With both large real and hidden opportunity costs associated with evaluating, implementing, and learning the quirks of a new analytics tool, it only makes sense to ensure that the tool one chooses will be able to keep pace with one´s future requirements. So in order to evaluate tools correctly people need to be aware of the changes that are occurring in the online world as this will influence what data will be important to collect and leverage in the future. In this article I wanted to outline some of the top trends that are impacting the online marketing world so that when someone makes an analytics tool evaluation they are creating a framework for the future.

Part 3 of Framing the Future: Data Filtering and Visualization Capabilities Matter
As we are increasingly becoming more accustomed to the ease of use and nice visual displays offered by iPhones and Google Maps, we will definitely start to demand that these same types of interfaces be available from our analytics applications.

Therefore, if an analytics tool is able to present data in a visually intuitive, easy to understand format, the end users of that data are much more likely to be able to take action on the information. A good example of data visualization from an analytics tool would be the use of heatmaps, which are overlays on any webpage that can visually display where people clicked and where they did not – even in areas that are not hyperlinked. This gives any web designer immediate, clear feedback about what is attracting clicks and/or what the visitors "think" should be clickable.

Ideally, a heatmap should also be configurable to display any variety of different groups´ behavior – i.e. where purchasers vs. non-purchasers clicked, or where people clicked that came from different cities or different search engines. In addition, a nice analytics tool visualization is the website overlay view, which can also show how many clicks happened on each link and then should have the capability to drill down on these clicks to see much more detailed views (what campaign, what search word, what geography these clicks came from, etc. )

Or an analytics tool could visually show the path that the visitor took through each page before converting into a buyer, or which marketing channels the visitor clicked on before the sale. In sum, any graph that makes the data easy to understand and interpret will add value by enabling the analyst to find useful insights.

Equally important to the visual display is the filtering capability offered by your analytics tool. Since typical tools tend to aggregate and integrate information, tools that offer the ability to filter the data first and then transform it into easy to use bits of information with nice visual displays will outperform the others.

As Greg Boutin says, just like Google became the doorman to the web because it filtered things better than others. Filtering, not aggregating, is where the money is. Not more [information]...just smarter."

As we generate and collect more data, the filtering and visual display of it enables the end user to make sense of it and leverage both the aggregate trends as well as the appropriate detailed views in order to take appropriate actions. Therefore evaluating your analytics tool to ensure that it has the ability to convey a message with useful visual displays of the data should also be on the list of factors to consider.

This is Part 3: Data Filtering and Visualization Capabilities Matter, of a Five Part Series; See also Part 1: Data Trumps Everything, Part 2: The Real Time Web Reality, and tomorrow - Part 4: Data Privacy Demands more Diligence.

Cecily Robyn Lough has over 15 years experience in pulling actionable insights from online marketing data. She is currently Director of International Sales at Webtrekk GmbH, a leading Web Analytics company based in Berlin, Germany. Please contact her through LinkedIn or at cecilyspeaks@gmail.com.

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