With retailers constantly adopting new strategies to beat the competition, many are beginning to deploy AI systems in stores. AI is important not just for staying competitive but also for enhancing customer experiences.
It is presently used to improve many processes, including demand forecasting, pricing, product ordering, placement optimisation, and data tracking from online channels to in-store.
Here are the details on how employing AI can revolutionise your retail store.
What Role Does AI Play in Modern Retail?
Bricks and mortar stores are following the footsteps of e-commerce businesses. First, the adoption of artificial intelligence (AI) in e-commerce became a crucial area for business development. Using AI has ceased to be a choice for businesses; it has become essential for survival and success. This brought about tremendous progress in the industry.
For example, the 2023 holiday shopping season saw the highest average discount rate since 2020 and increased AI-influenced purchases. Studies also show that adopting AI in e-commerce enhances the business performance of SMEs.
The success recorded by AI adoption in e-commerce and general technological development worldwide has triggered interest in AI adoption in retail.
AI in retail refers to improving retail business processes with high-level data and information that is leveraged to improve operations, enhance customer experience, and optimise merchandising, demand forecasting, and supply chain management.
Retail businesses have seen the need to improve customer experience and operational efficiency to stay competitive. Hence, AI adoption is no longer limited to e-commerce.
Brick-and-mortar businesses have long integrated digital marketing strategies into their operations. They’re now increasingly adopting AI to streamline the process and stay ahead of the game.
AI Applications in Brick-and-Mortar Stores
Here are six areas where bricks and mortar stores are leveraging AI in digital marketing:
a. Personalised Customer Experiences
AI algorithms are helping retailers create detailed customer personas that capture individual customers’ unique preferences, interests, and behaviours. How does this work? It utilises historical data to make predictions about future customer behaviour and preferences.
Such data include browsing history, purchase behaviour, and social media interactions. By analysing data from various sources, AI creates detailed retailer customer profiles and reveals preferences, behaviours, and needs.
Additionally, retailers are using facial recognition or loyalty programs for tailored recommendations. They can now personalise shopping experiences through facial recognition by offering tailored recommendations, promotions, and targeted advertising displayed on digital signage or through personalised mobile notifications based on individual preferences.
b. Inventory Management and Optimisation
Predictive analytics is helping retailers avoid overstocking or understocking. It analyses historical data, market trends, and consumer behaviour to forecast future demand. This allows businesses to optimise stock levels, reduce the risk of stockouts, and minimise overstock.
Accurate demand forecasting is essential for preventing overstocking. By analysing market trends, historical sales data, and seasonal patterns, retailers make more informed decisions about how much inventory to order.
c. Smart Shelf Technology
AI-powered sensors and cameras help retailers to monitor stock levels and shelf placement. AI-powered systems enable real-time inventory tracking throughout the supply chain.
By processing data from IoT sensors, GPS, and RFID tags, AI provides continuous visibility of stock levels, from warehouses to store shelves. This real-time monitoring allows companies to identify and respond to disruptions quickly.
AI is also useful in dynamic pricing based on demand and competitor analysis. AI transforms dynamic pricing strategies by leveraging advanced analytics, machine learning, and real-time data processing to optimise pricing models, respond to market demands, and maximise revenue.
d. In-store Navigation and Assistance
AI is helping retailers navigate their stores and help their customers through chatbots and virtual assistants that provide immediate support. In addition, AI enables visual search, allowing customers to find products using images, and increases fraud prevention to ensure safe transactions.
Some AI-based kiosks or apps guide customers to products. By leveraging AI-powered computer vision algorithms, kiosks can analyse and recognise objects or products presented by users.
In addition, AI helps integrate voice assistants for product queries. Voice assistants present personalised recommendations to users, enhancing the shopping process and improving sales outcomes.
e. Enhanced Security and Loss Prevention
AI-driven surveillance systems are being used to detect theft or unusual activities. These systems deploy advanced algorithms and machine learning to analyse real-time video feeds, greatly improving threat detection capabilities. By continuously monitoring for unusual patterns, movements, or behaviours, these systems can quickly alert security personnel to potential risks.
Brick and mortar stores are also using AI for pattern recognition to prevent fraud at checkout. AI algorithms can detect patterns indicating possible fraud, such as unusual transaction volumes.
f. AI-Powered Marketing Campaigns
Retailers are also using foot traffic data to launch targeted in-store promotions. Foot traffic data provides insights into when and where customers engage with a store, helping retailers strategically time their marketing efforts.
Here are some ways foot traffic data that retailers are using to launch targeted in-store promotions:
- Identify busy times: This helps to analyse patterns to determine the busiest times of day for customers.
- Identify popular entry points: Retailers use this to determine which entry points are most popular with customers.
- Identify dwell times: This helps to determine how long customers spend in different store areas.
- Identify products of interest: Determine which products customers show interest in.
Foot traffic data can be collected using several methods, including:
- People counting sensors, which can use thermal imaging, infrared beams, or Wi-Fi signals.
- Overhead beam counters, which are mounted to the ceiling and use a wired connection to gather and transmit data
- Video cameras, mobile location tracking, and Wi-Fi networks.
Foot traffic data can help retailers understand customer behaviour, optimise operations, and drive growth.
AI can also optimise store layout based on customer behaviour and observed foot traffic data. For example, by analysing customer traffic patterns and identifying areas of congestion, retailers can optimise aisle widths and product placement to improve the flow of customers and staff.
Summary
AI is enabling brick-and-mortar stores to leverage digital marketing by offering personalised and targeted marketing strategies. Retailers can now execute targeted marketing campaigns tailored to individual customers and deliver targeted promotions, discounts, and product recommendations based on unique preferences.
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