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How Fashion Analytics is Saving the Industry (2024)

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For the past few years, retailers have been faced with an exponentially growing gap between industry leaders and laggards. A McKinsey & Company report shows clearly that those who leverage analytics are outperforming their competition by 68%, and the gap continues to widen exponentially.

Digital transformation in the industry has created additional challenges for retailers to tackle. The traditional approach (which used to be good enough) has now become obsolete. Relying on the ‘good enough’ makes it impossible for a business to survive in the digital age.

In today’s landscape, when a new fashion retail trend latches on, companies need to have the ability to make the right decisions quickly. Retailers have found that machine learning and predictive analytics a game changers which is leaving slow-to-adapt competitors far behind.

So, why is advanced analytics so successful and what is the difference between the traditional approach and this new advanced approach to fashion analytics?

Fashion Retail’s Unique Challenges

In order to truly understand the benefits of analytics in the fashion industry, we must consider the unique challenges that fashion retailers face.

While digital transformation has affected every retailer, fashion retailers are among the hardest-hit verticals facing specific challenges which complicate the use of data analytics and demand forecasting.

This is because fashion retailers work with dynamic assortments and products with short lifecycles. A fashion retailer can’t bank on the popularity of a few cash-cow SKUs, year-over-year.

Fashion and apparel retailers also deal with additional complexity in the distribution of sizes, colours, and styles — all of which are complicated further by shifting seasonality. It’s not enough to predict a successful SKU — you also need to bring in the correct colours and sizes to each store at the right time. This often results in inventory distortion that leads to lost sales and costly markdowns.

And perhaps most importantly, the nature of fashion itself makes selling online a more difficult endeavour. The variations in fit and colour between brands and styles cause an increase in product returns. The cost of returns alone can make a retailer question the profitability of online stores.

With so many variables to consider, fashion retailers struggle to make good, data-driven decisions. Although retailers have unprecedented access to data, they do not have tangible recommendations for what to do next. In fact, Forrester research found that while 74% of retailers want to be data-driven, only 29% are able to connect analytics to action.

In other words, there is too much data. Traditional analytics approaches are just not sophisticated enough to offer accurate insights into decisions retailers need to make going forward.

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Traditional Approach to Fashion Analytics

Traditional analytics relies too much on past sales to plan and manage inventory. Unfortunately, past sales are often misleading.

Your business changes year to year.

New products, moving holidays, new promotions, changes in demand, competition, vendor costs and many, many other factors make last year’s sales data unreliable. These year-to-year changes pollute a fashion retailer’s data — creating forecasting exceptions (which increase the manual labour required to consolidate data and make it usable for real-world scenarios).

Solely relying on past sales to make important decisions going forward can be costly and misleading.

Imagine a retailer running a big promotion on a popular winter jacket brand. The promotion is a “hit” and they sell out of the winter jackets in all of their stores.

Can this be considered a truly successful promotion?

Because they ran out of stock, the retailer is not able to discover true demand for the product, nor are they able to calculate the actual promotional uplift.

Now when the retailer uses this sales data, they will be mis-forecasting next year’s potential demand for winter jackets — once again bringing in less inventory than needed to match demand.

But that’s just one major issue of traditional analytics.

An even bigger issue is the fact that traditional analytics is not “unified.”

In other words, each department and business unit uses its own patchwork analytics (either through spreadsheets or BI tools), without easy access to accurate data from other departments. By not Integrating key functions of the business, retailers are left in a position where the left-hand doesn’t know what the right hand is doing.

For instance, many retailers run promotions while lacking integration between promotion and inventory management processes. These retailers are therefore unable to account for the demand uplift resulting from the promotion. They do not know how much inventory they should have on hand to satisfy the uplift generated by the promotion itself. This means these retailers will:

  • Sell off existing inventory at a discounted price
  • Not match demand, losing out on potential sales
  • Experience lower customer service level

Successful retailers have found a way to improve their business by leaving traditional analytics in the past and moving toward advanced fashion analytics.

How Advanced Analytics in Fashion is Changing the Industry

Today’s leading fashion retailers are implementing a new generation of analytics. This new type of advanced fashion analytics has 3 distinct advantages over traditional methods:

  1. It is purpose-built for fashion retailers and doesn’t need to be customized or jerry-rigged to account for fashion-specific factors like size curves
  2. It doesn’t just give you simple data visualization (descriptive analytics). It leverages predictive analytics to forecast the future and run “what-if” scenarios (e.g. what would happen if you shrunk your assortment in men’s shoes?), and prescriptive analytics to automatically suggest the most profitable actions.
  3. It unifies business units that traditionally worked in silos (planning, purchasing, marketing), giving retailers a single source of truth for all data that impacts inventory profitability.

Let’s take a look at each of these in a little bit more detail.

Analytics Purpose Built for Fashion

Most fashion retailers use a complex combination of ERP reporting tools, data visualization tools like Power BI, custom homegrown reporting tools, and massive spreadsheets on good old Excel.

One of the primary reasons for such complexity in data reporting is that there are very few analytics tools on the market that were built for the specific nuances of fashion retail. You might be able to use traditional tools to quickly pinpoint your best-selling SKUs — but unless you’ve invested in customization, your software won’t be able to generate specific, granular data points that are most relevant to your day-to-day operation.

This is where analytics purpose-built for fashion really shines:

  1. It provides optimal distribution recommendations for every single style, colour, and size. Meaning retailers are never left with overstock because of a size issue.
  2.  It proactively accounts for short life cycles, dynamic seasonality, vendor lead times, and dozens of other factors that influence demand.
  3.  It recommends the best way to fulfill orders and accept returns, maximizing profitability and defending the GMROI.
  4.  It optimizes markdowns so retailers can get rid of end-of-life products while maximizing the GMROI. Often minimizing markdowns in the process.

Predictive and Prescriptive Analytics for Fashion

Beyond the “descriptive” analytics (data visualization) of legacy BI tools, fashion retailers are taking advantage of much more advanced forms of analytics, including predictive analytics (using AI and advanced statistics to forecast into the future) and prescriptive analytics (using algorithms and AI to generate profitable and actionable business recommendations).

Predictive analytics in fashion can be most easily summed up by the example of demand forecasting. This is an AI-driven forecasting process that automatically accounts for dozens of factors (previous sales, price elasticity, product cannibalization, events, etc.) to very accurately predict the demand of each SKU at each store. This allows retailers to cut costs on inventory (by only stocking products with demand) and maximize sales (by slashing out-of-stocks on inventory that typically sells out).

Prescriptive analytics, on the other hand, takes all of the predictions made by predictive analytics, runs them through algorithms and AI built for fashion retail, and automatically generates recommendations for retailers. For example, a prescriptive analytics platform may predict when to preemptively transfer inventory from one store to another to reduce markdowns increase sales, and automatically generate an inventory transfer request.

Unifying Fashion Analytics

Truly advanced fashion analytics also uses a unified approach to optimization — bridging the gap between key retail business functions and consolidating and reconciling all relevant data.

This is not just a feature of advanced analytics, it is absolutely necessary for its effective use.

Most retailers are heavily siloed across departments. Planners, purchasers, and marketers typically work completely autonomously — and this creates a drain on profitability. For example, marketing may want to run a promotion on a product to increase foot traffic to stores. In most cases, they will not carefully account for:

  • How much inventory needs to be purchased to satisfy demand without overstocking
  • How this promotion will have an impact on sales of other products within the assortment
  • What impact will the promotion have on gross margin targets

Conversely, merchandise financial planners will often set targets that don’t accurately reflect consumer demand, incentivizing buyers to purchase too much inventory, which then needs to be marked down, which in turn hurts gross margins.

Many of these problems stem from the fact that these different functions in fashion retail are not working from the same set of data and assumptions.

Advanced fashion analytics solves this by combining all data (financial plans, assortment plans, OTB budgets, demand forecasts, purchase orders, etc.) into a common platform, and automatically reconciling this data across the entire organization when changes are made in any department.

This allows planners to see the impact their plans will have all the way downstream to potential markdowns, and vice versa.

How Fashion Retailers Can Get Started with Advanced Analytics

Stop overthinking it.

Playing the “analysis paralysis” game means potentially losing additional market share to more decisive competitors. The biggest mistake fashion retailers can make in this market is to doubt and second-guess themselves. That’s why the gap between the leaders and laggards of the fashion space is increasing.

Now is the time to run a proof-of-concept for advanced analytics.

Retailers should run a proof of concept on their own data to discover quick wins and the overall benefits that advanced analytics can bring to their fashion retail business without having to dish out a major investment or roll out a full-scale solution.

Retailers using advanced analytics are seeing a decrease in:

  • Inventory costs
  • Needless markdowns
  • Manual labor costs

And a clear increase in:

  • Sales
  • Profitable promotions

Today’s fashion retailers have a decision to make; Invest in advanced analytics and maximize profitability or go the way of the dinosaur.

Request a demo of our technology when you’re ready to learn more about advanced analytics for fashion retailers.

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