Re-Imagine The Business Of Fashion-part 1

How Can Fashion Brands And Retailers Re-Imagine Fashion Through Advanced Analytics?

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My interest in Data Analytics began in 2020 when I stumbled on an article about different case studies of analytics in the Fashion Industry. Honestly, it was a long read, about 60 pages in all, but I enjoyed every bit of it, way more than any other fashion tech-related articles I had read.

This sparked my interest in Fashion Analytics, especially in Africa Fashion Businesses.

Sadly, I found little information as regards this subject in the African Fashion Industry, and each time I mentioned to people I was exploring Analytics in Fashion, it sounded somewhat strange to them, yet interesting.

I discovered that not many fashion professionals were aware of the application of data analytics in Fashion businesses.

Rather than enlightening people in my immediate environment alone, I came to the conclusion that “Fashion in Africa needs to be re-imagined” and I began to think about how I could contribute to that, hence the reason for starting a fashion tech blog.

Each article gears towards enlightening and educating fashion tech enthusiasts, young professionals, designers, retailers, and students on the disruption of Advanced Analytics and Machine Learning in the Fashion Industry.

If you belong to either of the categories mentioned above, this article is certainly for you!

Before delving into the application of Advanced Analytics let me shed some light on the impact of the covid -19 pandemic in fashion retail.

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The Impact Of Covid-19 Pandemic In Fashion

According to Fashion United, the fashion industry is valued at over 3,000 trillion dollars which is about 2% of the world’s Gross Domestic Product (GDP).

In 2019, the global apparel and footwear market was approximately $265 billion markets with Nike alone generating over $39 billion in revenue, and the market is projected to rack up to $3.3 trillion by 2030.

Irrespective of the unprecedented surprise of the covid 19 pandemics, fashion experts still projected a 20% or more growth in online businesses in 2021.

Interesting right?

Unfortunately, not all businesses were quick to adjust to the changes induced by the pandemic. While some businesses were quick to embrace analytics (doing better by 68%, Source: McKinsey & Company report ), others that stuck with traditional marketing or were late to adopt technology and innovations began to lose market share and some closed down.

This further justifies the sales statistics forecast that brands with digital channels will gain at least 20% revenue this year.

Despite the growth predictions, digital transformation still poses as the paradox of the fashion industry.

Interestingly, Mckinsey reports that 144% of the industry profits are generated by the leading 20% of the global fashion brands. This implies that for a fashion brand to make a significant profit, it needs to be amongst this 20%.

Just like me, you might be wondering what these fashion brands are doing to remain top in the Industry.

Keep reading…

The simple summary to describe this growth would be ‘Digital Transformation and Innovation’.

Zalando for instance reported a 32–34% growth in Gross Merchandise Volume (GMV) in the second quarter of 2020 and a 50% growth in GMV during the first quarter of 2021. (Source: Zalando Publication).

That’s a lot of growth especially bearing in mind the current state of fashion during a global pandemic.

Do you agree?

Prior to the COVID-19 crisis, companies that had built and adopted digital transformation such as :

• Advanced Analytics.
• Machine Learning Models.
• Artificial Inteligence
• Strong digital/e-commerce platforms, etc

had begun to outperform their competitors who had not leveraged either of the digital transformation listed above.

So it’s safe to say digital transformation did not begin in 2020, rather, the Covid-19 crisis assisted to accelerate the transformation and also broadened the gap between the two categories of companies discussed above.

For the purpose of this article, I will focus on Advanced Analytics and how top Fashion Companies have leveraged it in their businesses.

What Does Analytics Really Mean?

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Analytics refers to the scientific approach to discovering and communicating meaningful observations and patterns present in data. It tends to answer questions based on the patterns founds in a given raw data.

It isn’t a standalone approach per se, it relies on the application of computer programming, statistics, business intelligence as well as research in order to create explanations, quantify, and gain insights from raw data.

Analytics in fashion has become one of the most available tools to brands and retailers, and its application is not limited to sales forecasts. Some other areas include :

• Trend Analytics
• Digital Analytics
• Risk Analytics
• Web Analytics
• Predictive Modeling
• Sales Optimization
• Marketing and Advertising
• Logistics, etc

Applying analytics in either of the areas above depending on the business challenge(s), technical support, and funds available will not only increase a company’s sales but will also increase its competitive advantage in the long run.

It’s been a long read but I am glad we have covered the basics of Analytics. In order to better understand the benefits of leveraging advanced analytics, it is important to highlight some of the challenges faced by fashion brands and retailers.

Grab your coffee, and let’s do this!!

What Challenges Do Fashion Companies Have?

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The business of fashion is a rather complex one coupled with the fast rate at which fashion consumers’ preferences change.

With more awareness about advanced analytics and other technologies, fashion brands and retailers are seeking ways to optimize data analytics in order to improve product offerings through precise personalization for target customers. In turn more sales, more turnover, and more profits!

Here are some Fashion Business Challenges:

• Complexity and Life Span of Customer Preferences: The Advanced Analytics approach adopted in the business needs to put into account the sizes, colors, fits, styles, locations, seasons, lifestyle, etc. These attributes are changing rapidly thus, it is not sufficient to analyze your business based on just the sales data of the previous years or guts instinct.
• Lack of Data Integration: Isolating the data created by different departments in the company inhibits the effective use of data for analysis.
• Difficulty In Mitigating Product Returns: Due to the complexity of customer preferences,e.g size, retailers have little control over the number of returns. The more the returns, then the more the overhead cost.
• Inability to Optimize The Available Data: While some fashion companies possess numerous data but do not know how to use it, some brands do not have a data-driven culture.
• Inability To Accurately Predict Trends: As a result of the challenges mentioned above, brands and retailers are unable to accurately predict designs and sales trends. This could in turn result in excess production(which might end up in the landfill), unnecessary discount offers/clearance sales, store shutdowns, etc.

Here comes the part you have been waiting for…

Excited?

Me too!!!

How Is Advanced Analytics Re-Imagining The Fashion Industry?

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1. Analyzing Consumer Behaviour Preferences: Leveraging advanced analytics in the fashion industry can help companies to have a deep understanding of customer behavior and needs. Which in turn serves as a guide to answering questions such as :

• What ads to run, to who, and at what time?
• Who is likely to convert after receiving a discount?
• What product recommendations are suitable?
• How do we improve customer experience? etc.

The application of data-driven sentiment and behavior analysis can help companies better understand market trends, and in return improve conversion rates.

Zara for instance collects in-store data, sends it across to market analysts to make sense of the data and create insights on what the customers are in search of.

These insights are therefore sent to the design team who in turn produces the clothes and release them into Zara Stores.

Another example is the use of consumer data analytics in Ralph Lauren’s trend forecasts. With advanced analytics, Ralph Lauren can understand customer preferences such as colors, fabrics, price, etc.

Thus, increasing the accuracy of the trend forecast.

2. Predicting Inventory and Sales Distribution:

With Predictive Analytics, fashion companies can compare their prices and inventory with their competitor’s data, this could assist them with better ways to improve merchandising campaigns and increase the accuracy of sales and demand forecasts.

Some fashion retailers have also applied Natural Language Processing (NLP) to better understand the lingo of a specific target market to better understand consumer buying power, market trends as well as creating appropriate ads for the right audience.

Adapting Predictive Analysis could also help to prevent overstocking and understocking by identifying what product is in high demand or emerging. Consequently, reduces the time and money spent in optimizing inventory

Zara is an example of a fashion retailer that has successfully implemented this technology thus optimizing supply chain planning that ensures the right inventory is always sent to the correct store at the right time

3. Helping Customers Find the Right Fit

Finding the right fit is one of the challenges customers have especially while purchasing online. With advanced analytics such as Predictive Analytics(in combination with augmented reality (AR) and Image Processing), customers can easily find fashion products that fit them by using Virtual fitting rooms.

The combination of these three technologies is used to analyze a customer’s body measurement, predict which item of clothing will fit them best, and also shows them how it will look on them.

Sounds Fascinating right?

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Rent The Runway (RTR) ‘s data team analyzes customer reviews about their body data and clothing fits in order to provide more useful insights to customers with similar bodies.

This body data include body type, weight, bust size, dress size, height, weight, etc, and sometimes includes the events the clothes were rented for, pros and cons. These are then be used by RTW to create valuable insights for further purchases.

Advanced Analytics can help customers find a better fit, within a shorter time, thus making them happier, reduce the among of products returned, and increase the likelihood of the customers to keep buying.

4. Increase the Possibilities of Building A Sustainable Fashion Business:

Advanced analytics can help fashion brands and retailers mitigate wastes in each step of the value chain from the design phase to stocking and deliveries. By providing;

• a better understanding of market trends.
• better pricing options.
• better merchandise and inventory.
• better sourcing options.
• higher accuracy in the trend forecast.
• sales distribution strategy.
• better fit suggestions.
• better ads and discounts, etc.

Advanced Analytics can indeed make the business of fashion more sustainable and profitable in the long run.

Examples Of Fashion Brands Adopting Data Analytics

• German Retail Giant- Zalando
• Zara
• H&M
• Amazon
• Rent The Runway
• Stich Fix
• Ralph Lauren
• Net-a Porter

Examples of Tech Companies Offering Advanced Analytics Services:

• Heuritech
• WGSN
• Retalon
• Trendalytics
• Netgains

CONCLUSION

In as much as Advanced Analytics is still evolving and advancing, it has shown numerous prospects and created a pathway for fashion brands and retailers to be more profitable while being sustainability conscious.

Despite these promises and prospects, some fashion companies still have not adopted these technologies, either due to;

• Insufficient capital.
• Absence of strong data technical support team.
• Difficulty in creating insights from the data available.
• Unaware of the technologies.
• Or a complete lack of digital & data-driven culture

Though the end result of advanced analytics is improved marketing campaigns, better product recommendations, planning optimization, improved distribution & networks, and waste reduction, fashion companies need to understand their business challenges in order to select the right technology to apply.

Follow this link to know how Advanced Analytics is helping the fashion industry become more sustainable and profitable.

I hope you enjoyed reading this article?

Can you think of more ways to adopt Advanced Analytics in the business of fashion?

Let’s discuss this in the chat session.

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Cheers,
Fashion Data Queen (Kiitan)