Is AI Really Enhancing the Retail Experience?
Well, I caved….I finally wrote an AI focused article for retail. I’m definately not first to the market, after all this is a one man shop, but I hope this brings value. I have compiled data, research and industry outlook to bring a comprehensive view of the AI landscape in retail. The AI buzz is everywhere, and maybe the most household name in retail technology at the moment. Google, in their I/O developer conference keynote, mentioned AI over 130 times. In their latest product release event, Apple mentioned AI on eight separate occasions. ChatGPT has 180 million users and gets about 1.6 billion visits a month.
Is AI really enhancing the Retail Experience?
Artificial intelligence is revolutionizing retail and e-commerce by enabling personalized and curated retail experiences by utilizing consumer data in machine learning algorithms. This results in personalized recommendations, automated processes, and enhanced customer interactions, which for retailers, boosts revenue and operational efficiencies.
Below are some of the most common AI Applications in Retail:
Chatbots: AI-driven customer service agents that mimic human interactions.
Voice Recognition: Integration of AI programs like Amazon's Alexa with other devices.
Predictive Analytics and Demand Planning: Algorithms analyze consumer behavior to optimize inventory management relative to sales trends
Recommendation Engines: Suggest products based on past searches.
Visual Recognition and Inspection: Often paired with camera vision and image learning models, prevents counterfeiting and aids in product discovery.
AI Copywriting: Generates SEO content for websites or summarizes feedback and user experiences for the consumer to analyze.
Who’s Really Implementing AI in a Tangible Way?
Route: Enhances the online shopping experience with package tracking and product recommendations.
SHEIN, Amazon, Walmart and eBay: Using AI for personalized shopping recommendations and trend predictions. Amazon implemented AI for voice shopping, recommendation systems, and checkout-free stores. Walmart is using conversational AI to let customers shop as fast as they can talk and text, and helping astore associated find and locate items on the floor.
Smartly: Automates social media advertising with AI.
Schnucks Markets: Deploying a new smart salad bar concept through Picadeli. The solution enables full traceability of its supply chain. By scanning a QR code, Schnucks can ensure products do not stay out longer than allowed by receiving signals for the need to refill and reorder.
Gopuff: Optimizes delivery routes with AI. GoPuff finds cheap warehouses in markets where it wishes to grow and then uses an in-house AI-powered system that maps out routes for drivers so that they can deliver the most products to the most locations in the most efficient way possible.
Mondelez International: Enhancing R&D efficiency with AI.
Lily AI: Improves product discovery with AI-powered language and attribute identification. Lily AI turns qualitative product attributes into a universal, customer-centered mathematical language creating a personal and emotional experience.
Anaplan: Uses predictive intelligence for customer and sales forecasting.
Clarifai: Classifies and moderates content using AI.
DRINKS: a leader in alcohol, offers AI-based wine recommendations and consumer insights. DRINKS uses computer vision, machine learning, and AI to train computers to interpret wine labels the same way humans do – with your eyes. DRINKS’ algorithms look at thousands of qualitative and quantitative characteristics to predict the emotional impact a wine label will have on the customer.
IBM Watson: Personalizes shopping experiences with real-time data.
Alibaba: Uses AI for augmented reality, facial recognition payments, and content creation.
Fellow AI: Provides real-time inventory management with image recognition.
This is in no way the full comprehensive view, but a summary of soem examples I think people can relate to. Im sure in many ways, the majority of retailers are leveraging AI in some form or fashion.
While it feels like the investment in AI are taglines for retail earnings reports, AI doesn’t come without barriers to adoption.
Some Key Risks in AI Adoption are Highlighted Below:
Automation-Spurred Job Loss: The displacement of jobs due to automation. This is happening across the entire retail supply chain as organizations are tasked to do more with less.
Privacy Violations and Authenticity: Risks of data breaches and misuse of personal information and the creation and misuse of “fake” media.
Algorithm Bias: Biases arising from flawed data inputs. Bad historical data or inconsistent inward data flow can and will still produce bad insights.
Socioeconomic Inequality: Widening gaps between different socioeconomic groups.
Changing Retail Landscape: Retail is evolving faster than ever and traditional brick-and-mortar is being challenged hardened than ever. Retailers still need to prove new retail is better than old retail
Can AI tools mimic the Human Experience: Maybe “robots” aren’t always better than people
Current AI Utilization by Businesses (according to Forbes Advisor Survey):
Improving Operations: 56%
Cybersecurity and Fraud Management: 51%
Digital Personal Assistants: 47%
Customer Relationship Management (CRM): 46%
Inventory Management: 40%
Content Production: 35%
Product Recommendations: 33%
Accounting and Supply Chain Operations: 30%
Recruitment and Talent Sourcing: 26%
Audience Segmentation: 24%
Overall Adoption and Purpose: Businesses are widely adopting AI to automate processes, enhance customer service, personalize experiences, increase productivity, and analyze data. The primary goal is to gain a competitive advantage, not world domination.
My View on the low-hanging-fruit for AI implementation:
Price Optimization
According to Coresight Research, who looked at the effects of AI on pricing, reveals that, with an AI-based pricing solution, retailers across different verticals can increase annual revenues by 10%, on average, and improve margins by as much as 5%.
Returns Management
AI optimizes fulfillment processes, reduces return rates, and streamlines returns management to quickly reintegrate inventory into the supply chain.
Key Benefits of AI in Returns Management:
Faster Returns Processing:
AI disposition engines utilize real-time data to make quick decisions about returns allocation.
These engines evaluate product condition, resale price, processing costs, future touch points, transportation fees, and storage requirements.
AI can cut returns processing time by 75% by automating the calculation of total returns management costs and determining the best course of action for each return.
Identifying Causes of Returns:
AI analyzes data from customer reviews and returns communication channels to identify fulfillment operation issues leading to returns.
This allows businesses to react to consumer behavior, customize returns management operations, and adjust fulfillment processes to reduce return rates.
Maximizing Profit Recovery:
AI supports intelligent dynamic pricing, ensuring returned items are listed at optimal prices across multiple online marketplaces.
This data-driven approach helps recover higher profits from returned goods.
Optimal Routing of Returns:
AI routes returned items to the most suitable storage location, fulfillment center, or geographic region based on demand, item availability, and transfer costs.
This ensures efficient reintegration of returns into the supply chain, minimizing storage and transportation costs.
Shrinkage
AI offers significant solutions to the challenges of retail shrinkage by transforming inventory management and security. Its role in retail is becoming increasingly crucial, making AI not just a tool but a game-changer in retail management.
AI-Powered Demand Forecasting:
AI algorithms can analyze market trends, past sales, and external factors like weather to predict future demand accurately to help retailers maintain optimal inventory levels, reducing shrinkage from expired or obsolete items and preventing lost sales.
Automated Inventory Management:
AI gives the ability to move inventory management from a reactive to a predictive environment
AI systems track inventory in real-time, reorder products automatically, and detect discrepancies indicating theft or fraud.
Reduces human error, increases accuracy and efficiency, and allows employees to focus on higher-value tasks.
AI identifies supply chain issues like delayed deliveries or shortages, preventing understocking and overstocking.
Customer Experience
AI offers the greatest value to retailers by combining the personalization of a human experience in a digital world. AI has been shown to be very impactful in recommending fit and function, and analyzing customer feedback. AI is laso very useful in analyzing customer sentiment, or brand perception through by analyzing industry trends and social media.
Are we living in the AI bubble? In the moment, the conversations are everywhere and frankly at the center of every keynote and earnings call surrounding the industry. Retailers who are investing in AI are seeing value not only in processes but in shareholder value. In my honest opinion, AI becomes a tool used like any other piece of technology, and becomes more commonplace than groundbreaking.
Again, these views are one man’s opinion, and only time will tell. Thanks for reading!