We introduced Custom Formulas a little over a month ago as part of our Performance Analysis reporting tool. So far we’ve seen great interest from clients who want to use the increased flexibility to create custom metrics that map to internal proprietary metrics, drive instantly actionable information and share it with their teams/C-level executives.
Here are a few Custom Formulas customers leveraging Ad Engine are using, ranging from simple to more complex.
Average Order Value
For eCommerce companies, an AOV formula reveals the value/price of orders that users place over a certain time period – calculated by summing purchase revenue attributed to ads in a given time period divided by the number of purchases in that same time period. This metric is important because it helps show the efficacy of ad campaigns in terms of generating actual money for a business. Monitor this metric over time to make sure it’s growing (a sign that your campaigns are optimizing correctly) and use it to evaluate the profitability of different market segments (thereby justifying different ad budgets).
Formula: (Total Revenue / Total purchases)
Cost Over Bookings
Cost Over Bookings is calculated as (Spend / Revenue from New Users). It’s the inverse of what we’d normally look at for a return metric since in this case, lower COB is more efficient. The more revenue we have for a constant amount of spend, the lower the ratio. A lower ratio is better in this case because it means we’re generating more money for every dollar of spend.
This formula is best paired with mobile, which dovetails nicely with gaming user acquisition campaigns that have high volume goals. It provides a better understanding of how CPIs (costs per installs) will look once the data matures. To set up this formula, map the number of installs you get on the first day of launching an ad unit, and then scale by the percentage you expect to see after Day 1. Set that under Spend to get an eCPA based on pulled CTR, CPC and CPI information:
CPA1 = How much each install costs (spend/number of installs)
Full Maturity = The total number of installs you expect to get after X days of maturity or based on historical data that you’ve observed
This metric also allows you to evaluate how expensive installs are for specific audience segments, strategy groups or targeting parameters (e.g custom audience or specific interests). Effective CPA can be used to “scale” a given install maturity window to full maturity to estimate potential “true” CPA for a market segment.
Profit Based on Yield
This metric allows us to evaluate performance based on an agreed-upon index — let’s say 100%. Anything below 100% is considered positive ROI (good). Anything above 100% is considered negative ROI (needs improvement). If you need to hit 1344% yield (revenue/spend *100%) within 30 days of ad click, the formula would be (Yield / 1344) * 100%. Therefore, if an attribute (creative, targeting, bid type, date) comes in below 100% efficiency, good. If an attribute comes in above 100%, it needs improvement.
The benefit of this custom metric is that it allows for easy comparison of what is/isn’t working — especially when you download a CSV file from Performance Analysis and heat map the results.
Take Your Reporting to the Next Level
Not all of these formulas will apply to your campaign goals, but they should give you a solid idea of how adaptable Custom Formulas can be and just how granular you can get with reporting. These metrics save automatically in Performance Analysis, which means that you only have to set them up once. Another bonus? No more Excel calculations, because Ad Engine does all the work upfront.
Want to see how easy building your own metrics can be? Contact us today.