How AI is Reshaping the Retail Industry



Preamble


Retail data science provides significant benefits for optimizing supply chains, lowering costs, and targeting customers.


For years, the retail industry has been undergoing digital transformation. It has increased speed, efficiency, and accuracy across all branches of retail, owing in large part to advanced data and predictive analytics systems that assist companies in making data driven business decisions.


None of these discoveries would be possible without the internet of things (IoT), and especially without artificial intelligence. AI in retail has provided businesses with access to high-level data and information, which can be used to improve retail operations and create new business opportunities. In fact, it is estimated that AI in retail generated $40 billion in additional revenue over a three-year period.


Retailers looking to stay competitive should look no further than artificial intelligence in the retail industry. By 2020, it is expected that 85% of enterprises will be using AI, and those who do not risk losing insurmountable market share to competitors.


What AI Technologies and Solutions are Used in Retail?


Many industries use the term "artificial intelligence," but many people don't fully understand what it means. When we say AI, we mean a set of technologies, such as machine learning and predictive analytics, that can collect, process, and analyze massive amounts of data and use that information to predict, forecast, inform, and assist retailers in making data-driven business decisions.


These technologies can even act autonomously, converting raw data collected from IoT and other sources into actionable insights using advanced AI analytical capabilities. AI in retail also makes use of behavioral analytics and customer intelligence to gain valuable insights about various market demographics and improve a variety of touchpoints in the customer service sector of business.


What Does Artificial Intelligence Look Like in Retail?


The dynamic retail industry of today is based on a new covenant of data-driven retail experiences and elevated consumer expectations. However, providing a relevant and valuable personalized shopping experience at scale is a difficult task for retailers. As digital and physical purchasing channels merge, retailers who can innovate their retail channels will distinguish themselves as market leaders.


So, how exactly does that look? Here are some examples of how artificial intelligence in retail is reshaping the entire industry.


INVENTORY MANAGEMENT


Artificial intelligence in retail is improving demand forecasting. AI business intelligence tools forecast industry shifts and make proactive changes to a company's marketing, merchandising, and business strategies by mining insights from marketplace, consumer, and competitor data. This influences supply chain planning, as well as pricing and promotional strategies.


ADAPTIVE HOMEPAGE


Mobile and digital portals recognize customers and personalize the e-commerce experience to reflect their current context, previous purchases, and shopping behavior. AI systems are constantly evolving a user's digital experience to create hyper relevant displays for each interaction.


DYNAMIC OUTREACH


Through repeated interactions, advanced CRM and marketing systems learn a consumer's behaviors and preferences to create a detailed shopper profile, which is then used to deliver proactive and personalized outbound marketing — tailored recommendations, rewards, or content.


INTERACTIVE CHAT


Building interactive chat programs is an excellent way to leverage AI technologies while improving customer service and engagement in the retail industry. These bots converse with customers, answer common questions, and direct them to helpful answers and outcomes using AI and machine learning. As a result, these bots amass valuable customer information that can be used to inform future business decisions.


VISUAL CURATION


Algorithmic engines translate real-world browsing behaviors into digital retail opportunities by allowing customers to discover new or related products through image-based search and analysis, curating recommendations based on aesthetic and similarity.


GUIDED DISCOVERY


As customers seek to increase their confidence in a purchase decision, automated assistants can assist in narrowing the selection by recommending products based on their needs, preferences, and fit.


CONVERSATIONAL SUPPORT


AI-powered conversational assistants use natural language processing to assist shoppers in easily navigating questions, FAQs, or troubleshooting and redirecting them to a human expert when necessary, improving the customer experience by providing on-demand, always-available support while streamlining staffing.


PERSONALIZATION & CUSTOMER INSIGHTS


Intelligent retail spaces recognize shoppers and adapt in-store product displays, pricing, and service through biometric recognition to reflect customer profiles, loyalty accounts, or unlocked rewards and promotions — creating a personalized shopping experience for each visitor at scale. Stores are also utilizing artificial intelligence and advanced algorithms to determine what a customer might be interested in based on demographic data, social media behavior, and purchase patterns. They can use this data to improve the shopping experience and personalized service both online and in-store.


EMOTIONAL RESPONSE


By recognizing and interpreting facial, biometric, and audio cues, AI interfaces can identify shoppers' in-the-moment emotions, reactions, or mindset and deliver appropriate products, recommendations, or support — ensuring that a retail engagement is successful.

CUSTOMER ENGAGEMENT


By interacting with customers via IoT-enabled technologies, retailers can gain valuable insights into consumer behavior preferences without ever directly interacting with them. Consider the Kodisoft interactive tablet: Kodisoft created a tablet for use in restaurants, allowing customers to browse menus, order, and play games. This tablet, powered by the IoT Hub and machine learning, has leveraged consumer data and behavior trends, allowing businesses to increase customer engagement and success.


OPERATIONAL OPTIMIZATION


AI-powered logistics management systems continuously adjust a retailer's inventory, staffing, distribution, and delivery schemes to create the most efficient supply and fulfillment chains while meeting customers' expectations for high-quality, immediate access and support.


RESPONSIVE R&D


Deep learning algorithms collect and interpret customer feedback and sentiment, as well as purchasing data, to support next-generation product and service designs that better satisfy customer preferences or meet unmet market needs.


FORECASTING DEMAND


AI business intelligence tools forecast industry shifts and make proactive changes to a company's marketing, merchandising, and business strategies by mining insights from marketplace, consumer, and competitor data.


CUSTOMIZED SELECTIONS


Taking customer service to the next level, many retailers are leveraging AI to provide customers with unique, personalized experiences. And providing such services is lucrative. According to a Boston Consulting Group study, "brands that create personalized experiences for customers by integrating advanced digital technologies and proprietary data see revenue increase by 6% to 10% — two to three times faster than those that don't."


Why is AI Required in the Retail Industry?


Aside from the business intelligence and sheer speed that these technologies can provide, the digital transformation in retail is simply separating successful from unsuccessful businesses. There are numerous advantages to using artificial intelligence in the retail industry, but here are five of the most important ones.

CAPTIVATE CUSTOMERS


With so many innovative competitors offering immersive shopping experiences, traditional retailers must engage customers in a personalized and relevant way that is unique and inspiring across all touchpoints.


CREATE AN EXCITING EXPERIENCE


To maintain consumer interest, retailers must differentiate their products and provide compelling service and experiences. Retailers can lead with innovation rather than react to change by incorporating predictive analytics to gain more market insight.


DERIVE INSIGHTS FROM DIVERSE DATA


Faced with an onslaught of data from all aspects of their business, from supply chain to stores to consumers, retailers must cut through the noise to transform these disparate data sources into consumer-first strategies.


INTEGRATE OFFLINE AND ONLINE RETAIL


Digital and physical shopping channels typically operate under separate initiatives and approaches but treating them as separate business units creates friction for customers seeking a seamless shopping experience and leads to operational inefficiencies.


EMPOWER FLEXIBLE LOGISTICS NETWORKS


To meet a broader range of customer demands that are shifting from mainstream to niche, retailers must rethink their traditional supply chain in favor of adaptive and flexible ecosystems that can quickly respond to shifting consumer behaviors.

In Conclusion


By adding AI capabilities to both the edge and the cloud, Intel and our partner ecosystem are assisting retailers in transforming their data into powerful new insights. These data-driven solutions deliver highly personalized experiences and product recommendations, as well as

precise forecasting, inventory efficiencies, and overall smarter business.

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