Leveraging AI for Hyper-Personalized Product Recommendations

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AI is reshaping the future of marketing. Today, businesses rely on real-time and historical data to deliver amazing user experiences and hyper-personalized product recommendations powered by AI.

Netflix is ​​one of the notable brands that pioneered hyper-personalized recommendations based on real-time data.

In this article, we’ll explain how AI delivers a great customer experience and why personalized product recommendations are crucial to improving a customer’s lifetime value.

But before that, here is a list of interesting statistics that you should know,

Hyper-personalized product recommendations Data statistics
62% of customers expect brands to provide personalized product el salvador mobile database recommendations to maintain brand loyalty.
49% of customers say they will become repeat buyers if companies choose to offer hyper-personalized products.
AI-Powered Data Analysis
Data is the backbone of AI. The amount of data generated daily is 328.77 million terabytes of data . This gives marketers incredible opportunities to study target audiences and their preferences.

This infographic from ZDNET shows everything we need to know as marketers. It reveals the data lifecycle, from collection to decision-making.

data sources

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Source

Data collection and processing
Data is collected from a variety of sources. Some of the main data sources used by marketers include;

Cloud services include CRM, services, cases, digital footprints, tracking, e-commerce, social media insights, external insights, etc.
Mobile, web, and device devices that can provide data about app interaction, location, click patterns, and contextual data
Enterprise systems consisting of a system of records, end-to-end travel data
Virtual systems, including AR/VR technologies, metaverse, etc.
The massive data sets collected are analyzed using advanced technologies, artificial intelligence, machine learning, and deep learning to provide customers with hyper-personalized recommendations.
Advanced analytics for customer insights
To get advanced analytics for customer insights, marketers need to collect data on the following parameters;

Demographics and psychographics - Provides a complete overview of your ideal customer, including their location, gender, age, income, employment, interests, personal preferences, lifestyle and values.
Behavioral data - Includes online shopper behavior, including product purchases, abandoned cards, browsing history, and clicks.
Transaction History - Purchase history includes the number of purchases, their frequency, and the types of items purchased.
Engagement data - Includes all engagement rates across social media and your website, including bounce rates, email open rates, shares, comments, likes, follows, etc.
Sentiment Analysis - This is the measure of how satisfied your customers are with your product. It includes parameters such as feedback and customer reviews on your product pages.

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