Various studies over the years have examined the relationship between content relevancy and behavior. Almost everyone would agree that content must be relevant. But what is relevance? According to Wikipedia: "Relevance describes how pertinent, connected, or applicable something is to a given matter." A thing is relevant if it serves as a means to a given purpose.

In the context of this discussion, the purpose of content is to positively influence customer or employee behavior, such as increasing purchase frequency, purchase velocity (time to purchase), likelihood to recommend, productivity, etc.

When we ask marketers and others how they measure content relevancy, we often hear, "We base it on response rate." If the response rate meets the target, then we assume the content is relevant; if response doesn't meet the target, we assume it's not relevant.

Clearly there is a relationship between relevance and response. Intuitively we believe that the more relevant the content, the higher the response will be. But measuring response rate is not the best measure of relevancy. Many factors can affect response rate, such as time of year, personalization, and incentives. Also, in today's multichannel environment, we want to account for responses or interactions beyond what we might typically measure, such as click-throughs or downloads.

So, what is the best way to measure relevancy?

The best-practice approaches for measuring relevancy are many, and many of them are complex and require modeling. For example, information diagrams are an excellent tool. But marketers, who are usually spread thin, need a simpler approach.

The following three steps provide a way to tie interaction (behavior) with content. It's critical that you have a good inventory of all your content and a way to define and count interactions, because once you do, you'll be able to create a measure of relevancy.

The process and equation include the following:

1. Count every single piece of content you created this week (new Web content, emails, articles, tweets, etc.). We'll call this C.
2. Count the collective number of interactions (opens, click-throughs, downloads, likes, mentions, etc.) for all of your content this week from the intended target (you'll need to have clear definitions for interactions and a way to only include intended targets in your count). We'll call this I.
3. Divide total interactions by total content created to determine Relevancy: R = I/C

To illustrate the concept, let's say you are interested in increasing conversations with a particular set of buyers. As a result, this week you undertook the following content activities:

• Posted a new whitepaper on a key issue in your industry to your website and your Facebook page
• Tweeted three times about the new whitepapers
• Distributed an email with a link to the new whitepaper to the appropriate audience
• Published a summary of the whitepaper to three LinkedIn Groups
• Held a webinar on the same key issue in your industry
• Posted a recording of the webinar on your website, SlideShare, and Facebook page
• Held a tweet chat during the webinar
• Tweeted the webinar recording three times
• Posted a blog on the topic to your blog

We'll count those as 17 content activities.

For that very same content, during the same week, you had the following interactions:

• 15 downloads of the whitepaper from your site
• 15 retweets of the whitepaper
• 15 Likes from your LinkedIn Groups and blog page
• 25 people who attended the webinar and participated in the tweet chat
• 15 retweets of the webinar
• 15 views of the recording on SlideShare

That's a total of 100 interactions. It's likely that some of these interactions are from the same people engaging multiple times, and you may eventually want to account for that likelihood in your equation. But, for starters, we can now create a content relevancy measure:

R= 100/17 = 5.88.

Using the same information, had we measured only the response rate, we might have counted only the downloads and attendees—40 responses—so we might have had the following calculation:

R = 40/17 = 2.35

As you can see, the difference is significant.

By collecting the interaction data over time, we will be able to understand the relationship between the relevancy and the intended behavior, which in this example is increased "conversations."

I strongly encourage you to consider relevancy as a key measure for your content marketing. By tracking relevancy, you will be able to not only set benchmarks and performance targets for your content but also model content relevancy for intended behavior.