The relevance of Digital Analysis: the use of data-driven approach
The Digital Analysis is the analysis of qualitative and quantitative data of your company and the competitor, with the goal to obtain an improvement of the users online experience, to lead them towards the required results. For this reason, is essential for the Digital Marketing to realize the “customer-centric” strategies.
In this process the analysis results to be an integral part and the data interpretation is essential to communicate with the customers and to understand their needs. Anytime a customer can start his purchase path.
During the years we attended a continue evolution of Internet, and it wonder if we are taking advantage about these continues changes. The companies not only need a website, but also a corporate blog with social media profiles as Facebook, Twitter and Instagram.
To have a correct management of these pages, you need to have planning, purposes and suitables report.
The companies have to analyze the qualitative and quantitative data, to evaluate the results and to obtain a continuous improvement process. However, the web is a connection for the customers, so the data knowledge is essential to achieve success.
Which data are often overvalued?
Often in some cases we concentrate on insignificant data from the point of view of marketing decisions. The overvalued data are often:
Website traffic
The website traffic not developed is insignificant. What would be more appropriate to do if it should increase of decrease? First of all you need to identify and locate the traffic source:
Organic and paid ads, social media, etc.
If the sources are individually followed, you can understand the general trends. It could turn out important or not, to compare the time periods with the same time frame of the previous year.
For example, if you compare the last trimester with the same of the previous year, you can certify the real trends especially if you use new marketing tools.
Bounce rate
Once the bounce rate into the website should be about 40%. This “metric” is insignificant, because, different pages, are characterized by different bounce rates. In the case of, your homepage should have a bounce rate of 50%, what could you do? Is negative or positive?
The same question is for the blog post, with a bounce rate of 90%.
Usually, the users are interested to read the post and to go back without doing other actions, so the bounce rate could be high.
It is sure that, if the destination page generates a bounce rate of 90%, you should worry about it.
Which data you need to track?
Targets
If you create a campaign, how many users can convert into Leads or even into customers?
This is possible through LeadBI platform, using the available campaigns.
The same process is represented by a Form, for example a free download since to a “thank you page”, in which you can convert the users and change them into potential customers.
Canalizations
Once you decide the goals, you need to activate the canalization, and set a path that the users have to follow, to achieve the goal with the web address, such for example visiting the home page; to sign up the form; to download an ebook and to achieve on “a thank you page”.
Through the canalization, you can verify the number of users that followed every step of the path, and about the users that have not completed the steps. If all the users are stuck on the second step, it could be an error: the form doesn’t work in a correct way, is too long or the users don’t understand it. You need to carry out a test and to view the feedback achieved, to solve the potential problems and finally to check again if there is an improvement about a conversion rate.
Among the various aspects connected to digital analysis, some of them can reveal essential.
The conversions, as opposed to the traffic and the rate, turn out to be significant, such as the conversion from anonymous visitor to Lead.
Therefore, we can deduce that the real challenge is not obtain the data to analyze, but to know how to do it in a more efficient way to the business goals.
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