Digital technology and multichannel retail have dramatically transformed the consumer shopping experience over the last decade. Gone are the days when a trip to the high street was a shopper’s only option. Consumers can now decide on a whim whether they want to shop on social with their mobile phone, try on an outfit instore using a digital mirror, or order their item and pick it up in store, while taking a flat white in a store’s ‘experiential’ coffee house.
But how can ecommerce retailers ensure that every shopper experience is a great one when they’re faced with so much consumer choice and so many new paths to purchase? With an exploding number of data points that all signal shopper intent, behaviour and desire, the answer lies in a better understanding of data to enhance the overall shopper experience.
Artificial intelligence (AI) is proving to be the game-changer when it comes to understanding data and optimizing the shopper experience for the digital demands of a new generation. AI can act, sense and build upon stores of information in an automated fashion, while making smart decisions that can transform each shopper journey. It does this by detecting patterns and learning from shopper data trends, while executing tasks that take these trends into account in order to improve the online shopping experience. These breakthrough abilities offer endless potential for retailers to drive real value for their customers in smart and efficient ways.
The most successful retailers out there know how to create a sense of excitement with their visual merchandising process. Just think of the awesome shop windows and enticing product displays in your favourite brick-and-mortar department stores. Ecommerce merchandisers are now replicating this sense of excitement online with the help of AI-driven SaaS systems.
Modern-day applications of AI centre around machine learning, or the ability for algorithms to automatically make sense of data by detecting patterns and learning from trends. Machine learning algorithms can help merchandisers to rank key category pages with enticing products at speed, while managing stock replenishment and product recommendations automatically. Traditional black box SaaS systems can do this out of the box, but more intuitive systems are needed if retailers want to excite and inspire customers in an authentic way that showcases the value of their brand.
Such systems give retailers the ability to override ranking rules in order to showcase trend-led products or configure automatic ranking strategies based on a range of metrics that are vital to the business bottom-line. This gives teams complete control over amending product displays in creative and data-driven ways that impress every shopper while meeting overarching business needs.
The best shopping experiences are those that require the least effort and the quickest route to the perfect product. In in-store environments, this process is handled exclusively by friendly personal shoppers, who can fast-track customers to the items they want by listening to customer needs and personalising product selections based on these. Smart online retailers are recreating this personalised approach with the help of intuitive AI-driven systems.
Machine learning algorithms are adept at helping shoppers ask questions that will lead them to relevant onsite results. Algorithms can learn different synonyms that customers use for different products, while understanding customer typos and mistakes, and mapping all of these search terms to the correct pages that merchandisers want their customers to see.
Machine-learning algorithms can also generate unique onsite navigation menus based on the types of things that a customer is searching for. This helps customers to find what they want onsite in the fewest clicks while giving merchandisers more power than ever to surface relevant products that their customers will love.
Personalising the shopping experience to meet the unique wishes of each and every customer is a mammoth task for ecommerce retailers to handle, but AI can assist. Personalised product recommendations can be automatically triggered based on pre-purchase data history and real-time customer behaviour.
AI can intelligently comprehend what each customer wants to buy by analysing browsing and purchasing trends at rapid speeds. Automation of product recommendations can then be applied across diverse areas of the onsite shopping experience so that shoppers can access uniquely tailored selections across their favourite onsite shopping zones. Without the help of AI, this task just wouldn’t be possible at scale.
When shoppers can shop 24/7 across multiple territories and timezones, personalising and perfecting the online shopping experience may seem like another daunting task. But AI acts as the problem-solver yet again by being able to scale and replicate onsite strategies across different regions, territories and sites.
Ecommerce specialists can copy ranking rules to different localised websites for example or optimize onsite search terms based on a range of international languages with speed and precision. This gives brands complete control to create personalised experiences that tick every box and cover every customer need.