This is a part of a series of articles on digital analytics
Here's a situation for you to consider.
Company A has invested in a reliable web analytics tool. Data is collected at regular intervals as specified. Over a period of time, there is an impressive amount of data that has been collected. However, at the same time, the quality and quantity of insightful reports don't seem to have improved or increased. After a lot of tinkering around, the employees concluded that the web analytics tool can only deliver so much and one can't generate any more information from it. So the tool continues to crank out data day after day, the amount of unutilized data keeps piling up and the marketers remain unsatisfied because of the lack of actionable insights.
This scenario described above is surprisingly common among many organizations. Despite purchasing the best web analytics tool in the market and having the most analytical employees onboard, the whole practice of analyzing digital data can go into a nosedive. Like the axle that is central to the functioning of every wheel, when data is not targeted to specific business requirements, things can seem like they are falling apart. You can then expect your analytics vehicle to literally go nowhere.
What marketers go through in such situations is often described as 'analysis paralysis'. Due to the sheer volume of data, analysts don't really know where to start and how to go about fixing the problem in order to deliver what is required. Also, sometimes, the insights that marketers are looking for are quite simply outside the purview of web analytics data.
So how does one overcome this chaos of having too much that isn't of much use?
At the outset:
- Ensure that your marketing goals are clearly defined
- Carefully tie these marketing goals to your analytics framework to arrive at specific information that you're looking to gain
- Collect and measure data based on the priorities of each department / function
- Define metrics for each department / function
- Focus only on 2 metrics
Isolate business insights from existing data bloat by:
- Examining how a byte of information will empower better decisions
- Examining how correlated metrics has been computed and what it implies (if anything at all)
- Analyzing it from multiple angles to determine if it adds any value
- Looking for trends instead of data pertaining to individuals
When the rest of the world is intently (and strangely!) trying to get ahead on the data collection race, one needs to take a minute to consider whether such large volumes of data are really required for the organization in question.
Sure, granular information does facilitate better targeting but the question then is, do you really need to fine tune targeting to a point where you've started creating strategies for individuals?
The answer is simple and two-fold. Develop a keen understanding of your business requirements & aim at creating a balance in the applied solution.