Targeting customers for marketing campaign is a time series problem?

Background - We pitch a product to a number of prospective customers each month via mail as a part of a campaign. Some of them don't respond, some apply and get through the application process and some don't.

I have the customer engagement data for a marketing campaign conducted each month for the below months:

• MAR-2021
• APR-2021
• JUL-2021
• SEP-2021
• OCT-2021
• NOV-2021
• JAN-2022

The missing months are when we did not have any campaign. Data shows campaign date(mail sent date), some other attributes, their segment which indicates if they are more likely to respond or less likely to respond, whether the prospective applied and whether they actually got through the application process.

My requirement-

I would like to create a model that will help me target customers better in the upcoming campaigns. I want to classify them as most likely to respond or estimate propensity to respond to my marketing email.

My question-

The responses seem to decline in the recent months. Can I/Should I treat this data as a time series? I'm missing months worth of data. How can I fill in gaps here? If I don't to treat this as a time series dataset. How do I account for the declining trend in response?