THE FUTURE OF FASHION WITH BIG DATA AND ANALYTICS
Explore how big data and analytics are reshaping the industry, delivering personalised shopping experiences, and empowering designers to create trend-setting collections.
Introduction
With a constantly evolving digital world, the fashion business, which has always been famed for its creative expressions, has launched on a fascinating journey into a new era. The relationship between the worlds of fashion and technology may look weird at first glance, but for stakeholders, designers, fashion analysts, and fashion tech companies, technology has become the driving force behind the fashion industry’s revolutionary transition.
In recent years, the fashion industry has experienced a surge in adopting big data and analytics. To put this into perspective, let’s consider a statistic from the McKinsey & Company report:
Fashion companies that have effectively harnessed the power of big data have realized an average of 30% increase in digital sales. This growth is a testament to the impact of data-driven insights in fashion.
While big data and analytics undeniably have a broad impact across various sectors of the fashion industry, this article centres its spotlight on two critical domains: the personalised shopping experience and apparel design.
These domains stand as exemplars of how data-driven insights are not only reshaping but also revolutionizing the fashion landscape, from offering tailored experiences to consumers and empowering designers to create collections that resonate with market demands.
In the first section, I will explore what big data means and how it’s being applied to the fashion industry. Then dive into data-driven transformations in the fashion industry, focusing on personalised shopping experiences and apparel designs.
Following that, the key takeaways will be summarised in the concluding section to provide a glimpse into the future of data-driven fashion.
Ready to dive in?
Big Data in the Fashion Industry
Big data, as the name suggests, is a substantial volume of data characterised by its Volume, Velocity, and Variety, and these attributes are indeed reshaping the operations of the fashion industry.
While the traditional understanding of big data revolves around the three Vs, it’s crucial to recognise that over time, researchers have introduced additional Vs to the concept, further enriching our understanding of big data’s impact.
However, in the context of this blog, which focuses on the impact of big data in the fashion industry, the three traditional Vs of big data (volume, velocity, and variety) are sufficient.
These Vs adequately explain how data is reshaping the fashion industry, from the volume of data generated to the speed at which it’s processed, and the diverse types of data involved.
Let’s have a look at the definitions of the Vs.
Volume represents the enormous amount of data generated by the fashion industry, including customer data, sales information, and social media content.
Velocity denotes the speed at which fashion data is produced and processed, often in real-time, demanding rapid responses.
Variety encompasses the diverse types and sources of data in fashion, including structured and unstructured data from various sources.
These three Vs are fundamentally changing how fashion professionals operate, from enabling personalised shopping experiences to enhancing supply chain management and sustainability efforts.
Now that you have been introduced to what big data is, I will walk you through two examples of data-driven transformation in the fashion industry
1. Personalised Shopping Experience
The fashion business is undergoing a data and analytics-driven transformation. One of the most transformational parts of this change is the potential to give highly customised shopping experiences to fashion consumers.
Such personalisation, enabled by big data and powerful analytics, has profoundly revolutionised how individuals discover and select apparel and accessories. Furthermore, fashion manufacturers have taken advantage of the data’s enormous potential. They are collecting data from various sources, both internal and external, with a primary focus on customer behaviour.
This data includes customers’ preferences, purchase history, website activities, social media activities, and more. The goldmine of data collected is used to predict customer behaviour and, in turn, deliver personalised shopping experiences.
Fashion retail has long grappled with the challenge of forecasting styles and deciding what to produce. This often proved to be a challenging and inefficient process. However, the emergence of innovative fashion technology startups, such as Dream Dress, is reshaping the game.
What’s Dream Dress’s Innovative Solution?
Dream Dress, for instance, is at the forefront of creating a unique garment-cut body type matching tool, leveraging the power of big data analytics.
This cutting-edge tool is designed to assist online fashion retailers in providing customers with personalised fit recommendations. Not only does this significantly improve the shopping experience, but it also has a positive impact on sustainability by reducing the frequency of returns and excessive inventory ordering.
Personally, I think that’s fascinating! What do you think?
Dream Dress collects a wealth of data, not only from the customers themselves but also from various other sources, like many fashion brands, as mentioned above.
This includes information on customers’ previous purchases, style preferences, browsing history, and even social media likes and shares. Advanced algorithms and machine learning are used to analyse this data and create a comprehensive profile of each customer consisting of details such as;
- Customer’s body type
- Style preferences,
- Clothing fits history.
Furthermore, Dream Dress leverages these data to recommend clothing and apparel that are not only in line with the customer’s style but are also tailored to their unique body shape.
For example, if a customer is looking for a dress for a special occasion, Dream Dress’s tool would not only recommend dresses that match their style but also provide suggestions on the best fit for their specific body type.
That’s absolutely fabulous!
Read on…
It’s worth noting that Dream Dress is actively seeking beta users to collaborate with them, offering an opportunity for users to shape and refine their innovative products.
📌Take note, it’s not just startups; significant fashion companies have also recognized the power of data to personalize the customer experience.
As a result, they’ve witnessed substantial growth, with digital sales increasing by an impressive 30 to 50 per cent. This success is a testament to the transformative potential of data-driven personalization in the fashion industry.
Dream Dress, with its innovative use of personalised fit recommendations driven by data analytics, brings multiple advantages to the fashion industry.
- Firstly, it effectively tackles the issue of returns, a common challenge in fashion, by significantly reducing the likelihood of customers purchasing ill-fitting clothing.
- It enhances customer satisfaction. Moreover, by offering a more tailored shopping experience, Dream Dress contributes to higher customer contentment and loyalty. Beyond improving customer experiences, it makes strides towards sustainability in the fashion sector.
- The reduction in returns and excess inventory ordering leads to less waste, aligning with the growing eco-friendly and sustainable practises in the industry.
- In addition to the above, they are able to save time and resources. Dream Dress’s approach embodies the fusion of technology, personalised shopping, and sustainability for a brighter future in fashion.
Now let’s have a look at how big data is being leveraged in apparel design.
2. Apparel Design
The integration of big data into fashion empowers designers with invaluable insights into consumer preferences, trends, and the personalisation of products.
How does big data influence apparel design?
The integration of big data in fashion is revolutionising apparel design by providing designers with essential insights into consumer preferences, trends, and personalisation. Data-driven decisions have transitioned from being a luxury to a necessity, allowing designers to create collections that align with market demands in the ever-evolving fashion industry.
Let’s explore a few notable examples of Data-Driven Fashion Transformation highlight the impact of analytics:
1. Zara’s Fast Fashion Mastery: Zara, a renowned fast-fashion brand, utilises big data to monitor customer preferences and buying patterns. Real-time data on popular designs enables Zara to swiftly adapt production, stay ahead of trends, and meet customer demand accurately.
2. H&M’s Sustainable Styling: H&M embraces sustainability through data analytics, evaluating materials’ environmental impact. This data-driven approach guides the selection of eco-friendly fabrics for their collections, aligning with the sustainability trend in fashion.
3. Stitch Fix’s Personalised Picks: Stitch Fix, an online personal styling service, harnesses big data to tailor clothing recommendations.
By considering individual preferences, body types, and style profiles, they curate personalised clothing selections for customers, continually refining recommendations based on user feedback.
4. Lyst’s Trendspotting Expertise: Lyst, an online fashion shopping platform, excels at creating trend reports driven by big data. By analysing the browsing and purchasing behaviour of millions of shoppers, they identify emerging trends.
These examples underscore the transformative power of data analytics in shaping the future of fashion, from trend adaptation to sustainability and personalised design.
Whew!! We are finally nearing the end of this article.
Conclusion
Big data and analytics have certainly redefined the fashion landscape coupled with the influence of technology. Hence, the fashion industry is no longer solely about creating new apparel and accessories; it’s now about harnessing the power of data-driven insights to revolutionise every aspect of the industry.
Furthermore, the transformative potential of data-driven insights becomes evident when we understand the significance of the three Vs of big data: Volume, Velocity, and Variety.
These three dimensions are reshaping the fashion industry, from the sheer volume of data generated to the lightning-fast speed at which it’s processed, and the diverse array of data sources involved.
With this data-driven transformation, the fashion industry is embracing new opportunities, especially in two crucial domains: the realm of personalized shopping experiences and the art of apparel design.
In conclusion, the future of fashion is intricately linked with big data and analytics, offering the promise of personalised experiences and sustainability.
As fashion professionals continue to refine supply chains, enhance designs, and predict trends with heightened precision, the fusion of technology and creativity paves the way for a fashion landscape that is more innovative, sustainable, and customer-centric.
The journey has just begun, and the world of fashion is evolving with data as its compass and creativity as its guide.
Thank you for reading this far! You can share your thoughts in the comments below :)
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