THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #4: Consider product life when creating allocations

In prior posts we’ve discussed a few things to consider when determining the base of data to use when allocating, including: locations and clusters, products and product groups. Even the best decisions in these two dimensions can be nullified if the wrong time period is selected. This is particularly important when allocating product with short lifecycles like most fashion items.

You already know most allocated products have a distinct life, be it 6 months for a fashion basic or 3 to 4 weeks for a high trend fashion item. Lifecycle exists, but how can we understand and leverage it in the context of allocation?

There are two points when lifecycle can have a significant influence on allocations. In initial allocations, understanding the anticipated life of a product can help you make a better choice of what products to use as a base when allocating. More significantly, however, when there is an opportunity to re-allocate held back inventory or secondary receipts, understanding how a product is actually behaving relative to it’s life can have a huge impact on results.

Product Life cycle at Three Different Stores

Take a look at the chart above. It represents a product and it’s behavior in three different stores throughout it’s full price life (each line is a store representing indexed sales or demand across time). The yellow store took off with this item at introduction but has been falling off ever since (a very “fresh fashion” conscious location perhaps). The blue store built to a peak and has begun to taper off (a typical or core store). But the red store has had a slow build to it’s peak (possibly a “fashion follower” location). If we can understand this lifecycle variation it becomes very apparent that we can make better decisions at different points in time.

If we’re halfway through the life of this product how can we make a better re-allocation decision? At the midpoint all three stores may have sold the same number of units. If we only use ‘sales to date’ as our base, we’ve lost the opportunity to leverage understanding of lifecycle. Both the yellow and blue stores have reached their peak. The red store is still building and has a lot of potential. If we’re re-allocating this product at that point, more of our available inventory should be going to the red store, perhaps some to the blue, but ships to the yellow store will likely result in markdowns, probably deep markdowns before it’s through.

So how do we get to this understanding so we can use it in our allocation?

What you can do now

When constrained by older allocation technologies, your main weapon to use in the fight against lifecycle is your time selection. First and foremost, validate that the time window you are selecting does not include periods of high stock-outs or high markdowns. If it does, it’s not representing the lifecycle potential.   Select product(s) with a similar lifecycle to what you expect from the allocated product for a forward period representative of the period you’re allocating into. In doing this you begin to capture the lifecycle characteristics that will influence product behavior. If re-allocating, try to include the allocated product’s recent performance together with a product of similar volume that lived for the remainder of the life cycle expected from the allocated product if you can find one.

What you should consider when looking for new capabilities

Modern technology allows more advanced allocation systems to constantly monitor product lifecycle patterns within and across products and their lives. This learning about the reality of historical lifecycles can be used as a knowledgebase to apply to new and young items. Understanding of how items behave and how they are trending enables these systems to react to the unique lifecycle characteristics of products within each store so action can be taken on allocation recommendations. This maximizes full priced selling potential, reducing markdowns significantly.

This knowledge can also trigger alerts that notify merchants when products aren’t behaving within anticipated lifecycles. Awareness can open opportunities to either acquire more product (if available) when a product is going to live longer than anticipated resulting in missed opportunity – or to accelerate markdown plans when a product is going to reach end of life sooner than anticipated leaving too much excess inventory.

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Follow this series to learn all 10 strategies for improving allocation. We will be deconstructing the allocation process and exploring opportunities to improve within your current allocation processes and technology limitations. We will also review key areas to think about if you are considering investing in improved allocation capabilities.

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