Retail, CPG and Logistics
AI-Driven assortment planning and optimisation
A report on the study of Assortment and Space Optimisation (ASO) valued the global market in 2024 at approximately $2.1 billion. It projected the market to reach $5.1 billion by 2033 at a CAGR of 10.42%. Retail, at present, operates in a dynamic environment, changing rapidly with the consumer demands given to changing trends and consumption patterns. Optimising product assortments has become critical in meeting the ever-changing consumer demands, enhancing sales, and maximising profitability. Predictive analytics in retail has risen as an impactful tool in assortment planning.
Traditional retail assortment planning is often based on historical sales data and intuition, which is insufficient to compete in the growing data-driven marketplace. AI supports businesses in leveraging assortment planning through predictive analytics, customer-centric strategies, and real-time decision-making to stay competitive with market shifts.
The role of AI in assortment planning
AI-driven assortment planning augments conventional methods by integrating advanced analytics to optimise product selection and inventory management. AI analyses customer preferences, changes in local demands, and supply chain constraints, supporting retailers to balance stockouts and overstocking.
Predictive analytics in retail for demand forecasting
AI-powered predictive analytics processes huge amounts of data, including historical sales, market trends, and external factors such as economic patterns or seasonal shifts. Data analysis, supported by machine learning models, forecasts customer preferences to allow retailers to stock the correct number of products in particular locations at the right time to minimise lost sales opportunities.
Optimising product mix and space allocation
The dynamic customer demands require a product mix that helps retailers determine the length and depth of product lines for different store formats, regions, and customer demographics. Predictive analytics in retail analyses data to predict shopping trends for retailers to prioritise high-demand products while stock for underperforming or slack products is reduced or eliminated, defining a profitable product mix. AI also assist in space allocation to optimise shelf placement and visual merchandising for achieving maximum impact.
Customer-centric assortment strategies
AI supports hyper-personalisation in assortment planning by analysing customer behaviour, demographics, and shopping patterns. Retailers use these insights to create assortments to cater to specific customer segments and improve engagement and conversion rates. Personalised recommendation insights support customer satisfaction and drive brand loyalty.
Benefits of AI in retail assortment planning
AI in assortment planning delivers several key benefits that drive competitive advantage and improved customer sales:
Increased revenue and profitability
Predictive analytics in retail help retailers maintain the right stock levels of the products to reduce markdowns and maximise sales and revenue.
Enhanced customer satisfaction
By offering a product assortment that aligns with customer preferences, retailers improve the shopping experience, resulting in increased brand loyalty.
Improved inventory efficiency
Retailers optimise stock levels with the help of predictive analytics in retail. It helps in reducing waste overstocking, spillage, and spoilage due to overstocking or deadstock. Retailers also optimise the supply chain with retail assortment planning analytics.
Faster decision-making
AI enables real-time analysis, letting retailers adjust assortments dynamically to stay abreast of the changing trends.
Reduced operational costs
Automating the assortment planning system minimises manual effort and enhances efficiency across merchandising teams.
Key AI technologies in retail assortment planning
Retailers are increasingly adopting AI and machine learning in retail assortment planning to stay competitive in the dynamic market. Retailers use these technologies to define a profitable product mix for meeting customer demands and increasing the customer base.
Machine learning for trend analysis
Machine learning algorithms identify changing trends by analysing search trends, social media data, and online customer reviews. This allows retailers to anticipate demand shifts and plan assortment to lead the competitors in the dynamic market.
Natural Language Processing (NLP) for customer insights
NLP models extract insights from analysing customer reviews and chat support used by customers. This supports retailers in understanding customer requirements and refining retail assortment planning accordingly.
Predictive analytics in retail for in-store insights
Predictive analytics in retail and computer vision technology analyse store layouts, customer foot traffic, and product interactions to optimise shelf placement and inventory replacement. This ensures that high-demand items are easily accessible to customers.
How can Infosys BPM help leverage predictive analytics in retail?
AI-driven retail assortment planning supports retail businesses by improving decision-making, enhancing efficiency, and delivering superior customer experiences. By developing predictive analytics for retail, Infosys BPM has helped retailers integrate AI into assortment planning to optimise product lines, align with consumer expectations, and achieve long-term profitability. Investing in AI ensures keeping up with the changing market trends, meeting dynamic customer demands in time, and managing stock levels to leverage sales and profits.
