What are the 5 Types of Marketing Data Management? - 3 views

• 
  #1 paulmelton on 30 Sep 24

   

  There are multiple operations happening in an average digital marketing data management system. Here are the most common ones.
  
  Data integration
  As marketing typically involves dozens of data sources, from marketing channels to CRMs, data integration is a key process in the data management cycle. It involves taking data from multiple sources that may have different data types and formats and bringing it together in the data storage facility.
  
  Executing it correctly takes a deep understanding of the data systems of each of the sources used and transforms the data to conform to a uniform standard.
  
  Data refining
  Refining data is a process that searches for and eliminates data that is incorrect or corrupted. During data refining, your analytics tools should check for data entry mistakes and other inconsistencies, and catch wrong formatting like mixed-up date formats, duplicate or confusing column names, etc.
  
  This turns raw data into a format that is easy to parse and analyze.
  
  Data enrichment
  Datasets are rarely complete, but thanks to having access to multiple sources, there's a chance there is plenty of duplicate data. While the bulk of it should be cleansed during the refinement process, you can use some duplicate data to enrich your dataset. This means fixing missing data.
  
  This also means adding more columns of data to a dataset, for instance adding sales data to the Facebook ads dataset. The result is a dataset that may provide much more insight into the effectiveness of your marketing campaigns.
  
  Data stitching
  Data stitching operates on a similar idea. It's the process of taking customer data that is present across different datasets and integrating it into a single one. This allows for a deeper understanding of how customers interact with different departments like marketing and sales.
  
  Data mining
  Data mining helps businesses make more sense of large volumes of data. This process involves analyzing datasets to find trends and correlations. These are used for further business analysis and can help you arrive at conclusions about your marketing performance that may not be achievable with a simple marketing dashboard.

   

  <div class="cArrow"> </div><div class="cContentInner">There are multiple operations happening in an average digital marketing data management system. Here are the most common ones. <br /> <br /> Data integration <br /> As marketing typically involves dozens of data sources, from marketing channels to CRMs, data integration is a key process in the data management cycle. It involves taking data from multiple sources that may have different data types and formats and bringing it together in the data storage facility. <br /> <br /> Executing it correctly takes a deep understanding of the data systems of each of the sources used and transforms the data to conform to a uniform standard. <br /> <br /> Data refining <br /> Refining data is a process that searches for and eliminates data that is incorrect or corrupted. During data refining, your analytics tools should check for data entry mistakes and other inconsistencies, and catch wrong formatting like mixed-up date formats, duplicate or confusing column names, etc. <br /> <br /> This turns raw data into a format that is easy to parse and analyze. <br /> <br /> Data enrichment <br /> Datasets are rarely complete, but thanks to having access to multiple sources, there's a chance there is plenty of duplicate data. While the bulk of it should be cleansed during the refinement process, you can use some duplicate data to enrich your dataset. This means fixing missing data. <br /> <br /> This also means adding more columns of data to a dataset, for instance adding sales data to the Facebook ads dataset. The result is a dataset that may provide much more insight into the effectiveness of your marketing campaigns. <br /> <br /> Data stitching <br /> Data stitching operates on a similar idea. It's the process of taking customer data that is present across different datasets and integrating it into a single one. This allows for a deeper understanding of how customers interact with different departments like marketing and sales. <br /> <br /> Data mining <br /> Data mining helps businesses make more sense of large volumes of data. This process involves analyzing datasets to find trends and correlations. These are used for further business analysis and can help you arrive at conclusions about your marketing performance that may not be achievable with a simple marketing dashboard.</div>

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• 
  #2 samjonson on 30 Sep 24

   

  Choosing the right words and sending inquiry emails at the perfect time can make all the difference in grabbing customers' attention and encouraging them to share their experience, take a look at the customer review email template. By taking advantage of emails, you can improve your brand reputation and increase conversions throughout the customer journey.

   

  <div class="cArrow"> </div><div class="cContentInner">Choosing the right words and sending inquiry emails at the perfect time can make all the difference in grabbing customers' attention and encouraging them to share their experience, take a look at the <a href="https://nandbox.com/how-to-effectively-use-customer-reviews-in-your-email-marketing-campaigns/" rel="nofollow">customer review email template</a>. By taking advantage of emails, you can improve your brand reputation and increase conversions throughout the customer journey.</div>

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