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In February we launched our Performance Analysis (PA) product, which revolutionized ad reporting by allowing marketers to easily visualize data and take action on ad placements in real-time. We like to think of Performance Analysis as an advanced Excel pivot table… on steroids. Since launching PA we’ve continued to make iterations and enhancements with products like Custom and Shared Views, Charts, and Custom Metrics.
Metrics are a fundamental aspect of measuring and validating performance for every marketer and marketing campaign, but as online advertising evolves so do the variety of metrics used by marketers. Formulas and metrics need to be calculated to map to an advertiser’s internal key performance indicators, or mapped to factor in certain conditions of specific campaigns or goals. A great example of this could be if an eCommerce company needs to track average order value (formula: total revenue/total purchases) to determine the effectiveness of an ad campaign in generating actual money for their site.
To show you the power of our ‘Custom Metrics’ tool, we took a look at one of our largest eCommerce advertisers’ mobile app install ad campaigns.
Nanigans’ business intelligence product, Performance Analysis, features a robust set of tools including Custom Metrics. Custom Metrics empowers advertisers to create custom formulas for reporting in order to map traditional Facebook performance metrics to internal metrics and therefore glean more insights from their ad spend.
Nanigans partnered with one of the world’s leading eCommerce companies to launch a big mobile app install ad campaign, and soon realized their Day 1 Cost Per Installs (CPIs) were high. Why? An app download isn’t counted until it’s opened, and in most instances users weren’t opening their apps within 24 hours after initial download. Frustrated with high CPIs, the client needed a formula to account for the lag in install to the “app open” event in order to optimize and budget against — as well as assess — performance more effectively.
By leveraging Nanigans’ Custom Metrics feature, which allows marketers to build custom formulas directly in Ad Engine (rather than manually exporting to Excel and entering the formula), the eCommerce company created an effective CPI metric to account for the lag in install to the “app open” event. Custom Metrics benefits advertisers in several ways; not only does it eliminate the manual exporting/importing process, it also allows for one-click campaign adjustments and easy collaboration with other team members thanks to our Shared Views feature.
The formula the eCommerce customer created using Custom Metrics was:
Cost Per Install = Spend / (Day 1 installs * 1.4)
Day 1 installs = number of installs the company acquired the first day. Because of the download to app-open latency, our analytics group determined that total installs could be calculated by applying a 40% increase to the installs acquired on day 1.
The custom metric of Predictive CPI resulted in the company’s ability to bid and budget more effectively, assess performance more accurately, and most importantly — advocate internally for increased daily budget on mobile.