For a webshop, calculating the ROI for campaigns is relatively simple. But what if you don’t sell any products, and your primary income is generated by subsidies? How do you determine the impact of your content and campaigns? What is the return from your website, or from you as content marketer for your organisation?
Of course, you look at conversion targets on contact requests or interaction. And yes, these provide a good quality indication however, they also say very little about your conversion rate. A registration for the newsletter or a pdf download can have a variable value and absolute figures. How can you manage on this basis?
Digital Agency TamTam (part of Dept) and Vilans, a centre of expertise in the care sector, have been dealing with this for years. There is always higher demand than capacity from the internal organisation. How can you make it clear, internally, which content contributes towards the targets? How can you argue that certain content works and that you need to focus on that content, and not others?
Vilans has numerous conversion targets which provide indications on the interaction with the site, such as:
Nevertheless, these provide few pointers. The conversion rate as total is impacted by separate KPI’s. And it is ineffective and inefficient to assess content per target.
After much research with:
Every target that you set in Analytics can be ascribed a (fictional) value in Euro. We see this value as ‘points’. For example, a target such as ‘visit duration >4 minutes’ has a relatively high conversion rate but relatively low value, and thus is allocated 2 points. A newsletter registration is less common and more valuable so this is allocated 50 points.
The great thing about goal values is that they are saved as a numerical variable. And we can use these for calculations, just like turnover in the case of the e-commerce tracking code. You can then assess content and traffic sources on how they contribute to the total goal value.
Specific web pages are ascribed an average value with this setting. If you then multiply the page value with the number of displays, you obtain a more complete overview of the impact, quality or value of one specific web page.
An item, for example, that has 3,000 fewer displays than another item would have been considered less valuable. However by setting goal values, the lower performing item could well be valued more highly as this page has fulfilled more substantial conversion targets. Item 1 now has 6,000 points, for example as the page value is 2.0. And item 2 with 6,000 displays and a page value of 0.5 comes out at 3,000 points. The roles are suddenly reversed.
This gives an extra dimension to your statistics and a more accurate overview of the effectiveness of content.
But what does this actually mean? A couple of initial insights that we can take from the data already:
The step which you take when creating goal values is not revolutionary but provides a little extra depth for your statistics. As a marketer, you are thus better able to manage on the basis of which content and sources have to be employed.
The future? As a next-step, we would like to work towards a matrix: type of content vs subject. The idea is to be able to make searches concrete for new content; ‘blogs on X’ generally have a low contribution but an ‘interview on subject Y’ has the right effect.
This structure then becomes the basis for conversations about capacity and added value; Analytics as it should be!
*) This article was written in collaboration with Siem Valk, Content Marketer for Vilans