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Definition

The average value per transaction, or AVT, refers to a typical transaction value.  The AVT is a primary Key Performance Indicator (KPI) and measures the demand elasticity customers have to changes in season, pricing strategies, and merchandising strategies. The AVT is typically combined with foot traffic counts and/or conversion to determine the leveraging caused by a change in strategy.  For example, if a price reduction resulted in 5% increased foot traffic, and the AVT and conversion remains stable, then the overall margin and sales should increase proportionally.

AVT in the United States, Australia, and Canada usually refer to this metric as ADT, or average dollars per transaction.  The global variation, AVT, reflects the various currencies that can be used.

Calculation

AVT is calculated the following way:
AVT = Retail Sales / Number of Transactions

Dimensions

The AVT is generally calculated on the time and location hierarchy.  While it is possible to calculate AVT on the product hierarchy, it is used for very specific types of analyses.

Time Hierarchy:  Can be used on any time dimension such as intra-day, daily, weekly, etc…
Location Hierarchy:  Can be used for one location or multiple

Example

AVT is a straight-forward calculation.  But with any metric you should always cleanse and understand your data to ensure you’re obtaining the actionable values.  More about the nuances are discussed in the analysis section below.

AVT = Retail Sales / Number of Transactions

Let’s say we have three stores and we are interested in obtaining the AVT for last month and we also want to compare it to last year’s (LY) AVT.  Below is the data we’ll need.

For example, the AVT for Store 1 for last month is $80.00

AVT = Retail Sales / Transactions = $1,000,000 / 12,500 = $80.00

Analysis

The example above, as simple as it is, demonstrates the numerous ways AVT can be interpreted.  AVT belongs with other KPI metrics so that any changes from period to period can be evaluated in context.  Typically, AVT is combined with the following comp metrics:

• Transaction Count
• Traffic Count
• Discount or Markdown Rate
• Average Units Per Transaction (AUT)
• Conversion

In our example above there are three stores each with different store opening dates, different product assortments, and different sales volumes, each highlighting the need to evaluate AVT more carefully.

• Store Openings:  Store 2 opened this year so we don’t have a last year value for it.  This also means that when we add up last year data in aggregate we need to exclude this year’s data for Store 2 so we can obtain a comparative value.
• Product Assortments:  All three stores have different product assortments which makes an aggregation hard to interpret.  Ideally, the AVT should be broken down by the product assortment sub groups, like Men’s and compared only to other stores that sell Men’s.
• Sales Volumes:  Not every store sells the assortments in the same proportions.  If a store sells Women’s better than another and it sells twice the volume, it will disproportionally skew the AVT.  This analysis, above all others opens up another level of optimization because it means some stores may be more sensitive to promotions and affinities.

Other nuances to consider:

• AVT Stability: For some retail formats, like discount or outlet, the AVT tends to be very stable.  The stability can be used to determine the likelihood of substitution sales or the impact a stockout has on missed sales.  For luxury, or slow selling retail formats, AVT can vary widely.
• Promotions: Any reduction in price will have a direct impact on AVT.
• Discount or Markdown Rate: When comparing AVT from one time period to another, it’s worth taking a look at the overall discount or markdown rate to gauge whether the comparison is in context.
• Percentage of Assortment Tagged as Markdown:  When there is a relative abundance of markdown product in the store, customers tend to reach for the lower priced items.  For stable AVT retail formats, customers may spend the same but the AUT will increase.  The markdown product may provide an explanation.