To implement the discount logic, a conditional field would be used to check if the 'Total Amount' is greater than £500. If it is, the letter will include a message indicating a discount has been applied, along with the discount amount. If not, the letter will simply state the total amount. Conditional Field Syntax (example): 500. This means "if the Total Amount is greater than 500, include the discount message". The letter template would then include the following conditional logic: If 500 then Discount Applied: £[Discount Amount] else Total Amount: £[Total Amount]. The 'Discount Amount' would need to be calculated separately (e.g., as a field in the spreadsheet or using a formula within the mail merge software). This ensures that the invoices are personalised with the correct discount information, improving customer satisfaction and accuracy.

Sorting: Sorting the donor data by 'Donation Amount' in descending order would allow the charity to prioritise letters to its highest-value donors. This ensures that key supporters receive communication first. Filtering: Filtering the data to include only donors who have made a donation in the last year ensures that the letters are only sent to active supporters, avoiding unnecessary mailings to inactive donors. Conditional Fields: A conditional field could be used to display a different message based on the donation amount. For example, if the donation amount is over £100, a thank you message could include a specific phrase like "Your generous contribution is greatly appreciated." If the donation is less than £20, a different, more general thank you message could be used. These rules ensure the letters are tailored to the individual donor and the charity's specific needs.

A data source in a mail merge provides the information that will be included in each individual document. It acts as the repository for the recipient's details, allowing for personalized communication. The data source is typically a structured collection of data, often in a tabular format.

Here are three common types of data sources:

• Spreadsheet (e.g., Microsoft Excel, Google Sheets):

  • Advantages: Easy to create and maintain, widely accessible, familiar to most users, good for simple data structures.
  • Disadvantages: Can become unwieldy with large datasets, potential for data inconsistencies if not carefully managed, limited data validation capabilities.

• Database (e.g., Microsoft Access, MySQL, PostgreSQL):

  • Advantages: Excellent for managing large and complex datasets, strong data validation and integrity features, supports complex queries and relationships, improved data consistency.
  • Disadvantages: Requires database software and knowledge to set up and maintain, can be more complex to implement than a spreadsheet.

• Text File (e.g., CSV, TXT):

  • Advantages: Simple to create and edit, universally compatible, suitable for small datasets.
  • Disadvantages: Limited data validation, can be difficult to manage large datasets, prone to errors if data is not consistently formatted.