Making Sense of Content Marketing Data: Q&A with OneSpot Chief Strategy Officer, David Brown


OneSpot recently appointed David Brown as Chief Strategy Officer to lead the new Strategic Services division. As an early innovator and trailblazer in content marketing, David brings a wealth of experience working with Fortune 500 brands to establish highly-effective strategies around content.

We sat down with David to learn more about his take on content marketing data, exploring the common challenges and strategies for acquiring and making sense of all the data-driven insights marketers can get their hands on.

OneSpot (OS): Marketing studies continue to show that analytics is one of the top challenges for their organizations. This includes challenges with acquiring enough meaningful data to make decisions, but in some cases the challenge may be a case of data overload in which teams don’t know how to make sense of the data they have. What’s your perspective on these challenges?

David Brown (DB): There are two issues here. The first is that much of the data lives in a silo within a specific channel. That’s a good indicator of channel performance, but may fail to deliver key insights on the effects of integrated marketing activities. Because content sits across all channels, we need to be able to analyze data simultaneously across all channels to get truly meaningful insights. Marketers need to develop their data integration muscle to look at the cumulative and incremental impacts of these channels.  

The second issue is that there is a talent shortage for analytics experts. The worlds of CRM analytics and digital media analytics need to come together and create a pipeline of talent that can be trained in cross-channel content analytics. Data managers needs to present the data in a way that is useable for analysts to work with. Analysts needs to work harder to understand how the data really works, and where it comes from, to be able to create new approaches

OS: In your experience, which insights (or types of data) have proven to be most valuable for developing and optimizing content strategy?

DB: Search data is a good place to start. Google is the largest research panel with 8 billion searches daily, which gives us a great handle on consumer demand and intent, and can inform a content strategy. You can use search query data to inspire and quantify your content themes and topics. Content analytics are another key data type that allow organizations to measure content engagement and consumption and how those impact conversions. By monitoring your content engagement metrics, you can move your content strategy from a static to dynamic approach.

More traditional research approaches can help you understand the “why” as opposed to the “what”. The “why” helps you understand rational and emotional motivations that should inform your content strategy. Functional needs typically get you to a categorical content strategy that may be the same as your competitor. The emotional needs are often where breakthrough thinking comes from, and that can differentiate your efforts versus your competitors.  

OS: What are some of the most common pitfalls with content analytics? Where do content marketing teams continue to “get stuck” with acquiring and using data?

DB: Content teams sit often inside or alongside the digital team, and the digital analyst is asked to support content analytics. Too often, website metrics like visits, clicks and actions, are confused with content metrics, and web metrics are forced into the evaluation of content performance.

Overall, content strategy and analytics approaches are currently at a basic level across most industries. Research shows most marketers do not have a content strategy, let alone a measurement approach. It’s important for marketers to clearly define what goal they want their content to work against, and then ensure they have a way to measure against that goal with a series of more specific KPIs.  

For example, are you trying to raise awareness, position your brand as a thought leader,  affect a certain part of the customer journey, or focus on a specific conversion action? The measurement approach should then be designed against these goals and KPIs. If there isn’t a measurement plan against a goal, then you should question whether the goal should continue to be a goal. It becomes more of a hope!

OS:  We often hear that marketers complain that content analytics measures are too soft, and they desire metrics that are harder and better connect to business performance. How can content marketers overcome this challenge?

DB: This is because web metrics are used to evaluate content. Content analytics is a new discipline which forces a reappraisal of the right approach. For example, the well-established metric of bounce rate really does not work in a content world. If a piece of content exactly does its job and gives the customer what they need, they may leave the website at that point. Your bounce rate might go up, but you may have a satisfied customer.  

The way to harden content engagement measures is to break it down in its component parts, like content visits, content duration, content consumption and content re-circulation, and find ways to grow these numbers over time. Then connect them to your important actions and tasks across your digital ecosystem.

Marketers also need to be patient. Attribution cycles for content marketing are longer than a CTA display ad, for example. Therefore, looking at the ROI of content involves more longitudinal approaches with hold out control groups so you can understand the incremental effect of your content efforts. Care needs to be taken to avoid selection bias in order to separate differences from correlation from causation.

OS: Building a data-driven content marketing organization requires individuals and technology that can deliver on content creation while having the ability to understand content’s effectiveness. How can you structure a team for the optimal blend of content and data expertise?

DB: The hub of a content organization should be a content director and analytics partner to ensure constant and dynamic iteration of content strategy, development and optimization, with the channels owners playing supporting roles.

For many organizations, working with a content marketing agency partner is the fastest way to scale and get the analytics capabilities and talent needed to do content marketing well. In this case, it’s important for agencies to develop subject matter expertise for their clients and for the customers of their clients. In my experience the best starting point is making sure they understand the customer journey of their clients and of their brands because content has to be developed in a way which ties back to the customer journey.

Whether you build a team internally, or outsource, you should be careful to be able to report on the true cost of operations. Sometimes, internal costs are lost in ROI calculations until the CFO’s axe comes swinging, so be sure to allocate all of your costs into ROI measures.

For technology, there are over 5,000 options available to support your operational needs, across many categories such as development, sourcing, distribution and measurement. Before you commit to any new technology you should clearly define your needs, and check that your stakeholders align with those needs. And you should explore whether your existing technology stack already has the capabilities you are seeking. Typically technology is under-utilized once implemented, so the answer to your need may already be available and paid for.

Click here to get more insights from David and explore how OneSpot’s comprehensive strategic services offering can further your content programs—from identifying success factors and technology assessments, training and development to customer journey and roadmapping, as well as content strategy and deep content analytics.


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