Measuring and Linking Relevancy to Buyer Behavior

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Various studies over the years have examined the relationship between content relevancy and behavior. Almost everyone would agree with the statement 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 impact 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 or vice versa. Clearly there is a relationship between relevancy and response. Intuitively we believe the more relevant the content the higher the response will be. But measuring response rate is not the best measure of relevancy. There are many factors that can affect response rate, such as time of year, personalization and incentives. Also, in today’s multi-channel environment we want to account for responses or interactions beyond what we might typically measure such as click thrus or downloads.

So, what is the best way to measure relevancy? There are a number of best-practice approaches to measuring relevancy, many of them are complex and require modeling. For example, information diagrams can bean excellent tool. But for marketers who are spread a bit thin and therefore need a simpler measure, the three step approach below ties interaction (behavior) with content:
Count every single piece of content you created this week (new web content, emails, articles, tweets, etc). We’ll call this C.
Count the collective number of interactions (opens, click thrus, downloads, likes, mentions, etc.) for all of your content this week from the intended target (you’ll need a way to only include intended targets in your count). We’ll call this I.
Divide total interactions by total content created – R = I/C
To illustrate the concept, let’s say you are interested in increasing conversations with a particular set of buyers and as a result this week you:
Posted a new white paper on a key issue in your industry to your website and your Facebook page.
Tweeted 3x about the new white papers
Distributed an email with a link to the new white paper to the appropriate audience
Published a summary of the white paper to 3 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 3x
Posted a blog on the topic to your blog

We’ll count this as 17 content activities.

For this very same content during the same week you had:
15 downloads of the white paper from your site
15 retweets of the white paper
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

This counts as 100 total interactions. It’s both possible and likely that some of these interactions are from the same people engaging multiple times, and you may eventually want to account for this in your equation. But for starters, we can now create a content relevancy measure.

R= 100/17 = 5.88.

If we had only measured the response rate, we might have only counted the downloads and attendees, 40, so we might have had the following calculation

R = 40/17 = 2.35

The difference is significant. Over time, we can understand the relationship between the relevancy and the intended behavior, which in this example is increasing “conversations”. Tracking relevancy will enable you to :
Establish a benchmark
Set content relevancy performance targets
Model content relevancy for intended behavior