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Realizing the Potential of Business-Specific Analytics in Fashion Retail (2024)

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Close-up of clothes on hangers.

Business-Specific Analytics & AI for Fashion Retailers

The word “retail” gets used interchangeably as if all retailers are the same.

But let’s be honest, what do fast food restaurants, automotive, luxury fashion, and online pharmacies have in common?

Not that much.

Sure, on the surface it may all seem like retail to the uninitiated, however, anyone who’s actually worked in medium to large-sized retail organizations will tell you they are each a world of their own.

Once you start “peeling the onion”, you quickly discover that each retailer has business-specific considerations that are critical for their business.

For instance,

  • Destination resorts that run gift shops must account for occupation rates in their hotels as well as weather.
  • Liquor stores have legal restrictions such as wet/dry zones, and state lines, and deal with inventory in pallets, cases, and individual bottles.
  • Electronics and games retailers have presales and major demand spikes on game release days. They also offer trade-ins for used goods.
  • Jewelry retailers need to determine the right assortment mix that offers a good selection while having the right depth of inventory for each SKU.

That’s why fashion retail planning and analytics software needs to be unique and specific to fashion, with the ability to break down business considerations even further than that.

Close-up of different color plain shirts on hangers.

Without understanding these business-specific considerations, none of your data, or analytics will yield intelligent and reliable insight.

  • What are your customer profiles?
  • What channels do you promote through? What channels do you sell through?
  • What fulfillment methods are you offering? How do you handle returns?
  • What sizes should you offer for each product at each location?
  • Are you vertically integrated, manufacturing your own goods from fabric or are you buying and reselling merch?
  • How seasonal is your business?
  • Where are you positioned against the competition in terms of price point?
  • Do you run promotions? or markdowns?

Last Year’s Data is Unreliable

The expression “garbage-in, garbage-out” strongly applies here. It doesn’t matter how sophisticated your analytics tools are, if you’re simply feeding it last year’s sales data, you’re in for a major disappointment.

If you run out of stock on winter jackets at your busy locations, you will not have an accurate gauge as to what your true demand was last year and you will repeat the same mistake in your forecast for next year.

Have new products that you didn’t offer last year? Run a promotion last year that you don’t plan for this year? Has Easter moved to a different day this year? Did your vendor change costs, forcing you to alter prices? Did you open new stores in areas with new geo-demographic diversity? Are this year’s cold days starting much later than last year?

The one constant in fashion retailing is that there are no constants.

Simply relying on last year’s data is a big mistake, and you are not alone. This has been the typical ineffective approach fashion and apparel retailers have taken for decades.

Person holding a pen and looking over a printed graph.

Business-Specific Analytics

What makes Retalon unique and where our customers are finding success with our technology is our unique business-specific approach to AI and retail analytics.

Here is just one example of a progressive fashion retailer using business-specific analytics from Retalon:

Simons is one of the most exciting fashion retailers in Canada. Founded in 1840, the retailer features 100,000 sq.ft. flagship stores across the country.

Simons relies on Retalon’s analytics for an accurate demand forecast that allows them to maintain a healthy In-Stock % across all stores and channels while minimizing inventory costs.

Retalon automatically suggests the optimal size distribution for all fashion products to make sure they are not left with fringe sizes at the end of the season significantly reducing markdowns.

Finally, Simons uses Retalon AI to get recommendations for successful promotions and automatically accounts for the promotional uplift in their inventory to ensure they bring the right inventory to the right location at the right time.

Don’t just take our word for it. Here is a short interview with the president and CEO of Simons himself.

How you can get started with business-specific analytics

First of all, you will need to determine what makes your business unique. It will often be a unique combination of art and science that you’ve built into your business process. You should start by considering the following:

  • What makes your business unique? (For example your assortment, operations, marketing, etc.)
  • What analytics and decision-making are you doing today that is working? What works but needs to be scaled? and what is not working?
  • What constraints does your business have? (legal, macro-environmental, competitive, internal, etc.)
  • What unique features, processes, or variables you’d like to include as a business rule or want to be accounted for?

Avoid analysis paralysis! Introducing new technology and processes to your business requires a lot of thought and planning, however, overthinking is a common mistake in retail. While you schedule additional meetings to discuss your competition is picking up market share.


There are some easy and low-risk ways to dip your toes:

  • Get a personalized demo on a sample of your data so that you can see the system with your own stores and products and uncover hidden opportunities specific to your business.
  • Run proof of concept on a small department or category in your business.
  • Finally, when you are ready, work with Retalon experts to make the system your own. We offer best practices and assistance to configure the system for your business so you can get the most out of it.

The digital transformation is here, and analytics-driven retailers are going to take over. But having the most advanced analytics isn’t enough. It must also reflect your vision, your unique business process, and the reality on the ground.

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