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As with the typical journey to the restaurant, where paradox of choice will lead people to order meals recommended by the server, people have for long desired recommendations tailored to their individual taste when exploring any shopping experience. The retailers that win in the market are the ones that are able to seemingly recommend products that customers magically want. With the growth of e-commerce has come a seemingly unlimited array of products that customers can buy. While not all sub-industries within retail have been equally disrupted by the internet as they may have higher barriers to entry, as shown by the differences between fashion apparel and technology hardware, all customer expectations have certainly changed across the board. Customers now value companies that understand their needs and help them cut the chase right to the products they want. Accenture shares that 91% of customers say personalized offers make them more likely to shop with a brand.
In today’s world of hyper personalization, AI helps you offer products that customers are most likely to purchase. By developing individual or combined models that predict the likelihood a customer will accept different products, AI will inform your marketers on which products a customer is most likely to accept based on their individual characteristics and preferences. AI will also reveal the top reasons why customers are likely to accept each product with incredible granularity, ensuring that the predictions can be explained and easily understood. This allows your marketers to adjust their outreach to each customer’s highlighted priorities to maximize the likelihood they will resonate with your message. The outcome is that for both new and existing customers, you will increase your conversion rate for both new leads and cross sell / upsell opportunities.
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Retailers deliver better demand forecasting, marketing efficiency, transparency in the supply chain, and profitability through advanced AI applications and a myriad of AI use cases throughout the value chain: from store-level demand and out-of-stock (OOS) predictions to marketing channel modeling and customer LTV predictions. With the abundance of consumer data, changing consumption patterns, global supply chain shakeups, and increased pressure to drive better forecasting, retail businesses can no longer ignore the potential of AI in their industry.