Fashion analytics refers to the use of data analysis and technology to gain insights into various aspects of the fashion industry. It includes gathering and evaluating data concerning consumer behavior, market trends, product performance, supply chain, pricing etc.
In 2020, online stores boomed worldwide, and this trend is only getting bigger. Today, it’s crucial for business owners to understand their company’s data – it’s not just a luxury; it’s a must.
Key Areas in Fashion Analytics:
- Consumer Driven Marketing
- Product Recommendation
- Digital and Web Analytics
- Supply Chain Analytics
- Integrated Demand Forecasting
- Pricing Analytics
- Store Localization, Clustering, and In-store Optimization
- Artificial Intelligence for Predicting Fashion Trends
Navigating Analytics in Today’s Fashion Retail Landscape:
- Retailers face a plethora of options in the contemporary business landscape.
- The intersection of style, creativity, and analytics prompts a strategic approach to investments in the fashion industry.
- Analyzing market maturity and competition is essential for targeted analytics investments.
- Given the mature and competitive market, acquiring proven analytical applications becomes a priority.
- Start with established applications to ensure competitiveness and later explore innovative analytics areas.
- Aggressive adoption of analytics provides a competitive edge for successful fashion brands.
- Analytics plays a crucial role in driving innovation and disrupting traditional business models.
- Established organizations may face higher costs of change, while startups benefit from rapid innovation.
- Failure to invest in technology and innovation puts existing retailers at risk of losing market share in a shifting business model.
Leveraging Analytics for Competitive Edge in Fashion Retail:
- Customer Engagement and Targeted Marketing:
- Close relationships with customers based on deep understanding.
- Targeted advertising, promotions, and product offers to motivate purchases.
- Digital Analytics for Enhanced Consumer Experience:
- Improves consumer experience and conversion rates across all channels.
- Planning Optimization:
- Assists in product allocation, demand forecasting, and customer care.
- Optimizes warehouse operations and identifies optimal locations for new facilities.
- Inventory Management:
- Minimizes overstock or understock through intelligent technologies.
- Reduces lead times for introducing new products to the market.
- Environmental Sustainability:
- Addresses consumer consciousness about environmental issues.
- Utilizes analytics for better sourcing, raw material consumption, trend prediction, and consumer understanding.
- Impact on Fashion Industry’s Carbon Emissions and Wastewater:
- Fashion industry contributes to 10% of global carbon emissions and nearly 20% of wastewater.
- Analytics supports the entire value chain, promoting sustainable growth for both consumers and the planet.
Challenges in Adopting Analytics for Retailers:
- Information Overload and Technological Complexity:
- Abundance of data, technology, and analytics can be overwhelming.
- Daily introductions of new technologies pose a challenge for retailers.
- Need for Skilled Analysts:
- Difficulty in digesting data without highly skilled analysts.
- Overwhelming information flow from internal and external systems.
- Cultural Shift for Analytics Adoption:
- Companies must establish a digital and data-driven culture.
- Analytics adoption requires familiarity and comfort with analytical strategies.
- Effort in Technological Advancements:
- Adopting company culture and structure to technological advancements demands considerable effort.
- Economic Concerns vs. Technological Development:
- Companies prioritize staying in business over technological development in the current economic environment.
- Proactive retailers recognize the symbiotic relationship between business sustainability and technological advancement.
- Advantage of Pure Online Players:
- Pure online retailers have a competitive edge as analytics is a primary asset for them.
Examining online multi-brand retailers like Zalando and Tmall, both initially centered their operations on Clickstreams and Web analytics. As early pioneers of advanced analytics in this field, they have showcased their ability to leverage data and technology for a significant impact on the fashion industry.
Now, is analytics applicable to startups, established organizations, or both? Frequently, established brands underestimating newcomers often experience a loss of market share. Stitch Fix and Zalando serve as notable instances, illustrating how robust analytics play a pivotal role in propelling the rapid market expansion of emerging companies. Hence, it’s evident that strong analytics drive the swift market growth of these emerging enterprises.
Key Factors in Successful Fashion Analytics:
- High-Quality Data Access:
- Retailers have abundant data from various sources (point of sale, websites, loyalty programs).
- Data accessibility is crucial for effective analytics.
- Advanced analytics integrate internal and external data, with a trend towards cloud computing.
- Management Interest in Analytics:
- A data-driven leader is essential for successful analytics.
- Leadership belief in analytics drives teams towards analytics-based projects.
- CEO support is vital for approving investments and costs related to analytics tools.
- Level of Scale (Company Size):
- Companies of varying sizes can invest in analytics.
- Scale provides the ability to invest in analytics and skilled personnel.
- Successful firms started small and used analytics to grow their businesses.
