Average Order Value (AOV): Definition, Importance & Strategies to Increase It
Introduction
When working in User Acquisition (UA), numerous metrics help assess the performance of advertising efforts. AOV is one of them, enabling marketers to adjust segmentation and acquisition strategies accordingly. In this article, discover what AOV is and how to implement best practices to maximize it.
What Exactly Is Average Order Value?
Average Order Value is a key metric in marketing and e-commerce. It represents the average amount spent by a user or customer per purchase. AOV is calculated by dividing total revenue by the number of orders over a given period:
For example, if a mobile app generates €10,000 in revenue from 500 orders, the AOV would be €20.
Why Is Average Order Value Important in Mobile User Acquisition?
In mobile acquisition, Average Order Value measures the profitability of advertising campaigns and the effectiveness of marketing efforts. It is a key metric for four main objectives:
- Optimizing ROI: Increasing this KPI helps businesses offset customer acquisition costs (CAC), directly improving return on investment (ROI). For example, with one of our customers, Lalalab, we reduced CAC by 9% and maximized revenue by diversifying the marketing mix toward open web strategies by activating Moloco.
- Segmentation and Personalization: A high Average Order Value may indicate customers willing to spend more. This data helps create targeted campaigns based on purchasing behavior.
- Enhancing Lifetime Value (LTV): While AOV focuses on single purchases, a high KPI often reflects a positive user experience, encouraging long-term purchases. Again with Lalalab, this strategy increased the average cart size, achieving the highest recorded order value in the campaign’s history.
- Aligning with Freemium Business Models: In freemium apps, where only a small percentage of users pay, a high AOV compensates for the low proportion of paying customers.
Five Techniques to Increase Average Order Value
Here are practical recommendations to optimize Average Order Value and long-term revenue:
Offer Bundles and Packages
Combining complementary products or services encourages users to spend more. For example, a fitness app could offer a monthly subscription with exclusive coaching sessions.
Leverage Personalization
Product recommendations or tailored offers based on past behavior increase the likelihood of larger purchases. AI-powered algorithms can identify items that interest a user with minimal effort from UA teams.
Implement Cross-Sell and Upsell Strategies
Modern tools facilitate cross-sell and upsell opportunities. For example, in a retail app, a user purchasing shoes might be offered socks or a waterproofing spray.
Promote Free Shipping Thresholds
If free shipping is available for orders above €50, users are likely to add more items to their cart to qualify. While common in retail, this tactic can also apply to service-based apps.
Use Limited-Time Offers
Temporary promotions drive urgency, encouraging larger purchases. For example, offering discounts on orders above a certain amount can push users to spend more.
Best Practices for Sustained Performance
- Balance Optimization with User Experience: Excessive upselling can frustrate users. Maintain a balance between optimization and user-friendliness. Upsell suggestions should be relevant and non-intrusive.
- Conduct Regular Competitive Analysis: Users frequently compare prices across apps. If your pricing or bundles appear excessive, engagement may drop. Monitoring the market allows for pricing strategy adjustments and added value propositions.
- Personalization Is Key: Shopping behavior varies by region, culture, and user segment. Use analytics tools to segment users and tailor campaigns to their needs.
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Key Metrics Related to AOV
AOV should not be analyzed in isolation—it is closely tied to other performance indicators that help optimize campaign profitability and refine acquisition strategies. Here are some key concepts:
AOV vs. ARPU (Average Revenue Per User)
AOV measures the average order value, whereas ARPU (Average Revenue Per User) assesses the revenue generated per active user over a given period. ARPU is particularly useful for subscription models or freemium apps, where users may make multiple purchases.
AOV vs. LTV (Lifetime Value)
AOV provides a snapshot of order value, while LTV (Lifetime Value) represents the total value a customer generates throughout their relationship with an app or brand. In short, a strong acquisition strategy should consider both—high AOV can indicate immediate profitability, but if users don’t return, LTV remains low.
AOV & CAC (Customer Acquisition Cost)
CAC represents the cost of acquiring a paying user. A high AOV helps offset these costs more quickly, improving campaign profitability. Optimizing the AOV-to-CAC ratio ensures that each acquired customer generates sufficient value to cover advertising expenses.
AOV & Conversion Rate
A high AOV may coincide with a lower conversion rate if users hesitate to make significant purchases. Balancing an optimized AOV with a strong conversion rate is essential for revenue growth.
Why Are These Concepts Essential?
Understanding these metrics allows businesses to tailor acquisition and monetization strategies based on user behavior. For example, an e-commerce app may decide to prioritize high-LTV users rather than solely optimizing short-term AOV.
Conclusion
Average Order Value is a key indicator for optimizing mobile acquisition campaign profitability. By implementing strategies such as bundling, personalization, and incentive offers, businesses can maximize revenue while enhancing user engagement. However, maintaining a balance between profitability and user experience is crucial.
At Addict Mobile, we apply these techniques to help clients achieve their goals.
