01 Mar
This is the first blog post in our AI learning and awareness series for TrackMatriX.
It’s 2024, the year when businesses are flooding with an abundance of data, and retailers are drowning to process and analyze this data in as little time as possible. This scenario welcomes the concept of introducing AI and ML for analyzing, processing, and predicting the data for the retail industry.
AI and ML are now taking the retail industry by storm, and data shows that AI in retail will be valued at $9.65 billion in 2024. But how do AI and ML affect the retail industry? In this article, we will learn the effects, benefits, and usage of AI and ML in retail. So let’s get started:
Applications of AI in the Retail Industry
While AI applications in retail have gained substantial experience, their impact on physical stores holds significant potential for transformative change within the department. AI’s most promising areas in retail applications include:
- Personalized design and production.
- Forecasting customer demand, exemplified by retailers utilizing deep learning to anticipate orders.
- Inventory and loading management.
By leveraging collected data, AI predicts future customer purchasing behavior, understanding how the target audience interacts with the product and brand. This predictive analysis aids in identifying customer loyalty and potential attrition. Historical data analysis enables forecasting customer reactions to current trends or seasonal events, enhancing marketing teams’ demand forecasts for popular products, identifying unprofitable customers, and formulating effective strategies.
Personalization
Artificial intelligence solutions utilize personalized data to glean insights into customer preferences by aggregating both online and offline customer data. By analyzing clickstream data, consumer purchase history, demographic information, and individual preferences, AI can identify patterns in customer behavior, leading to tailored product recommendations for each consumer.
Intelligent image processing solutions play a crucial role in enhancing the sales success of retail stores and products. For instance, smart systems deployed on digital screens within the sales area leverage image records to analyze various parameters such as exposure, time spent viewing specific shelves, and the duration and mood of the decision-making process for purchases. This analysis enables the customization of advertising and customer interactions, facilitating the provision of personalized offers or similar products to positively influence purchasing decisions.
Furthermore, the integration of multiple devices allows these AI systems to learn from each other, optimizing both customer experience and sales effectiveness. The application of personalization through integrated devices empowers retailers to craft offers, incentives, and a positive shopping experience that resonates with their target audience.
AI Generated Content
While the field of artificial intelligence hasn’t advanced to the point of crafting opinion papers, it can leverage its capabilities to generate compelling and resonant content, effectively drawing customers to a website. AI content programs can sift through a dataset, extracting principles for a specific purpose and organizing articles in a ‘human voice’ style. Thus, artificial intelligence can indeed produce data-driven content with its primary focus on attracting audiences to the site.
Grounded in machine learning, this application harnesses online data for automated content generation, proving particularly valuable for crafting notification-based content or product descriptions. Artificial intelligence can select attention-grabbing news headlines, assess words that enhance traffic flow, and even provide future projections based on this data.
Chatbots
AI-based chatbots enhance the customer experience by streamlining interactions, refining search functionalities, providing notifications for new products, and suggesting items aligned with customers’ preferences. This innovation introduces a novel avenue for brands, businesses, and publishers to engage users without necessitating the download of specific custom apps. These chatbots operate seamlessly, are regularly configured, and are updated like pre-installed messaging apps, resulting in reduced costs for developing communication channels in electronic retailing.
Crucially, chatbots facilitate direct communication with customers, enabling companies to deliver accurate and effective information while minimizing errors arising from repeated information acquisition by employees. The ongoing challenge lies in enhancing the mutual understanding between humans and machines. While chatbots continually refine their knowledge of personalizing messages and improving protocols and responses, occasional typos or overlooked customer queries may still lead to error messages. Despite these challenges, it’s important to note that chatbots are not poised to replace comprehensive customer service in the immediate future. Instead, they will persist in enhancing the overall customer experience, serving as an additional layer of support for digital services.
AR and VR
Augmented reality (AR) and virtual reality (VR) are categorized as consumer-facing technologies since they directly engage and immerse consumers, whether they’re in a physical store or browsing online. Enhancing the shopping experience stands as a primary objective for digital retailers. Consumers often express that their online shopping experiences lack the richness found in physical stores, where they can engage with products, stores, and salespeople in various ways.
AR technology, accessible through devices like smartphones, tablets, wearable headsets, projectors, or fixed interactive displays, enables consumers to interact with virtual products in innovative ways. By employing interactive and immersive technologies such as AR, online retailers can forge positive psychological connections, shaping brand perception and customer relationships. Information garnered through these technologies is strategically integrated into communication and interaction channels, enriching the overall shopping experience.
Dynamic Pricing
Dynamic pricing algorithms empower retailers to assess the optimal price for a specific product, adjusting prices in real-time. This is achieved by scrutinizing competitive product and store prices, consumer behavior, location, time, and seasonal factors, aligning with the ever-changing dynamics of the market. Additionally, individualized pricing based on customer interests or other factors can be influenced by these algorithms. Possible scenarios for such adaptive pricing include considerations of temporary states of mind (e.g., happiness, sadness, stress), particularly if facial recognition technology, powered by AI-based programs, continues to advance.
Dynamic pricing operates as a strategy necessitating frequent adjustments in response to competitor pricing changes. While discounts are a common approach to boost sales, the challenge arises when customers paying the full price receive fewer benefits, leading to reduced profits. To address this, dynamic pricing employs machine learning to identify customers who require discounts to make a purchase decision. Machine learning establishes a trend pattern, indicating when a customer is likely to convert without the need for a discount. This approach enables increased sales before profit margins are significantly impacted, allowing for the maximization of profits.
Personnel Planning
Retailers leverage artificial intelligence technologies to optimize personnel planning, considering factors such as customer buying behavior, weather, and upcoming events. By calculating personnel requirements based on these external effects, AI facilitates the creation of efficient shift schedules. This proactive approach prevents overstaffing, minimizing unnecessary costs, and ensures an adequate workforce in the field. The outcome is enhanced handling of customer service and delivery, promoting rational staffing and overall operational efficiency.
AI is The Future of the Retail Industry
Artificial intelligence is transforming the retail industry, enhancing decision-making for shoppers and consumers. The technology provides accurate customer information, improving recommendations, search results, and personalized services. As AI advances, it extends beyond inventory tracking, ensuring precision through robots and automation.
While AI offers efficiency, reduces repetitive tasks, and improves customer service, potential misuse raises concerns about societal consequences. The success of AI in retail hinges on responsible application and adapting to changing market dynamics. Even small and medium-sized enterprises must embrace AI to survive, currently driven by large multinational players.
To stay competitive, retailers must be lean, flexible, agile, and swiftly adopt new technologies, responding to evolving customer needs and expectations.
In conclusion, start leveraging AI technology if you truly want to excel in your business.
Read the second blog post of TrackMatriX’s AI learning and awareness series here.
About TrackMatriX Technologies Limited:
TrackMatriX is a leading AI-enabled solution provider, technology developer, and system integrator dedicated to accelerating growth for retail, logistics, and manufacturing businesses. TrackMatriX provides brand protection, advanced digitalization, AI, AR, and SaaS development solutions for better business growth, personalized product experiences, and maximized customer engagement. TrackMatriX serves as your business partner for boosting revenue, protecting your brand identity, and providing you with advanced, scalable, and cyber-secure cloud-based, AI, and AR systems. In 2012, TrackMatriX® developed and patented the TrackMatriX® SaaS platform for digital authentication with mobile phone, product authentication, and consumer engagement.
Read the second blog post for TrackMatriX’s AI Learning and Awareness series here.
