If you’re anything like me (a loaded preposition), you’re enamored with measurement.
For me, the possible reasons are many, but there has been this nagging question that’s plagued me since my earliest research classes as an undergrad:
Does it really work?
Thing is, there are very few things in day-to-day living that you can plug in that says ‘when I do this, that happens.”
(Okay, putting a metal fork in a plugged in toaster or washing your car on a cloudy day prove otherwise.)
But how about in the marketing world? Or specifically, the content marketing world?
Can it be proven that if you invest in content marketing, X will happen? How can we measure content marketing and prove that it works?
The short answer is, yes, you can prove that X happens when you invest in content marketing.
Now, if you’ve been a visitor to CMI over the past few weeks, you’ve seen thoughts posted on how to go about it, including these:
- Michele Linn aggregated a slew of thoughts on metrics.
- Heidi Cohen offered 28 initial metrics to track.
- Heck, even I broached measuring content marketing initially way back (pre-CMI days).
While much of what has been discussed in this space revolves around content marketing metrics, demonstrating that your efforts are working requires both a hypothesis and experimental design to prove them out.
Let’s look at a couple of examples that can be tested for your business and content marketing efforts.
This is where you simply state what you’d like to prove. Important here is to not tackle an extremely broad test but one that is relatively isolatable and tied directly to marcomm objectives.
Bad hypothesis: Investing in content marketing will increase our company’s ROI by 30% in six months
Good hypothesis: Increasing the frequency of content-rich e-newsletters by 2x should increase our percentage of qualified to non-qualified leads by 20 points over the next 12 months.
Keep in mind that content marketing, like its cousins advertising, public relations, promotions, and others in the marcomm family tree, is a strategy to help accomplish goals. In this example, you want to manipulate the frequency of the content marketing effort (the independent variable) to see its effect on the overall goal of obtaining more qualified leads (the dependent variable).
There is a wide variety of other strategies that could impact this objective (like advertising, telemarketing, direct marketing, etc.); it is key for the design of the experiment to isolate out all other factors that could be the cause and focus solely on your hypothesis. This is done through:
Your experimental design
With your hypothesis in place and objectives clearly spelled out to demonstrate quantitative change (in units, dollars, time, behaviors or attitudes – there must be numbers attached) over time (place a start and end date on your measurement period), you now set up the design of your experiment.
There are multiple options of designs and features in experimental design. Content marketing seems best suited for simple cause and effect testing as well as pre-test/post-test measurement or even A/B splits of efforts.
Key to your experiment is the ability to obtain samples that are:
- Random (even within your list of only 500 clients, or 1,255 email addresses, etc. for example)
- Projectable to your overall universe (containing enough of the random client addresses to be statistically safe to project results to your entire database)
- Representative of your overall clientele (a sample not containing too many of one type of client profile or too few of another, but that is reflective of the larger total list).
Get a benchmark
In our example, we hypothesize that increasing the frequency of content-rich e-newsletters by 2x will increase the ratio of qualified leads to overall leads by 20 points over the next 12 months. Next we need to take initial measurements, or benchmarks, to determine either the “pre-“ or the base. This should be done for the sample(s) used in the experiment, likely two equal but separate samples of your broader list.
Begin the experiment
Next begins the experiment. Over the course of the next 12 months, Sample A will be exposed to, let’s say, monthly content e-newsletters, where Sample B will be exposed to 24 total e-newsletters.
It is vital to show that no other variables (advertising levels, sales calls, anything) are being manipulated in one way or another – only the frequency of content e-newsletters.
In this example, you want to show the impact the frequency has on the the percentage of qualified leads. Measurement of what your ‘qualified lead’ is will be dependent on your industry, but let’s say it can be measured by an action toward ‘more info’ – a click of a hyperlink, a call into an 800-number, returning a BRC, etc. After 12 months time, the same two samples are measured and compared, and your hypothesis is put to the test!
Your experimental design effort’s timing and variables certainly can differ from our example, and for content marketing can look at proving its efficacy by manipulating:
- Frequency of content
- Mix of content (of ratio to more promotional offers)
- Creative of content (A/B testing)
- Exposure to content (vs. not at all)
- and others.
Ultimately, you’ll be able to illustrate the positive effects of content marketing as well as leverage keen learning to improve your content marketing efforts!
So, go on, prove it!