Practical Applications of AI in eCommerce
September 9, 2019
Today it takes a lot of human intelligence to make sense of what artificial intelligence (AI) actually is and isn’t – with the notable exception of AI in eCommerce. Because there are so many practical applications, brands are embracing AI in eCommerce at an accelerating pace. Globally, AI is expected to grow at a CAGR of 42.8% in retail and eCommerce between 2019 and 2025.
Clearly, although the technology is pervasive in many industries, including financial services and healthcare, AI is gaining traction in retail. Companies are investing in AI to stay ahead of competition, enhance performance and assist with decision making around sales and merchandising planning, pricing strategies, and product promotions. Demand for more nuanced customer journeys is driving adoption, too. The technology is shaping the buying and selling experience for both shoppers and sellers, adding intelligence and personalizing to the way we purchase and trade goods and services. In fact, customer experience will be the most significant beneficiary of developments in AI. Consumer adoption of the technology will be a key driver for its success, with personalization a catalyst to winning over buyers.
Consider this: By 2020, the average person will have more conversations with AI-powered bots than with their partner, according to Gartner. It is likely that in the coming years a hybrid model will evolve that utilizes the best of man and machine in the eCommerce environment. Using each other’s strengths, AI in eCommerce can complement human intelligence, in a variety of areas.
eCommerce use cases
• Optimizing search – Relevance is crucial in the 21st Century world of eCommerce. Contextualizing, optimizing and narrowing down search results for online buyers is crucial. AI can utilize natural language processing, image, video and audio recognition to home in on what customers really want. AI software can tag, organize and search all forms of content.
• Customer relationship management and targeting customers – In the past, vast tranches of customer data have been left untouched, with little ability or knowledge in how to make sense of trends, purchasing patterns, and marketing leads. Now corporations can mine these data sources efficiently for new sales leads and remarket to existing customers using AI. Leveraging data is important and machine learning can help.
• Chatbots – Bots that interact with customers and sales leads are becoming mainstay interactions with shoppers, both online and on mobile devices. AI works 24/7 to provide on brand experiences using speech and text. They can help people find the right products, make complaints, check whether goods are in stock and assist in payments. Chatbots are evolving into more complex virtual assistants as well.
• Personalization – The more a corporation can appeal to individual needs, the more likely a browser will become a buyer. Deep learning and the predictive analyses of vast data sets is now achievable using complex algorithms. AI can assist in promotions based on previous user activity. Recommendation engines are now powerful tools allowing people to find products more quickly. AI can also help localize offers in terms of country and geography.
• Sales – AI can be used effectively by sales managers, and make a difference to the bottom line. It allows more accurate forecasts of what customers are purchasing and when, and can monitor sales trends and unfulfilled leads. It can pre-empt purchases and deliver sales insight. Virtual personal shoppers powered by AI can also help drive sales. Personalizing interactions with customers during the sales cycle can generate positive results.
• Inventory and product management – AI is increasingly being used to manage stock both virtually and in warehouses. In an age of instant gratification, the management of inventory and speedy delivery is paramount. We live in an era of great expectations. AI is being used to optimize sorting mechanisms, delivery schedules and product cataloguing, as well as supply chain management. Efficient logistics are crucial to eCommerce providers, as is product content management (PCM).
• Competitive intelligence – AI is not only being used to generate intelligence about people’s businesses, it is also being used effectively to create insight on competitors. This can include rapid and accurate pricing strategies. Omnichannel commerce providers need to be aware of what competitors are doing and be able to react in real-time. Monitoring competitors’ product mix and pricing is paramount, the ability to react and measure the results and how sales are affected is also important.
AI will not produce magical results in a short amount of time. Yet there is something to be said for first mover advantage and early learning in this space. It helps that the number of use cases are proliferating as AI’s potential is better understood. Certainly, pinpointing the use cases where AI can create the most value is crucial. In retail, for instance, the lowest hanging fruit is in forecasting, tracking customer transactions and history, as well as reducing revenue churn.
In a survey by PwC, the consultancy found that 72% percent of business leaders in the U.S. believe that AI will give them some form of business advantage in the future. This shows that there is a strong realization amongst the C-suite that AI can deliver value. But at this juncture investing depends on appetite for risk. There are low risk, low investment models for AI versus high risk, high investment ones. There are also several questions worth asking at this stage that will help you decide whether to invest now or wait until the technology is more mature. With AI in eCommerce: think big, act small, and scale fast.
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