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."
Enhanced product discovery through AI-powered recommendations
Recommendations AI surfaces the right products, to the right customers, at the right time. It provides an effective way to personalize the customer experience by helping customers discover products that match their tastes and preferences.
Google has spent years delivering high-quality recommendations across its flagship products like YouTube and Google Search. Recommendations AI draws on that rich experience to give retailers a way to deliver highly personalized product recommendations to their customers at scale.
Recommendations are based on user behavior and activities like views, clicks, and purchases as well as online activity, so that every prediction result is personalized in real-time.
Retailers can offer first-time users and loyal customers alike high-quality recommendations via the web, mobile, email, and more, anywhere in their journey from homepage to shopping cart to order confirmation and beyond.
How retailers are leveraging Recommendations AI
Retailers all over the world are using Recommendations AI to deliver highly personalized product recommendations tailored to their customers' preferences.
Multinational home furnishing brand.
“Customers were able to find products that they liked quickly and establish their preferred choice among other options more quickly as well, giving them confidence to make a purchase through much fewer clicks. Even though we previously already had well-tuned recommendations of several types, with Recommendations AI we measured +30% improvement in click through rates.
We were able to increase the number of relevant recommendations displayed on a page by +400% and Average order value saw a +2% surge.” Albert Bertilsson, Head of Engineering - Edge at IKEA Retail.
Beauty and personal-care goods.
"Since implementing Recommendations AI we’ve seen impressive results with a 50% increase in CTR on our product pages and a nearly 2% increase in overall conversion rate on our homepage relative to our previous ML-driven recommendations.” Jaclyn Luft, Manager, Site Personalization & Testing at Sephora.
Australian apparel and lifestyle brands.
“When we A/B tested the recommendations from Recommendations AI against our previous manual system, we identified a double-digit uplift in revenue per session.” Peter Luu, Online Analytics Manager at Hanes Australasia.
Wide range of products from electronics to clothes.
"In the past few months, we've noticed a strong increase in the usage of recommendations in general, with Recommendations AI performing with up to a 40% additional increase in CTR compared to the previous period." Christian Sager, Product Owner for Personalization at Digitec Galaxus.
One of the leading global marketplaces Kinguin is a haven for gamers.
Since adopting Recommendations AI, Kinguin has improved both customer experience and satisfaction. Search times have been shortened by 20 seconds. Additionally, their average cart value has increased by 5 EUR. Conversion rates have quadrupled since the outset. Click-thru rates have doubled, increasing by 2.16 on product pages and 2.8 times on recommendations pages.
“Google Recommendations AI has helped us evolve our service, increase customer loyalty and satisfaction. It has also contributed to a significant rise in sales.” Viktor Romaniuk Wanli, Kinguin CEO, and Founder.
Bazaarvoice is the leading provider of product reviews and user-generated content (UGC) solutions that help brands and retailers understand and better serve customers.
Recommendation AI solved Bazaarvoice's cold start problem by providing high-quality recommendations to both, existing and first-time visitors alike.
Bazaarvoice began by A/B testing Recommendations AI against their rules-based system. Early on in the experimental phase, they noticed a clear and consistent 60% increase in the click-through rate over their original recommendation system.
What Recommendations AI delivers to every retailer:
Capabilities of Recommendations AI as mentioned by Google Cloud :
- Custom models. Each model is trained specifically for your store's data.
- Personalized results. Recommendations are based on user behavior and activities like views, clicks, and in-store purchases as well as online activity, so that every prediction result is personalized.
- Real-time predictions. Each recommendation delivered, considers previous user activity like click, view, and purchase events, so recommendations are in real-time.
- Optimization objectives. Goals like conversion rate, click-through rate, and revenue optimization help you precisely optimize for your business goal.
- Automatic model training and tuning. Daily model retraining ensures all the models can accurately capture user behavior every day.
- Omnichannel recommendations. You can go beyond website recommendations and personalize your entire shopper journey.
Ensuring a highly personalized shopping experience for every customer is essential for building customer loyalty.
As customer expectations rise, the penalty for failing to meet those expectations rise significantly. 62% of customers say a brand will lose their loyalty if they deliver an un-personalized experience, up from 45% in 2021.
Personalized product recommendations are an effective way to build customer loyalty. They let customers know that you treat them as unique individuals and understand their needs and preferences.
How Shopify merchants are leveraging Google's Retail AI
Shopify merchants can implement Recommendations AI on their online stores with PersonalizerAI Recommendations Shopify app and deliver high-performing recommendations to any customer touchpoint.
AI-powered product discovery helps surface the right products, to the right customers, at the right time.
Fulfilling the customers’ needs results in stronger relationships, which means a more profitable and sustainable business in the long run.