RML Data Insights: Velocity Trade Offs

For retailers, suppliers and manufacturers evaluating a product’s sales velocity is an important factor for guiding product assortment and pricings decisions.  At its core, velocity shows you the rate at which a product sells in a given channel or store. At Revenue Management Labs (RML) it is a KPI we use to evaluate pricing effectiveness and validate implementation of price adjustments.

We recently worked with one of the largest convenience store chains in the world to help them understand the effectiveness of their private label strategy for the frozen section of their store.  At a high level, the strategy sought to target the top two national branded products and offer a comparable, lower priced alternative. The strategy was simple to define, however evaluating whether it was driving incremental profits or purchases posed a challenge.

Unlike most of our clients, the retailer had almost perfect data visibility with access to daily, store level sales. Unfortunately, they were drowning in millions of rows of data trying to assess true return. 

At the core velocity is defined as: 

But, understanding the impact across a portfolio of products and stores can get tricky. There are three basic approached to calculate velocity described below using a simple example below. These methods are useful when data is available at the granular product level and there is limited information on store size. At the the most basic level:

Method 1: Straight Line Average

Average Individual store’s velocity across the chain. Applicable when stores are similar in size and weeks sold.

Where N represents the total number of store.

Method 2: Weighted Average by Weeks

Store velocities weighted by number of weeks sold. Applicable when selling days need to be normalized due to new SKUs, store openings or closings.


Where w(A) represent the number of weeks product was sold for. 

Method 3: Weighted Average by Volume

Store velocities weighted by number of units sold. Applicable when there is a large variation in number of units sold by store.

Recall the above methods are a useful when data is available on a granular product or item level. In cases where data is aggregated the methods outlined below are useful to consider. The two methods use All-Commodity Volume (ACV) weighted distribution to determine product velocity. ACV is a weighted measure based on total store sales which accounts for store size. 

Method 4: Sales Per Point of Distribution (SPPD)

SPPD measures how quickly a product is selling in stores that it is in distribution. Applicable when comparing a single retailer or market as it accounts for the number of stores selling (weighted product velocity).

Method 5: Sales Per Million (SPM)

This method mirrors SPPD but normalizes the unit velocity to dollars per Million ACV. Applicable when comparing shelf velocity across markets, especially when the market sizes vary greatly.


Final Thoughts:

As you can see there are many different approaches to handling velocity and deciding which method to use is dependent on data available, the structure of the data and the outcome or result you are trying to achieve. It is essential to have a well-thought-out plan prior to evaluating velocity as the results can vary and impact the direction of key business decisions.

Revenue Management Labs was able to identify the relative success of the private label products, accounting for market level differences. During the next quarterly review, RML worked with the client to delist three ineffective products and introduce two based on insights generated from the velocity analysis. Today, the client has the necessary tools and methods in place to consistently track new product’s introduced into the market and gauge their success.

ABOUT THE AUTHOR Michael Stanisz is a Partner at Revenue Management Labs. Revenue Management Labs help companies develop and execute practical solutions to maximize long-term revenue and profitability. Connect with Michael at mstanisz@revenueml.com

Michael Stanisz


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