Data analysis to project most successful recurring billing dates
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£25/hr(approx. $31/hr)
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WordPress Development > Data Science > Game Development > Mobile App Development (Android +IOS) > Web Apps (MERN Stack + MEAN Stack ) > Woo Commerce Development > Shopify > Magento >
Rawalpindi
25891703091106301589256748745639676532951252489535118471509288837108869678892703645
Description
Experience Level: Expert
We are looking for someone who can help us to fully understand our data for reccuring card payment failures in different territories, then help us to optimise our billing algorithm to reduce the failure rates. It's going to be about the data as well as establishing trends and coming up with suggestions. Ultimately it's about optimising things to increase our success rates on one side and reducing the amount of money we spend on the other side with unsuccessful billing attempts in charges.
For example we operate in a number of different territories so we will need to come up with a specific algorithm for each territory based on our data that includes things like.
1. Most successful day of the week to re try
2. Most successful day of the month to re try
3. Other key dates that we can identify in each month (ie. such as maybe the last Friday of the month).
4. Analysis of dates for that user that were previously successful
5. Any other way that we can use the data that you can think of to aid success rates.
6. Optimum number of re tries on different dates before we stop trying to bill (as we get charged for each try)
7. We can also use our initial purchase dates and success data sets to help in each territory.
8. Certain card types that just never re bill in certain territories
So as a very rough example it could look something like this
In South Africa analysis indicates that the highest percentage of payments that have failed will go through after 8pm local time on the second Friday of each month.
The next most likely attempt would be to last day of the month etc etc.
Customers who pay using a zoom prepaid card will never be able to refill so never re try etc etc.
We will send you our existing rebelling methods as well for each model so you can see if you can improve on those.
There could also be ongoing work from this after in terms of optimising the costs of different membership packages in each territory as well as analysing what offers work the best etc.
For example we operate in a number of different territories so we will need to come up with a specific algorithm for each territory based on our data that includes things like.
1. Most successful day of the week to re try
2. Most successful day of the month to re try
3. Other key dates that we can identify in each month (ie. such as maybe the last Friday of the month).
4. Analysis of dates for that user that were previously successful
5. Any other way that we can use the data that you can think of to aid success rates.
6. Optimum number of re tries on different dates before we stop trying to bill (as we get charged for each try)
7. We can also use our initial purchase dates and success data sets to help in each territory.
8. Certain card types that just never re bill in certain territories
So as a very rough example it could look something like this
In South Africa analysis indicates that the highest percentage of payments that have failed will go through after 8pm local time on the second Friday of each month.
The next most likely attempt would be to last day of the month etc etc.
Customers who pay using a zoom prepaid card will never be able to refill so never re try etc etc.
We will send you our existing rebelling methods as well for each model so you can see if you can improve on those.
There could also be ongoing work from this after in terms of optimising the costs of different membership packages in each territory as well as analysing what offers work the best etc.
Martin M.
100% (9)Projects Completed
11
Freelancers worked with
9
Projects awarded
31%
Last project
1 Aug 2023
United Kingdom
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