Question 1

What are best practices for advertising effectiveness research?

Accepted Answer

Experts agree that there is no single, precise way to measure advertising effectiveness, but they generally recommend that you document specific objectives and metrics, test and refine your campaign before you launch it, pay particular attention to your target audience and to control markets, establish a pre-launch benchmark, and monitor competitors’ actions to anticipate how they will impact your campaign. At first, it may seem trivial to specify objectives, but it may be less trivial to translate those objectives into measurable goals, plan a set of metrics that makes your goals explicit, and identify ways to measure the impact accurately. An advertising campaign can have high-level objectives, such as establishing awareness of a new brand or category, or it might have very a specific purpose, such as increasing sales of a product among a particular target segment by a certain percentage. Given these objectives, you specify measurable goals which define success in achieving the impact you want to have. The particular components of the advertising campaign, how and where it is placed, and the kind of audience you target will determine how you conduct the research that will measure your impact. Before you launch the campaign, you will want to test and refine the campaign as needed and collect baseline measurements on your metrics. There are many ways to validate campaign elements, such as focus groups, in-depth interviews, surveys, and online A/B tests. However, they generally have the same objectives: to verify that the advertising is attention-getting, memorable, persuasive, and communicates your message effectively. Pre-launch testing can have multiple steps, such as neuromarketing to measure attention and intensity of reaction followed by some kind of follow up interview to understand what message was received and whether or not it was persuasive. In order to understand advertising effectives post-launch, you will need one or more points of comparison. These can include baseline measurements of the key metrics pre-launch or within a test market, and it could also include forecasts. For example, some digital publishers or platforms may offer simulations of a campaign’s likely effectiveness, and comparing your post-launch results to these initial forecasts might prove instructive. Some experts recommend creating a control sample that is not exposed to the advertising, does not overlap with your target audience, or is different from your campaign target in other ways. When you compare your post-launch results to the baseline, test market, forecast, or control group, you will be able to calculate the lift that was due to the campaign. Throughout each step in the process, you need to keep your target audience top-of-mind because those are the people who will ultimately make the difference between success and failure. If your campaign tests well with a general population but not with your target market, you will not enjoy the business results you might otherwise experience. Conversely, if it does not test well with a broader audience, you might overlook opportunities within your core audience who might react more positively. Experts also recommend monitoring competitor activity, such as spending on different channels, messaging and changes to messaging, and how much reach and frequency their campaigns achieve. These may provide insight into how well your campaign might perform or identify challenges that otherwise might be overlooked.

Question 2

What are 6 common pitfalls of advertising effectiveness research?

Accepted Answer

Six common pitfalls to avoid when conducting advertising effectiveness research include misapplying or misinterpreting metrics, focusing on outcomes to the exclusion of drivers, minimizing the importance of the cumulative effect of advertising, failure to control for important variables, overlooking competitive activity, and overestimating brand or product performance. As with any other situation in which measurements are used to make decisions, it is crucial to understand what an advertising effectiveness metric does and does not indicate. In particular, it is important to know how the data were collected and from whom, then to consider those facts as literally as necessary to decide what the metric means. For example, number of “impressions” is a commonly reported metric from digital analytics. Yet, a frequent criticism is that the researcher does not know if the advertising made an impression on anyone because they only know that something appeared on the screen, not whether it was seen or internalized by anyone. Click-through-rate (CTR) is another metric that is ripe for misinterpretation, but for different reasons. On the surface, it shows a direct relationship between advertising stimulus and the action it precipitated. However, very few people click on advertisements relative to the number who see them, but the advertisement may have played an important role in motivating non-clickers to subsequently shop for the product or brand. Experts can recommend better metrics than these. Another common pitfall is to focus advertising effectiveness research on measuring outcomes while failing to collect data that gives insight into what caused the outcomes. For example, behavioral tracking can help to understand how much the campaign contributed to lift, but it can’t tell you what worked and what didn’t work, leaving you with little diagnostic information with which to guide campaign adjustments. Understanding the cumulative impact of advertising across different channels is another thorny issue. Single-touch attribution models, for example, give you insight into which advertising exposure represented the final trigger for a person’s action, but it does not tell you anything about the exposures that led up to that moment. The cumulative effects of advertising may be impossible to measure with precision, but the issue must be considered and addressed. Your current advertising campaign may, in fact, be very persuasive; on the other hand, it may only be persuasive to someone who has already been exposed to relevant blog posts, social media mentions, outdoor advertising, and so on. Other potential pitfalls include failing to account for important variables and overlooking competition. For example, it is important to account for all relevant variables when determining the baseline, such as demographics, category affinity, and channel. Results will not necessarily translate across audiences and channels. Competitive activity might also have an impact on advertising effectiveness, for example, if they are spending in the same channels as you, if they have similar or contrary messages, or if they have made a big move that draws attention to them, drowning out your messages. This pitfall may be difficult to avoid, but it is wise to be aware of fit. Finally, a potential pitfall in advertising effectiveness research may be a disconnect between what the advertising communicates and what the product and brand represent. An otherwise perfectly designed and executed advertising campaign may fail to meet its goals because it promised something different (or more) than the product and brand deliver. Overlooking this possibility may lead you to invest more in advertising to solve a problem that can only be solved by investing elsewhere. Experts in advertising effectiveness research are familiar with these pitfalls and can help you navigate your way to results you can trust and apply meaningfully.

Question 3

What metrics are important in advertising effectiveness research?

Accepted Answer

There are two broad sets of metrics that are important in advertising effectiveness research: performance metrics and diagnostic metrics. Performance metrics enable you to understand the impact the advertising campaign has had, while diagnostic metrics enable you to decide whether the proposed campaign will meet your objectives and to adjust it if you find that it will not. Performance metrics apply to post-launch, control markets, and baselines, while diagnostic metrics may be collected at any time. Your advertising campaign seeks to convey information about your brand or product to your audience, persuade them to like your brand or buy your product, and keep your brand or product top-of-mind. To have an impact, the campaign must gain conscious or unconscious attention, create recognition, be memorable, deliver a positive message that resonates, drive consideration, achieve preference, and establish a lasting association with value. Therefore, you ideally want to measure aided and unaided awareness and recall, interpretation of the key message, willingness to consider, and preference compared to a strong competitor. Qualitative, quantitative, and neuromarketing methods can be used to explore some or all of these issues. When you collect data for these metrics, you will want to make sure that you can isolate the opinions of your core target audience and that the advertising is presented in each of the contexts that will be used in the campaign. You will also want to determine baselines from control groups, alternative concepts, or previous campaigns so that you can understand if the campaign had an impact. If you conduct such research post-launch, you may want to consider repeating it after different time intervals have passed or when you make an adjustment to the campaign. Post-launch research enables you to use behavioral data to measure a campaign’s impact. These may include amount of sales, number of new customers, store or web traffic, amount of search activity for brand or product, phone inquiries or requests for more information, and any other customer activities that you think may be impacted by the ad campaign. If you have linked advertising to a QR code, vanity URL, UTM (Urchin tracking module), hashtag, or coupon, you will also be able to trace changes in customer behavior to specific ad exposures. You also want to know how many exposures were required before the customer performed the desired action, and which types of exposures were most influential. Attribution models such as multi-touch attribution can help you quantify these issues, and experts in advertising effectiveness research can help you quantify the cumulative effect of ad exposures. When you have data on customer outcomes or other behaviors and a way to link them to campaign expenditures, other metrics become possible. These include return on investment (ROI) or on ad spend (ROAS), cost-per-lead (CPL), cost-per-acquisition (CPA), and cost-per-click (CPC). The more data you collect on each of the channels used by the ad campaign, the better you will be able to account for what was spent and how productive each expenditure was. Experts in advertising effectiveness research can recommend the right metrics for your objectives and ways to measure them. Some experts may be able to help reveal the financial performance of a campaign, and others may offer models that help you forecast performance and plan your optimal marketing mix.

Question 4

Which metrics are most important for digital advertising effectiveness?

Accepted Answer

Some advertising effectiveness metrics are specific to digital advertising. Number of impressions and click-through-rate (CTR) may be among the most familiar, but other metrics are more meaningful because they are better measures of how engaged your audience is. The number of impressions tells you how often an ad was served, but not how many times it was seen or whether it, in fact, made an impression. What counts as an impression for a customer who saw and liked the ad also counts as an impression for someone who scrolled passed it without noticing anything. To address this issue, some digital publishers offer time-based ad buys in which they guarantee that your ad will be displayed for a minimum amount of time. Some experts argue that the number of impressions is not meaningful, but the number of “viewers” is useful to know. The difference is that a single customer may account for a large number of impressions, but you ultimately want to know how many viewers of your ad were unique individuals so you can understand the ad’s reach. Look for this metric from your analytics provider. Another familiar metric, click-through-rate, can also be improved upon by focusing on engagement rather than on an isolated behavior. When you look more closely, you’re probably more impressed by the “not-clicked-through-rate,” which is likely to be staggeringly higher. Does this mean that the ad was ineffective? No. In order to determine that, you’d need to look at the number of people who saw the ad but didn’t perform the desired action until later, as well as understand how that exposure contributed to the cumulative effect of all your exposures. Each exposure is just a link, not the chain. Instead of click-through-rate, you want to focus on better representations of the quality of engagement. These include the amount of time spent on the site, the number of pages visited, the types of pages visited, the amount of time spent on particular pages, and what actions the customer took, such as making a purchase, adding to a wish list, subscribing to a newsletter, or asking a question. You can also get an idea of overall engagement as it relates to the advertising campaign by tracking how each customer found the page and how engaged they were once they arrived. For example, a visitor who was directed to your site by a web search engine might not be as engaged as someone who clicked on a social media mention. If you linked an ad to a QR code, coupon, URL, or UTM, you can hypothesize that customers who used them were influence by the ad campaign, and you may also be able to identify which were new customers versus those who probably would have visited anyway. Experts in digital advertising effectiveness can help you understand what each available metric means in terms of actual engagement, and they can help you gather them and analyze them so you can capture the most complete picture of your ad campaign, all of its paths to the customer, and its differential impact on behavior.

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