The 5 Content Marketing Metrics I Recommend to Track Performance

Marketing is a game best played with a scorecard, and the metrics you use will determine the type of game you end up playing. Picking relevant metrics matters when you’re playing to win.

At the time of this writing, the first search result for content marketing metrics lists social shares as the number one metrics teams should track. If that doesn’t tell you that content marketing has a measurement problem, I’m not sure what will!

At Shopify, I ran a content marketing program that drove millions of monthly sessions and tens of millions in revenue. We had exceptional support from our data science team and were able to go deep on measuring our performance. Below are some of my favorite content marketing metrics to use when gauging the health of a large-scale content program for a self-serve funnel (e.g., no sales team).

The content marketing metrics that matter

1. Retained Active Customers

Retained Active Customers (RACs) measure how well any page on your website converts and keeps active customers based on retention time frames that matter to you.

We used this measurement framework at Shopify, and I believe it’s one of the best content marketing metrics for any company with a self-serve funnel. RACs both expand the range of insight you can gather from your data and shortens the time to discover new insight; the hallmark of any good metric.

The best time to implement the RACs framework is after you’ve clearly defined important milestones in your funnel and when they’re reached. You likely have something in place unless you’re getting a new content or marketing program off the ground, but this is an example of what I mean:

  • Pre-Lead: Any email captured by any means other than a trial signup.
  • Lead: A trial signup that hasn’t yet made their first payment.
  • Gross Add: A signup that’s made at least one payment to any plan.
  • RAC(x): A paying customer on any plan who you’ve retained for at least X days.

You’ll have to define your own metrics here, and once you’ve collected enough data, it can be helpful to set a site-wide benchmark for conversion across each stage of the funnel, just so teams across the company are broadly aware of how well, e.g., a lead converts to a paying customer.

As noted, RACs also require time frames for customer activity; ideally, you’d choose time frames quantitatively based on your understanding of signup and success for your product (e.g., tracking RAC60 because customers are twice as likely to upgrade past Day 60), but you can start by picking best-guess time frames and updating them later based on what you learn.

For example, you could define sub-metrics like RAC30, RAC90, and RAC180 to denote a customer who signed up for your product, converted to a paying customer, and remained a paying customer for 30, 90, or 180 days.

Remember that the longer the time frame, the more lag there will be until you can confirm the data, so while it’s useful for many companies to track long-term retention like RAC365, it’s helpful to also include shorter time frames so you can make faster decisions.

2. Gross Adds

Gross Adds are customers added to your product before you factor in retention and reactivation. As we mentioned above, Gross Adds can be a helpful metric to get more immediate feedback on a campaign, tactic, or strategy without having to wait for retention timelines.

Although we also tracked leads and pre-leads at Shopify, we also tracked customer adds directly because our funnel was self-serve and because our data showed that pre-leads had a relatively small window to convert.

Gross Adds are a relatively straightforward metric, so the one other point I’ll add is that we typically did opportunity sizing with Gross Adds. The reason is because Gross Adds were an easy metric to calculate and because they offered a fast enough feedback cycle to tell us if a new tactic, topic cluster, etc. was working or if we needed to pivot.

3. Incremental Gross Ads by cycle

One of the most frequent measurement mistakes I see content marketing teams make is assuming they had a successful 6-week cycle, quarter, or half because their numbers are up—without confirming what’s driving the results.

The magical thing about content marketing is that it compounds: evergreen content with the right distribution can drive results long after it’s produced and published. But that also means that in order to successfully measure content marketing, you need to understand whether the net-new content shipped within the last cycle is performing to your expectations or if legacy content is masking a cycle where you underperformed.

So, what do you measure? The simplest measurement for a self-serve content marketing funnel is to track incremental Gross Adds for each cycle—both net-new content and results from content republishing. The latter, content refreshes, are why you need to track the incremental results rather than just the overall performance of pages you modified during a cycle. You’ll laugh, but I’ve seen people make that mistake.

The final product is going to look like a cohort analysis chart that shows how much each cycle is contributing over time. I’ll be honest: building this is no small feat for a non-data scientist, and it’s especially hard for content republishes. So, I’d start with net-new content and the best metric you can measure—even if that’s just getting a cohort view into leads added per month from each cycle.

4. Session-to-customer rate

The past few years have unmistakably shown marketers the kind of performance swings that are possible as a result of large macro events. I know many companies whose conversion rates thrashed wildly with the times, reset, or changed trajectory entirely.

That’s why it’s crucial to keep some sort of health check that scores how well your top-of-funnel traffic is turning into actual customers. The simplest measure is session-to-customer rate, tracked across properties and topics.

Session-to-customer rate is also helpful for tracking how newer, large-scale content initiatives are influencing your ability to convert customers. At the start of a content program, you might take a bottom-up approach and address all of the bottom-of-funnel opportunities, such as building a competitor conquesting campaign and pursuing “alternative” keywords.

Over time, however, you’re likely to move up the funnel and into broader topics that are only tangentially related to the problem your product solves.

As you push into these new areas, site-wide or property-wide conversion rates are bound to fall, but you need to be sure that’s why happening and to what degree—are you dropping across many topics, or is it just the new stuff? Are we sure the root cause is the top-of-funnel nature of the topic or is something else driving the decrease? And let’s not forget, you need to explain increases in conversion rates, too! Session-to-customer rate can reveal that answer, and that’s why it matters.

5. Lead to customer rate

One thing that session-to-customer rate can’t reveal by itself is whether you have problems deeper in the funnel. One example we experienced at Shopify was that our mobile onboarding was underperforming, and as content marketing began driving more mobile sessions, our customer adds weren’t going up at the rate we expected.

Lead to customer rate revealed that we had been adding customers at a reasonable rate, but they were dropping off at a worse rate than before because of our mobile onboarding experience. This not only revealed an opportunity for growth but also provided some air cover for content marketing—we had an explanation for the issue, and we were able to rule out problems that any reasonable person might expect, like maybe that we had been chasing pageviews at the expense of conversions.

Many content teams won’t track a conversion rate this deep into the funnel because content has very little leverage to affect this number directly. But I recommend tracking it across content properties to be able to conduct a deeper investigation when you see noticeable swings in conversion rates.