All posts by Michael Richardson, Manager of Analytical Delivery

Marketers are always challenged to expand sales beyond “business as usual,” while being good stewards of company resources spent on marketing. Every additional dollar spent on marketing is expected to yield incremental earnings—or else that dollar is better spent elsewhere. You must be able to determine return on advertising spend (ROAS) for any campaign or platform you add to your marketing mix. A key driver of positive ROAS is incremental customer actions produced by ad exposure. Confident, accurate measurement of incremental actions is the goal of an effective testing program. Why do we test campaign performance? Because demonstrating incremental actions from a campaign is a victory. You can keep winning by doing more of the same. Not finding sufficient incremental actions is an opportunity to reallocate resources and consider new tactics. Uncertainty whether the campaign produced incremental actions is frustrating. Ending a profitable marketing program because incremental actions were not effectively measured is tragic. Test for success When you apply rigorous methods to test the performance of campaigns, you can learn to make incremental improvements in campaign performance. The design of a marketing test requires the following: Customer Action to be measured during the test. This action indicates a recognizable step on the path to purchase: awareness, evaluation, inquiry, comparison of offers or products, or a purchase. Treatment, i.e., exposure to a brand’s ad during a campaign. Prediction regarding the relationship between action and treatment (e.g., Ad exposure produces an increase in purchase likelihood). Experimental design is the structure you will create within your marketing campaign to carry out the test. Review of results and insights.   Selecting a customer action to measure Make sure that the customer action you measure in your test is: Meaningful to the campaign’s goal. What is the primary goal of the campaign? Is it brand awareness? Web site visits? Inquiries? Completed sales? An engagement by the customer. Your measurement should capture meaningful, deliberate interaction of consumers with the brand. Attributable to advertising. There should be a reasonable expectation that ad exposure should increase, or perhaps influence the nature of customer actions. Abundant in the data. Customer action should be a) plentiful and b) have a high probability of being recorded during the ad campaign (in other words, a high match rate between actions and the audience members). Selecting campaign treatments It is best for treatments to be as specific as possible. Ad exposures should be comparable with respect to: brand and offer, messaging, call to action, and format. Making a prediction This is the “hypothesis.” Generally, you assume that exposure to advertising will influence customer actions. To do this, you need to reject the conclusion that exposure does NOT affect actions (the “null hypothesis”). Elements of an experimental design An attribution method that links each audience to their purchase action during the test. This consists of a unique identifier of the prospect which can be recognized both in records of the audience and records of the measured action during the measurement period. A target audience that receives ad exposure. A control audience that does not receive ad exposure. It provides a crucial baseline measurement of action against which the target audience is compared. Time boundaries for measurement, related to the treatment: Pre-campaign Campaign Post-campaign Randomly selected audiences (recommended) Some audience platforms, such as direct mail and addressable television operators, feature the ability to select distinct audience members in advance. Randomly selected audiences can generally be assumed to be similar in all respects except ad exposure. The lift of the action rate is simple to calculate: Campaign Lift = (Action Rate (target) / Action Rate (control)) -1 Non-randomly selected audiences are more difficult, but still possible, to measure effectively. There may be inherent biases between them that may or may not be obvious. To measure campaign performance, we must first account for any pre-existing differences in customer actions, and then adjust for these when measuring the effect of ad exposure. Typically, the pre-campaign period (and possibly the post-campaign period as well) are used to obtain a baseline comparison of actions between the two audiences. This is a “difference of differences” measurement: Baseline lift = (Action Rate (target) / Action Rate (control)) -1 Campaign Lift = (Action Rate (target) / Action Rate (control)) -1 Net campaign lift (advertising effect) = Campaign Lift – Baseline Lift Analyzing results and insights How large is the lift? This is generally expressed as a percentage increase in action rate for the target audience vs. the control audience. Are we confident that the lift is real, and not just random noise in the data? This question is answered with the “confidence level.”. 95% confidence means the probability of a “true positive” result is 95%; and the probability of a “false positive” due to random error is 5%. What was the campaign cost per incremental action? If you also know the expected revenue from each incremental action, you can project out incremental revenue, from which you can calculate return on ad spend. Other insights: Do the results make directional sense (we would hope that ad exposure will cause an increase in customer actions, not a decrease)? Does action rate generally increase with the number of ad exposures? Summary Well-designed testing and measurement practices allow you to learn from individual advertising campaigns to improve decision-making. The ability to draw confident conclusions from campaigns will allow experimentation with different strategies, tactics, and communication channels to maximize performance. These test-and-learn strategies also enhance your ability to adjust to marketplace trends by monitoring campaign performance. To learn how Experian’s solutions can help you measure the success of your marketing campaigns, watch our short video, or explore our measurement solutions.

One of the biggest challenges marketers face when planning digital advertising campaigns is getting an adequate number of impressions that yields measurable results at the lowest possible cost. As agencies, operators and advertisers are increasingly challenged about media budget, it is more important than ever to plan campaigns that generate enough information at the lowest possible cost. Through our design, deployment and measurement of advertising on a variety of platforms, Experian has developed best practices when planning digital advertising campaigns. We share some of these here, to help marketers with future campaign planning to maximize marketing effectiveness at a minimal cost. Using Path to Purchase to Determine the Right Number of Touch Points The Path to Purchase Funnel provides a framework to determine the number of touchpoints required to turn a prospect into a buyer. There are various phases the consumer goes through at each contact, and these phases dictate the number of touch points (or ‘touches’) required to induce a purchase. In the table below, we’ve described what these phases are, and the number of touch points required for those phases. The required number of touches will vary greatly among marketers, who must consider the expected time for a prospect to make decisions, competition in the marketplace, the novelty of the offer, and the level of engagement of the audience. For example, 15-second or 30-second audio and video ads played during scheduled breaks in programming may require more repetition than an ad played at a moment of high engagement, such as when the user interacts with the app, or during a “pre-roll” advertisement view prior to streamed content. Determining the Target Number of Impressions Needed to Persuade The next step involves determining how many exposures will be required to get the impressions or touch points needed to satisfy the consumer’s path to purchase. Let’s say a marketer has decided that four impressions are enough to make the case for the consumer to purchase, and that the marketer plans to reach 1 million prospects during the campaign. Perhaps the most intuitive solution is to provide four impressions for each reached prospect, such that 4 million impressions would be served during the campaign. However, during a normal digital campaign, some prospects will have zero impressions while others will have many. Because of this, we recommend planning to reach a target fraction (typically 80%) of the audience to receive the required number of impressions for purchase. The following table shows the predicted percent of audience exposed by average number of impressions served. Factoring Advertising Half-Life into Impressions Required Advertisement decay, or the fading consumer memory that reduces ad effectiveness, should also be factored into determining the right number of impressions for a successful campaign. For example, if a campaign length is planned to be 6 weeks, but the half-life of the advertisement is only three weeks, then more impressions would be needed to attain the number of touches required for the path to purchase. When planning campaigns for our partners, we adjust the target mean exposure frequency by the square root of the proportion of campaign length over advertisement half-life. For example, assuming a 3-week half-life and a 6-week campaign, we should multiply our target 5.5MM impressions by to get 7.78MM impressions. In the table below, we’ve demonstrated several scenarios of varying advertisement half-life. Other Considerations During Post-Campaign Analysis Once the campaign is completed, Experian recommends analyzing the distribution of impression frequency to determine how closely the actual impression frequencies matched to what was predicted. If frequencies do not align with the predicted, check to see if these assumptions are met: Make sure that advertising impressions are independent of each other. If rules are in place such that a prior impression affects the likelihood of a subsequent impression, this can affect the impression frequency. Check that the entire targeted population is on the ad platform long enough to be available for targeting. Some campaigns may have been instructed to be deployed in phases, which could limit the number of impressions to be delivered. Confirm that exposures can only occur one at a time, so that the impressions are deployed at distinct time intervals, giving the consumer the opportunity to view the advertisement Planning a successful campaign is critical for a test-measure-learn environment for an agency, operator, platform or advertiser. While initial up-front costs can be expensive, the long-term value to the business is significantly greater if tests are designed and administered appropriately. As a result, spending a little extra time thinking about your consumer’s path to purchase, exposure frequency, and the half-life of your advertisement can pay significant dividends in developing your digital advertising strategy.