Overview

Cost per Install (CPI) is a metric used to measure the acquisition cost of a user who installs an app. It captures the cost of campaigns used to acquire users.

What is Cost per Install?

Cost per Install (CPI) is an important ecommerce metric often used to calculate the cost associated with acquiring new users through campaigns, advertisements, and other forms of promotions. CPI is calculated by dividing the spend of a particular campaign by the number of installs resulting from the same campaign. This enables businesses to understand the cost of user acquisition, and make more informed decisions about their future ecommerce strategies.

Formula

CPI = Total Campaign Spend/Total Installs

Example

If a company spends $500 on a campaign and the campaign results in 300 installs, the Cost Per Install would be:CPI = $500/300 = $1.66

Why is CPI important?

Cost per Install is an important metric because it helps ecommerce businesses understand the cost of user acquisition, and assess the effectiveness of their campaigns. By measuring CPI, businesses can decide which campaigns are most profitable and allocate budget accordingly. The metric also provides visibility surrounding the return on investment (ROI) of campaigns and enables businesses to adjust their ecommerce strategies accordingly.

Which factors impact CPI?

The effectiveness of campaigns is impacted by a number of factors, including:1. Quality of creative: The visual elements of the advertisement such as layout, typography, and image selection can make or break the success of a campaign.2. Target audience: Campaigns should be targeted to the right audience for maximum success.3. Platforms: Different platforms can yield different results. Companies should be aware of the cost associated with each platform and optimize their campaigns accordingly.

How can CPI be improved?

There are a number of ways to improve Cost per Install. Companies should focus on creating high-quality campaigns that have engaging content and are targeted to the right audience. Additionally, measuring and optimizing user experience should be a priority and A/B testing can be used to validate the effectiveness of campaigns. Finally, tracking time-sensitive promotions can help reduce CPI.

What is CPI’s relationship with other metrics?

The Cost per Install metric is closely related to other ecommerce metrics, such as Return on Ad Spend (ROAS) and Lifetime Value (LTV). ROAS is calculated by dividing the return generated by campaigns with the total expenses associated with the campaign. LTV, meanwhile, tracks the revenue generated by one customer over the lifetime of their relationship. Together, these metrics provide insight into a company’s ecommerce performance and allow them to make decisions that will ultimately result in more profitable campaigns.

Free essential resources for success

  • Enhance marketing mix modeling

    Data Sources Checklist for Marketing Mix Modeling

    Build a robust marketing mix model by identifying and organizing the right data sources.

  • Marketing Measurement

    Mastering the Four Pillars of Marketing Measurements

    Learn how each pillar plays a unique role in measuring marketing effectiveness and improving ROI across channels.

  • In-Store Blind Spot Outer

    The In-Store Blind Spot

    The In-Store Blind Spot Why platform measurement isn't enough for retail brands, and...

Discover More From Lifesight

  • Measure-mingle-cover-

    Published on: August 31, 2026

    Four Marketing Conversations I Haven’t Stopped Thinking About

    Four honest takeaways on AI, authenticity, and measurement from our latest gathering of omnichannel marketing leaders.

  • CTV grew up

    Published on: August 26, 2026

    CTV grew up. Your measurement has to grow up with it.

    CTV has evolved beyond an awareness play, but measurement hasn't kept pace. Discover how incrementality, geo-testing, and causal MMM can prove CTV's real impact and ROI.

  • ensemble forecasting

    Published on: August 24, 2026

    Ensemble Forecasting

    New research from Lifesight on how forecasting models get chosen, and what happened when we compared picking a single model to combining several.