Siew, the merchandising manager of FashionCo, is unhappy. Her holiday line has performed badly and now the holiday is over – but the tail of products nobody wants to buy is still there! She knows the routine that will follow; the shelves need to make space for next season’s stock, so sale signs will be produced and her tail products will be relegated to back walls and basement floors, with markdown after markdown. Soon the sales signs will disappear from the store and the dreaded stock liquidation meeting will be around the corner.

Every merchandiser has been in this situation and it feels inevitable, disempowering and frustrating.

A recent study by McKinsey reveals that the bottom 50 per cent of a brand’s articles typically contributes only 5 per cent of sales. What they call “bad complexity” is being built into the product to respond to shorter cycles and customization. Imagine the impact this bad complexity has on margins. What can be done to avoid this situation? There are two potential solves: at the time the line is built, and once it is launched in the channels.

At the time of line build, Siew does not have the means to manipulate images of the products in her line along with their attributes and other data. Without being able to see the collection displayed together with key data she cannot visually determine whether her line is appropriately balanced to minimize the chances of a “long tail formation”. She has to depend on cut and paste techniques to build, review and freeze her line. Iterations are slow and tiresome, leading to many mistakes of redundancy and gaps.

What if Siew could work with, analyse and manipulate images and data together easily and seamlessly?

Kanvas is a SAAS-based platform that helps combine spread sheets with images and create new distinctions and analysis at the “speed of thought”.

Siew simply drags and drops the product images into Kanvas and can then assign tags and sort the collection along multiple distinctions that she can create on the fly, examining every distinction for overlaps and gaps. Using Kanvas, Siew can eliminate similar-looking products and improve the balance and consistency of her line, reducing the chances of developing a long tail at the line build stage well before the collection even enters the supply chain.

Once the line is launched, Siew’s inability to respond in a timely way to weak market signals slows her key tactical marketing decisions such as early markdowns, in-store promotions or cancelling product reorders. In-season performance reporting provides her only with an Excel sheet containing SKU codes as identifiers and several basic verbal distinctions about size, colour, etc., which is very limiting in an industry that depends on the “look”. Siew has to once again use her imagination and follow an extremely inefficient process using available catalogues, spread sheets and reports to arrive at enough distinctions to take quality decisions.

By using Kanvas she can understand her in-season performance much better. Her analysis and reports are no longer restricted to spread sheet data but also include images of her articles. First Siew uses Kanvas to filter the collection on sell through rates to understand in season performance. She then divides her collection into two categories – the worst performers and the next in line on performance. This whole process only takes a couple of minutes.

Based on this analysis Siew notices that most of the products in the worstselling category have something in common. They are all floral prints; a conclusion that would not have been available to her using spread sheet data only. To salvage some value before the stock piles up, she decides to pursue an aggressive promotion to improve their sell through. Using the Kanvas Layout feature she is able to create a quick in-store promotional poster for the “floral” collection to increase sales and improve sell through.

When she filters the second, better-selling category on inventory, she realises that she has many red products on her hands. Although the reds’ sell through rate is better than the florals this could still lead to problems downstream. She decides to put the reds on an in-store promotion, albeit a less aggressive one.

She selects the reds she wants to promote, opens the Kanvas layout section and prepares a communication flier for the “red” in-store promotion within minutes. She has analysed, hypothesised, made decisions and communicated them appropriately early and quickly, rather than waiting to be overtaken by events.

She has probably saved FashionCo at least a couple of points of margin on these lines, quite apart from any long term brand dilution effects.

Kanvas being on the cloud helps Siew create the underlying repository of all the information required for quality decision making in one place and work collaboratively with her colleagues avoiding a sequential iterative process.

The retail fashion industry continues to move more quickly, with ever-increasing numbers of cycles and drops, to increase scale in terms of the number of SKUs, and to complexify in terms of the different channels and their requirements. Decisions must be made earlier and more quickly: Kanvas can bring together everything you need to provide a better decision basis. Faster, better decisions help you save margins and improve response time.

Next holiday, Siew will not be unhappy.