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Case Study: Data Cleaning for Enhanced Analytics in Marketing and Advertising



Client Overview:

AMS, a mid-sized marketing and advertising company, primarily dealt with traditional marketing techniques. With the evolving market landscape, they recognized the need to leverage their customer data to gain deeper insights and make more informed strategic decisions.

However, the data, collected over years through various channels and stored in different formats, was untidy and inconsistent. AMS lacked the necessary expertise in-house to clean and standardize this data for analytics.


Challenge:

AMS's data, spread across different sources like CRM systems, social media, and customer surveys, was riddled with inconsistencies, missing values, and unstandardized entries. This prevented AMS from effectively analyzing the data and gaining valuable insights. They needed a solution that could clean, standardize, and integrate their data while ensuring data integrity and privacy.


Our Approach:

Our team began by conducting an initial examination of the data. We used Excel to understand the size, scope, and nature of the data. The initial analysis revealed inconsistencies in customer demographic information and significant missing values in customer behavior data.


Next, we moved to the data auditing stage. By employing OpenRefine, we were able to identify and tag outliers, inconsistent entries, and fields with significant missing data.

Based on our findings, we developed a data cleaning strategy. This strategy included methods to handle missing data, correct inconsistent entries, and deal with outliers. We used data imputation techniques for handling missing data and regular expressions for standardizing entries.


Upon execution of the data cleaning strategy, a quality assurance process was run. The cleaned data was reviewed for any remaining issues, and minor corrections were made.

Throughout the process, we documented our findings, decisions, and actions to ensure transparency and reproducibility.


Delivery & Impact:

The cleaned data was delivered in a SQL database, which was compatible with AMS's existing systems and tools. The database was designed to be easily queryable, making it user-friendly for AMS's team.


Post data cleaning, AMS was able to conduct a robust analysis of their customer data.

This led to the development of more effective, data-driven marketing strategies. AMS reported an increase in customer engagement and a significant uptick in their campaign conversion rates.


This case study serves as a testament to the fact that properly cleaned and well-structured data can significantly enhance the decision-making process in the marketing and advertising industry.

Conclusion:

By investing in professional data cleaning, AMS was able to unlock the potential of their data and gain valuable insights. The experience has made them understand the importance of data hygiene, and they plan to incorporate regular data cleaning into their data management practices.

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