This article first appeared in the Summer 2016 issue of The Record.
When it comes to assortment planning, retailers have been trying to successfully integrate art and science for years. Traditionally, assortment planning has been an art-based, subjective process where merchants and buyers developed assortments based on their knowledge of the business and customers. Now that retail planning systems are more advanced, science is playing a greater role. However, retailers’ data still has a good deal of untapped insight to offer when making assortment decisions.
Today’s merchant-led organisations advocate providing broader assortments to meet customer affinity, but this can quickly get out of control. On one hand, merchants want to address emerging trends and provide visually compelling assortments to draw consumers in. On the other, these decisions are often made without a sound financial plan, leaving retailers over-bought, over-assorted and at risk of margin erosion. Conversely, retailers can become so fixed on hitting their financial targets that they are unable to seize opportunities created by emerging market trends and become stuck in an eternal cycle of repeating last year’s assortment, losing out to more flexible competitors.
So how can retailers build a visually compelling assortment plan that draws customers in, without jeopardising financial success? Now that ever-improving technology is putting better data at our fingertips, retailers need to identify the key points in the assortment planning process where art or science should take the lead.
Traditionally, retailers have been guided by the structure of their product hierarchies when planning assortments. However, by drilling deeper into the data, they can hone in on areas of opportunity created by nuances in sales by location, time and consumer product affinity that would have otherwise gone unnoticed.
Adding new products to assortments each season is critical to success. Yet, making decisions about how many new products to add can be challenging, particularly when it comes to estimating how consumers will react to each new product to determine inventory levels. Incorrectly planned assortments can lead to stock falling short of consumer demand, or leave retailers with mountains of excess inventory at the end of a season.
Retailers must invest to take guesswork out of the process. By breaking down financial targets that are typically planned at higher levels of the product hierarchy into more attribute-based or trend-specific categories, they can identify focus areas for core business versus complementary offerings. This ensures that precious open-to-buy dollars are allocated to the areas of business where they will have the most impact and provide the greatest return.
For example, a traditional assortment plan might denote men’s short-sleeve t-shirts as a single area of the business, but there could be various subdivisions within that product category – t-shirts with humorous, novelty, destination, or all-over fashion prints – that could each have unique selling trends. Retailers should look at historic data to understand how each product category performed and which subdivisions represented a significant portion of overall sales when ordering inventory for the upcoming season. This minimises both stock-outs and leftover stock.
Selling trends can also vary widely across stores, even within the same geographic area. Recognising these differences and then grouping similar stores together allows retailers to localise their assortment offerings without planning for every individual store.
Tailoring assortments to locations based on factors like climate is not new, but there are a number of other location-related factors that can only be discovered using analytics tools. For example, analytics could pinpoint which destination-specific items are most in demand in major airports. Analysing option to productivity ratios by location cluster and combining that data with more refined product-level information enables retailers to develop customer-centric assortments that please their patrons and enhance their bottom line.
Seasons also play a role. Conventionally, product offerings are chosen for set windows of time, but problems arise when products cross these windows. The ability to evaluate historic product demand according to location in any timeframe allows retailers to adjust their assortment breadth and depth throughout the year to meet fluctuations in consumer demand. The busy Christmas and back-to-school periods are no surprise to most retailers, but there are also other events that happen at a more local level. The intersection of analytics by product, location and time paints the complete picture of ideal assortment size throughout the year.
Plus, every retailer occasionally experiences periods of less-than-optimal stock position, and trying to model future business expectations on these periods can be frustrating and produce highly skewed results. Having the ability to select alternate model timeframes for large areas of business, all the way down to a rate of sale for a single new item, is immensely valuable in fine-tuning assortment plans to maximise profitability.
Using historical data to dig deeper into customer preferences makes it possible for merchants and buyers to build compelling assortments that enable them to achieve financial targets, while addressing emerging trends. Through robust historical data modelling and process flexibility, the JustEnough Assortment Planning solution enables retailers to plan in a manner that feels customised to their specific business needs and move forward with confidence, knowing a sound analytical foundation lies beneath every step.
Keith Whaley is vice president of Retail Strategy at JustEnough
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