How Retailers Are Reaching Customers Better With AI

Granner Smith

Cognitive technologies and artificial intelligence (AI) are no longer science fiction or futuristic technologies, rather the reality we’re living in today.

AI is an umbrella term encompassing subfields such as machine learning, expert systems, general intelligence, robotics, natural language processing, and so forth. Many of the latest advances and innovations in medical diagnostics, aviation, stock trading, and so forth have AI at the core.

“AI algorithms play an increasingly large role in modern society, though usually not labeled ‘AI.’ It will become increasingly important to develop AI algorithms that are not just powerful and scalable, but also transparent to inspection,” said Nick Bostrom, superintelligence professor at Oxford University.

Facebook chairman and CEO Mark Zuckerberg says, “Facebook is already working on a feature that will let disabled people engage more easily with the platform. This includes an AI system that can automatically see what is inside an image and read it out loud to users who can’t see. The feature can also be used for reading newspapers or anything else.”

AI is becoming ingrained deeply into business, although it can’t yet be separated from human intervention. For example, because we learned how AI systems can be discriminatory, we’ve been able to design fairer, more inclusive AI for the benefit of many industries, including e-commerce and mobile commerce.

Applying AI to e-commerce

Machine learning and personalization can help e-commerce evolve for better purposes. Logistics, pricing, recommendations, and other market research data derived though AI can help marketers better understand and more accurately predict consumer behavior. Marketers can then use this information to focus on their customers’ needs and present better recommendations.

Following are some of the ways AI can be used in e-commerce.

  • Enhance search through image recognition: AI can improve the online shopping experience by presenting images of the best possible results when a user searches for a particular product. To get more information or make a purchase, the user just has to click the picture of their favorite product.
  • Recommend and purchase products: AI could use tracked data from a customer’s previous searches to recommend products. It could alert the user when a previously out-of-stock product is available, and even make the purchase without the user’s intervention if it fits into the user’s preset schedule or scheme.
  • Voice-activated apps: Customer satisfaction could be improved by swapping text forms with real-time, voice-activated apps or chatbots for seamless voice conversations.
  • Assortment tools: These tools help multichannel retailers track competitors and learn about their strategies as well as new products and services within 24 hours of release. This insight helps retailers provide competing products and services to preserve customer retention.

In summary, AI can be used as an intelligence agent for retailers that can fetch shoppers’ details, such as purchased, preferred, searched, or related products, to promote cross-selling and upselling opportunities. Further, by knowing the needs of the buyer and the seller, AI facilitates transactions and helps upgrade infrastructure.

Applying AI to mobile commerce

Mobile app developers can take AI-derived patterns in users’ data and apply them to create m-commerce tools that can predict shoppers’ needs more intelligently and enhance the user experience in subtle ways that the customer may not even notice.

Here are some ways AI can advance m-commerce.

  • Filter e-mail: Retailers can use AI to prioritize customer service inquiries and reduce e-mail overload on the customer service team, especially during peak traffic periods. With natural language processing and cloud computing, AI-augmented m-commerce assesses the nature and urgency of queries and responses accordingly.
  • Predict purchases: Retailers may be able to predict purchase behaviors and anticipate shipping. With enough data on purchase history, AI could prompt customers to purchase fast-moving consumer goods like toothpaste, soap, and cleaning supplies before they run out, and offer discounts, target advertisements, maintain stock, etc., to prompt and support sales.
  • Recommend services: AI can monitor shoppers’ choices and insert the data into its learning algorithm in order to make real recommendations to customers and increase upselling or cross-selling. For instance, if someone is buying a winter sweater, recommending a hat, jacket, or boots would be better options than blindly recommending a powder or a bath soap that the retailer also sells.
  • Detect fraud: Pattern-detecting algorithms can pinpoint unusual purchases based on previous shopper behavior. AI can learn from typical credit card statements and purchases and sense any deviation from the norm that could indicate fraud.

Conclusion

By bringing in more data and more conversions in less time, AI can help retailers achieve their targets more easily. But, according to professor Bostrom, working with AI is like allowing children to play with a bomb, because of the risks around using people’s personal data. Therefore, it’s essential to always consider the cognitive, emotional, and ethical aspects of decision making that incorporates AI-derived data.

For more on the privacy risks around using customer data to target your offerings, see Is Personalization Killing Your Relationships With Customers?


About Granner Smith

Granner Smith is a Professional writer. His skill set is vast, his greatest expertise revolve in the worlds of interactive design, development, UX, social media, brand identity design, content creation. He works with reputed company, Orange Mantra that provide web and mobility solution. Follow us on Twitter @Orangemantraggn Facebook @OrangeMantraindia