6 Common Data Analytics Mistakes every Marketer makes

6 Common Data Analytics Mistakes every Marketer makes

“Mistakes are the portals of discovery.”- James Joyce (famous Irish novelist). Data Analytics Mistakes may cost big to a Business but at the same time they help in discovering new trends and finding more patterns in the data set.

Data Analytics is becoming more and more important for businesses to help managers make informed decisions, drive the business forward, improve efficiency and achieve organizational goals.

Data Analytics is everywhere. Cautious and thorough execution of data analysis is therefore required by the organizations.

Analytics Mistakes by Marketeers

There are a few traps that marketers fall into in data and campaign analysis. Larry Alton, a freelance journalist covering issues related to technology and data security, in his latest article listed on these common mistakes.

1. Bad Data or Dirty Data

Bad or Dirty Data refers to information that can be erroneous, inappropriate, non-conforming, misleading, or without general formatting. Dirty data can wreak havoc on the entire organization.

Bad Data or Dirty Data

It can lead to higher consumption of resources, errors in product or service deliveries, lower customer satisfaction, increased churn rate, distortion of campaign success metrics, failure of marketing automation initiatives etc.

Bad Data cannot be removed entirely but through Data Management and centralized Data Governance, cleaner and more accurate data can be obtained.

2. Trusting Data Visualization Blindly

Bad Data or Dirty Data

Humans consume information easily when it is represented visually than in the form of texts. Consumers tend to blindly accept message portrayed in charts and graphs and hence it is essential for a data analyst to carefully interpret the results of data visualization before it turns out to be misinformation.

3. Limited variables

Basing the analysis on one or two key metrics does not yield accurate results. The sensitivity of results depends on it severely. Hence, the number of variables depends upon what precision is enough for a particular problem, i.e. the magnitude of differences needed to prove it to be statistically significant.

Limited variables

Incorporating all the variables is also a bad approach in getting the patterns of the data set. One should be fully aware of the adequate number of variables needed in order to obtain the best outcome from the process.

4. Confirmation Bias

Confirmation bias is the tendency to support and process data in such a way that it favors well with the pre-existing idea of the person. It leads to errors towards confirmation of the hypothesis under study.

To reduce this, researchers must continually re-evaluate impressions of respondents and challenge pre-existing assumptions and hypotheses.

5. Dropping Outliers

An outlier is a value distant from other data points due to variability or experimental error. Most statistical measures such as means, standard deviations, correlations are sensitive to outliers.

Dropping Outliers

Since it can change the result and assumptions of the study, it is important to investigate the nature of the outlier before deciding to drop it or not.

6. Building a Model on the Wrong Population

If the objective of a study is to model the customer influence patterns, then the model should be built on considering the behavioral data of customers who are highly influential and also those who are less influential but are likely to be influenced.

Otherwise, the biased consideration of population can skew the model and impact the customer segmentation.


In Conclusion


“Mistakes are the portals of discovery.”- James Joyce (famous Irish novelist). When it comes to data analytics mistakes help in discovering new trends and finding more patterns in the data set. However, it is essential for a data analyst to learn from the mistakes and avoid them in the future.


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