AI has moved from a futuristic concept to a practical tool that reshapes how fashion brands plan assortments, understand consumers, and react to market shifts. For merchandisers, the shift is especially transformative. Merchandising used to depend heavily on intuition, manual analysis, and delayed data, but AI now allows teams to make decisions that are faster, more accurate, and grounded in real time market insights.

Building an AI powered merchandising workflow does not require replacing human judgment. Instead, it strengthens it. AI handles the heavy lifting, while merchandisers stay focused on strategy, creativity, and commercial thinking. Below is a clear and comprehensive guide to building a merchandising workflow that integrates AI at every key stage.

1. Start with a Clear Merchandising Objective

Before introducing AI tools, brands need clarity on what they want to improve. Merchandising objectives vary, but common goals include:

• Reducing stockout risk
• Improving sell through performance
• Reducing return rates
• Increasing margin through better price decisions
• Understanding emerging trends sooner
• Optimising size curves
• Identifying slow moving or excess inventory early

Clear objectives guide which AI models, dashboards, and data sources to prioritise.

2. Centralise All Data Inputs

AI only works when data is unified and consistently structured. Merchandisers typically work across many sources. Centralising them makes it possible for AI to detect patterns that humans cannot see manually.

Useful data sources include:

• Sell through performance
• Pricing history
• Review sentiment
• Return reasons
• Competitor assortments
• Trend data
• Seasonality patterns
• Color sales
• Feature and material attributes
• Regional demand patterns

A centralised data layer removes guesswork and ensures that every decision is made with a complete view of the product and the market.

. Use AI for Assortment Planning and Depth Decisions

One of the strongest uses of AI in merchandising is assortment optimisation. AI models can evaluate thousands of attributes at once to determine what should stay, what needs more depth, and what should be dropped.

AI can support decisions such as:

• Predicting which categories will grow or decline
• Identifying high potential color and style combinations
• Recommending depth for each SKU
• Flagging items at risk of low performance
• Suggesting product gaps that competitors are filling

The goal is not to eliminate human involvement but to empower teams with objective evidence during buying and merchandising meetings.

4. Integrate Trend and Market Intelligence Tools

Merchandisers need a real time understanding of what is happening in the wider market. This requires tools that track competitor assortments, pricing movements, replenishment behavior, and emerging features in the industry.

AI market intelligence platforms help by:

• Monitoring new arrivals across competitor sites
• Tracking changes in color and silhouette trends
• Identifying attributes gaining traction
• Highlighting replenished products
• Revealing assortment shifts across regions

This ensures merchandisers stay ahead of changes rather than reacting too late.

. Use Predictive Analytics for Pricing and Markdown Planning

Pricing is one of the most sensitive aspects of merchandising. AI helps by modeling the impact of early, mid season, and end of season pricing decisions.

AI pricing capabilities include:

• Predicting optimal launch prices
• Forecasting the effect of price increases or reductions
• Identifying the best time to initiate markdowns
• Understanding elasticity across categories and regions
• Preventing margin loss caused by unnecessary discounting

This creates a pricing strategy that is both customer aligned and profit driven.

6. Use AI to Forecast Demand and Allocation

AI based forecasting models analyse seasonality, micro trends, weather patterns, regional behavior, and real time sales to predict demand more accurately than traditional spreadsheets.

Key benefits include:

• Improved allocation across regions and stores
• Fewer stockouts
• Reduced overstock
• More accurate size curve planning
• Better pre season buy volumes
• Stronger replenishment efficiency

AI helps merchandisers buy smarter instead of buying more.

7. Analyse Review Sentiment to Understand Fit, Quality, and Comfort

Review sentiment is one of the most valuable yet underused data sources in merchandising. AI can scan thousands of reviews and summarise the core issues affecting performance.

AI can extract insights such as:

• Whether customers feel the product runs small or large
• Complaints about fabric quality
• Fit issues related to body type
• Comfort related challenges
• Unexpected wear and tear

This feedback loop allows merchandisers to refine future assortments and collaborate with design teams more effectively.

8. Use AI to Identify Slow Moving Stock Early

AI models can detect slow moving inventory far earlier than traditional reporting. Instead of waiting until stock becomes a problem, merchandisers receive early warnings that allow for proactive action.

AI signals might show:

• Items with low view to buy ratios
• Products with consistent high returns
• Styles with ineffective imagery or weak descriptions
• Subcategories cannibalising each other
• Seasonal products entering decline faster than expected

This creates space for early corrective actions such as price adjustment, visual refresh, improved PDP copy, or reallocation.

9. Automate Reporting and Free Up Merchandiser Time

AI does not just improve decision accuracy. It saves hours of manual work. Automated dashboards and predictive reports allow merchandisers to focus on strategy while the system processes data continuously.

Automation supports:

• Weekly trade reports
• Competitor benchmarking
• Sell through summaries
• Trend highlight reports
• Review sentiment updates
• Forecast accuracy updates

This reduces reliance on spreadsheets and manual compilation.

10. Build a Cross Functional AI Culture

AI powered merchandising works best when the whole organisation embraces it. Successful teams create processes that let merchandisers, designers, planners, and marketers collaborate around the same data.

This includes:

• Regular cross functional meetings
• Shared dashboards
• Unified KPIs
• Training on data literacy
• Clear roles between human judgement and AI recommendations

AI becomes a partner in decision making, not a replacement.

Conclusion

Building an AI powered fashion merchandising workflow gives brands a significant competitive advantage. Instead of relying on delayed insights or intuition, merchandisers can make confident decisions supported by real time intelligence, predictive analytics, and deep customer understanding.

The future of merchandising is not about choosing between AI or human judgment. It is about combining both to create faster, more accurate, and more strategic decisions that strengthen assortment performance, improve customer satisfaction, and reduce operational risk.

About Woveninsights

Woveninsights is a comprehensive market analytics solution that provides fashion brands with real-time access to retail market and consumer insights, sourced from over 70 million real shoppers and 20 million analyzed fashion products. Our platform helps brands track market trends, assess competitor performance, and refine product strategies with precision.

Woveninsights provides you with all the actionable data you need to create fashion products that are truly market-ready and consumer-aligned.

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