Digital analytics develop rapidly recently as to facilitate analysts in interpreting online consumer data and afterwards allows management in establishing clear business objectives and strategies. Although web analytical tools as Google Analytics may strengthen company competitive advantages, data corruption can generate unexpected impacts on future business once inappropriately in setting up the Google Analytics profile. Today, we are going to introduce a solution to help analysts in avoiding data corruption, getting precise, accurate consumer data.

Basically, tracking customer behavior in an online platform is simple after installing a unique Google Analytics Tracking Code (GATC), which is the snippet of JavaScript to the website. Raw data will then be collected automatically in a routine basis and Google Analytics will start compiling data according to the specific setting, for instance, different profiles, goals and filters, etc. However, those data gathered may not truly portray the consumer insights as they merge with both internal traffic from company and actual customers. As you know, employees and customers behave in a totally different way. If analysts interpret and believe the consumer insights through this original set of corrupted data, the results will be horrible indeed. To avoid the situation, we have to differentiate the data by creating a so-called “Filter” in the Google Analytics before collecting the data. Of course, we have to test it before implement. In fact, one of the ways is to exclude the range of IP addresses that your company will use and even routine online navigation devices that employees use. Afterwards, they data acquired will become more truthful and insightful. A figure below shows how to setup a “Filter” in Google Analytics.

Google Analytics Filter Solution

Google Analytics Filter Solution

Here is a question on how do you avoid the filter nuke? Answer is simple, by setting up Safety Net Profiles. You are suggested to create two or more Google Analytics profiles for analysis such as “Test Profile” and “Raw Data Profile”. Within the “Test Profile”, try to establish your filters first. It’s not a big deal if you nuke your data. Next, reassure it works before applying the same filter to your main profile. For the “Raw Data Profile”, don’t try to apply any goals, filters, or others. Simply let this profile to collect data in case of a critical failure with your other profiles.

Hope the above solution helps resolving one of the challenges that digital analysts may come across in your routine analytical tasks.