More than ever, businesses need to rely on data to make informed choices and create strategies grounded in factual information. As a product manager, you can evaluate performance, identify gaps and needs, and quantify the aspirations of the sales and marketing teams by setting key performance indicators (KPIs) and related metrics.
One example of an influential KPI is average order value (AOV). AOV provides you with insights into customer behavior, allowing you to understand their spending habits.
In this article, you will learn what AOV is, how to calculate it, and best practices for implementing it to optimize revenue.
Average order value (AOV) is the average amount your customers spend per purchase within a certain period.
Most ecommerce businesses consider AOV one of the most influential metrics because it can significantly impact your bottom line. When you understand your AOV, you can immediately calculate if your marketing spending was in the proper channels or if your product pricing needs reconsideration.
AOV also directly impacts the revenue of the company. As you increase AOV, profitability increases, adding to the company’s revenue.
Imagine you’re a product manager for an online fashion retailer. Your company sells clothing, accessories, and footwear. You want to understand the AOV.
To calculate AOV, gather data on the total revenue generated and the number of orders placed within a specific period, say the previous month.
Suppose the online store generated total revenue of $50,000 in the previous month and received 1,000 orders.
To calculate AOV, divide the total revenue by the number of orders:
AOV = total revenue / number of orders
i.e., $50,000 / 1,000 = $50
In this case, the AOV would be $50.
AOV forms the basis of analyses that directly impact spending and strategies. Let’s continue with the example of the online fashion retailer to understand the importance of AOV for the following:
Regularly monitoring AOV over time can help to assess the effectiveness of your strategies and initiatives in driving higher sales and revenue growth. For example, evaluating the new design strategy is easy if you introduce a new collection and see a spike in order value.
Another example is implementing a plan to promote certain products in a particular niche. If the sales are still the same or have reduced, then you can assume that focusing on a specific niche was counterproductive.
Monitoring AOV will help determine if these efforts positively impact the revenue generated per order or if they need to be reevaluated.
The right pricing strategy plays a crucial psychological role in the customer’s purchase behavior. Your customers should feel like buying more benefits them. When purchasing three of the same products feels like a bargain instead of paying the higher value for one, most customers elect for bulk.
Let’s simplify this by continuing the fashion retailer example. Say the retailer introduces an offer where you can buy one t-shirt for $19.00 or buy three for $25.00.
By analyzing AOV, you can assess whether this pricing strategy leads to larger orders and higher revenue. If the AOV increases compared to the period before the new pricing, it indicates that customers are taking advantage of the offer and purchasing more t-shirts per order.
Companies pay a lot of money toward marketing campaigns. One of the quickest ways to assess the impact of a campaign is by analyzing the AOV before and after.
Let’s say the company launched a marketing campaign last month that offered a 20 percent discount on all dresses. You can evaluate the impact by comparing the AOV before and after the campaign.
If the average order value increased significantly after the campaign, it suggests that customers were more likely to purchase additional or higher-priced dresses to get more discounts.
Forecasting and planning helps businesses set financial targets and allocate resources effectively. By estimating future AOV trends, companies can project their revenue and plan their budgets accordingly. This enables them to make informed decisions about investments, pricing strategies, marketing campaigns, and inventory management.
To evaluate the performance of different product categories or individual products, you can use the data from AOV.
Suppose you find that high-priced designer shoes contribute significantly to AOV while inexpensive socks have a lower impact. This insight allows you to optimize your product mix, invest in popular high-value products, and introduce new products that align with customer preferences.
By observing AOV over time, companies can develop ideas and strategies to influence AOV. Digital retailers have mastered a few techniques that have changed the entire industry’s approach to average order value.
Shein is a real-world example of a digital retailer successfully implementing the best AOV influencing strategies. Shein is a Chinese online fast fashion retailer founded in October 2008. In 2022, Shein became the world’s largest fashion retailer.
Now let’s look at the three strategies Shein applied to impact AOV:
Cross-selling is when a retailer identifies product bundles that are most suited together. On the other hand, upselling is when a retailer offers upgraded products at a relatively lower price. By analyzing AOV, you can discover potential cross-selling and upselling opportunities.
For instance, if you notice that customers who buy dresses also purchase handbags and jewelry, you can utilize this information to create product bundles that promote these accessories together with dresses.
Shein always shows a window with product recommendations based on the customer’s cart during checkout to encourage additional purchases. They take this ahead one step by displaying a message to buy for an extra five dollars to receive a free gift, encouraging the customer to look at the recommendations and add more products to their cart.
Free shipping is one of the easiest ways to get customers to fill their carts. No one likes to pay five dollar shipping on a $20.00 purchase. Setting a minimum order value for free shipping gives customers an idea of how much they need to shop. Customers are more likely to add extra items to their cart to reach the free shipping threshold.
You could also offer tiered discounts based on the quantity or value of items in a customer’s cart. For example, “Buy two, get 10 percent off,” or “Spend $100, get 15 percent off.” This incentivizes customers to purchase more items or spend more to avail of the discount.
Continuing the example of Shein, they offer both a free shipping threshold, as well as multiple layers of volume discounts (e.g., purchase for $20.00, get 10 percent off, buy for $50.00, get 25 percent off). This encourages the customer to increase their AOV to gain more discounts.
Segmenting customers based on their AOV helps you tailor marketing and retention strategies. For example, you identify a segment of high-value customers that consistently have a higher AOV. Then you can implement personalized offers or exclusive perks for this segment to encourage them to make larger purchases, further boosting revenue.
Leverage customer data and purchase history to provide tailored product recommendations. By suggesting items that align with their preferences or previous purchases, you can increase the likelihood of customers adding those items to their cart.
Another way to utilize and influence AOV is by implementing a loyalty program that rewards customers for reaching certain order value thresholds. Additional benefits like providing exclusive discounts and early access to sales, incentivize customers to increase their spending.
Gamify the shopping experience by offering rewards or points for reaching specific order value milestones. Customers can then redeem these points for discounts or exclusive perks, encouraging them to increase their order value.
There are various ways to measure and track AOV data. You can utilize a readily available analytics platform, or you can create your own API integration. Based on the business’s needs, identify the best suitable approach for you at the given time.
The following are the most common approaches:
There are two major analytics platforms that offer similar features when extracting and evaluating data: Google Analytics and Adobe Analytics.
Google Analytics is a widely used web analytics platform that provides powerful tracking and reporting capabilities. Adobe Analytics is another robust analytics platform that offers advanced tracking and reporting features. Both of the tools let you set custom goals and funnels to track AOV and other relevant metrics.
You can integrate these analytics tools with your ecommerce platform to gain insights on numerous data points, including AOV trends and customer behavior. It provides comprehensive ecommerce analytics capabilities, including AOV tracking, funnel analysis, and segmentation.
Similarly the ecommerce ecosystem has developed exponentially over the years where platforms today provide integrated analytics tools as an add on feature providing businesses opportunities to be more data driven.
Shopify is one such popular ecommerce platform with built-in analytics features. It provides reports and dashboards to track AOV, sales trends, and customer behavior. Additionally, Shopify integrates various third-party analytics tools to enhance data analysis capabilities.
Another good plugin to look into is WooCommerce. It is a widely used WordPress plugin for creating ecommerce websites, offering extensions and integrations with analytics plugins like Google Analytics for tracking AOV and other ecommerce metrics.
If your business model is a bit complex or different from the rest of the market, you can integrate custom analytics solutions to track AOV effectively. This involves implementing tracking scripts or APIs to capture relevant data points and calculate AOV within your data infrastructure.
You can build your own system to track and measure AOV and other data points if the existing platforms lack the competence that your business needs.
Integrating ecommerce data into CRM systems allows you to analyze customer behavior, segment customers based on AOV, and personalize marketing efforts. There are multiple CRM systems like Salesforce, HubSpot, or Zoho CRM that can be used to track AOV alongside other customer-related data.
Consider your business needs and avoid having too many integrations that are not used or have no purpose to your business needs. These result in heavy maintenance and development costs, in turn creating unnecessary complexity.
If you are a retailer that has multiple suppliers and huge reach you might need systems like data warehousing and BI tools. Large ecommerce businesses widely use tools like Snowflake, Amazon Redshift, or Microsoft Power BI to store and analyze large volumes of data, including AOV.
These tools allow you to create custom reports, perform in-depth analysis, and visualize AOV trends alongside other business metrics. Like with any decision, conduct a proper pre-study with all stakeholders. Analyze the needs and complexity for your business.
Choosing tools and technologies that align with your business needs, scaling your growth, and integrating smoothly with your existing ecommerce systems is important. Consider factors such as ease of implementation, data accuracy, customization capabilities, and the level of insights provided when selecting the appropriate tools for measuring and tracking AOV.
When working with average order value (AOV) data, there are several challenges and considerations that product managers should keep in mind. The following are the most common concerns:
Data driven decisions are logical and safe as long as the data is accurate. Incomplete or missing data can create a completely different analysis leading to wrong decisions. AOV calculations require accurate and complete data on total revenue and the number of orders.
Only correct and complete data can lead to accurate AOV calculations, impacting the validity of the analysis. Ensure data collection processes are robust and address any data gaps as quickly as possible. Misleading data can cost a company dangerously.
Similarly, consistency in data collection methods and definitions is crucial. Ensure consistent revenue and order data tracking across all channels and systems to avoid discrepancies and ensure accurate AOV analysis.
People might interpret the same data in different ways. So it is crucial to set the common parameters and definitions across the organization that is clearly communicated and established while doing the analysis,
Customer segmentation based on AOV requires careful consideration. Striking the right balance between granularity and accuracy is important. Segmentation that is too broad may overlook important differences, while overly detailed segmentation may need more data points to be statistically significant.
Another pitfall can be segment overlap. Customers can belong to multiple segments based on purchasing behavior. Managing segmentation overlap and ensuring accurate categorization of customers can be challenging. Consider segment hierarchy or prioritization to avoid confusion and ensure meaningful insights.
Including a timeframe while creating customer segmentation will give another perspective. AOV can vary over time due to seasonality, promotions, or other factors. When segmenting customers based on AOV, consider if you want to use fixed periods or dynamic timeframes that adjust to changing AOV patterns.
Post pandemic customers purchased unexpected items and the hype of certain products was short lived. Because of this, consider external factors and their impact while creating a short and long term strategy that will serve the company.
AOV can be influenced by other external factors such as economic conditions, industry trends, or competitive landscape. It’s important to consider these factors when analyzing AOV to differentiate between changes caused by customer behavior and those driven by external influences.
Another missed parameter while evaluating customer segments can be order composition. AOV does not provide insights into the composition of individual orders. Two customers with the same AOV may have different order compositions, leading to distinct revenue and profitability.
Consider adding additional metrics to evaluate the number of products in the cart with respect to total order value to understand which tier of products the customer prefers. This will help in understanding not just the spend capacity of the customer, but also preference of high end or low end products.
As a product manager understanding average order value can be very powerful while prioritizing, implementing new features, and growing business. You can leverage AOV to drive revenue growth and optimize pricing strategies.
By understanding the average order value, the company can outlay the forecast revenue. AOV also gives a clear understanding of performance, which helps optimize marketing strategies, identify cross-selling opportunities, tailor customer experiences, and optimize product offerings.
It doesn’t matter if your business is B2C or B2B as long as you are in the ecommerce space, track and analyze AOV. Have a regular process to monitor and make conclusions based on the data from AOV. Use the data to increase revenue by implementing some best practices like creating product bundles or packages encouraging customers to purchase multiple items.
Featured image source: IconScout
LogRocket identifies friction points in the user experience so you can make informed decisions about product and design changes that must happen to hit your goals.
With LogRocket, you can understand the scope of the issues affecting your product and prioritize the changes that need to be made. LogRocket simplifies workflows by allowing Engineering, Product, UX, and Design teams to work from the same data as you, eliminating any confusion about what needs to be done.
Get your teams on the same page — try LogRocket today.
Phil Freo shares his experience scaling Close from its founding team to more than 100 people and transition to being a fully remote company.
Nick Fisk shares his experience leading transformations and transitioning companies to a product ownership model.
It’s easy to hide behind charts and justify decisions with shallow metrics. But great product teams resist that urge.
Tony Schumacher shares important lessons he learned and has taken with him from working under his former boss and mentor.
Learn more →
