Jun 24, 2020 2:38:49 PM
4 minute read

Reduce Your End-of-Season Deadstock

What's the issue?

Retailers know that it’s usually cheaper to over-stock than to under-stock. Ending the season with excess stock (deadstock) is better than being unable to meet customer demand for your product. Unfortunately, that deadstock comes with its own set of problems, including:

  • Opportunity Cost - all that money spent on extra stock could have been spent better elsewhere
  • Inventory Housing - paying to store all the deadstock
  • Discounts Diluting Brand Value - most fashion brands sell about 60% of their inventory at full price. The rest is put on sale, often at steep discounts

How can retailers reduce the amount of deadstock they end up with, while still ensuring they can meet customer demand?

Industry best practice

Inventory management and inventory forecasting. Most large retailers have robust systems to keep track of their inventory, and are aware of whenever a product is reaching its ideal reorder point, and able to place that order.

Retailers use inventory forecasting to predict demand by analyzing factors like their inventory turnover rate over a defined period. While this can be effective it’s difficult to form accurate predictions, especially without drilling down deep into every individual variant of every product.

What's the Fix?

ROI Hunter clients can use the Product Insights feature to find products most likely to become deadstock. This feature integrates product level insights from Facebook, Google Analytics, and Google Shopping, as well as business data exported from your systems, such as stock volume, estimated margin, return rates etc. This data is all combined within the ROI Hunter platform to create a single source of truth, down to the product level.

With this data, our clients can filter their catalogue by the actual performance of each item. They can find the items likeliest to become deadstock, according to the performance of the individual product, rather than relying on information on the successor the campaign as a whole. With these problem products identified, marketers can make sure these products will receive enough share in marketing promotions (for instance by using Facebook dynamic ads for a broad audience) and category managers can reduce reorders in time to avoid ending the season with a warehouse full of deadstock.

EXAMPLE: Product Insights deadstock filter:

0 transaction over the past 30 days; > 1 lifetime transaction

Adding the one transaction requirement helps us filter out new products that we don’t want to include in our deadstock measure. With this filter, you can quickly find the products that have gotten no recent sales, and are likely to become deadstock

Product Insights gives marketers visibility into the performance of every product, and this knowledge can be used to boost the profitability of their marketing. The data can also be exported and synchronised with category managers, giving them an understanding of the marketing efforts behind each product, as well as an idea of user trends other than sales. With this knowledge, they can determine if a potential deadstock product is actually just under-promoted.

Find out how ROI Hunter can add value to your business.
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