At Nanigans we recognize the need for performance-oriented data that enhances your advertising and enables data-driven decision-making. Below we will walk through four examples of metrics that you can calculate using Custom Formulas, a new feature within Ad Engine that provides the data necessary to drive decisions and high-performance marketing.
Audience Saturation – how much of your audience have you reached?
When you take into account the overall size of your audience and then the number of individuals that have seen, or converted, from an ad you can determine whether you are reaching new people, or seeing repeat visitors.
Want to set this up in Ad Engine? Let’s walk through the steps. For the sake of space, in this example I am shortening the name of “Action 1 – Unique Registration” to A1, and “Action 2 – All Registrations” to A2.
1. Go into Performance Analysis and click Custom Formula.
2. Enter a name for your custom metric, along with the format for your metric (percentage, integer, currency, etc.).
3. Add in the metrics relevant to the formula you are creating – in this case A1 and A2.
4. Indicate that you would like to divide the metrics by entering “/” in between each action.
5. After clicking ‘Save,’ Performance Analysis will automatically update and include the new metric in your report:
Depending on the percentage output you receive from this formula it may be worthwhile to expand or limit your targeting options. In the picture above this campaign just started, so audience saturation appears low but will naturally grow over time.
Inverse ROI – aka “Cost to Raise a Dollar”
Optimizing towards ROI can lead to significant benefits in your marketing campaign and is a great metric to gauge overall success or failure. Inverse ROI, or cost to generate a dollar can provide even further information on the efficiency of your campaign.
In the traditional ROI formula, we would calculate (Profit – Spend)/Spend. In the case of inverse ROI, we simply reverse the formula. The output of this formula will return a cost, such as $.05, meaning that within this campaign it costs 5 cents to generate a dollar in revenue.
Cost per Order (CPO)
Cost per Order is a good way of determining how much it costs your campaign to generate each order. Although this metric is heavily centered around eCommerce, it can be slightly changed to fit other verticals like a lead generation website by swapping out orders with registrations.
To calculate Cost per Order, take the social spend for the campaign and divide it by the number of orders (or purchases).
Based on the actual cost for each order you can get a good sense of the ROI and efficiency of each campaign. If costs per order are high, targeting a new group of users and also evaluating your website analytics for where users are exiting is crucial.
Cost per Unique Purchasing User
Cost per Unique Purchasing User is somewhat similar to Cost per Order. While Cost per Order will average the cost among all orders, Cost per Unique Purchase User will highlight the cost to generate a new unique purchase.
To calculate Cost per Unique Purchasing User, we will simply divide spend by the number of distinct purchasing users. Within Custom Formulas this can be done by simply adding these two metrics to your formula and saving the metric.
These are just a few examples of formulas that you can create within Performance Analysis. Once you have created the metrics you want to measure they will automatically be added to your report and saved, so each time you come back you can see the metrics that matter to you. Simply put, Custom Formulas allows you to forego downloading data and manually calculating metrics in a spreadsheet and instead build your formula once and have it automatically updated each time you view Performance Analysis within Ad Engine.
Try building a formula today by going into Performance Analysis and then Metrics, or contact us for a custom demonstration of not only how Ad Engine can help you achieve performance marketing at scale, but also measure the metrics that matter to you.