Average Order Value

What is Average Order Value (AOV)?

Average Order Value (AOV) is the typical total amount a customer spends each time they place an order. You can break this down further to look at how your AOV differs by device (desktop, tablet and mobile), by platform (website versus app) and channel (web, phone, app, in-store, social media) etc. 

The AOV is a key performance indicator (KPI) that businesses (especially eCommerce businesses) measure to understand buyer behaviours and it is typically measured as a moving monthly average. During sale periods, such as Good Friday or Boxing Day sales, the AOV typically decreases but the quantity of sales increases.

How is AOV calculated?

AOV is calculated by dividing total revenue by the total number of orders.

Average Order Value  (AOV) =  Total Revenue / Total Number of Orders

For example, if your eCommerce  sales for a given month totalled £50,000 and you had 1,500 orders that month, your AOV would be £33.33.

Why is Average Order Value (AOV) important?

Understanding the AOV and how this varies over time helps businesses evaluate the impact of marketing and other influencing factors (such as price, product, promotions etc). 

AOV can be used to provide a business with a benchmark for their shopper behaviours at a macro (cross-company) or micro (departmental, category or even sub-category) level. 

When used correctly, and in the context of wider market and internal influences, the AOV helps businesses set actionable goals, evaluate their performance and develop new strategies to grow the value of each order (and customer). 

Taking steps to increase the AOV can often be one of the most cost effective routes for a business. Relying on increasing traffic and order volumes alone can be costly whilst strategies to increase the AOV can be both quicker and deliver higher ROI.

How can you improve your Average Order Value?

Throughout the buying journey, there are opportunities to encourage each customer to either add more products to their basket (cross-selling) or by presenting them with more expensive products (up-selling).

These tactics can be employed throughout the shopper journey. Here are just some examples of tactics to employ on your own online platform(s): 

  • Cross-selling: 
    • Product Details Page: 
      • Suggest add-ons to the item (e.g. personalisation, extra features)
      • Fashion retailers often use this as an opportunity to ‘shop the look’ which includes all items in the outfit the model is wearing
      • ‘You may also like’ encourages shoppers to see recommendations based on previous shopper purchasing behaviours
  • Shopping Cart : 
    • Promote products ‘Often bought with’ those in the customer’s basket (e.g. beach kaftan alongside a swimsuit) 
  • Up-selling: 
    • Product Listings Pages: Bestseller badges can be a great tool to nudge shoppers towards higher-value top-selling items within a category
    • Shopping Basket: ‘Upgrade’ options can be particularly powerful for travel sectors 
  • Both: 
    • Free shipping: This can work for both cross-selling and up-selling by encouraging shoppers to round up their order either by adding more items or up-grading those in their cart
    • Volume discounts: Volume discount messaging can be shown up front to customers or triggered as they get close to a certain quantity (e.g. “3 for 2 offer on all dresses”)
    • Loss aversion: Limited time offers or scarcity messaging can help encourage shoppers to add items to their basket and is often combined with social proof messaging to weave this in across PLPs, PDPs and checkout stages of the buyer journey
    • Coupons: Offering coupons to different audience segments helps you tailor messaging whilst also encouraging customers to return. Using exit overlays when a customer looks like they’re just about to leave the site with a customised offer can be a particularly powerful tactic to keep customers engaged 
    • Loyalty programmes: Signing up for a newsletter or a loyalty programme frequently comes with early bird discounts, personalised recommendations and exclusive deals

Including social proof  at each stage of the buying journey has been shown to statistically increase the Average Order Value by helping users during the product discovery, consideration and purchase stages. 

For statistical significance, A/B testing should be used to measure relative uplifts in performance.

Measuring AOV alongside other eCommerce metrics

AOV should not be viewed in isolation. You may have a very high AOV but only a handful of orders. Holistic reporting should include: 

  • Total orders : All orders for the reporting period
  • Items per order: The average number of items per order
  • Revenue Per Visitor (RPV): The value of each visit to the site – this is arguably a more important measure as it is a strong indicator of non-engaged visits
  • Conversion Rate (CR%): Orders per visit as a percentage
  • Return on Investment (ROI): Return on investment divides the net profit (or loss) from an investment by its cost

Depending on your business model, there may be additional metrics to monitor alongside the KPIs mentioned above.

Using A|B testing and machine learning to improve AOV

A|B testing can be used to understand how changes to titles, descriptions, imagery and layouts can affect performance. Social Proof Messaging A/B testing with Taggstar is no different. 

Our results are scientifically tested and proven. We support all industry A/B testing software to independently validate results, or use our in-built capability with an experiment dashboard to track results in real-time.

As leaders in social proof machine learning, our technology delivers the right message at the right time in the customer buying journey to continually optimise conversion.

Using real-time information ensures a true picture of product popularity that customers can trust and avoids message fatigue of repeatedly sharing the same message.

Taggstar’s Social Proof Messaging solutions presents real-time shopper behaviour that statistically and scientifically increase online conversion rates and AOVs whilst simultaneously reducing cart abandonment rates.

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