5 Mistakes of Businessmen in Conducting Data Analysis

How do you know how your website is performing? The easiest answer is to use tools like A / B Testing and Analytics. Not using these two tools makes you make a big mistake. Both of them can produce important data for decision making related to website evaluation and improvement.

Website evaluation and improvement are important so that the resources you spend on website operations are not wasted. So data from Analytics is very valuable. It’s just that reading the data needs to be careful because it can lead to wrong conclusions. As a result, media programs with poor performance are retained, while those that are good are deleted. To avoid these errors, reading the data needs to be done by avoiding the five mistakes that business people often make. Here are five mistakes that often occur.

Confused or biased perspective in reading data

You will certainly feel pity to stop a program that has been executed and struggled for a long time. While on the other hand, your competitors will do anything to tackle the strategic steps that you take. Competitors will use every bad result they come across to stop your digital campaign.

The pressure of these conditions will make you not neutral in seeing the problem. As a result, you have ambiguous perspectives and thus bias the interests in reading the data. Even reading the data objectively becomes difficult. No matter how much you love a work program, you need to be realistic about reading the data. Poor results exist as an evaluator of what to do. This is your opportunity to learn more about website work programs. Remember that the original goal of a business is a Return of Investment (ROI), You can also use lcs2 software to get big leads through online marketing, not wasting resources without results or a mere hobby.

Cannot distinguish between “correlation” and “cause”

When reviewing data from your website’s Analytics account, be careful not to conclude cause and effect based solely on correlated trends. You will need to dig a little deeper and find out if there is a link between the two items, or if other outside factors are affecting the variables in the game. You may need to run additional tests to find out.

A simple example is a correlation between weight gain and a person’s stress level. The two items are correlated or related, but not necessarily the level of stress is the cause of weight gain. It could be that the higher the level of stress makes people need sugar consumption to produce hormones that can calm the brain. This means that weight gain in people suffering from stress is caused by excessive sugar consumption.

Confusion between statistical significance and actual significance

Analytics will generate large amounts of data sets, especially when you want to know how your website is performing in real-time. For example, there is a change in the trend of content themes accessed by website visitors within an hour. The cause can be due to external factors or the driving of visitors to certain content with the settings you make.

It is of statistical significance but not of actual significance, where you must take action on the full course of business. You can ask the following media and marketing campaign teams. Would a 1% increase or decrease in conversion rate cause an actual difference sufficient to warrant a change in my marketing campaign? Businesses must weigh the costs and problems involved in changing the actual significance of the results.

Failure to properly format data before performing analysis

The activity of ensuring formatting and double-checking the accuracy of the data can consume a lot of time in the data analysis process. You may be tempted to simply go over the data and draw conclusions rather than taking the time to organize everything so that it is formatted correctly. This can cause significant errors in the analysis, and lead to more work revising and reworking the data in the future.

Instead, select some data and check its accuracy against different sources. For example, make sure the name and date of purchase are aligned correctly, and that there are no missing information or blank fields. Take the time to organize your data in order so that you can spot trends and build the right reports. A simple way to ensure that the data structure and format are in order is to check and check with other staff. Even though it looks like a hassle, it will minimize errors and save overall time.

Still asking if Visits and Views are the same

Always remember that Visits and Views are not the same things. Visits are a count of the number of visits to your website from URLs outside of your site. Visitors can view various pages which will then be counted as the number of Views. A visiting session will end when the visitor’s IP address closes the browser window of your website or is inactive for a certain period. You have to keep in mind that misreading Analytics will influence digital campaign decision making. So as much as possible keep your viewpoint unbiased when reading Analytics data and stay objective.