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How AI and ML Optimize the Full Retail Cycle from Pre-Season Planning to Post-Season Analysis

3 MIN READ
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As the pace of artificial intelligence (AI) adoption in retail accelerates, the impact on the industry is becoming clear. A significant majority of retailers are already harnessing AI, with 64% of major retail companies using AI and 22% piloting AI programs. According to a forecast by IHL Group, generative AI (GenAI) built on machine learning (ML) will have a $9.2 trillion impact on retail through 2029.

Leveraging AI has already contributed to increased revenue for 69% of retailers and 72% of retailers say the technology has contributed to a reduction in costs through improved gross margins, operational efficiencies and better consumer experiences.

With advanced AI tools and integrations, retailers are empowered to improve core processes across the full retail cycle and reach key business targets faster than ever. Here, we start at the beginning with pre-season planning and make our way to product end-of-life to demonstrate the value of AI/ML-driven strategies, data analysis and automated processes at each stage.

The Power of AI Analysis in Pre-Season Planning 

When pre-season planning is NOT built on a foundation of accurate current trends and customer insights, retail brands are making critical decisions based on guesswork and gut — which leads to a cascade of missed targets and opportunities and unnecessary inefficiencies.

With AI-powered retail planning tools like Centric Planning™ supporting pre-season planning, your team has the power to analyze market trends and customer preferences.

In retail, markets and shopper expectations are constantly evolving. Integrated AI/ML algorithms streamline traditional market analysis, eliminating the need for time-consuming manual interpretations of historical data. AI planning tools empower planning teams to swiftly identify trends, make strategic decisions about product selections, collection development, allocation and business growth strategies.  

AI-powered KPI setting for sales, profit margins and pricing

AI trend forecasting and customer preference tracking tools play a crucial role by enabling retail teams to set more accurate KPIs. But there are additional KPIs and metrics to track for AI-driven optimization, including:  

  • Price elasticity to watch how pricing changes impact sales
  • In-season performance and competitive pricing data to drive dynamic pricing shifts in real-time
  • Cost structure analysis to pinpoint opportunities for production cost reductions that don’t impact product quality or customer experiences

The fashion industry is leading the way in AI adoption, with 62% of fashion executives saying their companies use the technology. Beyond trend forecasting and pricing, AI in fashion and retail use cases include: 

  • Early-stage creative design collaboration and ideation 
  • AI-driven fit improvements that reduce the volume of returns 
  • Reducing fashion waste due to returns and poor collection sell-through  
  • Predictive supply chain management in fashion guides strategic stocking decisions 
  • Fashion marketing that targets customers with personalized messaging based on behavior

Data-driven pre-season planning

AI tools are used for ‘feature extraction,’ which analyzes visual and product use similarities, and ‘clustering,’ which groups similar products so they are compared to historical performance data. With this information, planning teams make data-informed decisions about expanding or contracting buying of specific product types.  

Real-Time Inventory Allocation and Dynamic Pricing with AI-Powered Insights

Retail inventory managers have always had to thread the needle between too much and too little stock. Today, finding that balance and being able to quickly shift inventory when and where needed is more important than ever with the rise of omni-channel shopping (where customers expect seamless experiences online and in-store) and near-continuous supply chain issues. Agility is a differentiator and essential for retailer success.   

Minimize overstock and stockouts

AI and ML technologies are transformative tools for meeting retail market demands with agility. Retailers fine-tune inventory with AI tools that analyze widespread data sources including variations in demand, supply lead times and inventory levels across warehouses in different locations. With this information, rapid strategic decisions are made to move stock where needed most, update orders in real-time, and implement pricing changes to optimize sell-through.

Intelligent re-ordering and replenishment

Integrated AI and ML tools connect the dots between historical sales, current trends, supply chain challenges and seasonal demand fluctuations to increase the speed and accuracy of re-ordering and replenishment activities. Automating processes ensure timely and continuous replenishment.  

AI is also a powerful inventory optimization tool for:  

  • Adjusting buffer stock levels with greater accuracy to prevent overstocks
  • Supplier lead times, with reordering timelines based on past supplier performance
  • Detecting and responding quickly to inventory level anomalies due to trending products

Optimize Post-Season Analysis and Exit Strategies with AI/ML  

Post-season analysis and exit strategies are core stages of the retail cycle that AI and ML tools are transforming, accelerating and calibrating to maximize revenue and minimize losses for retailers.  

How to increase Product Sell-through Rates

Integrated AI/ML algorithms analyze data and spot when products or lines are underperforming and compare that with market trends and customer behavior metrics to pinpoint products that need a redesign, refreshed packaging or should be phased out completely. When products reach the final phase of their lifecycle, AI-driven tools guide strategic pricing decisions for clearance sales to maximize profitability. Throughout this phase, AI-powered solutions also optimize clearance marketing with personalized offers and suggest discount levels that balance clearance timelines with revenue goals.

Increase the strategic value of retail post-season analysis

AI-driven post-season analysis enables retailers to understand more quickly what worked and what didn’t within a particular product line, capsule, targeted marketing campaign or other retail initiative. This data guides pre-season planning, forecasting, budget and KPI setting. Post-season analysis powered by AI also empowers retailers to: 

  • Monitor customer sentiment and feedback
  • Identify high-potential market opportunities
  • Evaluate the results of past marketing activities and promotions
  • Compare sales and promotional performance across sales channels

With AI-led analysis of these data points, retailers are positioned to make better pre-season decisions, meet current trends and customer expectations, respond with agility to sudden market shifts and develop strategic market expansion plans. 

Advanced AI/ML capabilities are built into Centric Software’s integrated solutions, including Centric Planning, Centric Pricing and Inventory™ and Centric Market Intelligence™. Empower your retail teams with intuitive AI-powered tools designed to optimize the full retail cycle, drive better strategic decisions and improve efficiency and profitability across your business.