The modern analytics package is quite robust, with more possibilities available than any one company can or should try to utilize at once. Aggregating big data is only the first step of the process. Let's take a look at some of the questions you should ask yourself to optimize your results.

Are you drilling down into your analytics function using customized KPIs?

Your analytics program will present you with a bevy of great looking statistics including unique visitors, bounce rate, average time on site, total page views and social media traffic referral. It is up to you to first determine which of these stats are actually relevant to your company.

State your purpose and agree upon it interdepartmentally: Are we all looking for increased customer acquisitions? Are we trying to increase participation in our loyalty programs? Does our strategy need to be localized in some way? Determining what you are looking for will allow you to begin drilling down into the numbers that really mean something; moreover, you will have a clear understanding of how the raw data relates to itself.

Is the company devoted to predictive analytics?

Big data gains its true power from an attitude of continuous improvement. Markets move more quickly than ever, and the primary purpose of data aggregation and marketplace analytics must be to step ahead of the curve, not follow it.

Predictive analytics also work best when choices begin from a wide spectrum of thought and narrow themselves as your analysis continues. In short, less data means more ideas on the table. As you gain more data, improve your subsequent predictions by removing the least efficient options from the table. It will be tempting to begin testing elementary ideas during an intermediary phase - try to curb this inclination through robust brainstorming sessions from the beginning of the process with the underlying theme of "speak RIGHT now or forever hold your peace."

Data is never black and white.

Right and wrong are usually not what data will show you (unless you have an ad that gets 0 clicks; then of course scrap it). Instead, data tends to lend itself towards an interpretation of less correct and more correct. If you make it a point to drill down into data looking for hard answers, you are likely to be disappointed.

For instance, the most effective ad may be a composite of your current efforts, but you will never know unless you are considering the nuances of each dataset during multivariate testing. A keyword from here, the sentence structure from there, a sentence length from a third place - this type of  thought process will likely lead you to an optimized solution in an efficient timespace.

If things get confusing, focus on a specific buyer profile.

Is it more profitable to focus the interpretations of your data (and the budget that your analysis will invariably call for) on first responders, loyalists or new prospects? Perhaps you are looking to optimize sales during a hot season, in which case, you may need to focus on your loyalists. However, introducing a new product during a downswing may require a redirection towards your first responders. Determining your core audience (within your core audience) will streamline the interpretation of your data as well.

For example, a focus on new prospects means that total time on site may not be as important as total pageviews. If you are looking to attract your first responders back, then your bounce rate might take a higher priority than it normally does.

Consider other methods of aggregating data.

Without overdoing the aggregation aspect of your analytics, consider a link shortener, UTM tracking codes and social media platform metrics to bolster the volume of your data. This tip is usually best for new companies without enough data to form an optimized sample size. However, at least one test run of these solutions may benefit more established companies as well; you never know what data you may be missing if you completely ignore these avenues.