AI in Marketing
Generate more leads and increase sales potential with hyper-personalization
AI in Marketing Can Create More than Text and Images
When it comes to AI in marketing, many people think of generators such as ChatGPT and DALL-E, which have been on everyone's lips for some time now. These AI-supported tools generate texts and images that are virtually indistinguishable from human-generated content, if at all. However, AI only reveals its full potential in marketing when it is used to hyper-personalize websites, landing pages, online stores, and apps.
AI in Marketing for Effective Hyper-Personalization
Artificial intelligence is no longer a niche technology but is integrated into many tools and business processes, for example,, in marketing and sales. Ideally, both departments work together seamlessly: Marketing creates content that supports sales in acquiring new customers and maintaining existing customers -¬ from sales pages and presentation templates to advertising flyers. The better companies design the customer experience, the more leads they generate. Assuming that more landing pages lead to more leads, user-specific landing pages play a central role.
Why Are Generic Landing Pages Outdated?
If users search for a specific topic in a search engine, they are taken to a company's generically generated landing page. This means there is a landing page for everyone - regardless of whether they are longstanding existing customers or potential leads coming into contact with the company for the first time. To meet the different information requirements, companies must create many landing pages and use A/B tests to check which variant is best received by which user group. More human, time, and financial resources must be needed for this. But what if the sales department demands more sales-promoting landing pages from marketing? With the current approach, it is neither sensible nor possible to create dozens of landing pages on one topic that focus on a different aspect of content but have a similar basic message.
Genuine Customer Centricity Thanks to AI in Marketing
This changes as soon as AI comes into play in marketing. This makes it easy to play out personalized landing pages. There are different levels of hyper-personalization.
- AI combines existing content stored in the backend and displays it in a user-specific way.
- Taking into account click paths and pages visited, each additional landing page is prepared in such a way that it corresponds better to the visitor's assumed interests. The more data is available from different sources, the more accurate the result.
- Based on predictive analytics, the front end is built dynamically for each user in real time. The AI even generates the displayed content in real-time. The only solution that currently supports this is Microsoft's D365 Marketing Copilot.
"Together with our technology partners and customers, we aim to provide every user and website visitor with their own landing page. We want tow everyone what they are interested in more quickly."
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Download our practical guide and find out where AI is already used in marketing today. Find out how hyper-personalization can revolutionize your marketing using artificial intelligence for dynamic landing pages and individual product recommendations. In 15 illustrative application examples, we show how companies can benefit from generative content creation in real-time. Download the Fourpager now and take a step towards innovative marketing!
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What Are the Advantages of AI in Marketing?
What Are the Requirements for AI in Marketing?
Data consolidation
No hyper-personalization without customer data. You must collect and store customer data - including demographic information, purchase history, online behavior, social media activity, and more. Data from different systems - from CRM to CMS to analytics - and platforms such as a CDP need to be consolidated centrally to do this. This is achieved via an API management platform or direct system integration in the case of individual development. This is the only way to enable smooth data flows from various source systems to the desired target systems and display personalized landing pages.
High data quality
For a personalized customer experience, you need up-to-date customer and prospect data that is as complete as possible and always available in real-time. If you set cookies in your website visitors' browsers, you need a legally compliant consent banner in advance. When users consent to data processing, you can tag click paths and link them to encrypted IP addresses. This is because every single signal about users supports hyper-personalization. It is essential that all relevant content is tagged correctly and that collected data is synchronized directly. This ensures a high level of data quality.
Data protection
When collecting and processing data, ensure you continuously comply with legal requirements, such as the GDPR. It is essential to inform customers and interested parties about the collection of data and its processing for advertising purposes and to obtain their consent (double opt-in). If you highlight the advantages of hyper-personalization, you increase the chance of obtaining many consents. Good to know: If you use the support of a company with server locations in Germany for AI in marketing and hyper-personalization, you are on the safe side legally.
- Data analysis
You need advanced tools and platforms to effectively evaluate even extensive data sets to make the collected customer data usable for hyper-personalization. Due to the complexity of this task, it is best to rely on the support of an experienced service provider who will check the quality of your data and create the infrastructural prerequisites for ongoing data analysis. Only then does it make sense to use technologies that analyze customer data in real time and use AI in marketing to make hyper-personalization a reality?
- Team of experts
If hyper-personalization is to work with the help of AI in marketing, an interdisciplinary team of experts is essential. In addition to data scientists and analysts, this group should include marketing and sales specialists, product managers, and IT and business intelligence experts. Provided experts from different company areas work together smoothly, this team is ideally positioned to interpret data reliably, derive meaningful recommendations, develop personalized strategies, and translate these into appropriate measures.
- Change management
However, you will only succeed in establishing AI in marketing in the long term if you create a company-wide understanding that the future of your commercial success lies in data-driven decision-making. This includes creating a culture of continuous learning on the one hand and constantly questioning your actions on the other. Feedback loops are of crucial relevance: Monitor the reactions and behavior of your customers - and use this as a basis to design your hyper-personalization strategy in an increasingly targeted manner. This is the only way to make AI a real game-changer in marketing.
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Frequently Asked Questions About AI in Marketing
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Which systems are required for hyperpersonalization?
To benefit from AI in marketing and hyper-personalization, seamlessly integrated systems are needed, including a Customer Data Platform (CDP) for managing, tracking and analyzing customer interactions; a customer relationship management system for managing customer data; a content management system (CMS) for managing subsequent landing page content and the D365 Marketing Copilot for dynamic content creation in real time. Other data sources may include systems for e-commerce, product information management (PIM), digital asset management (DAM), video management, social media platforms and others
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What are the strengths of AI in marketing?
The purpose of AI in marketing is not to replace employees, but to support them as an assistant. With this in mind, hyper-personalization technologies have the potential to significantly improve the efficiency and effectiveness of marketing teams. This is because companies can - as a first step - concentrate on designing and creating high-quality content. AI takes care of the personalized delivery. In the final expansion stage, AI in marketing will be able to publish hundreds or even thousands of landing pages in real time - i.e. at the moment website visitors request this information - without the intervention of employees.
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What opportunities does AI open up in marketing?
AI in marketing allows companies to significantly increase the quantity of their output. Instead of manually designing one landing page or product page after another, creating corresponding texts and images, entering them into the CMS and keeping an eye on how up-to-date they are at all times, companies no longer have to actively take care of all this. AI generates landing pages in real time that meet the assumed requirements and information needs of users.
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What are the weaknesses of AI in marketing?
The same applies to AI in marketing as to other AI services: Its success stands and falls with the underlying database and training. It is important that this is done in an ethically and morally responsible manner. If an AI is trained with certain expectations or biased patterns, this will be reflected in the results. Training should therefore be based on a clear set of rules with defined processes. The quality of the prompt also determines the quality of the content of the pages displayed. It is the responsibility of the marketing team to draw up precise instructions for the AI. Otherwise, there is a risk that it will generate content that does not meet expectations.
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What are the risks associated with AI in marketing?
To benefit from AI in marketing and hyperpersonalization, companies must trust the underlying technology. If the AI generates landing pages in real time, companies can no longer check them in advance with regard to content, conceptual, contextual, graphic and data protection aspects. AI in marketing operates within the framework of the guidelines set by the company. This is precisely why the rules and training are so important. If the results do not meet expectations, there is a risk that employees will reject the technology. To prevent reservations from arising in the first place, accompanying change management is essential from the outset.
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