As we all know that analytics is important for SEO. Most of the Marketers spend more time on the data. We all think ourselves to some extent as a part of analyst. At the base of a good marketing team, analytics is a platform which provides a ready to use information. We must always sense that the information is just a log in away. With this we can use the data for great recommendations, solve serious issues, and anticipate our efforts accurately. We must all feel totally in command of our analytics, and use them daily.
Maybe a fistful of you work on teams that are doing all they can do as it relates to analytics. Perhaps some of you have even provided your team with a fistful of full-time analysts. More likely, most of the people are not using analytics in exhaustive manner or as effectively as you wish you were.
Here are the different types of analytics and common places to start; first reason is that the marketing teams aren’t as data driven as they are supposed to be because data is intimidating. However, knowledge trumps performance demoralizing. The more knowledge you have with it, the more comfortable you will be with analytics, so let's learn it.
The target of all data analytics is to leave us more learned than before so we can execute better in the future. Sounds simple but not really because a common misunderstanding among marketers is that all analysis is similar, but it isn't true. There are actually three types of analytics:
1.Predictive
2.Prescriptive
3.Descriptive
Most marketers consume their time on one analytics i.e. descriptive. As you can imagine that leaves lot of good content on the table.
Let's go through the three analytics and their differences.
Descriptive Analytics
Descriptive analytics is when we deposit our concerning past events performance for insights. Often, we are concerned about getting context or tell a tale with the data. This is done by most of the marketers on a daily basis, especially in their web analytics. We look at how is our performance and we try to see what is happening and how is it affecting everything else.
Predictive Analytics
This analytics takes us one step further. It is less about the questions, and more about the suggestions. It includes looking at your historical information, and coming up with predictions on what to await next. This is most easily used in our industry when we try to foretell how following month will perform based on this month's accomplishment. This seems like the next step for analysis is to find out how many marketers stop at descriptive, and fall through to push into this arena. Often, it's because this involves predictive modeling which can, again, be very horrifying.
Prescriptive Analytics
In this analytics things can get fun because it takes a step further then previous analytics methods. With prescriptive analytics, you inevitably mine data sets, and apply business regulations or machine knowledge therefore you can do predictions and next move faster. Marketers do not look after this as their responsibility but as someone else to think about and solve it. Therefore it can be super dangerous attitude, which hits the company's business. This analytics can be a good accelerator for success at a company.
So, are you doing enough?
When we ask this question with marketers they began to think that descriptive analytics was their job, and other stuff was for someone else to figure out. One should break through this stereotype work. Data can be fun and it is accessible to everyone therefore it should be part of everyone's job and it really should be.
Imagine this for a second: just think about how much job could get done if every team felt together authorized to tell a story with the data, make forecast off of it, and prescribe next ladder for the biggest gains.
Probably, study of these types of analytics above is a great remembrance that there is more than just descriptive analytics.
Maybe a fistful of you work on teams that are doing all they can do as it relates to analytics. Perhaps some of you have even provided your team with a fistful of full-time analysts. More likely, most of the people are not using analytics in exhaustive manner or as effectively as you wish you were.
Here are the different types of analytics and common places to start; first reason is that the marketing teams aren’t as data driven as they are supposed to be because data is intimidating. However, knowledge trumps performance demoralizing. The more knowledge you have with it, the more comfortable you will be with analytics, so let's learn it.
What are the different types of analytics?
The target of all data analytics is to leave us more learned than before so we can execute better in the future. Sounds simple but not really because a common misunderstanding among marketers is that all analysis is similar, but it isn't true. There are actually three types of analytics:
1.Predictive
2.Prescriptive
3.Descriptive
Most marketers consume their time on one analytics i.e. descriptive. As you can imagine that leaves lot of good content on the table.
Let's go through the three analytics and their differences.
Descriptive Analytics
Descriptive analytics is when we deposit our concerning past events performance for insights. Often, we are concerned about getting context or tell a tale with the data. This is done by most of the marketers on a daily basis, especially in their web analytics. We look at how is our performance and we try to see what is happening and how is it affecting everything else.
Predictive Analytics
This analytics takes us one step further. It is less about the questions, and more about the suggestions. It includes looking at your historical information, and coming up with predictions on what to await next. This is most easily used in our industry when we try to foretell how following month will perform based on this month's accomplishment. This seems like the next step for analysis is to find out how many marketers stop at descriptive, and fall through to push into this arena. Often, it's because this involves predictive modeling which can, again, be very horrifying.
Prescriptive Analytics
In this analytics things can get fun because it takes a step further then previous analytics methods. With prescriptive analytics, you inevitably mine data sets, and apply business regulations or machine knowledge therefore you can do predictions and next move faster. Marketers do not look after this as their responsibility but as someone else to think about and solve it. Therefore it can be super dangerous attitude, which hits the company's business. This analytics can be a good accelerator for success at a company.
So, are you doing enough?
When we ask this question with marketers they began to think that descriptive analytics was their job, and other stuff was for someone else to figure out. One should break through this stereotype work. Data can be fun and it is accessible to everyone therefore it should be part of everyone's job and it really should be.
Imagine this for a second: just think about how much job could get done if every team felt together authorized to tell a story with the data, make forecast off of it, and prescribe next ladder for the biggest gains.
Probably, study of these types of analytics above is a great remembrance that there is more than just descriptive analytics.
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