Online Advertising Metrics: A Simple Framework

When planning an online advertising campaign, or reviewing results, the data can be overwhelming. It seems everything can be measured sixteen different ways. Other times, it seems nothing that matters is measured. Lots of data, but little to no actionable information.

When digital campaigns are not driving a single discrete activity, like an ecommerce transaction, selecting metrics requires care. We cannot measure the ultimate outcome (sales) with a long sales cycle and be agile marketers. It takes too long and there are not enough sales to provide meaningful insight into each element of a program. We have to select metrics that have sufficient volume and are available quickly as proxies for our long-term objective, sales and revenue.

The following framework is a useful starting place, based on information available from most web analytics packages and ad servers.

Step One: Choose Measurements
Select one or two activities that are good early proxies for future sales. Leads are an obvious choice for many B2B marketers. Also, content downloads and interactions are a good indication that you are reaching an audience that is interested in your category and what you are offering. This is a particularly useful metric for campaigns designed to highlight and elevate a company’s thought leadership. [Choosing metrics is tightly linked to strategy and marketing’s contribution to overall business challenges and opportunities. This is a likely topic for a future post.]

Step Two: Create Metrics
Create two to four metrics for each activity identified, using the quadrant below. Always use the top two boxes, which gives you a cost-efficiency and a quality metric. These metrics directly drive results for your campaign. They belong on your dashboard.

Step Three: Measure and Optimize
Use the quality and the cost efficiency metrics together. Since you are not directly measuring sales, the quality measurement is critical. It helps avoid the mistake of chasing volume from lower quality sources.

What programs should be canceled? Anything that is both low quality and expensive should go. If that doesn’t seem right, check your choice of metrics. Is there a major benefit that isn’t being measured? Does it have a different objective? If so, carve it out and measure it differently! If not, get rid of it.

Step Four: Learn and Apply
Learn from what is working, and from what isn’t. This is where returning to the metrics quadrant above and using metrics from box three and four is beneficial. Although they may not be primary success metrics, they give context to the performance.

For example, a site with high audience quality measures (bottom right box) is reaching the right audience. If the audience isn’t responsive, look at the placement. Is it obscured by other ads? Is it well below the fold? Is it in a transactional environment, where someone isn’t likely to click, and you need to bring additional functionality directly into the creative?

This framework is simple to apply for online advertising programs. It balances quality and efficiency, and it is easy to expand on (for example, try using reach and unique metrics for quality measures). The key is to always include efficiency and quality measurements and to consider the audience and their response to the campaign separately.

Ultimately, online advertising metrics are merely a tool. Metrics shape our final program, but by themselves they are useless. Use metrics to measure success, identify areas for further investigation and, most importantly, take action and improve your programs.

Do you have a simple approach or framework you use? If so, share it in the comments.

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Comments

  1. says

    Thank you for the great article.

    As a Web Analyst with experience gauging the impact of online advertising on brand’s bottom lines, I always try to approach the situation by asking and answering the same two questions:

    Question 1) What does the client (what do we) want the audience to do?
    Question 2) How do we get them to do it?

    (It may seem overly basic but I think review of the simple process is helpful sometimes. )

    Then go on to define metrics for each question and a corresponding answer:

    Answer 1) Make a purchase online.
    Answer 2) Go to the website.

    Metric 1) Number of purchases made online.
    Metric 2) Number of people who went to the website.

    You will find “Metric 2” is the “who touched it last factor?”–the necessary requiremet or prerequisite all of the online revenue stems from.

    So what did the people who went to the site and made purchase do right before they went to the site? How does it relate to our efforts to encourage this behavior?

    Then a simple a, b, c.

    a. get the numbers
    b. do the analysis
    c. report the results

    –Martin

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