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Daphne Duong

September 13, 2021

The Buyer and Merchandiser's guide to assortment planning

How to leverage retail market intelligence to perfect your assortment planning.

Getting the right product at the right price at the right time is the trillion dollar question. However, less than 20% of retailers use AI and big data to support critical decision-making. 

The rise of digital channels such as ecommerce and livestream platforms has forced the industry to adapt quickly and inevitably made it more unpredictable. This means brands need to react faster than ever before if they want to win in retail. 

Retailers have historically made these decisions based purely on gut instinct and they’re learning quickly that they need to be more data-driven. It’s never been more important to equip their teams, particularly Buyers and Merchandisers, with the resources needed to analyze this ever-changing market. 

While every team in a retail business is important, we want to focus on the primary roles that dictate the products that get pushed out and the price points they sell at. One decision can either make or break a business, with the potential of gaining or costing them millions of dollars. 

In order to understand how critical these roles are, it’s important to roughly break down what they do. Buyers are devoted to making sure present and future product ranges are aligned with current trends and consumer demand within a set budget. By bridging design and merchandising, Buyers ensure the right product is always in stores and online, with a finger on the pulse of the next big trend. Their counterparts are Merchandisers or Planners - labeled differently depending on where you are in the world. This role is exceptionally analytical as they’re in charge of making sure products are available, online and in-store, and in accurate quantities and sizes. In general, Buyers and Merchandisers have to not only consider consumer demand, but also adapt to market changes when planning their product lines. 

All in all, assortment planning is not an easy task, especially if you have too little or not enough stock. With no straightforward strategy out there, we use retail AI to outline some of the best practices a Buyer or Merchandiser should consider when tackling their upcoming range. 

With over 10 years of historical data, EDITED has helped countless brands including Mango, Puma and Abercrombie and Fitch to make better decisions. If you want to know more about the use cases we’ve set up in this report, reach out to a dedicated Retail Specialist for a demo today

Issues to consider before tackling your range planning

It’s no secret to anyone how wasteful the fashion industry is. It’s currently responsible for 10% of the world’s carbon emissions, according to the UN, and costs US retailers $50 billion a year from deadstock alone. 

Besides navigating fashion’s waste problem, retail professionals also have to wade through the additional challenges that globalization presents. There are more brands breaking into the scene every day, while emerging trends are no longer isolated to one part of the world. Whether you’re an established player or a new entrant, the air of competition is hot and heavy. 

With sustainability at the forefront and the necessity to keep up with all the latest trends from around the world, retail teams are under an immense amount of pressure to get their assortment right while protecting margins. 

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Image via Reformation Email US - Dec 8, 2020

To avoid costly decisions, it’s time to figure out smarter and more strategic ways to take a data-led approach when planning an assortment range. With the help of advanced retail AI, the machines do all the heavy lifting to gather the competitive data and analytics needed for a brand to navigate various markets with more efficiency to drive sales and react quicker to new trends.

While data is not a new concept to retailers, fewer than 40% of companies who invest in retail AI see gains from it. According to an HBR article, this could be due to failure to understand what problem a retailer is solving, inability to recognize the difference between value of being right and costs of being wrong, as well as how to leverage AI to make more frequent and granular decisions. 

To hear directly from the article’s co-author, Michael Ross, listen to our EDITED: Inside Retail podcast episode called ‘How to optimize your retail AI’. 

The EDITED Retail Intelligence Platform is here to help you avoid running into those above issues and improve your assortment planning processes for the better, equipping your business to prevail in this new age of retail. 

Think big, but start small. You don’t start with a big grand vision and you don’t start with data. You start with looking at a decision and decisions that have a big revenue attached to them and how we can make decisions differently and better.”

- Michael Ross, SVP of Retail Sciences at EDITED

A new way to look at traditional planning

Stage one of assortment planning is to identify the missed opportunities from last season or year. Right now you do that by looking at what you had and comparing it with what you’ve learned from your online or store sales. This means spending lots of time manually sifting through tons of products and aligning with other internal teams.

Access to data offers a better way to view the entire market, so you have context as to why your pieces were a hit or miss. Instantly you can flag up what you didn’t have – something a data-free approach can’t answer. Retailers are able to analyze their competitors’ assortment, what sold out fast or at full price, and what stock they have left. 

This is why data transparency across all teams is important. With retail AI, machines aggregate global data into one place so category managers can customize these dashboards for their assistants to access this data in real-time. With a few clicks, you can analyze any retailers you want to benchmark against, alongside the category you’re buying for. You’re also able to see what success last season looked like and then do the same for failures. 

When using external data, set your own metrics for what constitutes a failure or a success – whether that means something that was reduced by 50% after two months online, or whether it was swiftly replenished after its first sell through. Set that in line with how you define bestsellers and duds within your own business.

After getting an overview of last year’s missed opportunities you need to know where your successes lie. There’s a series of calculations involved with assortment analysis and planning which you are probably pretty familiar with. This is the simplest place to start. 

Break your assortment out into its key categories and compare the percentage of sales in each category against the percentage of receipts in each. That’ll reveal the key growth areas. If sneakers made up 50% of sales, but only 30% of receipts, you’re going to want to keep investing there!

assortment planningLet’s take a look at the data behind the western trend that’s taking the industry by storm, with both mass and luxury brands stocking it. The popularity of cowboy boots kicked off this trend, but now we’re seeing even more prominent western products coming to the fore. And if you have yet to jump on the bandwagon, EDITED data can help you determine what opportunities to capitalize on that others have yet to do. 

Looking at US and UK products in stock, fabrication is most often cited in western-inspired product names, indicating it’s a focal point in differentiating one brand’s product from another in the market. “Denim” was found in 6% of Western-labeled products, while “suede” was used often to describe dresses, skirts, outerwear and footwear, making up the same percentage as “denim”. “Leather” was the most common fabric used in product names, ranging across apparel, footwear and accessories. 

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With this analysis, you’d probably want to stay away from offering too many boots and focus on a category and fabric option others haven’t approached yet. For example, a western style suede jacket might be an area of opportunity, especially with the anticipation of festival season next year. 

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Images via Nasty Gal, Lucky Brand, Nasty Gal

Use case #1: Tackling your wholesale strategy

The rules of wholesale retail have changed due to the ecommerce boom during the pandemic - one thing online wholesalers like ASOS, Farfetch and Zalando have all benefited from. Compared to other retailers and brands who struggled all of 2020, these heavyweights raked in big profits, making it even more attractive for brands to expand into new regions and categories. 

However, as many retailers know, there is a level of uncertainty involved, including little control over the way your products are merchandised and discounted. 

If your business is willing to take on the risks associated with going wholesale, make sure your buyers and merchandisers have data on their side to provide visibility on how your partners are stocking, pricing and discounting competitors. 

Let’s take a look at price positioning. It is a major pain point for retailers to streamline a consistent pricing strategy across all its retail and wholesale channels. Ensuring your first price is the right price is critical to maximizing your full price sales so you’re less reliant on the discounting drug. With so much of the business’ profitably at stake, it makes any merchandisers’ role incredibly stressful and difficult. 

This is where retail AI comes into play. Access to global data allows your team to hone in on a particular product to see how it’s performing across various markets and places. For example, one luxury brand we work with had a best-selling bag that was being discounted at a wholesale site. The retail team was immediately notified of the changes in pricing via real-time analytics and was able to present the data findings to their wholesale partner. The inaccurate promotions were quickly revised and the brand was able to maintain their high-end positioning in the market. 

Read about our case studies comparing ASOS and Zalando’s strategy here. 

With the power of data, you’ll also be able to empower your retail business when it comes down to negotiation and insight into new platforms. While luxury retailers are fine-tuning their DTC strategies, it’s still important for these brands to leverage retail data if participating on third-party sites to ensure its positioning in the market hasn’t been negatively impacted. 

Prada, for example, is shrinking its presence on wholesale retailers. The number of options stocked YoY in the UK fell by 84% at Farfetch, 47% at Net-a-Porter, 25% at Mr. Porter and 26% at Selfridges. It’s highest priced goods are also housed on its own site instead of elsewhere. They can then reinvest in other innovations as well as ensure consistent pricing across all third-parties platforms.

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Having this kind of information at your fingertips is valuable if you’re experiencing pushback from a wholesale partner suggesting your product is priced too high or too low for their site. 

Use case #2: Tackling your ecomm strategy

We mentioned in our forward-thinking report on 2021’s overarching themes to conquer the retail landscape, the digital experience will be an area to watch. Nine months later… and, boy were we right. 

The economic volatility from the pandemic only further cemented the changing of the guard from brick-and-mortar to ecommerce dominance. Within the span of 3 months, the retail industry experienced 10 years of ecommerce growth, according to McKinsey. From live-stream shopping to buy-now, pay-later partnerships, everyone’s all in - with more brands looking at these channels much differently and investing on innovations to support a digital-first approach. 

So, how can you excel in your role knowing there’s all this global competition and rapid turnover of trends? 

The answer is simple. Think of AI as your new colleagues who will propel your brand forward. The bonus is that it also makes your job easier too.  

Just like the wholesale strategy, retailers need to implement data into their everyday processes and break down silos across other internal teams when it comes down to strategizing for its ecommerce platform. 

In the midst of the pandemic, retailers have most likely readjusted their discounting strategy, a lot. As explained in our Black Friday 2020 trends unearthed, deeper discounts don’t always equal higher sell outs. EDITED data found retailers favored the 60-70% discount bucket; however, products reduced between 50-60% off saw the highest number of sellouts. Remember, aggressive markdowns do not always resonate with consumers. As a merchandiser, use insight like this to adjust your sales strategies, such as understanding when your competitors are communicating discounts and at what price range. 

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As the holiday season approaches, it’s important for savvy retailers to create bullet-proof strategies backed with retail analytics for upcoming events to streamline the decision-making process across your entire team. With retail market intelligence by your side, retailers are more equipped to react quickly to any market shifts and avoid damaging their margins. 

While the industry continues to evolve, it’s important for retailers to utilize retail AI to support their assortment planning and recognize where there is room to capitalize on gaps in the market. 

There are trends retailers cannot plan for 6-12 months ahead of time based on how quickly consumer tastes and preferences change. That’s why retailers leave a portion of their budget ‘open to buy’ to chase and maximize new trends. Think of an Instagram-fueled trend or something that spurred off the back of a Netflix series. Be able to confidently use that budget to back a trend like Regencycore with the right market data findings. 

By ensuring your team has the competitive advantage to look at the market and your business as a whole at this micro level, it can not only save you time, but also drive big profit gains. 

For more tips, tune into our podcast episode called ‘best practices for assortment planning in the new retail landscape’, featuring Carla Tuttlebee, Head of Buying and Merchandising at Vero Moda. 

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