Rottmann Lab

Understanding Generative AI and Its Role in Personalized Communication

In today’s digital landscape, businesses are constantly seeking ways to engage their customers and stand out from the competition. One of the most effective methods for achieving this is by implementing personalized communication strategies tailored to specific target groups. With the advent of generative AI, organizations have the potential to revolutionize their approach to personalization, creating more meaningful and relevant experiences for their customers. In this blog post, we will explore the different levels of personalization, how generative AI can support your journey from the first level to a higher degree of personalization, and look at the critical steps to finding the right personalized communication strategy for your purpose.

First, let’s discuss what generative AI is and why it is connected to personalized communication strategies. Generative AI is a subfield of artificial intelligence that focuses on creating new content, such as text, images, or audio, by learning patterns from existing data. Today’s quality in generative AI is high enough that the exponential growth in output required to implement a personalized communication strategy has become much more feasible for everyone.

From Basic to Advanced Personalization: Identifying Your Starting Point

To understand which level of personalization you already apply and which is your target, we differentiate the following three personalization levels:

Level 1: Basic Personalization

• Basic personalization involves using minimal user data, such as a customer’s name or location, to create a slightly more customized experience.
• This level of personalization typically requires limited data collection and analysis and can be achieved with simple (cloud-based) tools and techniques at a very low budget and with little data literacy in your organization.
• This level of personalization is rather a commodity and won’t really help you in building a more complex relationship with your customers.

Example: An online retailer sends out a monthly email newsletter with the customer’s name in the subject line and the greeting, such as “Hello, [Customer Name]! Check out our latest deals!” This strategy uses only the customer’s name to create a more personalized touch, without tailoring the content of the email to the individual’s preferences or behavior.

Level 2: Intermediate Personalization

• This level uses already much more diverse data and more sophisticated personalization tactics
• Intermediate personalization goes beyond basic data points by incorporating user preferences, behavior, and additional demographic information to create more tailored experiences.
• Compared to level 3, level 2 organizations do not personalized the product development based on the user profiles. We will not discuss Level 3 in this blog post. If you are though curious how such a Level 3 example could look like, read the Uplyfe Reference Case.
• This level of personalization requires more sophisticated data collection and analysis, for example, data on:
  • User preferences: what users liked, disliked, and showed interest in in the past, which can be collected either through explicit inputs like surveys, feedback forms, social media data or inferred from behavior data (see next point).
  • Browsing behavior: Information on users’ browsing patterns, such as pages visited, time spent on pages, clicks, and navigation paths, and labeling of these pages if they should be accounted for the interest in a specific offering, distribution channel, or stage in the customer buying cycle, etc.
  • Purchase history: Shows you details on users’ past purchases, including product types, purchase frequency, and average transaction value, share of returns/claims behavior, payment morale, etc.
  • User interactions: Information on users’ engagement with content, such as likes, shares, comments, or content ratings. This data can help determine the types of content that resonate with users and inform personalized content creation. And channel selection to deepen the engagement with a specific user group.
  • Demographic information: Data on users’ age, gender, location, occupation, and other demographic attributes, which were probably the first thing you were thinking of, so here it is!
  • Device and platform usage: Information on users’ devices, operating systems, and browser types, which can help optimize personalized experiences, conversion funnels, and communication formats to be preferred in your interactions. 7. Social media activity: Data on users’ social media engagement, such as likes, shares, comments, and follows, can provide insights into user preferences, interests, and influencers.
  • Referral sources: Information on how users arrived at your website or platform, such as search engines, social media, or referral websites. This data can help identify user intent and tailor personalized content accordingly.

Harnessing the Power of Data for Targeted User Experiences in Your Personalization Strategy

That’s a lot of analytical ideas in here. The key point is to understand in the very first step: WHAT EXACTLY YOU WANT TO ACHIEVE AS A BOTTOM LINE IN BUSINESS with the personalization efforts. This is the most important step in your transformational journey. We normally invest a significant amount of time to define and operationalize these goals with a larger group of stakeholders across the organization. So, please keep that in mind while we go back to the main topic of this post: Personalization and how to find the level that is right for you and how Generative AI can support you in getting there.

Maximizing Engagement Through Personalization: How Generative AI Streamlines Content Creation

Basically, personalization means more relevant interactions with each of your customers. This implies that you have an exponential growth of assets to handle. Generative AI can help you dealing with the creation of all this additional content. Let’s assume we prepare the communication for a product launch, such as a new car. We will need many different assets for different user groups with different topical literacy and interests, and we will need to create content for many channels with their specific rules of success (e.g., a blog post, a teaser for the homepage, a briefing for a video script, a questionnaire for a podcast interview with the lead engineer of the new car model, etc.). To do this, we will input all the data we have on the user group, our tone of voice and brand identity, our offering, and what we want to achieve with the user group in focus into a SEPARATE ‘chat’ in ChatGPT (this will train the model for your specific purposes and make the outcome more specific). Always use the same chat for all subsequent interactions on this target group.

We can also use generative AI for our initial intelligence on the user group and ask which product features of our new offering were of key interest to each of your user groups and why. We can ask which touch points per user group we might consider and which kind of information and formats to use for each of the touch points. In the end, we will ask the Generative AI to create these assets, different versions to have proper A/B testing. This usage of generative AI is mainly to generate fresh ideas from an outside-in perspective and to generate assets that we can test in the next phase. The subsequent testing data will show us which of our/AI’s assumptions were right and where to focus for the upcoming interactions with the target group. Such personalization tactics will increase engagement with our target groups and establish digital authority on our focus topics for this specific target group, thus improving SEO in the longer term.

Developing a Successful Personalization Strategy: Key Steps and Considerations

To sum it up, here are the key steps that bring you from level 1 to level 2 of your personalization strategy by using generative AI:

1. Establish the business goals of the personalization with all relevant stakeholders. What is the business bottom-line of your personalization strategy?
2. Make sure, this goals are measured over time and communicate it across the stakeholders to keep the focus on the right aspects.
3. Define with your stakeholders which data you would ideally need to detect the profiles you are looking for, and see where or if you can find this data. If not, you might start collecting it (survey, cookies, social media data, etc.).
4. Start collecting the data and setting up the targeting for these user groups.
5. In parallel, start with content planning: a) Specific messaging planning if only relevant for one user group (e.g., special employer branding content only for applicants). b) Mass-personalized messaging planning, consisting of general messaging that is tailored to each of the user groups. Focus on the key pains and gains of each specific user group in relation to your topic and put this into the focus of the mass-personalized messaging (e.g., a new car with very different selling points for environmentally conscious clients vs. the user group of tech feature pioneers).

The Future of Personalization: Adapting to Ever-Changing Customer Preferences

The path to achieving a higher level of personalized communication strategies requires a clear understanding of your business goals, data collection and analysis, and the use of cutting-edge technology such as Generative AI. By following the outlined steps and leveraging AI-generated content, you can create more engaging and tailored experiences for your users, ultimately leading to improved customer satisfaction, increased engagement, and better business outcomes.

Remember that personalization is an ongoing process; continually refining your strategies and adapting to the ever-changing preferences of your target audiences will ensure the long-term success of your personalization efforts. With generative AI as your ally, the possibilities for personalization are endless, helping you create a unique and memorable experience for each and every customer.