How AI-driven customer personalization is driving the top line
AI is enabling the world’s largest brands and retailers to aggregate disparate customer data so they can better understand their consumer base, enhance their user experience online and offline and make more forward-looking product development and strategy decisions.
Olay’s Skin Advisor doubles conversion rate
Leveraging 25 years of expertise in image recognition, which helps it identify skin problems and improvement areas for its users, skincare and beauty brand Olay launched its mobile Skin Advisor nearly 2 years ago and has since seen its conversion rates double. According to Venturebeat, the brand uses machine learning technology to analyze a customer’s skin based on selfies.
The team noticed consumers were facing decision paralysis mainly in store, due to the plethora of options and shades available, but they often lacked the ability or desire to consult with an in-person expert on the best choice for their skin. Enter the mobile Skin Advisor experience. The product was built using Olay’s “massive proprietary database of face and skin images from a wide variety of ethnic and demographic backgrounds.” The tool provides the brand with access to additional insights, including the most popular customer preferences, demographics and shopping behaviors.
Olay isn’t the first beauty brand to offer shade and product recommendations based on aggregated data. Early adopters like Laura Mercier, Maybelline, Bare Minerals and more have offered online shade finders for years. But Olay’s is one of the first to incorporate an AI-driven tool based on years of detailed image data. As a result it’s one of the more accurate applications incorporating facial recognition technology and machine learning for more significant personalization.
Wayfair’s AI-driven personalized search tool
Just as we look to celebrity styles for the latest fashion inspirations, many look to replicate home furnishing styles seen on social media or in celebrity homes, searching for similar items at an affordable cost.
Mass furniture retailer Wayfair looked out this behavior as well as new visual search technologies developed by companies like Pinterest and Google. They created an AI-driven visual search engine in an effort to enhance the customer experience with more personalized recommendations.
“Using either a camera or their photo library on web and mobile, online shoppers can take a picture or upload a photo they’ve already saved to see if Wayfair has something similar,” TechCrunch reports.
The tool’s advantage for Wayfair lies beyond a better search function for consumers. It provides Wayfair’s decision-makers with access to instant external customer insights, enabling the customers to act as scouts that bring the latest trends and styles to the Wayfair team, with significant proof of interest. They can use this data to better plan new designs, promote bestsellers and understand how preferences are changing. Applying machine learning, they can better predict individual user preferences and secure their place as the go-to source for furnishings across the entire home.
Walmart doubles down on tech innovation in the rapidly digitizing retail space
In the race to implement AI solutions in the larger e-commerce space, retail incumbents often struggle to move beyond basic AI innovations that tend to impact just a peripheral segment of the overall business. However, Walmart has managed to remain ahead of the game by looking out at retail newcomers that can provide them with the necessary innovations and access to the digitally savvy audience they need to stay alive in a digital-first world.
Walmart has been making big moves in the digital space in recent years. Its surge of patent applications in the digital space point to a heavy focus on innovation, including potential in-store drone assistants and a blockchain ledger. Its purchase of ecommerce sites jet.com and Bonobos speak to a larger strategy of enhancing its e-commerce offering and better understanding online consumer behavior in an effort to compete with Amazon and offer a more cohesive customer experience online and in-store.
Lauren Desegur, VP of customer experience engineering at WalmartLabs told Forbes, “We’re essentially creating a bridge where we are enhancing the shopping experience through machine learning. We want to make sure there is a seamless experience between what customers do online and what they do in our stores.”
For an idea of the results from these efforts, in the quarter following its purchase of jet.com, Walmart’s ecommerce revenue rose 63 percent year over year. Today it serves 140M customers on a weekly basis, a number of which increasingly comes from its online store.
What do Olay, Amazon, Wal Mart, Wayfair and Netflix have in common? They’ve all mastered use of predictive analytics to create a highly personalized customer experience that’s impacting their conversion rates.
This post was originally published on Outside Insight. An online resource designed to help business executives understand the power of external data and the impact of forward-looking insights on the decision-making process.