Lead Management for Relevant Marketing
Posted: Sun Feb 02, 2025 4:41 am
Lead Management Tips
The 10 most important tips for higher data quality
Lead management is the generation, processing and evaluation of prospect and customer data. The goal is to use the data to conduct even more relevant marketing in order to generate more sales at the end of the day.
Lead management and generation with AGNITAS AG
Unfortunately, the success of lead management is often compromised by the insufficient quality of the database. This can mean that the status of a lead in the purchasing process cannot be precisely identified, that it is not possible to create an exact target group, or that the lead is not contacted correctly.
In general, when it comes to data quality, a little preparatory work can often save a lot of time and money. We have therefore compiled our best practical tips below to help you ensure that your lead data is of high quality.
10 Tips for Successful Lead Management through Good Data Quality
1. Define in advance which character encoding you want to use for your data so that umlauts and special characters are not destroyed during data transfers.
Our recommendation: If you are only using the software in German-speaking countries, use ISO8859-15 encoding, otherwise use UTF-8. Make sure that all external data suppliers provide the data with the same character encoding. This can be determined by visually checking some test data sets (e.g. open the file in Excel with the desired character encoding and check whether umlauts are displayed correctly).
2. Work with a separate and unique number (ID) per record that you use for references.
If you were to use an existing field such as the email address as a reference and this were to change later, all references for this record would be lost.
3. Consider in advance which fields must be alphanumeric rather than numeric.
For example, a field for the postal code must be alphanumeric, because otherwise the postal code “01234” would automatically be shortened to “1234”. A field for the telephone number should also be alphanumeric to allow entries such as “++49 89 / 55 29 08-0”, which is much clearer than the number “49895529080”.
4. Define a uniform format for displaying date values.
Example: “31.12.2015” or with time: “31.12.2015 23:59:59”
This clearly defines the order of the date components.
5. For current reasons: Internally choose a numerical value for gender.
This means that the list can be expanded and is not limited to the classics “male”, “female” and “unknown” (see the development on Facebook).
6. Perform pre-validation for external data sources.
Examples of validations: Check whether all mandatory kuwait phone number data fields are filled in, whether an email address contains the @ sign and the address ends with a valid top-level domain, whether postal codes are 5 digits long (Germany) or 4 digits long (Austria and Switzerland), etc.
7. Define a separate data source ID for each data source.
The data source ID is a unique identifier for each data source. This allows you to later determine how a data record got into your database.
8. Add the fields “CREATION_DATE” and “CHANGE_DATE” to the lead records.
Define the CREATION_DATE field in the database so that it is populated with the current date when the record is newly created. Define the CHANGE_DATE field so that it is populated with the current date every time the record is changed. This way you always know when a particular record was added to your database and when it was last changed.
9. Standardize and automate manual data processing processes.
Standardize and automate comparisons and validations as much as possible to eliminate human error sources.
10. Periodically check the quality of your data, for example by determining the number of duplicates and conducting a random visual inspection.
The latter measure occasionally allows you to find errors that were not detected by automated validations, such as fake data or incorrect formatting.
The 10 most important tips for higher data quality
Lead management is the generation, processing and evaluation of prospect and customer data. The goal is to use the data to conduct even more relevant marketing in order to generate more sales at the end of the day.
Lead management and generation with AGNITAS AG
Unfortunately, the success of lead management is often compromised by the insufficient quality of the database. This can mean that the status of a lead in the purchasing process cannot be precisely identified, that it is not possible to create an exact target group, or that the lead is not contacted correctly.
In general, when it comes to data quality, a little preparatory work can often save a lot of time and money. We have therefore compiled our best practical tips below to help you ensure that your lead data is of high quality.
10 Tips for Successful Lead Management through Good Data Quality
1. Define in advance which character encoding you want to use for your data so that umlauts and special characters are not destroyed during data transfers.
Our recommendation: If you are only using the software in German-speaking countries, use ISO8859-15 encoding, otherwise use UTF-8. Make sure that all external data suppliers provide the data with the same character encoding. This can be determined by visually checking some test data sets (e.g. open the file in Excel with the desired character encoding and check whether umlauts are displayed correctly).
2. Work with a separate and unique number (ID) per record that you use for references.
If you were to use an existing field such as the email address as a reference and this were to change later, all references for this record would be lost.
3. Consider in advance which fields must be alphanumeric rather than numeric.
For example, a field for the postal code must be alphanumeric, because otherwise the postal code “01234” would automatically be shortened to “1234”. A field for the telephone number should also be alphanumeric to allow entries such as “++49 89 / 55 29 08-0”, which is much clearer than the number “49895529080”.
4. Define a uniform format for displaying date values.
Example: “31.12.2015” or with time: “31.12.2015 23:59:59”
This clearly defines the order of the date components.
5. For current reasons: Internally choose a numerical value for gender.
This means that the list can be expanded and is not limited to the classics “male”, “female” and “unknown” (see the development on Facebook).
6. Perform pre-validation for external data sources.
Examples of validations: Check whether all mandatory kuwait phone number data fields are filled in, whether an email address contains the @ sign and the address ends with a valid top-level domain, whether postal codes are 5 digits long (Germany) or 4 digits long (Austria and Switzerland), etc.
7. Define a separate data source ID for each data source.
The data source ID is a unique identifier for each data source. This allows you to later determine how a data record got into your database.
8. Add the fields “CREATION_DATE” and “CHANGE_DATE” to the lead records.
Define the CREATION_DATE field in the database so that it is populated with the current date when the record is newly created. Define the CHANGE_DATE field so that it is populated with the current date every time the record is changed. This way you always know when a particular record was added to your database and when it was last changed.
9. Standardize and automate manual data processing processes.
Standardize and automate comparisons and validations as much as possible to eliminate human error sources.
10. Periodically check the quality of your data, for example by determining the number of duplicates and conducting a random visual inspection.
The latter measure occasionally allows you to find errors that were not detected by automated validations, such as fake data or incorrect formatting.