You Can’t Spell Retail Without AI: How To Optimize Your Chain Retail Store Design With Data Science
By Seth Redmore, Lexalytics
The old saying “retail is detail” has never been more true. From what’s in stock to the size of the customer service desk, data science rules the decision-making process.
While machine learning has been the darling of large chains for years, increasing accessibility, ROI and AI hype are encouraging uptake among mid-sized chains looking to maximize operational efficiency alongside delivering superior customer service. Here are some of the ways AI can be used to drive growth, efficiency and profit.
Store Location
Location is king in retail, and the difference between an okay and a prime location can have a significant impact on your bottom line. AI can help crunch the data around population trends, shopping habits, travel patterns and competitor reach, identifying the factors that represent an ideal location for your store. These factors, and their associated profits or costs, can be used in decision-making around new store openings — and also for closing down lower-performing stores.
Layout Optimization
Cookie cutter doesn’t cut it any more. Retail has long been moving towards the experiential. But retail store design still needs to meet the needs of consumers — while ensuring efficient processes for staff and suppliers. AI can tap into local demographics, consumer preferences and operational efficiency to help develop layouts that deliver the best possible customer experience, while still providing a consistent brand experience across each store.
Product Optimization
Effective product display and merchandising is crucial for moving stock. AI can be used to help optimize product position and space allocation, finding the sweet spot that drives maximum value. Data around the effectiveness of shelf displays can also be gathered through customer monitoring: Procter & Gamble is currently piloting technology that gathers customers’ facial expressions as they encounter products.
Dynamic Pricing
Stock that moves too slowly affects margins, while selling out too quickly affects brand reputation. Using AI, pricing strategies can be optimized for variables around seasonality, demand and availability. Dynamic pricing can be used to encourage movement of perishable or seasonal stock, and can help handle issues with overstock as well. AI can also be used to predict sales numbers based on local store demographics and behavior across your other stores, helping you to optimize your orders in the first place.
Opening Hours And Personnel
Effective staff allocation is critical to your bottom line. Being understaffed affects both the customer experience and staff morale, while being overstaffed is inefficient and costly. According to research from McKinsey, taking a data-driven approach can save up to 12% in staffing costs. AI can be used to monitor and predict peak times day-to-day and seasonally, as well as to gauge demand and preferences around checkout type. Using this data you can adjust your opening hours, along with staffing levels and allocation. For example, for off-peak hours or times when people make quick trips, you may rely more heavily on self-checkout. Notably, Macy’s has recently launched “scan and go”, while Amazon Go has no cashiers at all.
Fulfillment
Chances are that your retail chain also has an online presence. Traditional fulfillment processes one order at a time, resulting in split shipments, higher delivery costs and slower shipping. AI can be used to optimize order fulfillment across all orders, juggling multiple orders at once in order to ship like orders together, improving efficiency and boosting margins. Canadian brand Aldo Group has saved millions doing so.
Store Monitoring
Real-time intervention can help ensure a positive customer experience. Store camera footage is now cheap and easy to store and monitor, and can be combined with computer vision technology to identify customers in need of assistance. Staff members can be easily dispatched to assist customers in the short term, while this data can also be used to help inform future store improvements.
Voice Of The Customer
Finally, AI-powered natural language processing can also be used to analyze text-based customer feedback posted on social media or review sites. This allows you to gain insight into what customers are saying about you — or your competitors — and to adapt accordingly. Retail giant Kroger uses information from social listening to create personalized communications and offers.
AI is a powerful tool that allows chain retail companies to use existing data to predict trends, behavior and opportunities. By optimizing your store design, practices and related services, you can improve the customer experience, create more efficient processes and maximize profits.
But you don’t need to be a Walmart, Amazon or Target to benefit from AI. Medium-sized chains can partner with AI, NLP or computer vision providers to determine which optimizations will have the greatest impact on profitability and customer experience, and go from there. Be strategic about getting started: start small with something like voice of the customer monitoring, and expand your touch points as resources allow.
With over 20 years of combined experience in product management, marketing, text analytics and machine learning, Seth Redmore is currently the CMO of Lexalytics, the leader in "words-first" machine learning and artificial intelligence. Prior to this role, Redmore served as Vice President of Product Management and Vice President of Marketing at Lexalytics. He has held executive positions at both hardware and software companies, and was co-founder of Netiverse (acquired by Cisco Systems). During his tenure at Cisco, Redmore built Cisco's first internal text analytics solution for reputation management. He has a degree in Chemistry from Carnegie Mellon University
