AI in the Retail Industry: Understanding Consumer Behavior Better

Overview of AI in Retail Industry  

AI is spurring economic expansion by streamlining processes and enhancing consumer experience, beyond automation, benefiting businesses in ways never seen before. These solutions can be customized as per project objectives and business’ vision for gaining maximum profits. Consumer behavior analysis involves scrutinizing the mechanisms of selecting certain products, services etc. and involves customer behavioral responses, media and sentiments. Artificial intelligence in retail can assist in analyzing the increasing number of touchpoints by empowering natural language processing, machine learning, computer vision, data visualization, statistical analysis tools, deep learning and more to create a cohesive system. 

All in all, it aids retailers in providing the required product to their customers at the right time and place by understanding their requirements in a 360-degree manner, user buying journeys, behaviors, sales patterns etc. Furthermore, it analyses generated databases like brand and product reviews, browsing histories, demographics, sales transactions and social media activities to assist customers in a highly personalized manner while taking various personal, psychological and social factors into account. As of 2021, the global market for use of artificial intelligence in retail was approximately valued at US $4.84 billion and is expected to reach a projected value of US $31.18 billion by 2028. 

Expected rise in market value & adoption of AI solutions in retail from 2020 to 2028 

Integration of AI Solutions in Retail 

Components  

1. Data Management Systems: To collect, store and process data obtained from multiple sources. This may include databases, data lakes, cloud storage solutions that ensure that the data is available at all times for analysis of customer behavior. 

2. AI Models: Includes machine learning algorithms for demand forecasting, customer segmentation, predictive analytics, natural language processing for understanding reviews, feedback, and responding to customer queries in chatbot interactions, computer vision for customer behavior analysis as one of the retail store solutions, inventory management as per demand and monitoring customer interactions with sales personnel. 

3. User Interfaces: Used by both customers and staff to interact with AI solutions deployed in the stores such as mobile apps, websites, in-store self-checkout kiosks etc. 

4. Integration Frameworks: Advanced and newly developed solutions leveraging artificial intelligence in retail can easily integrate and work in harmony with existing surveillance, cloud-based ERP system, CRM software, logistics inventory management systems etc. with the help of these frameworks 

Deployment 

1. Assessment & Planning: Areas where AI in retail industry can add value to the current functioning of stores or e-commerce must be evaluated by retailers. Goals, objectives, targeted segment, type of data required, method of collection, cleaning, labeling, storage analysis, cloud migration solutions, and financial requirements need to be set clearly. 

2. Model Development: A suitable AI model or artificial neural network is selected, custom developed or established and trained with existing databases containing historical sales data, customer queries etc. Post-training, it is subjected to rigorous and iterative testing for refining accuracy and effectiveness of results and analysis. 

3. Pilot Deployment: Pilot programs can assist in testing, improving AI-based solutions and identifying potential functional issues in the long run for making timely corrections. 

4. Full Implementation: Post pilot testing, the solution can be deployed across all retail store branches and seamlessly integrated with existing systems. Resources and staff can be trained in due time for adjusting to new technologies. 

5. Feedback & Optimization: The overall solution needs to be assessed for its performance prior to and post deployment of artificial intelligence in retail. Continuous maintenance and regular updates are essential to improve the model’s accuracy and adapt with the evolving market conditions. 

Workflow example of AI solutions in retail: Chatbot-Customer Interactions 

Use Cases of AI in Retail Industry 

Improved Consumer Experience 

With the help of AI-based cameras, retailers can understand their customer viewpoint, purchase pattern and buying behavior, conducts, tastes etc. They can utilize these analytics to adapt their offerings, products and services as per individual customer’s needs and personalize consumer interactions. Retailers are enabled to understand, anticipate and fulfill consumer demand with AI predictions that depend on historical sales and real-time buying pattern observed in stores. With continuous availability of products and other customizations, they can build stronger consumer relationships, bonds, loyalty and customer lifetime value. 

Demand Forecasting 

Artificial intelligence in retail solutions can analyze browsing histories, historical sales records, social media preferences, market trends and other types of customer data to predict buying behavior. AI-powered predictive analytics helps retailers meet forthcoming consumer demands accurately and maintain their inventories accordingly for increasing product availability. 

Informed Pricing Strategies 

AI algorithms can perform minimum advertised price (MAP) analysis which is utilized by retailers to enforce uniform pricing policies and regulatory compliances across the retail outlets. Here MAP is the lowest reselling price which is usually set above the price at which the product was bought from manufacturers. This is necessary for maintaining the brand image, ensuring fair competition with respect to demanding costs and protecting profit margins. 

Product Recommendations 

Models powered by AI in retail industry can efficiently analyze and store large amounts of customer data such as order and browsing history, service reviews, chatbot interactions, product searches, demographic information, interests etc. This helps retailers to identify services and products preferred by customers and present individualized offers specific to customers generated by these models. Subject to convenience of recommendation and personalized relevancy of next best offers, customer satisfaction levels and loyalty usually increase. Examples of product recommendation engines include Amazon Personalize, watsonx.ai etc. 

Increased Operational Efficiency 

AI-enabled solutions can improve departmental and organizational efficiency through streamlined retail inventory management, round-the-clock support with customer-chatbot interactions and strategically devised pricing of products and services. These can help retailers in minimizing costs and increasing efficacy with ample resource utilization. Mundane tasks like anomaly detection and customer insight analysis can be automated and performed accurately at lower costs, so that human resources can be involved in more creative and strategic job roles. 

Sentiment Analysis 

AI solutions in retail can analyze, store and manage consumer feedback, online surveys, videos, textual, auditory and image-based reviews, comments, age, geography, language, pricing, product features, and other social media preferences to gain deep insights, identify user personas and sentiment trends that help retailers in developing marketing, branding strategies, campaigns, tailored products and services. 

Streamlined Supply Chain 

Predictive analytics and demand forecasting can lower a retail business’ costs related to its supply chain management depth through optimization of logistic routes, planning, scheduling, purchasing, avoiding potential issues and enhanced visibility of traversal. AI solutions in retail can keep all components of a supply chain in balance by finding relevant patterns and relationships that improve logistics from cargo freighters to distribution centers. 

Numerous use cases of artificial intelligence in retail 

Virtual Trials 

Creation of hyper-realistic images using augmented reality allows consumers to try out products, interior designs, wall paints, jewelry, nails, accessories, virtual clothing try on, and even experience 360-degree viewpoint of tourist destinations. It enhances the customer’s online and offline shopping experience, satisfaction, operational efficiency, reduces product return rates and the need for physical trials by providing a virtual fitting room-like experience. 

Optimized Inventory 

AI models scrutinize past sales data, market trends, weather patterns, economic conditions etc., showcase intelligent video analytics and proactive approach to minimize carrying costs by calculating optimal maximum and minimum product levels, avoiding excess inventory and stock-outs. AI-based stock monitoring system can accurately forecast stock demand and allow teams to remove cash from inventory to save on carrying costs, automatically preserve productivity and ensure return on marketing investment. 

Virtual Assistants 

AI-powered chatbots and virtual assistants enhance customer service, support by handling multiple queries round-the-clock in real-time simultaneously and offering tailored assistance to customers without any additional resource for monitoring their functioning. Some common queries may include delivery time, location, return assistance, order tracking, product related questions, appointment schedules, specific item search, stock check, shopping process, previous purchases etc. that are swiftly answered by chatbots to streamline response time and customer interactions. These bots and assistants like Microsoft’s Personal Shopping Assistant, evolve and improve through continuous interactions for improving engagement, reducing resource costs, automating routine inquires, thus offering a personalized experience. 

Predictive Maintenance 

Solutions developed with AI in retail industry can analyze sensor data, usage pattern, electricity consumption etc. and send alerts regarding impending machinery failure during peak hours or faults in equipment performance present in retail stores such as POS systems, refrigeration systems, retail self checkout systems etc. Retailers can get assistance from corrective application maintenance services or schedule maintenance, repair prior to potential breakdowns, reduce downtime, disruptions in operations and avoid unexpected expenditures in the long term.  

In-Store Analytics 

These solutions are useful to estimate customer waiting time in queues, overall footfall and visitor frequency in the store for determining peak hours, human activity recognition, total operational hours, theft, concealment or asset removal detection, presence of guards in exit and entry doors of the store, loose electrical wires, planogram compliance, preventive and predictive maintenance, detection of item in hand, shelves, trolleys or baskets, obstruction of shopping aisles by any object like heavy boxes, region, aisle or shelf with maximum sales, market basket analysis, ad view analytics, cross or up selling product placement at point of sales etc. 

Instigate Holistic Strategies with KritiKal’s AI Solutions in Retail 

It is fair to conclude by saying that AI is revolutionizing the retail industry by empowering innovative ways of improving operational efficiency, optimizing supply chains, understanding customer journey paths, behavior, requirements, improving both backend processes, front end experiences and personalizing customer experiences. Retailers can gain valuable customer insights and devise their targeted marketing campaigns, inventory optimization and effective reach out strategies as per their shopping behavior, market trends and preferences. 

KritiKal Solutions has assisted various SMBs, startups, Fortune 500 companies in designing, developing, deploying advanced AI-based retail technology solutions that can effectively analyze, understand customer behavior and requirements. Not only do we establish a secure retail environment through real-time surveillance, theft alerts and triggers, but also conduct sentiment analysis with the help of our state-of-the-art computer vision, generative AI technologies etc. This helps retailers in establishing a greater brand value, earning customer loyalty by improving product availability, and enhance customer experience via hyper-realistic try-ons, relevant product recommendations etc. Please contact us at sales@kritikalsolutions.com to know more and avail our services. 

Nitesh Kumar

Nitesh Kumar currently works as an Associate Architect at KritiKal Solutions. Apart from being an IIIT alumni, he has extensive experience of more than 12 years and is proficiently skilled in handling various software platforms, languages and databases. With an aptitude to thrive in dynamic environments, he has assisted KritiKal in delivering multiple projects with optimal results.