Strategizing with Retail Data Science [+ Use Cases!]
Retail data science offers important advantages for optimizing supply chains, reducing costs, and targeting customers.Read the Customer Story
The digital transformation of the retail industry has been going on for years. It has increased speed, efficiency, and accuracy across every branch of retail business, thanks in large part to advanced data and predictive analytics systems that are helping companies make data-driven business decisions.
None of those insights would be possible without the internet of things (IoT), and most importantly, artificial intelligence. AI in retail has empowered businesses with high-level data and information that is leveraged into improved retail operations and new business opportunities. In fact, it is estimated that $40 billion of additional revenue was driven by AI in retail in a 3-year span.
Retailers looking to stay competitive need look no further than AI in retail business. It is forecasted that 85% of enterprises will be using AI by 2020, and those who don’t risk losing insurmountable market share to their competitors.
What Technologies & Solutions Are Used for AI in Retail?
Artificial intelligence is a term that is thrown around in many industries, but many people don’t fully grasp what it means. When we say AI, we mean a number of technologies, including machine learning and predictive analytics, that can collect, process, and analyze troves of data, and use that information to predict, forecast, inform, and help retailers make accurate, data-driven business decisions.
These technologies can even act autonomously, using advanced AI analytical capabilities to convert raw data collected from the IoT and other sources into actionable insights. AI in retail also utilizes behavioral analytics and customer intelligence to glean valuable insights about different market demographics and improve many different touchpoints in the customer service sector of business.
What Does AI in Retail Look Like?
Today’s dynamic retail industry is built on a new covenant of data-driven retail experiences and heightened consumer expectations. But delivering a personalized shopping experience at scale — that is relevant and valuable — is no easy feat for retailers. As digital and physical purchasing channels blend together, the retailers that are able to innovate their retail channels will set themselves apart as market leaders.
So, what exactly does that look like? Here are some examples of how AI in retail is reshaping the entire industry.
Inventory Management – AI in retail is creating better demand forecasting. By mining insights from marketplace, consumer, and competitor data, AI business intelligence tools forecast industry shifts and make proactive changes to a company’s marketing, merchandising, and business strategies. This also impacts supply chain planning, as well as pricing and promotional planning.
Adaptive Homepage – Mobile and digital portals are recognizing customers and customizing the e-retail experience to reflect their current context, previous purchases, and shopping behavior. AI systems constantly evolve a user’s digital experience to create hyper-relevant displays for every interaction.
Dynamic Outreach – Advanced CRM and marketing systems learn a consumer’s behaviors and preferences through repeated interactions to develop a detailed shopper profile and utilize this information to deliver proactive and personalized outbound marketing — tailored recommendations, rewards, or content.
Interactive Chat – Building interactive chat programs is a great way to utilize AI technologies while improving customer service and engagement in the retail industry. These bots use AI and machine learning to converse with customers, answer common questions, and direct them to helpful answers and outcomes. In turn, these bots collect valuable customer data 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 using image-based search and analysis — curating recommendations based on aesthetic and similarity.
Guided Discovery – As customers look to build confidence in a purchase decision, automated assistants can help narrow down the selection by recommending products based on shoppers’ needs, preferences, and fit.
Conversational Support – AI-supported conversational assistants use natural language processing to help shoppers effortlessly navigate questions, FAQs or troubleshooting and redirect to a human expert when necessary — improving the customer experience by offering 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 custom shopping experience for each visitor, at scale. Stores are also using AI and advanced algorithms to understand what a customer might be interested in based on things like demographic data, social media behavior, and purchase patterns. Using this data, they can further improve the shopping experience and personalized service, both online and in stores.
Emotional Response – By recognizing and interpreting facial, biometric, and audio cues, AI interfaces can identity shoppers’ in-the-moment emotions, reactions or mindset and deliver appropriate products, recommendations or support — ensuring that a retail engagement doesn’t miss its mark.
Customer Engagement – Using IoT-enabled technologies to interact with customers, retailers can gain valuable insights on consumer behavior preferences without ever directly interacting with them. Take the Kodisoft interactive tablet for example – Kodisoft developed a tablet to be used in the restaurant setting for customers to use to browse menus, order, and play games. Supported by the IoT Hub and machine learning, this tablet has leveraged consumer data and behavior trends, allowing companies to increase engagement and success with customers.
Operational Optimization – AI-supported logistics management systems adjust a retailer’s inventory, staffing, distribution, and delivery schemes in real-time 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 fulfill unmet needs in the marketplace.
Demand Forecasting – Mining insights from marketplace, consumer, and competitor data, AI business intelligence tools forecast industry shifts and make proactive changes to a company’s marketing, merchandising, and business strategies.
Customized Selections – Taking customer service to the next level, many retailers are using AI to help them provide unique, personalized experiences for customers. And, there’s big money in providing such services. “Brands that create personalized experiences by integrating advanced digital technologies and proprietary data for customers are seeing revenue increase by 6% to 10% — two to three times faster than those who don’t,” according to a study by the Boston Consulting Group.
Why You Need AI in the Retail Industry
Aside from the business intelligence and sheer speed that these technologies can provide, the digital transformation in retail is simply setting successful businesses apart from unsuccessful ones. There are countless benefits that can be credited to artificial intelligence in retail business, but here are five primary ones that retailers can count on.
- Captivate Customers – With a plethora of innovative competitors providing shoppers with immersive shopping experiences, traditional retailers need to engage customers in a personalized and relevant manner that is unique and inspiring across all touchpoints.
- Create Exciting Experience – To drive continued interest, retailers need to differentiate their products and offer consumers compelling service and experiences. By integrating predictive analytics to gather more market insight, retailers can lead with innovation rather than react to change.
- Create Insights from Disparate Data – Faced with an onslaught of information from all aspects of their business from supply chain to stores to consumers, retailers need to filter through the noise to transform these disparate data sources into consumer-first strategies.
- Synchronize Offline & Online Retail – Digital and physical shopping channels typically operate under a different set of initiatives and approaches but treating these channels as distinct business units adds friction for customers seeking a seamless shopping experience and leads to operational inefficiencies.
- Empower Flexible Logistics Networks – In order to service a wider range of customer demands that are moving from mainstream to niche, retailers need to rethink their traditional supply chain in favor of adaptive and flexible ecosystems that can quickly respond to consumers’ shifting behaviors.
Implementing the systems to support AI in retail can seem overwhelming, but it doesn’t have to be. With a technology solutions partner like Hitachi Solutions, you will be supported and guided through every step of the process, and even after deployment. Reach out to one of our experts to learn more about Hitachi Solutions for retail business.
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