Assignment
Budget: ₹600 – ₹1,500 INR
It has -
1. Installs & Spends data from Facebook Ads. One Campaign can have multiple Ad Sets and one Ad Set can have multiple Ads.
2. Revenue data from our backend where you can identify each payment against the campaign, ad set, ad name, install date, registration date, payment date & the revenue
3. Mapping of Ad Set Names to Target Groups (TGs)
4. Mapping of TGs to broader buckets
5. Mapping of each Ad Name to the Ad Type
This data is from the last 42 days of digital marketing expenditure on Facebook Ads.
We want to know the following -
1. Week on Week overall spends vs. payback % within Day 0 of install, Day 1 of install, Week 1 of install, W2 of install & overall payback. Payback is basically the Return on Investment (Revenue/Spends, please refer to the sample output sheet for further clarification).
2. Which TG & which Ad Type is giving the best payback in Week
3. What's the Age & BMI distribution of the revenue.
4. iOS Vs. Android comparison of revenue and payback within Day 0 of install, Day 1 of install, Week 1 of install
5. How much net revenue is coming from new installs Vs. existing users reinstalls and their respective Day 7 RPI (defined as the Revenue from a particular audience segment within 7 Days (<=7) of Install Date divided by the amount spent to get those installs).
Important Notes -
1. If the install date is after the payment date then it's a reinstall from an existing paid user & this revenue should not be included in payback calculations.
2. If the Install date is after the joining date (registration date) but before the payment date then it's an existing user reinstall & the revenue should be considered.
1. Installs & Spends data from Facebook Ads. One Campaign can have multiple Ad Sets and one Ad Set can have multiple Ads.
2. Revenue data from our backend where you can identify each payment against the campaign, ad set, ad name, install date, registration date, payment date & the revenue
3. Mapping of Ad Set Names to Target Groups (TGs)
4. Mapping of TGs to broader buckets
5. Mapping of each Ad Name to the Ad Type
This data is from the last 42 days of digital marketing expenditure on Facebook Ads.
We want to know the following -
1. Week on Week overall spends vs. payback % within Day 0 of install, Day 1 of install, Week 1 of install, W2 of install & overall payback. Payback is basically the Return on Investment (Revenue/Spends, please refer to the sample output sheet for further clarification).
2. Which TG & which Ad Type is giving the best payback in Week
3. What's the Age & BMI distribution of the revenue.
4. iOS Vs. Android comparison of revenue and payback within Day 0 of install, Day 1 of install, Week 1 of install
5. How much net revenue is coming from new installs Vs. existing users reinstalls and their respective Day 7 RPI (defined as the Revenue from a particular audience segment within 7 Days (<=7) of Install Date divided by the amount spent to get those installs).
Important Notes -
1. If the install date is after the payment date then it's a reinstall from an existing paid user & this revenue should not be included in payback calculations.
2. If the Install date is after the joining date (registration date) but before the payment date then it's an existing user reinstall & the revenue should be considered.