AI-Powered Personalization: The Future of Customer Engagement

With the surge in market competition, personalization has become the new normal for businesses today. Whether it is Amazon, Facebook, Flipkart, or something else, they always present the users with content they like.

From running ads to recommending purchase offers to customers, personalization has greatly helped businesses. As per McKinsey, companies that excel at personalization can generate 40% more revenue. Moreover, 50% of the consumers are ready to share information on the products they like to get personalized discounts.

But, as the amount of data is increasing at an exponential rate, the need for powerful technologies has risen. This has led to AI-powered personalization. As AI algorithms can seamlessly make sense of structured and unstructured data, their use for personalization operations has increased.

Let’s read ahead in this blog and learn how AI-powered personalization is helping businesses boost customer engagement.

Trends in AI-Powered Personalization

Well, before we move to the trends, let’s see what AI-powered personalization is.

AI-powered personalization is the use of machine learning and other advanced AI algorithms to analyze a large amount of data. Further, this leads to the identification of trends and patterns in customer behavior while ensuring data security and privacy.

As per stats, companies that excel at personalization earn 40% more than their competitors. This number can be further boosted with AI-powered personalization. With AI supporting your personalization goals, your business can process more complex data, such as a vast array of data, and automate the personalization process.

Let’s analyze some AI-powered personalization trends!

• Hyper-personalization using machine learning algorithms

Hyper-personalization takes a step forward from AI, where data of all types is accessed to develop advanced and smart marketing campaigns. ML, along with NLP and computer vision, is used to analyze behavioral, transactional, and demographic data to dive deep into customer preferences.

Furthermore, the use of AI in personalization also promises auto-creation and updation of user profiles based on each interaction.

Here is an example of Secret Escapes. The travel business adapts its website to the keywords the user types and the ads they click. Therefore, clicking on an ad takes the users to a personalized landing page. This led to an increase of 26% in the sign-ups.

• Real-time personalization across channels and devices

Real-time personalization is another cool trend of AI-powered personalization. In this, instant communication is started with the customers based on their website activity.

For example, when a customer browses a product on the website and adds it to the cart but doesn’t buy it, a personalized message, push notification, or an email with the product link can be sent to remind them to make the purchase.

An example of this is Google Play Books, which informs users about the price drop on the items they add to their wishlists.

• Increased use of NLP for customer interactions

Natural language processing technology enables comprehension of human language, including chats, emails, and phone calls. The AI courses and AI-backed technology can be used for sentiment analysis to understand user emotions regarding a product or service.

Tech giants like Amazon and Netflix use NLP for sentiment analysis and target personalized recommendations to their users.

Practical Applications

AI has many uses for personalization. Here are some of the primary ones!

1. Dynamic Content Optimization: Dynamic content optimization or DCO is a process where AI uses customer data like viewed products, location, search history, and even live data to target them with ads and product recommendations.

An example of this is Amazon, which uses dynamic user data to craft personalized ads and homepages for each user.

2. Predictive Analytics: The use of current and past customer data to estimate future outcomes with the help of AI and machine learning comes under predictive analytics. With current and past user data, future customer needs can be identified just like those of IKEA. 

IKEA leverages predictive analytics to optimize customer experience and know what they need. Further, it uses predictive analytics for inventory management.

3. Conversational AI: If you have heard of ChatGPT, it is the perfect example of conversational AI. Conversational AI is a technology that uses AI and ML to respond to human language. It can be used to create interactive chatbots and virtual assistants like Sephora’s Kik.

Sephora’s virtual artist chatbot provides personalized product recommendations to users based on their skin tone and other facial features. Moreover, within 2 years of launching the chatbot, Sephora witnessed 200 million shades tried on.

Industry-Specific Use Cases

As I mentioned earlier, every industry is using AI for personalization, which is why the market of AI-powered personalization is surging at a CAGR of 4.80%. Here are some interesting use cases of AI-powered personalization.

1. E-commerce

In the e-commerce sector, AI-powered personalization is used to provide personalized product recommendations to users. Besides Amazon, several brands monitor user activity to provide personalized recommendations. For example, H&M targets its users with related product recommendations after they have made a purchase. Based on this, the brand achieved a boost in the revenue stream and customer loyalty.

Source

2. Healthcare

With the help of AI, data elements, such as genetic data, lifestyle, and patient’s medical history, can be analyzed to curate personalized treatment plans. Furthermore, with the AI assessment of the same data, tailored patient engagement support can also be catered to.

IBM Watson for Oncology analyzes patient data to suggest personalized cancer treatments. Doctors take around 20 minutes to recommend a treatment while IBM Watson does it in 40 seconds. Moreover, Watson was able to predict the right treatment in 96% of the cases.

3. Financial Services

With the use of AI-powered algorithms, financial user data, including bank accounts, policies, investments, past transactions, etc., can be analyzed to provide personalized investment advice, financial products, and banking solutions.

For example, based on the age of the individual, Ally Bank offers personalized financial products to the users. It segments the users based on their age and targets them with personalized products for wedding planning, vehicle purchase, retirement etc.

Top 5 Practices for Implementation

As the dependency of the modern business landscape is pretty heavy on AI-powered personalization, it needs to be implemented well. However, some healthy practices can be used to make the most of it.

1. Ensure the Best Data Quality

Data is an indispensable resource for an artificial intelligence or AI algorithm. Therefore, when using AI-powered personalization, ensure that the data is up-to-date, accurate, and free from bias. Plus, ensure that it is ample in volume and diverse for precise recommendations.

2. Customer Journey Mapping

Besides data, it is also crucial to understand the customer journey. Check where the customers are coming from and how they are moving in the customer funnel. Identify all the customer touchpoints, such as social media ads, website ads, video ads, reviews, etc. This analysis will help you aim for the right touchpoint.

3. Continuous Testing and Optimization

A/B testing is the key to the process of personalization. Continuous testing of various strategies will help you understand the best channel for targeting recommendations. Moreover, it will be easier to shortlist the best personalization strategy among personalized emails, sales follow-up, live chat, context-based support, etc.

4. Real-time adaptation

The needs of the users do not remain the same. Therefore, based on their changing preferences, AI systems should adapt to keep the product recommendations relevant for the users in the future.

5. Keep ethical considerations in mind.

As human data is involved in the entire personalization process, it is crucial to ensure fair and ethical use of it. Make sure your users are informed about how their shared data is used. Provide them the option to opt-out from sharing their data. Furthermore, compliance with data privacy regulations must also be ensured.

Future Directions for AI-Powered Personalization

AI is a pivotal technology for personalization. But that's just the tip of the iceberg. AI can do a lot more by collaborating with other innovative technologies. As the Co-Director of the Stanford Institute, Fei-Fei Li, says, “Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity.”

Here are some future prospects of AI-powered personalization.

Edge AI: Right now, AI carries all the analysis operations at the central hub in the cloud. However, in the future, reliance on cloud-based systems will be eliminated. Live data from devices and systems will be analyzed by the algorithms at the edge of those devices, saving time and faster recommendations.

Explainable AI: As an ethical practice, the business must know that the recommendations provided by AI are true and fair. This is where explainable AI comes into the picture. It is also an AI system that helps build the trust of the users in its results and establish transparency.

Human-AI Collaboration

While AI might be super proficient in data-based tasks, the sixth sense is not a part of it. Effective human-AI collaboration, combined with powerful AI capabilities, can help achieve unmatched results in the future.

Besides these future prospects, here are some interesting stats about AI-powered personalization.

Conclusion

With in-depth data analysis of online user activity, precise product recommendations can be provided to the customers, leading to faster sales and boosting revenue. While this practice is carried out by every business today, this is not the end of it. As there are several other innovative technologies, AI has endless applications when used side by side. One such technology is edge computing, which can help with software scalability while offering the same personalization features.

Rest assured, as the use of generative AI is also surging, it can also be seen as potentially useful in AI-powered personalization in the future.

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