COMMON MISTAKES TO AVOID DURING DATA ANALYSIS STAGE
Data is an important skill in the field of marketing. Data collection, making a report on data collected and making a good data analysis is also important for a growing marketer. The best marketing skill requires great skills too at data analysis and its application.
Several mistakes should be avoided if you want to become a great data master. They include;
1 .Unclear objective
A campaign started without a clear objective lead to poorly collected data, unclear results and unprofitable analysis.
When starting your campaign, have a clear hypothesis to test and have specific questions that will put clear your intentions. Determine your Northern Star metric and define your parameters, like the times and locations you will test.
2. Sampling bias
Sampling bias refers to when you make a conclusion for a certain data that is not related to the population you are trying to understand. Do your campaign and make conclusions on different platforms. Avoid depending on a few platform that might end up giving your unclear and biased results.
3. Uneven comparison
Comparing two or more sets of data can at times bring in unequal comparison hence one must exercise caution. However, comparing multiple datasets helps a lot in avoiding sampling bias.
Uneven comparison comes in when two or more data sets that are not or equal weight are compared to each other. For example, it is like comparing two reports that are from two different locations.
Before making any conclusion on any data you are working on, always make sure that they are having the same weight.
4. Understanding the meaning of a metric
It is dangerous to assume you understand the data while you do not. Metrics can be labeled or could have different meaning in different contexts.
For example, marketers believe that bounce rate are visitors leaving the site too quickly yet this is not the case. Bounce rate is the percentage of visitors who divert away from a site after viewing just one page. High bounce rate is an advantage.
To avoid confusion and having a solid data analysis, understand the meaning of each metric that is within your specific context.
5. False causality
Different marketing strategies do affect each other. If for example you start a Facebook campaign and then you realize there is a sharp uptick in organic traffic, you might conclude that Facebook campaign drove traffic to your sight. Such an assumption can cause you a lot.
Do not assume that since two events look alike, one directly caused the other. Treat each independently. Correlation is not equal to causation.
6. Losing direction of your Northern Star
Northern Star is the one metric that you should consider for your growth. Northern Star has the ability to measure accurately the success you are looking for. If you have other metrics that you are depending on, they should reunite with your Northern Star.
Avoid metrics that adds no value to you. Put effort in the main metric and focus on the indicators that directly influence it.
7. Data searching
After getting your data and testing your hypothesis, avoid the temptation of testing several new hypotheses using the same set of data. Any new correlations you make will most probably be the result of chance because you are primed by your first result to see connections that are not there in the first place. This is not useful.
Each set of data should be faced with a new, clear, fresh objective and when hypothesis changes, start your analysis with a fresh set of data.
8. Tunnel vision
Marketers get confidence through data validation because it gives quantitative truth. However, depending on raw data can mislead. As a marketer, you should avoid focusing too much on metrics.
Even though your organic traffic is up, you should check if your visitors are buying any product or service. Even though your social media presence is growing, you should check if people are engaging.
Look back at your metrics within the context of the big picture to clearly see what needs to be done.
9. Absence of statistical importance
Since you have done a test, got the data and got a winner, do not depend too much on this without real statistical importance. Have the high probability and real statistical importance in order to identify the winning variation.
10. Depending in the summary
Giving your data a quick skim and getting a conclusion out of it only gives averages of your overall metrics. Data sets that have similar averages have big variances hence depending on summary metrics to make a decision can be a bit messy.
Avoiding summary metrics and going back to your raw data can be the best method when making sound decisions.
11. Failing to differentiate between mobile and desktop
Use of mobile has caused major challenges.
Both mobile and desktop need different form of campaigns. Do not bring together your sets of data and start analyzing them as one.
Users behave differently on traditional computers and mobiles. It is important to keep the data on each device separate and analysis it separately for you to get the best results.
12. Focusing on oddities
Web data is complex and oddities will automatically appear in the process of getting the information. Oddities are a factor too but should not give you a headache. The best you can do is to ignore them.
13. Pick and choose
Picking and choosing to depend on one metric can be harmful to your activities. Depend on several metrics when making conclusions to avoid depending so much on one indicator.
14. Overfitting
Overfitting is statistical word that describe a mathematical model that exactly fits a given set of data. It explains the current set of data without giving a clear view of the future pattern.
Many marketers tend to overly depend on this, which is unhealthy to your future. As you go through your data, be sure to consider the wider, overarching patterns of habits your data uncovers inside of proving every difference. Be straight, simple and movable.
Are you looking for statistician for DNP project online? then look no further but https://helpwithdissertation.com for experienced team of data analysts
Several mistakes should be avoided if you want to become a great data master. They include;
1 .Unclear objective
A campaign started without a clear objective lead to poorly collected data, unclear results and unprofitable analysis.
When starting your campaign, have a clear hypothesis to test and have specific questions that will put clear your intentions. Determine your Northern Star metric and define your parameters, like the times and locations you will test.
2. Sampling bias
Sampling bias refers to when you make a conclusion for a certain data that is not related to the population you are trying to understand. Do your campaign and make conclusions on different platforms. Avoid depending on a few platform that might end up giving your unclear and biased results.
3. Uneven comparison
Comparing two or more sets of data can at times bring in unequal comparison hence one must exercise caution. However, comparing multiple datasets helps a lot in avoiding sampling bias.
Uneven comparison comes in when two or more data sets that are not or equal weight are compared to each other. For example, it is like comparing two reports that are from two different locations.
Before making any conclusion on any data you are working on, always make sure that they are having the same weight.
4. Understanding the meaning of a metric
It is dangerous to assume you understand the data while you do not. Metrics can be labeled or could have different meaning in different contexts.
For example, marketers believe that bounce rate are visitors leaving the site too quickly yet this is not the case. Bounce rate is the percentage of visitors who divert away from a site after viewing just one page. High bounce rate is an advantage.
To avoid confusion and having a solid data analysis, understand the meaning of each metric that is within your specific context.
5. False causality
Different marketing strategies do affect each other. If for example you start a Facebook campaign and then you realize there is a sharp uptick in organic traffic, you might conclude that Facebook campaign drove traffic to your sight. Such an assumption can cause you a lot.
Do not assume that since two events look alike, one directly caused the other. Treat each independently. Correlation is not equal to causation.
6. Losing direction of your Northern Star
Northern Star is the one metric that you should consider for your growth. Northern Star has the ability to measure accurately the success you are looking for. If you have other metrics that you are depending on, they should reunite with your Northern Star.
Avoid metrics that adds no value to you. Put effort in the main metric and focus on the indicators that directly influence it.
7. Data searching
After getting your data and testing your hypothesis, avoid the temptation of testing several new hypotheses using the same set of data. Any new correlations you make will most probably be the result of chance because you are primed by your first result to see connections that are not there in the first place. This is not useful.
Each set of data should be faced with a new, clear, fresh objective and when hypothesis changes, start your analysis with a fresh set of data.
8. Tunnel vision
Marketers get confidence through data validation because it gives quantitative truth. However, depending on raw data can mislead. As a marketer, you should avoid focusing too much on metrics.
Even though your organic traffic is up, you should check if your visitors are buying any product or service. Even though your social media presence is growing, you should check if people are engaging.
Look back at your metrics within the context of the big picture to clearly see what needs to be done.
9. Absence of statistical importance
Since you have done a test, got the data and got a winner, do not depend too much on this without real statistical importance. Have the high probability and real statistical importance in order to identify the winning variation.
10. Depending in the summary
Giving your data a quick skim and getting a conclusion out of it only gives averages of your overall metrics. Data sets that have similar averages have big variances hence depending on summary metrics to make a decision can be a bit messy.
Avoiding summary metrics and going back to your raw data can be the best method when making sound decisions.
11. Failing to differentiate between mobile and desktop
Use of mobile has caused major challenges.
Both mobile and desktop need different form of campaigns. Do not bring together your sets of data and start analyzing them as one.
Users behave differently on traditional computers and mobiles. It is important to keep the data on each device separate and analysis it separately for you to get the best results.
12. Focusing on oddities
Web data is complex and oddities will automatically appear in the process of getting the information. Oddities are a factor too but should not give you a headache. The best you can do is to ignore them.
13. Pick and choose
Picking and choosing to depend on one metric can be harmful to your activities. Depend on several metrics when making conclusions to avoid depending so much on one indicator.
14. Overfitting
Overfitting is statistical word that describe a mathematical model that exactly fits a given set of data. It explains the current set of data without giving a clear view of the future pattern.
Many marketers tend to overly depend on this, which is unhealthy to your future. As you go through your data, be sure to consider the wider, overarching patterns of habits your data uncovers inside of proving every difference. Be straight, simple and movable.
Are you looking for statistician for DNP project online? then look no further but https://helpwithdissertation.com for experienced team of data analysts
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