer rewards and bonus programs that are based on the individual preferences and shopping habits of customers. It is also possible to dynamically adapt loyalty programs to user interactions and customer feedback. Such cross-channel personalization is a real game changer for marketing, e-commerce, and data specialists in retail and pays off in terms of competitiveness, in-depth customer understanding, and long-term customer loyalty in digital commerce.

In theory, the potential of hyper-personalization for successful campaigns with satisfied customers and high-quality conversions is as diverse as the selection of channels themselves. At the same time, companies face major challenges in data management, as practice today is often characterized by insufficient, incorrect or outdated data in various places. However, a prerequisite for the personalization of channels and campaigns is the targeted analysis of extensive data, which makes customer interactions traceable and marketing workflows more efficient.

Typical sources of customer information and data in digital commerce are

1. The Customer Relationship Management (CRM) as the foundation for analyzing and evaluating customer information
2. The Customer Data Platform (CDP)to also analyze customer interactions of anonymous customers and to efficiently manage and use the collected data

Through the use of generative AI customer data from customer data platforms (CDP), customer relationship management systems (CRM) and other customer data sources (e.g. Google Analytics) can be merged and combined with a content management system (CMS), for example, to create personalized front-ends - e.g. a variety of landing pages. Through the support of AI assistants from leading hyperscalers such as the Microsoft 365 Copilot this data can be used for intelligent and detailed customer journeys for specific target groups, allowing companies in digital commerce to react more flexibly to omnichannel presence.