If you’ve ever participated in a data analytics implementation, you may be familiar with the indescribable excitement around the project. Who wouldn’t be eager for a solution that makes it easier and more efficient to understand and serve your customers?
This week we’re wrapping up work with a client that focused on using data analytics to facilitate marketing automation based on web traffic. For example, the client wanted to message only to a group of members who visited a particular page or series of pages on their website. By visiting the page, they showed implicit interest in the topic, which means they might be interesting a related publication or event.
We've demonstrated the importance of both leveraging the data that your association already has along with extending beyond the walls of your organizations to understand customer journeys. Incorporating publicly available data provides many creative opportunities to further create association analytics to drive data-guided decisions.
This is a continuation of our previous blog on a Guide to Analysis. The previous blog covered defining your S.M.A.R.T. goal. In this section we will discuss preparing and checking your data for analysis.
Did you know that approximately 70% of the body’s sense receptors reside in the eye? Of all 5 senses, vision stands out dramatically as our primary and most powerful channel of input from the world around us! Not only that, but apparently, “the eye and visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers.”* I envision a super highway from the eye to the brain. This helps explain why data visualization is so powerful.
One of the most important factors in successfully adopting a data strategy is the data champion. This person is able -- through influence, education or example -- to advance the cause of data throughout the organization. His or her role will vary between organizations, but should include some of the following:
Many associations want to do more advanced analytics projects using R — a programming language used for statistics — but are not sure how to start.
Tableau 9.3 was released last week and has several exciting new features. These are a few of my favorites.