Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- As assortments are becoming larger, more diverse, and increasingly include third-party brands, fashion retailers need a different approach to space planning.
- They must determine the minimum credible offer for customers using customer decision hierarchies and the associated need states or the types of a product within a category.
- Retailers can use data and modelling to understand the space the optimal assortment occupies and ensure it is tailored to customers’ needs.
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The challenges of space planning in fashion retail
Increasingly, the assortments of fashion retailers are growing and becoming more diverse.
Retailers are introducing third-party brands, both physically and online. As a result, there can be pressure on physical space, colleague capacity, assortment availability, and full price sell-through. Overcoming these challenges requires a more sophisticated approach to assortment and macro and micro space planning.
Despite the array of sophisticated tools available, few retailers can effectively cope with today’s apparel requirements. In fashion, the products and ranges do not conform to the convenient and consistent sizes and shapes seen in easier-to-manage grocery environments. Additionally, buying cycles and merchandizing processes differ, with a stronger focus on visual merchandizing in fashion.
It is a misconception that complex sourcing paths in fashion make it more difficult to plan and manage assortment, macro, and micro space. Therefore, a shift in approach is required. The real challenge lies in understanding data related to physical retail space, including equipment used, and the product attributes.
When the product lifecycle is plotted out end-to-end, it makes perfect sense to embrace this challenge. For example, there is little value in curating an exciting new range of summer fashion with a new brand, only to hamper retail execution due to a lack of physical display space. It would result in sub-optimal exposure for this range, or another range, at its expense. This can impact sales, increase markdowns, and disenfranchise retail teams.
Overcoming the challenge is simple in theory, but the approach is key to unlocking the benefits.
Space as a strategic lever
The growth of the Metaverse means retail ‘space’ is taking on a broader meaning – it could be online, physical, or virtual space.
We focus on physical stores, where fashion retailers can leverage space to execute their central strategy. This could be creating space for a third-party brand to drive footfall or increasing space to a category, aligning to market trends.
Leveraging space effectively is key to executing the right strategy:
- Planning: Once the strategy has been finalized, it is key to develop the supporting plans. For example, if the strategy is to grow womenswear sales by 5% over two years, increasing space can help, but it may be dependent on the buyer sourcing more products for this. Equally, retailers may identify physical space from underperforming areas, which can be utilized. The clothing collection may be spread across several floors and careful planning is required to effectively release space.
- Measuring: Understanding space performance is critical. However, performance data is more than just sales and margin. It is important to know what space each store has for a section, what equipment is used to display the product, and departmental adjacencies or which floor a product is on when reviewing sales. It is equally crucial to consider volume, markdown, or customer end-use. One area may have low sales, but if a retailer’s most loyal customers buy into it once a year, it may be worth retaining for this customer base. Today, the ability to leverage artificial intelligence (AI) and machine learning (ML) makes this analysis simpler, and quicker, offering retailers greater flexibility to interrogate results and model options.
- Simplifying: Retail changes can be complex and costly. Therefore, harmonizing space changes, minimizing disruption for customers, and reducing costs can be valuable for retailers. Aligning projects, such as space rebalancing and store refurbishment, can be helpful. Understanding space is the key to unlocking strategic goals.
- Partnering: Executing a successful space strategy requires cross-functional partnerships; decisions cannot be made in silos. A decision to increase physical space for one category requires the donating category to plan their space reduction, such as reducing stock keeping unit (SKU) count or buy quantity. Building collaborative plans to execute the strategy is key.
The range-to-space equation
Once the available retail space is analyzed, retailers can build the equation determining what range can be placed in the available space.
There are several layers to this process. It is crucial to understand the minimum credible offer for customers and then, within that offering, the range of sizes and variants required.
The answer lies in creating customer decision hierarchies and associated needs within these. Algorithmic tools can assist in this process, using the retailer’s data and insights into the target market and the competitive landscape, in conjunction with their experience and knowledge. We are familiar with the headline departments of womenswear, menswear, childrenswear, footwear, accessories, outerwear, gifting, beauty and cosmetics, and possibly home where applicable. However, it is crucial for retailers to determine the minimum credible offering within these areas before further optimization or offering of additional services.
Using product attributes to inform space
Once the minimum credible range is determined, retailers must understand how much space this is likely to occupy.
This is a specific challenge in fashion due to the rotation of product, seasonality, and size variability. However, it is possible to categorize by groups of products and display methods. For example, we know the average size of a necklace in the accessories range and a winter jacket in the outerwear range, and the equipment used to display them respectively. Equally, we know we may wish to display garments folded or hanging, so it is possible to understand the differences in space occupied as a result.
It is important not to overcomplicate this but achieve an acceptable level of accuracy and breadth of product types. These attributes can be maintained over time, without the need for the physical dimensions of each product, in every rotation. This is a process of equivalence, easily taken from season to season with automated attribution by product type.
With this attribution and dimension data at hand, it is possible to automatically calculate the space the minimum credible range will occupy. This can be calculated in both a linear manner and square ft/m as required.
Now retailers can expand upon the minimum credible range through the customer decision hierarchy, adding further breadth and choice as space and trading intensity allows. There are tools and processes to assist retailers in this layered optimization.
Tailored offers through clustering and localization
The assortment can be further optimized through clustering and localization.
There may be some adjustment to the minimum credible range here, however, it can be expected to remain largely identical across a given country. This layer of optimization shapes how assortment grows over and above the minimum credible range by location within it at a regional level. There can be differences driven by factors such as location, demographics, competition, and environment. For example, proximity to the coast, ethnicity, affluence, and climate.
Where there is an existing estate of stores not clustered in this way, algorithmic tools can be used to analyze sales relationships and help define clusters for stores and departments within them. The assortment can then be built out over and above the minimum credible range based on these factors.
Assortment, macro, and micro space optimization
The next layer is the final optimization of the assortment, macro, and micro space.
After retailers have determined the minimum credible range, necessary clusters, and the space these require, they need to make optimal use of the remaining space. There are several outputs at this stage, and using algorithmic tools, the ideal assortment can be modelled within the given cluster. However, retailers also need to consider constraints in the macro and micro space, with two extremes to be understood here.
First, is the optimal assortment too big for the available macro space? This is not uncommon, and retailers should use already created hierarchies to trim the assortment, in order of priority, to fit the available space. They may choose to use this output to help drive future changes in store size or layout to achieve greater macro space in the required areas.
Another common scenario is when macro space is too great. This is often where retailers fill this space, which can lead to issues with full price sell-through and rotation of ranges. Using the right data, retailers can decide on how best to fill this space, at the lowest risk, or make informed decisions on downsizing or remodeling.
Overall, retailers can use data and modelling to make the best use of the assortment in a space-aware way. They can understand the space the optimal assortment occupies and ensure it is tailored to customers’ needs in a given location, while informing future strategic space decisions.
Display guidance and planograms for in-store execution
In fashion, stores are given varying levels of display guidance, ranging from detailed brochures to general implementation and sales floor flow guides.
It can be quite difficult to execute the centrally prescribed plan when volumes and assortments are graded by store. Therefore, a high level of visual merchandizing and interpretation is required.
There are a number of pitfalls in this traditional approach. First, it is labor-intensive for central teams to produce and distribute. Additionally, implementation is varied due to the nuances of space, volume, and range by store. This leads to inconsistent execution of the central strategy.
In an increasingly competitive and dynamic fashion environment, a data-led algorithmic approach to space planning is not just a strategic advantage – it is a necessity. By leveraging the power of data and analytics, retailers can optimize space for maximum profitability, enhance the shopping experience for customers, reduce operational costs, and stay ahead of market trends.
