Introduction

Historically, fashion industries have earned a notorious label of being one of the slowest sectors to utilize modern technology and innovative data science applications. Many popular retailers used to operate with confidentiality, keeping strategic knowledge to themselves and sticking with traditional methods of forecasting demand.

Unfortunately, using the traditional closed-book approach left many brands without important information to make decisions, such as insightful pricing trends and patterns. It’s taken some time for brands to consider using this technology to their benefit, but nonetheless, many are making strides with this science. Brands are recognizing the worth of these analysis tools to as they’ve improved sales through advanced consumer segmenting, cut down on costs and wastage, and predicted upcoming trends to appropriately manage inventory.

Follow along as we break down the benefits of applying advanced data analysis tools and find out exactly why data science has become the hottest trend in the fashion industry.

1. The Traditional Analysis Method & Its Challenges
2. Precise Personalization for Target Consumers
3. Early Trend Identification
4. Promotes Cohesiveness and Saves Costs
5. Fashion Data Analysts Are There to Help!
6. Conclusion

1. The Traditional Analysis Method and Its Challenges

Before we can understand how data science has transformed the industry, we must recognize the challenges fashion brands face in order to better understand the advantages of implementing said tools. Almost every retailer has been influenced by the digital revolution, but fashion retailers are among the hardest hit verticals that face unique obstacles that could be overcome with this technology.

Traditional Method

The use of data analytics is not a new concept to designers, manufacturers, and retailers alike. However, many traditionally preferred to rely on creativity and intuition to guide them. Now that knowledge and data from digital platforms are readily accessible, the fashion industry can abandon archaic ways of storing and deriving information from datasets. There is loads of data waiting to be put to good use, and we’re going to cover how. First, let’s identify some challenges these traditional ways present.

Challenges

• Traditional analytics rely very heavily on past sales metrics. Past sales, unfortunately, can be deceptive. The various changes from year to year eventually contaminate the data, generating forecasting exemptions. New goods, moving holidays, new deals, demand shifts, competition entrants, and other variables make sales statistics from last year unreliable.
• Fashion brands deal with hyperdynamic assortments and short lifecycle spans. They often struggle with determining the supply of sizes, colours, and types, many of which are further complicated by changing seasonality.
• Most colours, styles, and other materials for clothing are usually scattered around as unstructured data.
• Other critical pieces of the puzzle are lacking, such as strategic intelligence, pricing, patterns, perspectives and other essential data when trying to make data-driven decisions.
• Although some brands now have incredible access to data, they do not have concrete ideas for what to do next. Research from Forrester indicates that only 29% are able to utilize analytics compared to 74% of retailers who want to be able to make data-driven decisions.

With so many details to consider, the fact is that traditional analytics are not advanced enough to provide reliable insights into the choices that retailers need to make in future planning.

Now that we’ve covered the obstacles brands face, let’s examine some of the ways data science has improved operational efficiency.

2. Precise Personalisation for Target Customers

Many brands are looking for avenues to optimize data analytics to improve their product selections. The most significant reason data science has become the hottest trend in the fashion industry is that it offers precise personalization for target consumers.

Fashion is subjective and relevant to each individual person, community, or larger demographic location. For example, a product trending in one country may be considered an outdated trend in a different country. But in general, consumers prefer to have a personalized set of products offered to them as opposed to a generic set they would not be overly interested in.

One company using advanced data science to personalize their product offering is called Stitch Fix. Stitch fix is a business that leverages data science to deliver personalization at scale. Using AI Algorithms, the application presents specific fashion designs to consumers based on their recommendations, browsing history, and past purchases. After the first round of selecting broad style of designs is complete, the system then moves to the second tier of choices to present to the customer. Round by round the customer finalizes their design/choice of product, and the order is submitted to Stitch Fix’s development team for production.

Spanish apparel brand, Zara, is also transforming their business with big data. Traditional retailers order ‘bulk clothes’ for the entire season. Zara only orders a minimal amount of merchandise. keeps track of its distribution statistics, then analyzes sales data  for a certain SKU, or Stock Keeping Unit. Zara has perfected the art of fashion data science to the point where they now manufacture and distribute within 21 days of collection conceptualization.

Advanced data science like this helps companies market products accordingly by providing insights into specific preferences of customers across all demographics. It also gives them the ability to consider feedback from millions of consumers in a way that humans simply cannot. Thus, application of data analytics is becoming an extremely important weapon to maintain relevance and foster genuine personalization on a large scale.

3. Early Trend Identification

To thrive in the retail industry, brands need to anticipate trends and keep in line with ever-changing customer tastes. Thanks to data science tools such as predictive algorithms, photographic data, visual search, stylists can detect trend patterns before they even happen. This helps companies satisfy the needs of their end customers and stay ahea of their competitors.

Predicting Purchase Trends

One primary reason fashion brands are incorporating more advanced algorithms, is to predict when products will run out on e-commerce platforms. Have you ever visited a website that has banners or text saying something along the lines of “Almost gone!” or “12 left in stock”? This is the work of data science. The algorithms approximate a “shelf-time” of products on the website and notify the customer if it’s going to run out soon. This helps retailers and manufacturers calculate proper production amounts to which we’ll later cover, also helps to reduce costs.

Fashion brands often used to rely on focus tests to determine whether or not a collection would be successful. Now, many are also using trend prediction software to forecast a new product launch’s success.

Predicting Success of Product Launch

Well-known brands like Ralph Lauren use technology that is known as ‘active product intelligence’ to assess how changes in product fabric, design specifics, color and price impact the customer’s purchase decision.

For example, their PoloTech for athletes featured a single large monitor built into the shirt that captured real-time data on the position and activity of the wearer. It tracked biometric data such as cardiac and respiratory speeds, the amount of steps taken, and the calories consumed.

Imagine how useful this data could be! Companies could predict which type garment is most likely to sell based on how much value it brings to their customer. All sorts of consumer data can be gathered by this form of wearable technology to forecast new fashion patterns far beyond the running track.

Following the collection of the data, it is then is processed using the appropriate algorithms. A favorable combination is discovered, and brands will double down to manufacture similar goods with greater speed and accuracy.

The neat thing about using data technology like this, is that all of the research is gathered before the collection is fully developed or launched. If the company can predict what types of products would trend the most, they can reduce the costs and the probability that it will be a commercial failure. Isn’t that amazing?

Next, we’re going to expand on how reducing unnecessary waste is another a key reason data science is becoming increasingly popular trend in the fashion industry.

4. Promote Business Cohesiveness and Saves Costs

Data science unifies business divisions that have historically worked as separate units (ordering, planning, marketing). Unification through intelligent data storage systems facilitates easy of communication between these units by providing a common point of certainty for all data that influences the profitability of inventories.

Here is an example of how isolated departments run into problems when approaching an opportunity. Let’s say the Marketing department, for example, would like to run a discount on a product to improve traffic to stores. Without appropriate data to present to the other departments, the Marketer may fail to carefully account for:

• How much merchandise has to be bought to meet demand without over-stocking?
• How will this campaign influence the sales of other items within the collection?
• What would be the effect of promotion on gross margin objectives?

Companies may encounter overproduction if they do not have the tools in place to effectively address these questions across all sectors. Overproduction, or oversupply, means you have too much of something than is necessary to meet the demand of your market. The resulting excess leads to lower prices and potentially unsold goods.

Take Stitch Fix for example.

Before using advanced software, Stitch Fix had to allocate inventory for each client at a time, holding up the queue and increasing customer wait time. Many companies might think that having “extra” inventory to cushion is a cure to the long queues. Not Stitch Fix. After implementing a better data analysis algorithm, Stitch Fix’s system today considers the preferences of many customers at a time to determine which materials should be available to stylists as they fulfill orders. Stylists now have an appropriate inventory level to meet all of the customer’s wants irrespective of the order data and time. This algorithm helps the company save inventory costs and shortened the time to complete a “Fix”.

5. Fashion Data Analysts Are There to Help!

Fashion data analysts collect data, analyze, and describe essential data to all major departments of a business. Their job is stay on top of the market trends and consumer patterns before competitors, but also before they even start to develop. A key part of an analysts occupation is to draw out relevant information from all data sources and properly organize to create easily digestible presentations for the rest of the divisions in the business. These analysts utilize all of the algorithms above, but also offer a valuable human intelligence to prescribe comprehensive and realistic solutions.

6. Conclusions

Big data is certainly a massive development for both apparel manufacturers and consumers who are excited and eager for improvement. Brands that incorporate data-driven decision making have the opportunity to grow and retain their competitive advantage better than rivals in their industry.

Three primary benefits of using advanced data science tools are:

1. Hyper-personalization of products to attract customers
2. Early market testing and trend prediction to follow each season or major collection launch
3. Business department cohesiveness which reduces costs and improves operational efficiency.

Always remember, highly insightful data must be gathered and analyzed by qualified programs or industry experts to ensure it is reliable information to base important decisions on. Fashion analysts are dedicated to translating complex data insights into comprehensible presentations for the purpose of delivering leading-edge products in both customer and market segmentations.

There is little evidence that points to a slowdown of developments in computer learning, artificial intelligence, and other prominent fields of data technology , making it a very it a perfect time forms businesses to incorporate data science into their strategic planning.

We hope you enjoyed reading about data science’s impact on the fashion industry. Hopefully, you’ll take away some tips from this post and apply them to your own business!

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