Love it or hate it, statistical data plays a crucial role in the survival of any business.
Even more so in industries such as fashion where consumers have a billion alternatives at a click of a button. The reality is a harsh one; fail to meet consumer needs and wants and your chances of survival are slim.
Within the fashion industry, data is perhaps the least used resource available to designers. In an environment where artistry is highly regarded, designers often use personal preferences, artistic visions and inspirations to determine their next collection. After all, fashion is a form of art, so why not rely on your intuition?
However, relying on your intuition comes with it its own set of problems; what if you’re not a seasoned veteran of the industry? What if your visions don’t meet consumer demands? Whilst your artistry may greatly contribute to your brand image, a unique brand image does not equivocate a successful business.
As industry expert Kim Wisner says in her article “…image isn’t business. Business is about creating healthy cash flow by selling a product or service that people want, delivering growth and profitability”
So how does data help you? Consider these two dilemmas fashion retailers often face:
- Does my product assortment meet consumer demand?
- At what price do I set my products?
For ease, we’re using a fictional retailer, Retailer X to illustrate how data can help you decipher these problems:
Retailer X is an already established fashion retailer, currently located in Singapore. They want to expand into the S.E.A market and are beginning with Thailand.
Using Data to Decipher Product Assortment – What Do The Consumers Want?
During the planning stage of their collection, Retailer X would be faced with the question “what kind of items should I produce to meet consumer demand in Thailand?”
For Retailer X, understanding what established retailers like fast-fashion magnates Pomelo and Asos’ best-sellers are could indicate what the best selection of items to produce for the Thai market as best-selling items often reflect consumer’s preferences.
As fast fashion turnover cycles only last between two to five weeks, any period longer would not be relevant to Retailer X’s decision making. Thus Retailer X has decided to analyse Pomelo and Asos Thailand’s Dress product assortment over the month of March 2018. Using the Omnilytics Dashboard their analysis of the Thai apparel market shows as below;
The above graph illustrates the sell out rate of Dresses for Pomelo Thailand. Total SKUs is shown by the left y-axis and the right y-axis indicates the sellout percentage. The x-axis is made up of the top 18 sub-categories of dresses available at Pomelo.
Based on the information, Midi Dresses with 70 SKUs was the most stocked item. This was followed by Mini Dresses with 54 SKUs. For both categories, the sellout rate was above 60% indicating it was a well-performing item. SKUs with poor sellout rates were Off Shoulder Dresses with an 18% sellout rate and Long Sleeve Dresses with a 22% sellout rate. SKUs with low stock counts but high sellout rates were Sleeveless Dresses with 15 SKUs and a sellout rate of 73%, Asymmetrical Dresses with 7 SKUs and a sellout rate of 71% and lastly Cold Shoulder Dresses with 5 SKUs and sellout rate of 100%.
Thus from this information, Retailer X should consider stocking timeless categories such as Midi Dresses, Mini Dresses and Maxi Dresses as they have a good sellout rate. When considering SKUs with “high” sellout rates such as Sleeveless, Asymmetrical and Cold Shoulder Dresses, Retailer X would need to delve further into the data and analyse the weekly movements of these individual SKUs. This is because all these SKUs were stocked at a low rate, thus less the less SKUs, the higher the sellout chances which results in skewed data.
The same subcategory was analysed for ASOS Thailand.
At Asos Thailand, the highest stocked SKUs were Midi Dresses with 3091 SKUs, Mini Dresses with 2492 SKUs, Maxi Dresses with 2374 SKUs and Lace Dresses with 2050 SKUs. All these dresses had a sellout rate of 83% – 85% again indicating a high performance amongst consumers. The only poor performing SKUs were Wrap Dresses with a 77% sellout rate that was below the average 80% sellout rate. Low stocked SKUs with high sellout rates include Bodycon Dresses with 711 SKUs and a 88% sellout rate – the highest sellout rate for dresses in Asos Thailand. Others were Cami Dresses with 576 SKUs and a sellout rate of 84%, Plunge Dresses with 486 SKUs and a sellout rate of 86% and lastly Shift Dresses with 462 SKUs and a sellout rate of again, 86%.
Through analysis of the data, Retailer X should again consider including average performing dresses such as Midi, Mini, Maxi and here Lace dresses to it’s collection. Reiterating the point made with Pomelo Thailand, if Retailer X wanted to include SKUs popular with consumers such as Bodycon, Cami, Plunge and Shift Dresses, further analysis would be required to ensure that the data has not been exaggerated by low SKU stocks that skew the sellout rate.
Hence, Retailer X has multiple options in terms of deciding what to include in their collection for Thailand. Whilst the decision remains theirs, the data has pointed to Mini, Midi and Maxi dresses being a reliable choice that performs well amongst consumers.
Using Data to Understand Pricing – How Do I Price My Products?
Whilst most retailers may have a perception on the value of their products, consumers may not share the same sentiment. Especially for fledgling fashion retailers, it is always better to analyse the market and determine your price range by referring to your peers and competitors.
Again, Retailer X will be analysing Pomelo and Asos to understand their pricing strategies.
The above graph shows the performance of price band for Pomelo Thailand. The left y-axis shows the total SKUs for each price band. The right y-axis shows the percentage of sellout corresponding to a certain price band. The bottom x-axis shows the price bands in Malaysian Ringgit (MYR).
The price band with highest stocked SKU and average performing sellout rates, that around 25% sellout, were MYR 50-100 with 600 SKUs and MYR 100-150 with 702 SKUs. Price bands unpopular amongst consumers were MYR 250 and above. In fact, after MYR 350 there were no sellout despite Pomelo having some SKUs priced at that point and above. Price band with low SKU counts but above average sell out rates were MYR 0-50, MYR 150-200 and MYR 200-250 indicating a consumer preference for SKUs priced at that point.
Thus Pomelo SKUs performed well around the prices of MYR 250 and below.
ASOS Thailand was analysed similarly;
At Asos Thailand, there seemed to be an even performance amongst all price bands with most price bands averaging at 40% sellout rate. The price bands with the highest sellout rate amongst consumers were MYR 0-50, MYR 50-100 and MYR 100-150. The one with the highest SKU count was MYR 0-50 with 16,586 SKUs and a 65% sellout rate. Price bands that had low sellout rates, thus unappealing to mass consumers were MYR 300-350, MYR 350-400 and MYR 550-600.
Based on the data above from Asos Thailand, Retailer X should concentrate most of its assortment around the MYR0-250 price range, as that yielded the highest sellout rate for Asos. For any price higher than MYR250, Retailer X should consider allocating less than 10% of its total assortment there. This is because unlike Asos, he is not yet an established label which gives consumers the confidence to buy higher priced items.
The result of these findings indicates to Retailer X that it should be pricing its products around the MYR 0-200 range to meet mass consumer expectations.
Through these examples of using statistical data, Retailer X is now a more informed retailer as it ventures into new markets.
The key with statistical data is to ensure that the information is accurate and its implications made clear to the user. The use of data is imperative to the success of a business. From a fashion point of view, data is that diamond ring that ties off your outfit – a discrete item with great value and an altering impact.
Learn more about how you can use data to amplify your business decision making. Drop us an email at email@example.com and we’ll be in touch!
The data above was obtained from Omnilytics, real-time retail data platform. The numbers and statistics may vary, as the platform is updated every day. The time period of the information taken was between August 01, 2018 to March 31st, 2018.