In the early 2000s, website owners first started touting the now-common mantra “content is king.” Back then, when asked about which content mattered the most on a site and which content mattered the least, content writers would either point to metrics on pageviews or leads generated directly from specific pieces of content. These limited data points guided content writers to focus very heavily on content that converted quickly. But unfortunately, pageviews and direct leads don’t really provide enough context for how strong the content is or how it is used in complex sales cycles.
To give one example, let’s look at how visitors research and apply for mortgages on banking websites. Our research shows that, on average, users visit the site 3.2 times before starting an application for a mortgage product. When we analyzed the click paths of the first, second, and third visit, we found that some content, such as rates-related content, is viewed repeatedly on each visit. Other content, such as the product comparison pages, is viewed only on the preliminary visit. And finally, the “About Us” content is mostly only viewed on the third visit.
This multi-visit analysis poses a problem to traditional content analytics reporting. In traditional reporting, the “About Us” content might appear as the highest value because it was the last page viewed before the visitor started the application, when in reality the successfully converted customer likely paid much more attention to the rates-related content. This demonstrates that if Web analytics are going to be truly useful in tracking content value, they need to revolve around the sales cycles of the product.
To fully understand how content reporting needs to be customized to the sales cycle, let’s look at three different products and different sales cycles.
- Selling Low-Cost Market Research to Independent Realtors: In this sales cycle, the realtor likely knows what they want before they arrive at the website. The typical prospect probably looks at a few pages on the site to quickly analyze the value of the research and then makes a buying decision based on the relative cost. This sales cycle typically involves one site visit and multiple pageviews from one decision-maker.
- Selling Commercial Banking Solutions to Small Businesses: When a small business (i.e., 20 employees) is looking for a commercial banking provider to cover areas such as payroll, checking solutions, and insurance products, there is typically a single information gatherer who is also the sole decision-maker (i.e., the president). There are many providers in the market and making a wrong decision carries bad but not critical consequences. There are relatively low contractual commitments, but switching providers can be costly. For this reason, the decision-maker typically makes multiple visits to the website of the prospective providers, viewing multiple pages with each visit before making a decision. The time in between visits can be hours or days or even weeks.
- Selling Inventory Management Solutions to Global Manufacturers: In this sales cycle, the product is expensive and the commitment from the customer is large in terms of time to implement and support contracts. With these types of solutions it is very costly to switch from one provider to another. For these reasons, stakeholders are very deliberate in learning as much as possible about every prospective provider. Decision-makers will do everything in their power to avoid making a wrong decision. There is typically more than one influencer and multiple decision-makers. In this example, we can assume that an operations manager with oversight of the warehouse, an IT manager that will be responsible for integration of the solution, a sourcing/finance person tasked with understanding the costs and contracts, and an executive for final approval will all be involved in the sales cycle. Each of these roles will likely visit the websites of prospective providers multiple times over a long sales cycle and will look at multiple pieces of content with each visit. To make things more complex, they will sometimes look at specific pages repeatedly across multiple visits (i.e., technical specifications) and they will sometimes look at specific content on only one visit (i.e., vendor locations).
With each of these sales cycles there are different reporting needs to understand how effective the content is to the users as they research and make decisions. However, there is one common thread – in each sales cycle, content engagement metrics are important in understanding how the copy educates and drives the loyalty of prospects. In monitoring engagement we look at metrics such as the following:
- Landing page views and sessions: This gauges how effective the content is at attracting users from important digital channels such as search engines, social sites, and other referrers.
- Repeat visits: For pages that provide content that is useful throughout the sales cycle is it important to see that the content can bring visitors back for more consideration.
- Repeat visitors: For content that is more important later in the sales cycle, is important to know how many visitors that have been to the site before are looking at a specific page for the first time or repeatedly.
- Time on page, scrolling, mouse movement, and heatmaps: These important data points give us an idea of how many people are actually reading most or all of the content.
- Bounce rates and exit rates: Knowing how many people leave the site after viewing content is important. However, keep in mind that a high exit rate might actually show us that the content was successful in helping the visitor accomplish their task.
Engagement data is important in understanding the quality of content. However, typical content engagement reports don’t take into account the complex sales cycles such as the one described in the third example above (Inventory Management Solutions). When we have multiple users involved in a sales cycle visiting a website multiple times and viewing multiple different pages specific to the visitor’s role in each visit, content reporting becomes more complex. In sales cycles like these we need to understand what content is most important to each user role and when in the sales cycles it is most critical. Three reporting techniques help shed light on these models:
- Combined Click Paths/User-Based Reporting: In reporting, by combining the click paths of a single user over multiple visits we can start to see which content is important at the start of the digital relationship and which content is important toward the point of decision. For this type of reporting, we need to know the time of the first visit relative to the time of the last visit.
- Accurate Segments: When there are multiple influencers/decision-makers in multiple roles, it is important to know which content is favored by different roles. This is very difficult in Web analytics. While asking users to identify their role by having them click on a link almost never seems to work, we can often get a hint at the role of the user by the content they view. In example three above, we might be able to segment the IT users when they view technical specification from the finance users that are using TCO calculators on a site. Ad networks and some Web analytics platforms are starting to offer demographic information on anonymous site users, which helps out tremendously in determining the role of the user.
- Organization-Based Reports: Using service provider reports in Web analytics platforms or using third-party IP lookup firms such as DemandBase, we can see what companies are visiting a website. This usually only works with mid-to-large organizations, as many small companies use randomized IP addresses through an Internet service provider. The organizational-based reporting allows us to see how many different users in an organization visited a site and how many visits each unique user has had. We can also see which pieces of content were popular with multiple users on a visit-by-visit basis. When we start to compare and contrast organizational reports amongst different companies all buying similar products, trends in content consumption during the sales cycles start to emerge.
With these three types of reporting, we can start to create different models for valuing content in complex sales cycles. If we understand the average length of a sale cycles, which roles are influencers and which are decision-makers, and the average amount of visits in a sales cycle, we can start to develop content scoring models that show how strong content is at generating leads and assisting the sales team throughout the research and decision-making phases.
Content reporting in Web analytics has come a long way in the last few years, helping us to better understand how content attracts and engages users. But most Web analytics platforms focus reporting on very simple sales cycles and do little to help site owners understand how content performs in more complex sales cycles. I’m optimistic that Web analytics platforms and other tracking sources, such as marketing automation tools, will start to offer more insightful reporting for complex environments in the upcoming years.
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