63% of consumers expect personalized experiences, making it a must for businesses to integrate into their marketing strategies. Achieving true personalization requires gathering and analyzing vast amounts of data to understand customer needs and identify the best engagement tactics.
Advanced AI and machine learning technologies allow for scalable marketing personalization. They use real-time data to fine-tune campaigns instantly and predict future customer behaviors. Let’s explore what marketing personalization entails and how AI can enhance your efforts.
AI Personalization Marketing Strategies
1. Unique Emails and Messaging
AI and machine learning empower marketers to gather and analyze customer data, creating truly personalized emails and messages. They predict the content that will engage each individual and craft appealing messages automatically.
With AI, emails and messages are sent at the optimal time for each customer, using automation rules based on historical and real-time data. AI also delivers targeted messages in response to specific behaviors, such as current online activity or location.
For example, push notifications can appear on a customer’s mobile screen with suggestions like “Still interested in [product]? Head back to our site for 10% off.” These personalized prompts lead to higher click-through rates.
2. Chatbots and Virtual Assistants
Chatbots and virtual assistants are crucial for personalized marketing, offering interactive and tailored customer service. With the right components, these tools provide engaging and efficient personalized experiences.
One successful example is Sephora’s Color IQ app, which uses facial recognition technology to recommend makeup products based on customers’ skin tones.
Harvard highlights Sephora’s success through digital and technological adaptation. Besides the Color IQ app, Sephora launched its Innovation Lab in 2015, fostering collaboration among marketing, product development, and technology teams to introduce new technologies for omnichannel shopping.
3. Tailored Recommendations
Another way AI engages customers is by recommending products and services they actually want. AI models learn from user behaviors and site traffic data, becoming smarter with more interactions.
For example, Spotify and Amazon use recommendation engines that suggest options to customers dynamically. They employ content-based filtering and collaborative filtering to recommend similar items based on characteristics or user interactions.
AI can also make personalized recommendations on retail websites. A chatbot might suggest items based on conversation keywords and previous shopping history. When a customer makes a purchase, it could offer loyalty-based discounts and complementary item suggestions.
4. Customer Segmentation
Customer segmentation helps marketers identify groups of customers with similar traits and target them with customized campaigns. This personalized approach is more likely to boost conversions.
A Homburg & Partner case study showed that AI-powered customer segmentation increased revenue by 4% and reduced manual effort by 80%.
AI-driven customer segmentation is scalable and efficient, quickly identifying segments using extensive data. Marketers can automate real-time segmentation, eliminating the need for manual analysis. This allows for accurate segment profiles by instantly analyzing customer data. AI also reveals hidden trends and patterns, uncovering new campaign opportunities.
5. Dynamic Websites
Using AI, you can go beyond personalized messages and recommendations to offer each customer a unique version of your website. This technology leverages live behavioral signals and past browsing or purchase history to create dynamic websites that adapt to individual users.
For instance, Netflix customizes its homepage for each user, showing different movies and shows based on past actions. Similarly, machine learning can tailor content using data like trending topics, common searches, or user location. Some websites even use local weather conditions to suggest related items, such as sunscreen ads during a heatwave.
Keep in mind that any AI solutions require high computing power. If you add them to applications, you need to make sure that the smartphone performance is sufficient. You should also provide instructions on how to optimize the smartphone. For example, you can use Cleanup App to speed up the device. After launching the phone cleaner, it finds similar photos and images, frees up memory from them, and thus frees up more resources for applications to run.
6. Assistive Search
Recent advances in natural language programming are enhancing assistive search capabilities. This means search engines can now guide users more effectively, delivering results tailored to their individual preferences and behaviors, which increases the likelihood of a purchase.
“Smart search” features play a key role in this evolution. They offer personalized autosuggestions and understand context well enough to correct misspellings automatically. For instance, if someone types “contract management system” instead of “contract management system,” the smart search can still interpret the intended query accurately.
Additionally, image recognition technology allows users to upload photos of items they wish to find. The search engine then locates similar products. Pinterest’s Chrome extension already offers this functionality. This tech also enables brands to track where their products or services appear online.
Conclusion
AI-based marketing personalization is becoming essential as customers face an abundance of choices. By leveraging data, you can send highly-targeted marketing messages and anticipate user behaviors, ensuring they stay engaged with your brand.AI also simplifies marketers’ tasks by automating labor-intensive processes like manual segmentation. However, success requires the right tools. Currently, less than 25% of businesses have the necessary technology to consistently deliver personalized experiences across channels.
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