What is your story's perspective? Telling stories with analytics at different levels...
Today’s post on The Tuesday Standard comes after a couple weeks off. If you are in the insights and analytics world, you, like I, have been consumed with developing stories of what happened in 2017 now that the full year of data is available. At my organization, we develop Insights Reports that tells our members, and the public, what happened in the last year from a data and perspective. (See earlier editions here - new edition to come soon).
This week, I wanted to write a response to a comment from someone in a management position at a product goods company, on an earlier post on the blog. They were struggling with their current analytics program because the data was too detailed for them to use in their fast paced planning process. They cared less about the fine details of interaction on each element of each product page of their website, and more about the big picture of the journey of different visitor types. Their long tail search activity, what their return behaviour was, etc. It turns out, their analytics program is run from an IT perspective. It serves developers and technical product managers well. Their marketing department focuses on data related to campaigns – mostly offline but also some online. This organization seems to lack a 1000-foot view of the website as a channel and its overall effectiveness – or even ROI. This has become a problem with management when this view is needed.
From my perspective, I believe there are three main view levels of analytics or insights:
- 1000-foot perspective – As mentioned above, this is where you are talking high level stats and trends such as the behaviour of all visitors to your website or users of your product. This view is at the macro level trends (this link or page isn’t working for ANYONE). You might look at groups of people, or segments (discussed in an earlier post), but they tend to be quite large – such as everyone that visited the site or used the app on Tuesday – or used an iPhone device. It can also include overall visitor sources, conversion rates, etc.
- 100-foot perspective – Now we are talking about segments big or small such as groups of identifiable visitors or app users. Think in terms of commonalities: everyone that came from a google search that visited a product page and clicked ‘How to Buy’, in the case of an e-commerce site. Here you can discover click behaviour and understand what may be getting in the way of groups of people finding their way.
- 10-foot perspective – This is where you get to see specific visitor level data and find out what journey an individual is on, who they are - and why they behave the way they do. Here is an example of a narrative from REALTOR.ca that would come from this perspective. New home buyers that are looking for a home in the next 6 months will often start their search for a property type in a city - and then navigate to a neighbourhood that has the size of property that matches their needs. This is different than those upgrading or downsizing – these older buyers generally know exactly where they want to live (unless relocating) and will often confine their search to this area.
Enhanced Visitor Measurements – Analyzing Logged-in Visitor Analytics for the 10 foot perspective
It should be noted that the 10-foot perspective is difficult without having your visitors log in to your website where you are able to identify them, and collect segmentation type data to truly know who they are. There are ways to set-up current analytics platforms to not only measure the basics about visitors – gender or visitor source – but also to understand the behaviour of visitors that fit certain segmentation criteria, as well as different behaviour types. For example, how do citizens that are new graduates in IT behave on a government website that focuses on entrepreneurship. And is there a difference between province. And does this change as events such as an unemployment rates rise or decline are taken into account.
How can this be accomplished?
Authentication tools such as Gigya with Google Analytics give you the ability to identify more closely, who is visiting and what they are doing. Using your website’s data layer and pulling data from forms, etc. into your analytics platform (i.e. Google Analytics or Adobe Insights), you can start to bring together data that helps you tell stories at the various levels as required. E-commerce platforms such as Shopify will also provide data about completed and abandoned purchases that help at all view levels. This can be used to help an individual or groups/segments – or the overall performance of the shopping site.
What do you think? Thank you,