Ways to Protect Profits—and the Customer Experience—with AI

Ways to Protect Profits—and the Customer Experience—with AI

Consumer spending drives 70 percent of the U. S. economic growth according to a recent article in The Balance, making every fluctuation in retail sales important to a myriad of businesses. Manufacturers, suppliers and logistics companies, as well as all the industries that support them benefit from strong retail profits. As store sales trend upward, retailers are turning to artificial intelligence (AI) to help protect profits and improve the customer experience.

As this Forbes article points out, Amazon gained some of its competitive advantage by breaking down its information siloes and allowing the benefits of AI to flow within the company. Stores and their ecommerce counterparts would be well advised to do the same, and then use AI to prune away actions and processes that do not contribute to net sales or customer satisfaction.

What Is AI?

Artificial intelligence is created by using advanced analytics to develop models that are then employed by a computer to analyze data from a broad range of sources. As the models are used, the outcomes are fed back into them, which incorporates the information in future actions or decisions. The system learns and adapts without human intervention. Examples of artificial intelligence include machine learning, deep learning, natural language processing and generation and image/facial recognition. Examples within retail are vast and include inventory management, fraud detection, marketing/consumer targeting, discount and pricing optimization, among many others.

How Can It Protect Profits?

AI replaces repetitive, individual-driven analysis. Instead of having 10, 100, or 1,000 people performing the same analysis day-in and day-out, AI can deliver answers immediately for each end user specific to his or her job role. Not only is this far more efficient, but it ensures consistency across the retailer’s stores. The same data and criteria are applied universally.

The AI may take the form of facial recognition, fraud detection, marketing message targeting, or any number of routine determinations that can be made based on data. It can be used at the company headquarters to find under-performing stores and determine what steps to take to improve their profitability. It can be employed at store-level to cut through complexity to determine a fair and impartial answer to a process, such as accepting a consumer’s refund request, allocating resources, or identifying stocking errors.

With AI to help them, employees in the stores and in the corporate headquarters can have better information faster and deliver more consistent outcomes than before the technology was implemented.

Retailers frequently use it to reduce losses resulting from error, faulty processes and intentional fraud. According to the 2018 National Retail Security Survey, 52 percent of retail shrink (the difference between actual on-hand inventory and the inventory level recorded in the computer system) was due to employee fraud or paperwork errors. Luckily, these types of fraud leave a traceable data signature that can be detected through data analytics.

AI requires huge amounts of data from diverse sources. Retailers, especially omnichannel retailers, generate tremendous amounts of data every day. The data range from HVAC trends to the amount of time consumers spend in front of a display. These data trails, when analyzed with AI, can predict trends and reduce losses.

AI can also enable the retailer to identify and respond to a consumer quickly. Chatbots are one example. The bot answers routine questions and (hopefully seamlessly) rolls difficult ones over to a customer service agent. In a store, different technology can be used to generate real-time, targeted marketing messages to shoppers that drives more value from the existing foot traffic. These are just two examples of how AI can enhance the customer experience and drive more sales while eliminating waste.

Another way that AI can protect profits is to model the effectiveness of chainwide loss prevention solutions. Retailers invest heavily in surveillance and monitoring equipment—cameras, CCTV, software to store and analyze the CCTV feeds, EAS tags, EAS tag readers and exception based reporting systems. Instead of buying and trying, a retailer can predict their usefulness prior to purchase. AI can also model the outcomes for policy changes to determine if they will be beneficial. For instance, a retailer may think that developing a strict return policy will save the company money, but during testing they discover that the reduction in sales—the diminished customer experience—outweighs the savings on returns. This is an outcome that is best discovered before store operations and the overall customer experience are disrupted.

With AI to help them, employees in the stores and in the corporate headquarters can have better information faster and deliver more consistent outcomes than before the technology was implemented.

While ecommerce uses AI to emulate the person-to-person experience shoppers have in stores, stores use AI to emulate ecommerce through streamlined processes and expanded marketing touchpoints. This combination of AI and humanity will drive future industry growth.

Key Takeaway

Artificial intelligence is a tool with broad implications, in retail and beyond. This quick glimpse into how AI is evolving is designed to stimulate your thinking. If AI can become deeply embedded in as personal a business as brick-and mortar retail – to the benefit of shoppers and employees – how can it help you improve your business? What untapped data do you have that AI can address? Or, what results is AI delivering that you have not yet put to use?

Predictive analytics can help you reduce shrink, decrease returns, and improve the customer experience.

here’s how

Author

David Speights, PhD - Chief Data Scientist, Appriss

David Speights, PhD, chief data scientist at Appriss, Inc., is the lead author of “Essentials of Modeling and Analytics: Retail Risk Management and Asset Protection” which was co-authored by Daniel M. Downs, PhD, and Adi Raz, PhD. He resides in California, USA, and speaks internationally on the intersection of artificial intelligence and retail.

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