Today retailers must take advantage of data and AI conversational business applications to elevate the customer experience in newer, more meaningful ways.
Retail, as we know it today, is omnichannel, multi-purpose and personalized. Digital has established a new world in which consumers are more connected, communication is more pervasive, and shopping experiences are more fluid.
There are infinite pathways for customers to connect, engage and purchase from retailers. With AI and machine learning becoming a benchmark for business practices, retailers have an opportunity to leverage data and AI conversational business applications to elevate the customer experience in new and more meaningful ways. The most innovative retailers scale AI capabilities across the entire value chain, including solutions, data and digital platforms, talent, and culture.
So, here is how you should leverage AI to address the top four retail use cases.
To help retailers broaden and deepen their connections with their existing customers and expand their audience of new customers, businesses must deliver personalized customer experiences. Customers crave personalization, with 73% of consumers saying they prefer to do business with brands that take their personal preferences into account.
And thanks to the right data, technology and AI, businesses have successfully moved from mass promotion to personalized offers at scale. Leading retailers now use machine learning, deep learning, natural language processing, etc. , to personalize a brand's marketing messages, content, products and services. This also includes recommending products based on past purchases and search history, as well as offering personalized pricing and discounts.
Marketing personalization is now the most important point of advantage for retail brands. For example, Starbucks has discontinued mass promotions (such as Happy Hour and Treat Receipt). Instead, it presents gamified, personalized offers tailored to each Starbucks Rewards member when you open its application or click on a mobile offer push notification. This has resulted in sustained 8% year-on-year growth in member spending.
Conversational commerce, also known as chat commerce or conversational marketing, represents a way for online retailers to unleash the potential of conversation to sell their products and services and can include a chat app, a chatbot, a voice assistant or a messaging platform. Many retailers use conversational AI platforms to communicate with customers and provide personalized assistance with tasks such as placing orders, tracking shipments, and answering product questions. And with 80% of customers willing to pay more to get a better experience, this suggests that it is not always products or the price that have the most significant impact on conversion.
Conversational commerce provides an intelligent way for customers to experience a retailer's brand. It provides an intelligent way for retailers to connect with customers during every step of their retail experience and compensate for the lack of personal touch of online stores. Not only can conversational AI recreate the experience of a typical "corner store", but it can markedly improve on it too. Conversational commerce platforms are a great way to support customers considering a purchase. It provides upsell and cross-selling opportunities through personalized recommendations while a customer is in purchase mode. It can also help reduce cart abandonment issues by connecting with customers who have not yet finalized their order and offering a discount to incentivize them to place an order. And it can be used for conversational marketing.
For example, one large multinational clothing company takes conversational commerce to the next level through proactive conversational marketing. The clothing manufacturer utilizes a conversational agent to market new products, sales, and items related to what the recipient may have been recently viewing. Working in much the same way a bricks and mortar personal stylist or a sales associate, the conversational platform provides recommendations based on the customers' preferences, having asked them just a few basic questions.
Leading retailers use AI to analyze customer data to personalize their shopping experience and make product recommendations. For example, Thread, a fashion company based in the UK, utilizes AI to offer personalized clothing recommendations for each individual customer. Customers can participate in style quizzes to provide information about their personal style preferences. Every week, customers receive personalized recommendations that they can either give a thumbs up or a thumbs down. Based on this data, Thread's AI algorithms can identify patterns in what each customer enjoys and customize its recommendations accordingly. The more data the company gathers from a customer, the more accurate the recommendations become.
Some retailers are using AI to analyze images of products and make tailored recommendations based on the style and characteristics of the item. For example, British e-commerce retailer ASOS utilises machine learning to improve customer experience by recommending a customer’s outfit based on a given seen product. It does this by developing a model that understands a customer’s style incorporating a broad range of data, including colour, shape, pattern, fabric, fashion trends, customer’s style preferences and an awareness of the context in which the outfit will be worn.
Find out how a leading Australian retailer goes digital and enhances customer experience using conversational AI.
Today retailers are grappling with factory shutdowns, the global pandemic, geopolitical disruptions, talent and sourcing scarcity, and extreme weather. This is the nature of modern retail. So, to help them better manage retail supply chains, retailers are investing in AI and digital solutions to optimize their supply chain operations. This includes leveraging AI to help managers make more accurate predictions about future purchasing behaviour, forecast demand, optimize inventory management and improve logistics management. AI helps retailers reduce demand and supply fluctuations across their supply chains and optimize storage space.
Retailers are also using AI to improve supply chains from within their own operations. For example, they are using AI to create digital workers that can intelligently automate back-office tasks such as document processing, automated invoice sharing and payment reminders. And they are going one step further and combining conversational AI with RPA to create intelligent conversational business applications. Conversational AI-infused applications provide intelligent answers to supplier queries, such as the status of a particular invoice, and answer questions about shipments, contracts or company payment terms. With more consistent and predictable communication, retailers can also use AI to improve supplier relationships.
Furthermore, in retailers' production facilities, AI-enabled technologies such as CoBots are helping drive efficiency, productivity, and safety in retail warehouse management. For example, Amazon’s smart warehouse robots mean that the warehouse can hold 50% more stock, retrieve it three times faster, and reduce the overall fulfilment cost by 40%.
Each AI solution helps retailers avoid shipment or production delays, as well as reduce lead times, stock-outs and disruptions in the retailers’ supply chains. All of which improve supply chain resiliency and, ultimately, their ROI. AI is making retail supply chain operations smarter and more resilient. In fact, modern supply chain automation would not be possible without AI.
AI holds significant economic potential for retailers and is fast becoming necessary for those who want to keep up with changing customer needs. It is revolutionizing how retailers operate and interact with their customers. Its versatility allows it to be employed in various retail contexts. With 95% of executives recognizing the need for their businesses to adapt faster than their customers, it is clear that AI is an essential tool for retailers looking to attract and retain customers, boost customer spending and increase basket size. While we have only just begun to tap into the capabilities of AI in terms of personalizing customer interactions or intelligently automating retailers' operations, the technology is rapidly evolving, and we will see even more exciting developments in the future.
This success story is about Auchan embarking on a collaborative journey with DRUID to establish new benchmarks for operational efficiency and growth.
Use Cases for Conversational AI in Retail show how employees, clients, and partners may easily communicate within your company.
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