Artificial Intelligence in Retail
AI-powered innovation is fundamentally transforming the way ecommerce platforms and physical storefronts operate. AI-enabled retail analytics methods are helping retailers maximize the value of their data, with opportunities to thrive in an increasingly competitive industry. In this article, we’ll explore how the use of artificial intelligence in retail is driving innovation, including concrete examples of how AI is helping retailers level up data-driven decision-making and improve customer satisfaction.
Types of AI used in retail analytics
While many types of AI can be applied to retail analytics, three stand out as having the most opportunity for impact. Let’s look at each, focusing on how they address specific challenges within the industry.
Computer vision enables machines to extract relevant information from digital images and videos. Using computer vision-equipped cameras and sensors, retailers can collect and analyze vast amounts of visual data, including demographic information and the ways that customers interact with products and store layouts. This data helps retailers optimize product placement, improve their messaging, monitor product pickups and put-backs and identify trouble spots where customers tend to disengage.
Computer vision is also heavily used in retail theft prevention. By analyzing video footage, computer vision-enabled analytics tools can detect patterns of behavior that indicate theft, such as a customer putting an item into their pocket or purse or a cashier placing an item in a customer’s bag without scanning it, automatically alerting security personnel.
Natural language processing
Natural language processing (NLP) uses machines to generate meaning from language, both written and spoken. NLP-enabled analytics tools can analyze customer feedback, reviews and social media comments to gauge customer sentiment and identify consumer trends. With this information, retailers can improve their marketing, proactively respond to customer concerns and align their product offerings to capitalize on emerging trends.
Deep learning plays an important role in predictive analytics, helping retailers tackle complex demand forecasting. Predictive analytics tools use deep learning to analyze historical sales data, seasonality, economic factors and weather patterns to more accurately predict demand for products. With this information, retailers can optimize inventory levels, reduce overstocking or understocking and create more efficient supply chains.
Clicks and bricks: use cases for artificial intelligence in retail
Whether operating exclusively online, in physical storefronts or a combination of both, retailers have many opportunities to apply artificial intelligence to help reach their goals.
Visual search helps customers find products for sale via snapping a photo with their smartphone or linking to a photo on another website. Retailers can use AI-enabled visual search to suggest similar products that closely match or complement the item in the customer-provided photo, simplifying the shopping process for buyers and boosting conversions for retailers.
Personalized content and offers
Advanced data analytics and machine learning algorithms allow retailers to serve personalized content and offers at the individual customer level, often in real time. By analyzing a diverse mix of customer data, including purchase history, browsing behavior and demographics, artificial intelligence can identify patterns and preferences, ensuring that customers receive content and offers targeted to their individual interests.
AI can help retailers identify early warning signs of customers who are at risk of moving to a competitor by pinpointing specific behaviors or trends that signal potential churn. Retailers can re-engage these customers using personalized retention offers, outreach campaigns or tailored customer service interventions.
Ensuring that planograms are executed in-store as intended can be difficult to verify, especially for retailers with numerous locations spread across a wide area. Computer vision-enabled technology helps retailers quickly identify discrepancies between the shelf set and planogram, alerting appropriate personnel that corrections are needed and providing a detailed to-do list of specific actions that must be taken to regain compliance.
Customer occupancy analysis
By analyzing video footage from stores in real time, AI helps retailers accurately count customers and staff. This assists retailers in better aligning their staffing levels to accommodate periods of peak demand, ensuring customers can access help when they need it and move quickly through checkout lines.
Eliminating phantom inventory
Phantom inventory is inventory that shows as in-stock in a retailer’s ordering system but isn't actually there due to unreported theft, damage or being improperly accounted for. This discrepancy can short-circuit auto-replenishment systems and result in lost sales opportunities and dissatisfied customers. AI technologies identify and correct phantom inventory quickly, reducing the negative effects of stockouts.
Build your AI capabilities with Snowflake
The successful deployment of AI in retail contexts requires a modern data infrastructure capable of processing, analyzing and storing data at scale. As AI-enabled retail analytics technologies advance, Snowflake's robust architecture, data-sharing capabilities and scalability empower retailers to drive AI innovation and fuel transformative business strategies.
Streamlined access to all relevant data
Snowflake facilitates discovery and access to structured, semi-structured and unstructured data for data science workflows, including retail analytics. With Snowflake, retailers can easily access relevant data in its all forms, augmenting it with shared data sets from their business ecosystem and third-party data.
Freedom to use preferred languages and frameworks
By providing developers with a single platform that supports their language of choice, as well as popular open-source and commercial solutions, Snowflake enables developers to work efficiently. Snowpark, Snowflake’s developer framework, allows data engineers, data scientists and data developers to code in a variety of languages—including Python, Scala and Java—and execute data pipelines and machine learning (ML) workflows faster and more securely, all within Snowflake.
Superior performance across ML
With its elastic and performant multi-cluster compute architecture, Snowflake can handle large amounts of data and users simultaneously. The infrastructure automatically scales to meet machine learning resource demands without bottlenecks or user concurrency limitations. Pre-installed open-source libraries and seamless dependency management via Anaconda integration accelerate Python-based workflows, further reducing time-to-insight.
Top-tier security and governance
Retail analytics and other applications of artificial intelligence in retail often involve the use of sensitive data, making data security and governance a top priority. Snowflake's scalable data governance and security features enable retailers to consistently enforce enterprise-grade governance controls and security. With a multi-layered security foundation that includes encryption, access control, network monitoring and physical security measures, retailers have the tools they need to protect the quality and security of their data.
Cost savings through resource automation
Resource automation enables greater efficiency and minimizes operating costs. This is especially important for AI-enabled initiatives, which require scale, expertise and planning for an unpredictable number of resources. With Snowflake, storage and compute resources can be automatically spun up and down to meet changing resource needs, eliminating the performance issues that come with fixed-capacity resources while lowering costs. As a fully managed platform with near-zero maintenance, Snowflake frees retailers to focus their full attention on maximizing the value of their data.
Future-proof your artificial intelligence initiatives with Snowflake
Prepare for an AI-driven future with Snowflake. With the Snowflake Retail Data Cloud, retailers can consolidate multiple data types and structures, uniting diverse data sources into a single source of truth and making that data easily accessible for sharing and collaboration. From data preparation to model building to application deployment, Snowflake streamlines the process of developing and deploying artificial intelligence solutions, providing retailers with the data infrastructure required to realize the full potential of data.