Announcing new product: Content Analytics
We’re extremely excited to announce that our game-changing Content Analytics product is now available. Content Analytics enables B2B marketers to measure the impact of content on pipeline and revenue.
In this post we’re covering the challenges Content Analytics overcomes, how we’ve overcome the challenge of tracking, what Content Analytics looks like, and what this means for measuring content.
Contents:
Content Analytics
It’s a truism that content is an immensely important pillar of any go-to-market strategy. Unlike paid ads, content brings value to those consuming it in its own right, it also helps showcase products and set the tone for the brand. But also unlike ads, content takes time and effort to create and has been challenging to measure in any meaningful way.
To the frustration of B2B content marketers everywhere (myself included), proving the value of content in dollars and deals often hasn’t been possible, until now that is.
Sure, we’ve been able to count unique page visits, time on page, downloads of gated content, and dozens of other proxy KPIs to showcase our contribution. But proving that those consuming our content go on to become leads, opportunities and won deals, no señor.
Dreamdata’s new Content Analytics consigns this frustration to history.
In this post, I’m going to walk you through how Content Analytics is a game-changer for tracking and measuring B2B content performance.
Measuring influenced pipeline for B2B content: the challenge
Tying content to pipeline and revenue data is notoriously difficult to do - especially in the B2B setting.
The B2B customer journey involves multiple sessions by multiple stakeholders intersecting with the buyer journey over a course of weeks and months.
Yet the only tools available to B2B content marketers have been limited in their tracking and so unable to measure content’s performance beyond simple conversions.
Tracking limited to same-session conversions
The solutions available at present track content performance in the following two ways:
Scenario A - where a conversion, say a sign-up, occurs directly after the content is viewed.
Scenario B - where a user views the content and visits other pages before converting later in the same session.
In both scenarios, readers go from content to sign-up in the same session.
These same-session conversions, however, leave content influence on conversion and pipeline events later in the typical B2B customer journey unaccounted for.
To compare, a B2B buying scenario looks something like this.
Here we see that sign-ups and subsequent pipeline conversions take place at a much later point (days, weeks or even months later) than when the content was consumed, and that later pipeline events are driven by different stakeholders from the same account.
This makes it immediately clear how tracking same-session conversions by a single user, (Scenarios A and B), captures only those rare conversions that happen in the same session, and say nothing about what happens after.
But for a B2B content marketer, this obviously doesn’t cut it - hence the frustration. The journey needs to be tracked across all pipeline stages and stakeholders to get the full picture of how your content is influencing deals.
This is where Dreamdata’s Content Analytics enters the stage.
Connecting Content data to revenue and pipeline: the solution
Dreamdata overcomes this tracking challenge and ties content views to pipeline and revenue.
Let’s take a quick look under the hood to show you how.
Content Attribution
In a nutshell, Dreamdata is able to tie content to pipeline and revenue generated because the platform tracks every page view, of every session, by every user of every company.
Dreamdata does this by connecting every tool in your go-to-market tech ecosystem - from marketing automation tools to ad platforms, CRM, and CS tools. As well as tracking on-site user behaviour (using our tracking javascript which you can read more about here).
This allows Dreamdata to connect the dots in the complex B2B customer journey, no matter how many stakeholders are involved in the buying decision, or how long it takes. See this recent customer journey which took 1131 days from first-touch to won - yes that’s four years!
Dreamdata then transforms all this data by cleaning, joining, standardising and modelling it to allow for actionable analytics insights… including the influence of content on pipeline and revenue.
The Content Analytics Dashboards
The Content Analytics product centres on two dashboards: Content Analytics (which you find under the ‘Revenue Analytics’ tab and Content Performance (which sits in the ‘Performance’ tab).
The dashboards differ mostly in their intended use.
Content Analytics is a look-back dashboard that summarises how much pipeline (leads/prospects/deals) and value has been influenced by content so far. In other words, it answers the question: “How did content perform between these dates”?
Content Performance, on the other hand, is a look-forward dashboard, where the reports look at touchpoints and metrics showing potential. In effect answering the question: “how is my content performing and is there scope to optimise and improve performance?”
But there’s more. You can also deep-dive into who exactly is viewing which content: at both account and user level. Helping you tailor your content to the right audience and the right time.
Find 8 things you can do with Content Analytics in this post 👀
Both dashboards benefit from the same range of filters, which enable a high degree of customisation.
Starting with the date range and the pipeline stage you want to examine, and moving on to an array of filters which you can learn about in much greater detail here - but the main ones are:
Content Category selects content clusters, e.g. blog, videos, podcasts, product, success stories, whitepapers, etc.
This enables high customisation of how you want to analyse your content; whether that’s by format, site location, etc.
Get all the details on how to set up your content categories here.
Group By: determines the granularity of the content, e.g. URL will show the URL of the content, whereas Content Category aggregates multiple URL’s into the specific categories that are setup.
Secondary Group By, provides the ability to further split the content, e.g. by Session Channel, Source or Campaign.
Moving towards KPIs that really matter
Right, so now we know how Dreamdata overcomes the tracking challenge and how impressive the dashboards look. But what does this mean for the B2B marketer in practice?
Above all it means marketers can analyse their content’s performance from an entirely new perspective: pipeline and revenue influenced.
From this, marketers can scale the content that works and optimise the content that isn’t quite doing the trick. Check out this post with 8 use cases for Content Analytics.
Traditional Content KPIs
When it comes to measuring content’s performance, the limited tracking we discussed above has forced B2B content marketers to rely on proxy KPIs, including:
Traffic/unique page visits
Conversion rates (simple conversion rates like newsletter sign-ups)
Click-through rates
Time on page
Downloads (of gated content)
SERP ranking
Bounce rates
None of these, however, actually indicate whether those visitors consuming the content are then going on to become paying customers.
Don’t get us wrong, these metrics do offer decent insights. I mean, long avg. time on page and low bounce rates are good indicators of quality content.
But ‘quality’ content, where visitors spend time drinking from your fountain of knowledge doesn’t necessarily correlate with quality content that ropes in leads and sees them through to purchase.
I mean, a super-fantabulous blog post on orangutans isn’t going to get readers purchasing your SaaS product.
Ok, leaving silly examples aside, the point is remains the same. You can produce quality content that ranks well, retains readers and generates downloads but doesn’t influence the bottom line.
In fact, these traditional KPIs leave a lot of questions unanswered and instead force content creates to make assumptions when creating and optimising content. Which they are then unable to prove or disprove. A vicious cycle.
Victor Ijidola and Ran Yousef illustrate this here in this must-read article, when listing seven critical questions to make sense of blog-to-newsletter subscription conversion rates:
Are people subscribing because they want more of our content or just because they wanted a piece of content we gated?
How many of our subscribers fit our Ideal customer profile (ICP)?
If they don’t fit our ICP, how can we get potential customers on our email list?
Are we getting good open and click-through rates on our emails?
Do people unsubscribe after receiving a few emails?
Are we getting more valuable sales leads as our email list grows?
Or is this just one of those vanity metrics we shouldn’t waste time with?
It’s little surprise that Victor and Ran go on to suggest that “the more relevant the KPIs you’re tracking, the better you can impact revenue with your content.”
And what is more relevant to “impacting revenue” than actually linking viewed content to pipeline and revenue - no matter how complex the journey?
Content influence on pipeline and revenue: new perspective, new KPIs
Dreamdata’s Content Analytics does just this. It helps answer the fundamental question: are the viewers of my content turning into leads, prospects and new business?
Which means that you can now place revenue and pipeline-centric KPIs at the centre of your content marketing strategies.
At Dreamdata we’re tracking these three KPIs for our content efforts:
Content pipeline influence (by MQL and SQL stages)
Content revenue influence
Influenced Opportunities per Session - to compare the performance across pages, in this case, how many SQLs do we generate for every content view.
This has enabled us to track and measure content performance from an entirely new (and much more business-relevant) perspective.
It’s also meant that we’ve been able to level the playing field between content marketing and rest of our go-to-market efforts - finally.
And you can get started on measuring the value of your content in metrics everyone can understand with Dreamdata today.
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