More than a decade ago, Amazon became a pioneer in reforming the eCommerce experience by using artificial intelligence (AI) to optimize retail processes, recommend products, and improve customer experience. Since then, this tactic has been adopted across industries in companies ranging from Netflix to Spotify to SurLa Table.
These days, machine learning, a branch of artificial intelligence, is the key to many of the trends we are seeing today. Three important areas where machine learning can be used to improve your processes are:
1. Product discovery
Many of us have seen the statements, “frequently bought together,” or “people who bought this item also purchased this item...” when conducting transactions online. These types of consumer suggestions have helped us discover additional products or make better purchase decisions. Behind these offerings is a product recommendation engine. Recommendation engines help customers explore relevant products that they may never have encountered otherwise. Machine learning algorithms work behind the scenes to analyze the data and make connections for the consumer, resulting in a better, more personalized experience for the user.
Because these types of recommendation engines are becoming more readily available by tech-savvy companies, customers are increasingly expecting a personalized experience. Businesses that want to compete for consumer attention and customer loyalty should continue refining their algorithms to ensure that they remain relevant as consumers' needs change and product offerings evolve.
Dynamic content personalization, which changes offerings based on a user’s persona and preferences (their likes, dislikes, etc.), occurs when machine learning is combined with customer data to create a unique, individualized shopping experience. This can be experienced in different stages of the customer journey, where pages are presented to individual customers based on the purchase history, browsing patterns, popular items, and location information.
This can be taken off site, too, by reaching customers via email and encouraging them to convert their abandoned cart or to keep them informed about new products.
Read the case study: See how we helped one of the largest organizations in the world create a personalization tool for their clients.
3. Forecasting and optimization
Demand forecasting uses statistical machine learning models to analyze the impact of pricing, promotion, and seasonality on sales. This allows retailers to anticipate demands on products, consider discounts or marketing campaigns, and help make decisions on stock and prices. This especially helps medium and small sized retailers to get better data insights and manage their inventory effectively. There are other aspects of predicting churn, such as dynamic pricing, where machine learning can help optimize different stages of retail processes, thus focusing on betterment of the customer experience.
So, where do you start?
While there are many real-world examples of how retailers are using machine learning, it is far from a normal trend. There are still many retailers who have the opportunity to adopt machine learning and artificial intelligence to boost their customer experience and reap business benefits as a result. At the same time, there have been tremendous tech offerings in machine learning services, which have made it more economical to implement, giving businesses the ability to prove its ROI on their bottom line.
But don’t panic if you aren’t well-versed in AI/ML or don’t have access to resources to help you get this kind retail optimization in place. Working with an established digital transformation partner can provide you with the ability to be thorough, strategic, and ongoing in your approach.
Mobiquity is well established in artificial intelligence/machine learning (AI/ML), omnichannel experiences, such as mobile, web, and voice, and can help you unearth new digital solutions to elevate your digital transformation efforts.
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