Don’t Get Lost in a Maze of Metrics

There’s a lot of data out there. More than any one marketer needs at any one time. The new frontier in using big data in multichannel marketing is learning what data you need. And that starts with clearly defined marketing objectives. The proliferation of data has caused many marketers to get caught up in minutiae that are not relevant to their objectives. With all the data that’s available, it takes discipline to focus only on the metrics that are relevant. Too often the most important metrics like cost per acquisition and customer lifetime value are overlooked while we’re looking at things like email bounce rates and time on site, which certainly have their place, but should be viewed in the context of how they can be leveraged to improve lifetime value.

There’s a lot of data out there. More than any one marketer needs at any one time.

The new frontier in using big data in multichannel marketing is learning what data you need. And that starts with clearly defined marketing objectives.

The proliferation of data has caused many marketers to get caught up in minutiae that are not relevant to their objectives. With all the data that’s available, it takes discipline to focus only on the metrics that are relevant. Too often the most important metrics, like cost per acquisition and customer lifetime value, are overlooked while we’re looking at things like email bounce rates and time on site. Those are metrics which certainly have their place, but should be viewed in the context of how they can be leveraged to improve lifetime value.

How Many Metrics Do You Need?
Every semester, more than one student in my “Advertising Research” class asks:

How many questions do we need to have in our quantitative questionnaire?

My answer is always the same, and always initially perplexing to them:

As many as you need.

The ensuing discussion is a lesson in the importance of setting clear objectives:

What are you trying to find out? Write down what you need to learn from your survey, and develop questions that will get you that information. Once you’ve done that, count the number of questions you have. That’s how many you need.

That lesson applies to marketing measurement, as well. With all the metrics that our marketing analytics platforms can provide, it’s easy to get buried in a landslide of statistics that don’t really relate to your business objectives. If your objective is lead generation at a landing page, why measure time on site? (Of course if you find that the abandonment rate on the data capture page is high, then look at time on site. You may be asking for too much information.)

Define What You Need to Know
If you’re looking to optimize your cost per lead or maximize lead volume, you’ll need to track cost per lead by individual tactic. You’ll find an interesting approach to maximizing lead volume in a previous “Here’s What Counts” post. But if you’re looking to enroll people in a CRM program and every one of your touchpoints is essential, then you may be able to skip that level of analysis. (If that idea seems foreign to you, check out this “Here’s What Counts” post that talks about a real world scenario where it wasn’t necessary to track cost per enrollment by vehicle.)

Every end has a beginning. Measurement always starts with the objectives you set at the start of a campaign. If they are clearly defined and you focus only on those metrics that are related to the objectives, you won’t find yourself buried in data that’s not relevant to measuring your success.

How to Maximize Your Lead Volume Within Your Allowable Cost per Lead

Many times marketers running lead generation programs shortchange their lead volume in order to maintain tight controls on their cost per lead. Their fear is that if they rollout media that tested at a cost per lead (CPL) that’s just equal to or slightly below their target CPL that a variation in response might put their overall CPL over the top. As a result, they roll out only those media properties that are performing below their target CPL.

Many times marketers running lead generation programs shortchange their lead volume in order to maintain tight controls on their cost per lead. Their fear is that if they roll out media that tested at a cost per lead (CPL) that’s just equal to or slightly below their target CPL that a variation in response might put their overall CPL over the top. As a result, they roll out only those media properties that are performing below their target CPL.

This conservative strategy ends up cheating you out of volume that could significantly increase your program’s total revenue and positively impact your ROI. The fact is that every well-constructed media test has its big winners as well as its big losers. The trick is to leverage the big winners in a way that allows you to include the “little losers” in the mix and still meet your overall target cost per lead.

With a few simple spreadsheet tricks, you can maximize your lead volume and still hit your target CPL by including media that actually generate higher lead costs than your target CPL! Think about it this way. If your target cost per lead is $15, for every $10 lead you get from a “big winner” media, you can accept a $20 lead from a “little loser.”

Let’s walk through the simple spreadsheet manipulations you need to manage this process.

Start out with your basic results spreadsheet like Table A that shows your media cost, responses, and cost per response for each media. For this example, we’ll look at a 500,000 impressions test (10 properties,
50,000 impressions each, with a roll-out potential of 15 million. The target CPL is $15.

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As you can see, the test yielded 700 responses at a cost of $11,425 or a total CPL of $16.32. But there are 7 out of 10 properties that are performing worse than the target CPL of $15.

The first thing you need to do is rank the results in ascending order of CPL using the Data Sort function, and you end up with Table B below. (Make sure you don’t include the total line in your sort).

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Here we see that properties H, B, and C are below the target of $15 per lead while all the others are higher. The combined roll-out quantity of these three properties is a disappointing 4,050,000 impressions out of the total potential roll-out quantity of 15 million. But let’s look at what the actual roll-out potential is when we leverage the “big winners” against the “little losers.”

To the spreadsheet that you sorted by ascending CPL, add columns for cumulative responses, cumulative cost and cumulative CPL. Table C, shows the formulas for calculating those.

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Looking at the results of this calculation in Table D, we get a better picture of the potential roll-out universe.

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If you look at the cumulative cost per lead column, you can see that taken together, 8 out of 10 media properties produce an aggregate cost per lead under $15. That leaves only properties E and F with their high CPLs out of the mix, creating a potential rollout of 12,250,000 impressions. (Note: If you decide to re-sort this spreadsheet do not include the cumulative results columns in the sort).

Now, some words of caution. Don’t roll all these marginal media out before retesting them in a larger quantity, say 250,000 impressions to make sure that you’re going to repeat your results. A test quantity of 50,000 impressions generating less than 100 responses does not create a high level of statistical confidence. So be especially careful with properties like A and I that have higher CPLs. You’ll also want to retest your “big winner” properties with a greater number of impressions to make sure the test results are not an aberration.