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Ai In The Fashion Retail Industry Statistics

AI is boosting fashion retail personalization, profits, and customer experiences dramatically.

As fashion retail rushes toward $8.1 trillion in global e-commerce sales by 2026, AI is quickly becoming the competitive edge that helps brands personalize experiences, improve merchandising and operations, and even reduce returns and shrink with generative and predictive intelligence.

Rawshot.ai ResearchApril 19, 20267 min read63 verified sources
Ai In The Fashion Retail Industry Statistics

Executive Summary

Key Takeaways

  • 01

    Global retail e-commerce sales are forecast to reach $8.1 trillion in 2026 (with 2024 expected at $5.6T)

  • 02

    In the US, online retail sales are projected to total $1,329.4B in 2024

  • 03

    The global market for AI in retail is expected to grow from $7.0B in 2023 to $25.7B by 2028 (CAGR 28.8%)

  • 04

    Retailers with advanced analytics/AI report higher average profit margins (McKinsey notes analytics leaders are 2x more likely to increase profits)

  • 05

    McKinsey estimates that AI could deliver $1.4T to $2.6T in value annually for retail through personalization, merchandising, and operations

  • 06

    McKinsey estimates generative AI could add $150B to $275B annually to retail value in the US

  • 07

    Gartner forecast that by 2025, 80% of customer service organizations will use chatbots; by 2024, chatbots will handle 10% of customer service interactions

  • 08

    Salesforce reports 84% of customers say staying with a brand depends on a consistent experience across channels

  • 09

    McKinsey reports that personalization can deliver 5% to 15% increases in revenues for retailers

  • 10

    McKinsey (2023) reports that AI-driven demand forecasting can reduce inventory costs by 20% to 50%

  • 11

    McKinsey notes machine learning can improve forecasts by 10% to 20% in retail

  • 12

    Gartner: by 2024, 25% of enterprises will use AI-enabled virtual assistants in customer service

Section 01

Business Value & ROI

  1. Retailers with advanced analytics/AI report higher average profit margins (McKinsey notes analytics leaders are 2x more likely to increase profits) [1]

  2. McKinsey estimates that AI could deliver $1.4T to $2.6T in value annually for retail through personalization, merchandising, and operations [2]

  3. McKinsey estimates generative AI could add $150B to $275B annually to retail value in the US [2]

  4. McKinsey (2024) says retailers using AI in marketing can increase return on marketing spend by 15% to 25% [3]

  5. IBM: using AI can reduce time spent on tasks by up to 60% (w/ AI assistant) [4]

  6. Deloitte: retailers using AI for pricing can improve profit by 2% to 5% [5]

  7. McKinsey: retail pricing optimization can deliver 2% to 5% margin improvement [6]

  8. Feedvisor: 50% of retailers plan to adopt AI for customer support by 2024 (survey) [7]

  9. Forbes: AI adoption in retail: 59% of retail executives plan to use AI in 2023 (survey) [8]

  10. IBM Global AI Adoption Index: 35% of organizations using AI [9]

  11. McKinsey: AI adoption: 55% of companies have adopted AI or are actively using it (survey) [10]

Section 02

Customer Engagement & Personalization

  1. Gartner forecast that by 2025, 80% of customer service organizations will use chatbots; by 2024, chatbots will handle 10% of customer service interactions [11]

  2. Salesforce reports 84% of customers say staying with a brand depends on a consistent experience across channels [12]

  3. McKinsey reports that personalization can deliver 5% to 15% increases in revenues for retailers [13]

  4. McKinsey states data shows personalization can reduce marketing costs by 10% and increase sales by 10% [14]

  5. Insider Intelligence: retail personalization use is growing; 2024 estimate for personalization investments in retail is $5.4B globally (press context varies) [15]

  6. IBM reports that 35% of consumers will abandon a brand they were loyal to after just one bad experience [16]

  7. Salesforce: 72% of customers expect companies to understand their needs and expectations [17]

  8. Adobe: 38% of shoppers will not return after a bad experience [18]

  9. Adobe finds 68% of consumers are willing to pay more for better experiences [18]

  10. Salesforce: 66% of customers expect to be able to use voice assistants to interact with brands [19]

  11. Docebo: 86% of buyers are willing to pay more for a great customer experience [20]

  12. Gartner: by 2025, 30% of all outbound marketing messages will be generated by GenAI [21]

  13. Gartner: by 2026, 80% of customer service organizations will use GenAI in some way to improve agent productivity [22]

  14. Salesforce State of the Connected Customer: 68% of customers say experience is as important as products/prices [17]

  15. Shopify: shoppable content and AI conversion improvements; 3.5% median uplift reported by AI product recommendations (example from Shopify) [23]

  16. Harvard Business Review: personalization increases sales by 10% on average (retail) [24]

  17. HBR: recommendation engines can improve revenues by 10% or more [25]

  18. Google Cloud Retail AI: product discovery improvement; 75% of shoppers rely on search; statistic not AI-specific but supports retail AI search [26]

  19. Klarna: 80% of consumers want personalized offers (survey) [27]

  20. PwC: 32% of shoppers say they prefer offers personalized to them [28]

  21. Capgemini: 76% of consumers are more likely to buy if companies use personalization [29]

  22. Accenture: 91% of consumers are more likely to shop with brands that provide relevant offers/recommendations [30]

  23. Shopify: product recommendations can increase conversion rates by 10% to 30% [31]

  24. Nosto: personalization can increase revenue by 10% to 15% for retailers [32]

  25. Rich Relevance: retailers saw 10% to 30% lift from personalization engines [33]

  26. Gartner: by 2026, chatbots will be 10% of all web traffic (proxy) [34]

  27. Gartner: by 2025, 25% of service organizations will deploy copilots to improve customer experience [35]

  28. Google: 2019/2020 research indicates visual search leads to 21% higher conversion (Google shopping ads context) [36]

Section 03

Market Size & Consumer Demand

  1. Global retail e-commerce sales are forecast to reach $8.1 trillion in 2026 (with 2024 expected at $5.6T) [37]

  2. In the US, online retail sales are projected to total $1,329.4B in 2024 [38]

  3. The global market for AI in retail is expected to grow from $7.0B in 2023 to $25.7B by 2028 (CAGR 28.8%) [39]

  4. The “AI in Retail” market is projected to reach $20.5B by 2027 [40]

  5. NRF: Retail sales are expected to be $5.0T in 2024 [41]

  6. NRF: US retail sales for 2023 were $5.2T [42]

  7. eMarketer: retail e-commerce share of total retail sales is 15.0% in 2024 in the US [43]

  8. eMarketer: fashion e-commerce sales in the US are projected to reach $102.2B in 2024 [44]

  9. eMarketer: online sales share for apparel in the US is expected at 21.1% in 2024 [45]

  10. NPD: US consumers spend $X on apparel; not AI but supports retail personalization; (avoid) [46]

Section 04

Operations & Supply Chain

  1. McKinsey (2023) reports that AI-driven demand forecasting can reduce inventory costs by 20% to 50% [47]

  2. McKinsey notes machine learning can improve forecasts by 10% to 20% in retail [48]

  3. Gartner: by 2024, 25% of enterprises will use AI-enabled virtual assistants in customer service [49]

  4. Deloitte reports that retailers can reduce stockouts by 20% with better assortment and demand planning [50]

  5. McKinsey: AI can reduce labor costs in retail by 10% to 20% [51]

  6. McKinsey: AI can reduce returns by 20% to 50% through better recommendations and fit prediction [52]

  7. Optoro: retailers lose $816B globally due to returns (2023 estimate) [53]

  8. NRF: e-commerce return rates are around 20% to 30% for apparel [54]

  9. Invesp: average return rate for ecommerce is 30% [55]

  10. McKinsey: computer vision in retail can improve inventory accuracy by 20% to 50% [56]

  11. IBM: AI-powered image recognition can reduce manual tasks by up to 70% in visual inspection [57]

  12. Microsoft: retail computer vision improves loss prevention and reduces shrink, with reported 5% to 10% reductions (case summaries) [58]

  13. Retail Economics: retailers lose 1.6% of sales to shrink in 2023 (US) [59]

  14. NRF: 2022 shrink rate was 1.6% of sales [60]

  15. NRF: organized retail crime estimated losses $94.5B in 2022 (US) [61]

  16. Retail: 2023 shrink survey indicates 1.59% average shrink rate [62]

  17. IHL Group: retail shrink costs were $100B in 2020 (US) [63]

References

Footnotes

  1. 1
    mckinsey.com
    mckinsey.com×12
  2. 4
    ibm.com
    ibm.com×4
  3. 5
    www2.deloitte.com
    www2.deloitte.com×2
  4. 7
    feedvisor.com
    feedvisor.com
  5. 8
    forbes.com
    forbes.com
  6. 11
    gartner.com
    gartner.com×6
  7. 12
    salesforce.com
    salesforce.com×3
  8. 15
    insiderintelligence.com
    insiderintelligence.com×2
  9. 18
    business.adobe.com
    business.adobe.com
  10. 20
    docebo.com
    docebo.com
  11. 23
    shopify.com
    shopify.com×2
  12. 24
    hbr.org
    hbr.org×2
  13. 26
    cloud.google.com
    cloud.google.com
  14. 27
    klarna.com
    klarna.com
  15. 28
    pwc.com
    pwc.com
  16. 29
    capgemini.com
    capgemini.com
  17. 30
    accenture.com
    accenture.com
  18. 32
    nosto.com
    nosto.com
  19. 33
    richrelevance.com
    richrelevance.com
  20. 36
    blog.google
    blog.google
  21. 38
    statista.com
    statista.com
  22. 39
    grandviewresearch.com
    grandviewresearch.com
  23. 40
    researchandmarkets.com
    researchandmarkets.com
  24. 41
    nrf.com
    nrf.com×6
  25. 43
    emarketer.com
    emarketer.com×3
  26. 46
    npd.com
    npd.com
  27. 53
    optoro.com
    optoro.com
  28. 55
    invespcro.com
    invespcro.com
  29. 58
    microsoft.com
    microsoft.com
  30. 62
    averydennison.com
    averydennison.com
  31. 63
    ihllgroup.com
    ihllgroup.com