Permission e-mail marketing can serve as an excellent tool for capturing the attention of your audience and driving customer interactions, but how do you evaluate the results of those interactions and quantitatively compare e-mail marketing campaigns?
The basis of any e-mail marketing strategy should be establishing a consistent framework for deciding which responses and interactions are most important to reaching your objectives, then refining your e-mail marketing tactics based on their relative performance over time.
Analysis frameworks, at the very least, should include two primary elements. The first element, data capture, is the indispensable foundation upon which all optimization strategies will be based.
The second element is the formation of a standardized methodology to help you evaluate and compare the responses and interactions of a single campaign or multiple campaigns to determine whether they proved successful.
Perhaps the most important aspect of gauging e-mail marketing campaign performance is the acquisition of relevant, insightful and actionable response data.
Beyond simple click-through or message-delivery statistics, data capture should include conversion data. Only conversion data – the tracking of each sale, download, registration, etc., from a given campaign – truly illustrate the return on a marketing investment.
Far too many marketers rely on click-through data to evaluate the performance of their campaigns when it is widely understood that click-through alone can represent very little value to the performance of a marketing program.
In many instances, companies may offer products or services that allow a customer to convert in more than one way. For example, one company may want to generate page views and sales while another may want a combination of sales, downloads and newsletter sign-ups.
In such cases, when multiple conversion types are possible, it can be challenging to determine the success of one campaign over another.
Marketers can overcome this challenge by creating a standardized analysis methodology. Once the marketer has formalized the trade-off among the desired actions by creating an index of common units, the marketer can calculate a score for each mailing.
Say, for example, an organization is trying to drive both home page visits and sales. If the first e-mail message generated 600 sales and 15,000 home page visits, is it preferable to a second mailing that generated 700 sales and 10,000 home page visits? The answer depends on what value the marketer places on each response type.
Using this scenario, it can be assumed that the marketer favors sales to home page visits, so in building an index, a sale may be weighted with a 1.00 while a home page visit may be weighted as a 0.05. In other words, a sale is valued at 20 times that of a home page visit. Using such a weighting scheme, the marketer can objectively evaluate the performance of the mailings relative to each other.
By multiplying the total number of each response type by its index weighting, the first mailing would have a score of 1,350 (600 x 1.00 + 15,000 x 0.05 = 1,350). The second mailing would have a score of 1,200 (700 x 1.00 + 10,000 x 0.05 = 1,200).
Therefore, the first mailing outperformed the second mailing. It is preferable, in this case, to have 600 sales and 15,000 home page visits rather than 700 sales and 10,000 home page visits – an outcome that may not have been obvious by a simple comparison.
By tracking which aspects of your e-mail marketing messages are compelling your customers to respond, and by using an index to help quantify those responses, you can more effectively optimize your campaigns and improve the dialogue with your customers.