The role of a merchandiser is not an easy one. How does one ensure profitability from a collection whilst remaining true to your brand identity? It requires experience, creativity and the ability to understand the implications of figures. In essence, a merchandiser must be both “left and right” brained.
Not an easy feat, we know.
Before you, the retail merchandiser, hike up your skirt and start running for the hills – do not fret. In this article we explain how you can use data to help your merchandising role and obtain the best results for your brand.
For the purpose of this article, we used fast fashion retailer Asos as a way to illustrate examples.
Navigating the Numerical Data
Amongst the sea of data available to you as a merchandiser, there are three crucial pieces of statistical information that will aid your decision-making process for upcoming collections.
Looking at Trends
For collection planning, a merchandiser must look to the past, present and future in order to gain insight into what pieces to select during buying periods. Through understanding the past, present and future, the merchandiser will be able to gauge and navigate the styles available, thus preventing feeling overwhelmed with all the options presented during buying season.
When evaluating trends, the merchandiser must look at predicted past trends to see how they perform and compare it with its present performance to understand how the trend is performing amongst retailers and consumers.
For example, take kitten heels that were reported as Spring 2018’s next big trend. In 2017, kitten heels made a comeback with reports from editorials suggesting they would be the “it” shoe style.
A look at Omnilytics data for Q4, 2017 showed the results below;
Kitten heels had 72 SKUs on ASOS with a sellout rate of 18.1%, which was quite low as an average sellout rate is at least 25%. In Q4 of 2017, 59.7% of SKUs for kitten heels were discounted again indicating a lack of faith in this trend amongst the retailer.
However, when analysing the data for Q1 of 2018, the data shows as below;
Kitten heel SKUs were at 104 SKUs for Asos in Q1, 2018. This showed an increase by 32 SKUs showing an increased in confidence from the retailer, Asos. Additionally the sellout rate for Kitten heels also reached 42.3% showing a positive response from consumers for this trend.
Thus, the lesson learnt here for merchandisers is that even though certain trends may have performed less than well at the initial mention of the trend – here Q4 of 2017- trends have an ability to become fashion styles over time and increase in popularity over time. The skill needed by the merchandiser is to continuously monitor trends in the past and present to aid in their purchasing.
In order to predict future trends, the merchandiser would need to continuously monitor the literature and retail intelligence available to them to help gauge future trends and remain ahead or on par with competitors.
Once the merchandiser has narrowed down several trends for the brand, the second order of business would be to determine the breadth and depth of their collection.
The breadth refers to the number of styles that will be included in the collection while the depth refers to how many SKUs would be purchased per style.
In order to determine breadth, the merchandiser will need to understand the following key factors regarding their brand;
The brand identity
How much retail space they want to occupy (if you’re an online store, this would be the number of products per page)
Who are their target consumers and what are their profiles like?
Determining the depth of the collection requires collaboration between the merchandiser and the buyer. Both parties must evaluate the previous retail history and current trends in the market to ascertain what SKUs would require deeper counts in comparison to others.
For example, an Asos Merchandiser would perhaps choose to stock more slingback heels than heel sandals as these have a greater sell-through chance once put on the retail floor.
To maintain a healthy sell-through percentage – that is ensuring products are sold at full price – it is advised that both the merchandiser and retailer should rely on data and their gut instincts to ensure a successful collection.
Determining Price Points
Price points are a difficult balance to achieve for the merchandiser. On one hand, the merchandiser needs to set prices that will bring in revenue for their brand, however those price points need to be attractive enough to appeal to their consumer base.
If a retailer, for example, was a brand retailer such as Asos that caters to a variety of consumer demographics, the merchandiser would evaluate their consumer base and competitor’s price points to aid in determining Asos’ own price points.
Below is an illustrated example of the various price points available at Asos for kitten heel SKUs for Q1, 2018.
The smallest price point, here MYR 50-100, is known as the ‘point of entry’. This is the lowest price point that is attractive to value buyers and mass consumers.
The ‘point of exit’, here MYR 950- 1000 is the highest price point, often positioned for luxury products. This would allow a brand to ensure that the needs of consumers for high-end merchandise are also met.
As a merchandiser, the key in determining price points is to evaluate your profit margin and also understanding your consumer profiles; you want to price your products in a way that heightens your retail business success while retaining consumers’ attention for your brand.
The Key Takeaway
Overall, the role of the merchandiser is complex. However, it is an achievable feat with the aid of retail intelligence that acts as a navigation for merchandisers during buying season. Additionally, the merchandiser must remember not to act in isolation but work together with the buyer to achieve business goals.
Want to know how you can use retail intelligence to aid your merchandising skills? 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 27th October, 2017 to 31st March, 2018.