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Fashion Risk Or Return: What To Manage For?

| 3 min read

image courtesy: steemit

In this edit, we will introduce a construct to enable fashion business managers and their teams to effectively manage and differentiate fashion risk and return.

The essence of portfolio management is the management of risks, not the management of returns. Well managed portfolios start with this precept 

Benjamin Graham

This is a great perspective for any portfolio including that of fashion. Benjamin Graham considered the “father of value investing” is also an inspiration for Warren Buffet.

Managing a fashion business is much more complex than managing an FMCG brand (for example an FMCG brand would have 6 pack sizes of say 12 flavours = 72 SKUs vs a fashion brand that would have to say 500-800 options with 5 sizes at a minimum = 2500-4000 SKUs. In addition, there is distribution complexity and time sensitivity).

Prudent management of a diverse set of products and making the best return out of them on an ongoing basis is non-trivial. Those who have a robust Portfolio approach will win over the others. 

While there is always a saying that in fashion what worked in the past is not a reflection of the future, it is untrue to a large extent. At an abstract level, consumer taste does not change significantly over time neither does their relationship with the brand. 

The framework we recommend is grounded on two dimensions (modelled around Prof Robert Blattberg’s recommendation)

What works for consumer (what is good for consumers) —> Revenue Potential
and
What works for the brand/retailer (what is good for brand/retailer) —> Margin

Introducing The Fashion Role Matrix (TFRM) :

This construct slots all categories/products and assigns them a role based on what is good for customers and what is good for a brand.

The X-axis is revenue velocity, Rate of Sale (ROS against absolute dollars) – you can always use absolutes, we recommend ROS. In our view revenue velocity is real consumer response and absolute dollars is a function of supply and response. ROS is a metric that is calculated incorrectly not because of effort but the way many ERP systems do not preserve the stock data on a daily basis. Real ROS is calculated only considering sales for the period when the stock was available. Getting this information at every POS level is challenging. Stylumia FIT enables the calculation of the Real ROS.

Y-axis is margin, we recommend gross margin % (after discounts) to be used.

We recommend that you populate this framework with all categories first to see category level roles. For example in your overall portfolio Jeans may occupy a flagship role while a t-shirt may play a traffic driver. This would help you to structure the category mix augmenting what works and eliminating what does not.

The next step is to do this across categories first to get a holistic view of the overall portfolio. Then you can populate the matrix with individual options at each category level. For example, you could have TFRM for Men’s Jeans or Women’s dresses with the list of products and their images occupying each box.

Demand is not generated at the corporate level. It is at the level of POS i.e Channel and store. You can consider making this for each channel/store/state/region. This would give a very clear picture of the strategy of the brand vs response by consumers. Localisation has its own benefits.

This TFRM framework transparently provides what works and what does not. This is a reflection of what the brand did. There is a huge amount of learning from what others did too (competition and inspiration). Stylumia CIT (Consumer Intelligence Tool) fills this gap in intelligence.

With the market dynamics constantly changing, and product cycles getting shorter, managing TFRM at all the levels of distribution and product hierarchy is challenging. Yet, it is important for the sales, planning and product teams to see the information seamlessly.

We understand the importance of the framework and how it can help any brand or retailer to be on top of consumer needs. In order to facilitate this, we are introducing soon TFRM as a part of the Stylumia FIT (Fashion Intelligence Tool) in the coming weeks for your own data. You can use the results of the TFRM for structuring your portfolio. One important question remains. How do you know where new product launches (which are untested) fall in the matrix?

This needs prediction capabilities. In order to solve this problem, we built a one of its kind Fashion prediction tool Stylumia APOLLO which is now delivering prediction accuracies of 30-50%* better than current practices.

Hope this edit gave you a new tool to your arsenal in managing your product portfolio.

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