AI has become ubiquitous across various industries, and almost every organization invests significantly in leveraging AI to stay at the forefront. Looking back just a few years, organizations already invested in building a digital foundation managed better during the pandemic than their peers. Today, we find ourselves at a similar inflection point. Following years of disruption caused by the pandemic, industries are transforming rapidly. Unlike any previous technology, AI is advancing by automating tasks through its ability to see, reason, learn, and communicate. The unique benefits offered by AI apply to organizations across all sectors.
What can AI do for you?
The excitement around AI is undeniable, with its potential to reshape every industry, job role, and business function. However, the crucial business question is: What benefits can AI bring to your organization? A recent study found the substantial impact of generative AI across various industry sectors, particularly in retail and consumer packaged goods, projecting a potential annual impact ranging from 400 billion to 660 billion. So, organizations are focusing on optimizing AI capabilities while ensuring the security of their business, data, and workforce.
AI is already present in many organizations across industries, including retail. Zabka, for instance, has integrated AI-powered computer vision technology into its Nano stores, capturing and processing data in real-time as customers shop. Built on the Microsoft Azure platform, the deep learning system monitors the entire customer shopping journey. Smart analysis helps analyze the shopping data to be leveraged to elevate the experience of personalized upgrades. Carrefour, a French-based company, introduced Hopla, a chatbot powered by ChatGPT integrated into their website. Hopla assists customers in their day-to-day shopping by providing support with product discovery based on budget, dietary restrictions, recipe ideas, and more. The Americanas Group, boasting over 2,000 restaurants and 25 food production sites across the Middle East and North Africa, required enhanced insights into their operations to improve strategic decision-making. They addressed this need by implementing a custom configuration of Microsoft Azure services. The data and AI services empowered Americanas' sales, HR, and operations teams, providing quick access to crucial data and reducing the time spent on repetitive tasks by 80%. Customers, in turn, experience improved services and shorter wait times thanks to the streamlined workflow.
Microsoft Cloud
Microsoft Cloud is crucial in empowering organizations globally to lead the AI transformation in this new era. With a proven track record and a comprehensive range of products and services across various sectors, Microsoft Cloud has been a driving force in the digital transformation of organizations across industries for years. As entering the next stage of AI transformation, Microsoft is poised to serve as a reliable and trusted partner.
AI innovation is set to transform the retail industry throughout the shopping journey. Retailers can leverage AI to efficiently organize and collect transaction data to build more complete customer profiles. This enhanced capability opens doors to more meaningful personalized shopping experiences, both online and in-store. Also, AI promises operational innovation, enabling retailers to cut costs and ensure continuity through dynamic AI-powered supply chains. Frontline workers are empowered to enhance customer experiences using AI tools, which provide timely access to the right customer information, increasing store associates' productivity and customer awareness.
Microsoft Cloud for Retail
Microsoft Cloud for Retail assists retailers in connecting with their customers, employees, and their data. Tailored for the retail industry, Microsoft Cloud for Retail offers 15 capabilities across four key customer scenarios. The first scenario aims to maximize the value of your data by unifying disparate data sources across the enterprise to unlock greater value. The next scenario focuses on elevating the shopping experience, providing retailers with tools to offer seamless and personalized experiences, fostering strong customer relationships, increasing online and offline sales, and driving loyalty. Currently, the most critical challenge for retail organizations is achieving stability and agility in their supply chains. This is why Microsoft Cloud for Retail offers solutions that leverage AI to provide visibility across retailers' supply chains, predicting and managing changes in supply and demand at all levels. Another key aspect is the empowerment of the store associate scenario, which focuses on the retail store associate as a valuable asset. Microsoft Cloud for Retail delivers modern work solutions that enable the retail workforce to enhance customer satisfaction, increase employee productivity, and boost morale. These capabilities within Microsoft Cloud for Retail are supported by industry-specific data models, APIs, and partner ecosystems purpose-built for retail scenarios.
In Microsoft Cloud for Retail, two application templates, Store Operations Assist and Smart Stores Analytics offer insights into how AI-infused solutions enhance retailers' efficiency.
Store Operations Assist
Store Operations Assist is a Power Apps-based application designed for store associates and managers. It helps them to perform daily tasks more efficiently and empowers them to better serve customers by providing necessary data at their fingertips. Also, it offers rich insights and analytics on various aspects of store operations and workforce management. The Store Operations Assist solution helps retailers to optimize their in-store operations. A standout feature is the AI-powered store performance analytics built on Power BI. This feature provides a multifaced view of store operations, enabling managers to make data-driven decisions and optimize actions within the store.
The upper section of the dashboard features actionable tiles that offer quick insights into tasks scheduled for today. For instance, it displays the number of tasks due today and how many are categorized as high priority. Retail managers can click on each tile, redirecting them to a filtered view of tasks based on the tile specification. For instance, clicking on the rightmost tile will lead to tasks pending review. All reports on this page can be filtered by a specific store or aggregated at different regional or district levels, depending on the user role access. The following features can be regarded as AI capabilities in Store Operations Assist.
Outlier detection report: This provides a swift overview for retail managers to observe trends in the count of tasks and identify unusual patterns. Organizations can set their own tolerance range across the report and highlight any deviations from the expected range of count. Retail managers can analyze trends across two distinct data points, the count of created and the count of completed tasks in separate tabs. In addition to detecting outliers, this report automatically provides managers with an explanation of the reasons behind the observed variations.
Task count drilldown report: As the name suggests, this feature enables retail managers to drill down on the task count data based on their preferences. Managers can break down the total task count and analyze data points across various variables. In this report, retail managers can leverage AI-based visualizations to instruct the system to drill down based on the highest or lowest factors influencing the number of tasks. The primary goal is to allow users to drill down on data according to their specific needs and evaluate actionable outcomes.
Q&A report: This feature transforms natural interactions from store managers into a structured report format, empowering managers to query data according to their needs. The Q&A report allows them to pose questions using natural language and receive well-structured responses.
Smart Store Analytics
Smart Store Analytics is an analytic application template designed for retail stores built in close collaboration with AiFi. It helps retailers improve in-store experiences and achieve operational efficiency through AI-powered analytics and insights related to store layout, shelf placement, product catalogues, and inventory. This empowers retailers to make data-driven decisions that have a measurable impact on their business.
Smart Store Analytics, a Cloud for retail offering, is designed to assist store managers in understanding their in-store data. Since its introduction, store managers at several retailers have started using the app's features, such as Smart Store KPI tracking, data visualizations, and data-driven insights, to optimize various aspects of their store operations for business benefit. The insights and recommendations section of the app includes three AI models, each designed to analyze in-store data and generate insights.
Frequently bought together: This model helps the store manager identify combinations of products likely to be purchased together and placed, revealing numerous revenue-generating opportunities. Start by selecting a specific store or the entire retail chain. The products are ranked by the strength of association, a statistical calculation indicating how likely customers are to buy them together due to their relatedness rather than random chance. Also, the time taken between picking up both products is displayed, measuring how closely or distantly the products are placed. The product-specific drill-down view is another way to enhance a data-driven approach to product clearance strategy.
Product substitutes: This model pinpoints products that customers are likely to perceive as simple alternatives to one another. In this process, AI technology develops the product context for each item by analyzing what other products were put into the basket during the same session and the sequence in which all products were selected. Products are then compared based on their context, with a higher likelihood of two products being identified as substitutes if their contexts are more similar.
Foot traffic forecast: The model utilizes historical data on foot traffic and external information, such as local holidays, to predict customer footfall. This allows retailers to predict expected foot traffic and adjust store activities accordingly. The dashboard comprises four key areas, including top-level filters for the store and the day of interest for prediction. KPIs cover forecasts for the selected day and the week, providing comparisons against actual data from the previous week. Time series graphs display predicted foot traffic by the hour of the day and by day for the next seven days. There's also a consolidated view enabling quick identification of spikes and slumps presented as a two-dimensional grid of hour by day. With this data, retailers can formulate optimized staffing schedules for peak and off-peak periods. They can strategically schedule tasks such as shelf replenishment and restocking before peak hours and optimize restocking schedules based on footfall patterns.
In conclusion, retailers are leveraging all the models along with Smart Store KPI tracking and immersive visualizations to make daily decisions on store operations, layout, product catalogue, shelf placement, inventory, and more. These decisions are helping retailers continuously enhance the in-store shopping experience and significantly improve business performance.
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