The data we collect might be interesting enough, depending on what we want to accomplish, to create visualizations from. I like visualizing movement and progress – that has always been one of my favorite things to do – so that’s why I decided to write about it in this column.
But, to visualize progress (or the lack of) we need to have a framework set up to measure the progress being made toward our goals and a way to both describe and act on the information we ideate, collect, and visualize.
Challenges to Data Measurement
The biggest “gotcha” in the Convergence Assessment (which I’ll describe shortly) is figuring out just how perfect frameworks for goal measurement actually need to be.
I was thinking a lot about the degree of “precision” needed when designing measurement tasks for undergraduate students in one of my Baruch Zicklin marketing analytics classes that is executing pro bono projects, under my direction, and decided higher precision becomes more important when the measured outcome is critical to the organization whose goals are being measured.
However, when the outcomes being measured are still considered to be “exploratory,” ballpark figures are usually good enough.
Another challenge with benchmarking is determining what constitutes excellence in any particular “dimension” being measured. Sometimes, it’s easier to get started by drawing lines in the sand to begin with. If we don’t do that, it’s easy to get stuck waiting for a perfect yardstick that doesn’t yet exist within our organizations.
But common sense ought to be our guide here. If a lot of business resources are tied to the outcome being measured correctly, then perhaps more time needs to be spent on making sure data can be collected and accessed fairly and properly. If not, then you can afford to be more “creative” with how the data is marked up and applied.
The Convergence Assessment
Convergence analytics really should be about looking at several aspects of online and offline activities and evolving ways to inform them simultaneously so as to create better business outcomes. In other words, convergence becomes a focusing mechanism, while analytics acts as the byproduct of convergence, measuring business activity and results.
With this in mind, in my classes at Baruch College we are just beginning to work with organizations seeking to improve their business outcomes, yet still stuck trying to understand where the outcome gaps are, and the next steps that need to be taken.
Precision isn’t needed (yet) because the next steps aren’t fully determined, or business goals nailed down.
Figure 1 – Online Assessment of an Arts Organization in Brooklyn, N.Y., October 2014
To start with, I created an assessment of a local Arts Organization in Brooklyn, New York, to test out this idea.
Currently existing tools were used to create criteria* (a full list of tools is in the footnote). In an ideal world, entirely home-grown assessments could be developed, and perhaps this could be more meaningful. But, to begin with, what really counts is just moving forward and charting progress in order to see progress and growth and precision becomes important when more is riding on the outcome.
Figure 2 – One Year of Progress (Anticipated)
Therefore, a chart such as what I depicted in Figures 1 and 2 can be done just by using off-the-shelf tools, mostly free, to do some of the heavy lifting, to generate a quick view of current and likely future (with some work) growth – the arrows show predicted and attainable growth in specific dimensions being measured over a period of the next year. While using third-party tools is not a perfect solution (third-party tools can disappear or be acquired, therefore no longer available for use), they still good enough to measure the changes in our marketing efforts and output.
And, when the Convergence Assessment is done on a regular basis, changes in the radar diagram can be tracked, accounted for, seen, and used for insight development and goal setting, leading to more satisfying outcomes.
The Limits of What We Can Collect – Other Dimensions of Data to Eventually Report On
The dimensions I chose to represent in figures 1 and 2 are based on my popular ongoing Rutgers course focusing on social media and art, but there is much more one could represent, such as viral marketing and aspects of geo-location that would be nice to add to this assessment, but are much more work, too, as there is no common definition or tools that can fully predict what might go viral, so I left that out.
In addition, many of the exciting updates to Web analytics probably should be included in a Convergence Assessment, but it might be too early to add them.
For example, Google Analytics reporting now includes demographic and geo-demographic data, provided Google Universal Analytics is being used and demographic profiling has been enabled. With HTML5 widely implemented, geo-data from Web browsers isn’t too hard to collect, and provides a level of granularity in the reporting for most stakeholders, that has been missing from Web analytics until now.
Figure 3 – Geo-Data Reporting in Google Analytics
Figure 4 – Geo-data is fascinating to report on, but also highly sampled and difficult to put into an assessment at this point.
What’s challenging beyond the Convergence Assessment (even if the data could somehow go into the Radar Chart, in what would be a new custom dimension) is how to act on from the data being collected.
Analytics Delimited by the Language
As human beings, we’re very much creatures of the language we use, the words and terms we have. It’s much harder to talk about or act on information we don’t have a good way to describe. I fear that’s the case with a lot of the newer “convergence analytics” data – the ability to act on the data will only come about when we can set business goals with that same data. And for most of us that hasn’t happened yet, because the way business is described and enacted is still too far removed from the data we are able to collect – both need to “converge.”
In short, while many aspects to the data we collect would be nice to include in the Convergence Accessement, until stakeholders and business owners can adopt lexicon that allows them to act on this data (which is slowly evolving – so there is hope), we might not be able to include it.
Next month, I’ll talk about how to evolve the right lexicons and language so we can better ideate and use the new “converged data” we’re collecting more and more of.
*Third party tools used: WebSEOTools.com Blog Analyzer, OnlineVideoGrader.com, Klout.com, PeerReach.com, Likealyzer.com Followerwonk.com, Foursquare.com, SEMRush.com, PinAuthority.com, Marketing.Grader.com.
What’s behind a successful data-driven marketing strategy?
One of the major challenges in the martech industry is getting the attention of prospects in a world where they are bombarded by content and emails on all sides.
Facebook is addressing one of the biggest missing pieces of its chatbot offering: analytics.
ClickZ caught up with EditorEye CEO Nick Gregg to find out about the Catalyst marketing tool, and how competitor intelligence and news tracking can help marketers stay ahead of the curve.