The bar for personalization is getting higher. Personalization became extremely relevant during the pandemic. Customers are becoming very loyal to their preferred brands as evident since the lockdown.
They are also spending significantly more when their experience is engaging and personalized. Nearly 80% of business leaders say consumers spend more (34% more on average) when their experience is personalized.
As customer expectations grow, businesses will need more sophisticated personalization strategies for better customer loyalty.53% of brands are focused on improving existing channels to give consumers a better experience.
AI can improve customer experience, for better customer loyalty.To meet customers on their terms, retailers are upgrading their personalization strategy by replacing or complementing their existing solutions with AI.
Not all AI algorithms are created equal
Most existing recommendation systems consider recommending as a static process and deliver recommendations based on a fixed strategy.They neglect the dynamic nature of the users’ preferences which leads to their failure.
Recommendations AI emphasizes individual customers rather than a product, which enables it to piece together the history of a customer’s shopping journey and serve them with personalized product recommendations.
It can continuously update its strategies during the interactions until the system converges to the optimal strategy that generates recommendations best fitting users’ dynamic preferences.
How Recommendations AI solves cold start problem?
Recommendations AI handles recommendations remarkably well in cases with long-tail products and cold-start users and items.
New visitors to the online stores usually experience the “cold start problem”, i.e their data profile is too shallow to provide relevant predictions. How do you figure out what to recommend to new people without their history, behavior, or preferences?
Recommendations AI gives you the option to input and train on unknown users, and by providing metadata on products, it can provide high-quality recommendations to both, existing and first-time visitors alike.
Unlike most existing recommendation systems, Recommendation AI is deep learning.
As explained by Google Product Manager Pallav Mehta, "Its “context hungry” deep learning models use item and user metadata to draw insights across millions of items at scale. They constantly iterate on those insights in real-time in a way that makes it impossible for manually curated rules to keep up."
