u/Mananino

When I bought my car in 2022, I deliberately chose Proton because I was tired of the Honda, Toyota and Suzuki monopoly in Pakistan. I had criticised their inflated prices, stripped-down cars and lack of competition for years. When it was time to spend my own money, I didn’t want to become a hypocrite and simply buy a Honda City.

My other options were the Changan Alsvin, which was over budget, and a used Toyota Aqua. Even a 2016 or older Aqua was around Rs 30 lakh, while the Proton cost me Rs 23.5 lakh.

May Allah give hidayat to Al-Haj Group. They successfully managed to mishandle Proton, much like they did with FAW. With proper after-sales support and continuity, we could have had a genuinely good sedan for the masses.

Anyway, I love data, so I recorded almost every fuel purchase for four years: date, odometer reading, litres, price per litre, total cost, fuel type and sometimes the station.

I cleaned 171 entries and converted them into this dashboard to understand:

• My actual fuel average and running cost
• Whether high octane was worth the extra price
• How fuel prices affected my driving
• Whether summer and winter changed the average
• How my usage changed over time

Some headline results:

• 62,222 km driven
• 13.5 km/L overall average
• Approximately Rs 12.9 lakh spent on fuel
• High octane gave a small improvement and was almost worth the premium*
• Almost no meaningful difference between summer and winter mileage

The detailed report is attached. I would love to hear from other Proton owners and data enthusiasts. Feel free to ask anything about the car, the data or the analysis.

I also work with data and BI, so if anyone has professional feedback or knows a team that values this kind of real-world analytical work, I’d be happy to connect.

Car usage dropped as the fuel price increased around March 2026 however it seems I am now becoming more accustomed with the new standards

Surprisingly fuel efficiency slightlly increased for the first two years as the mileage increased

I tracked High Octane efficiency manually and the results were better than this. I got 14.35 km/L. To simplify if High Octange is less than 10 percent more the cost of normal petrol than it is worth the premium.

Another thing that was unexpected was that the fuel efficiency didnt change much across the seasons. I was expecting lower mileage in summer.

I’ve been working with Amazon for about five years, mostly private label now. I’m also a data/analytics guy, finishing a master’s in Robotics and AI, so I wanted to test whether AI could actually improve how I manage PPC.

I looked into dedicated AI tools and MCP integrations. Almost all options are subscription based that I dont want to invest in before having proof of work. So I started with something that anybody can do. Generated Amazon reports, bought ChatGPT for a month and made a spreadsheet.

The reports covered roughly six weeks: $15.2k ad spend, $36.8k attributed sales and 1,692 orders. Those are account totals, not sales I’m claiming AI generated.

My workflow:

1. Export campaign, targeting, search-term, placement and advertised-product reports.
2. Give ChatGPT the margins, break-even ACoS and actual objective: improve efficiency while protecting sales.
3. Ask for specific changes, supporting numbers and what could go wrong.
4. Check the recommendations, implement a small batch, and record the old setting, new setting and date.
5. Upload the next reports and compare spend, sales and orders per day.

Many encouraging results: for example after reducing a category-related target’s bid, its ACoS moved from 46.3% to 18.1%. Daily spend fell 67%, while it retained roughly 84% of its daily attributed sales. Useful improvement, although the comparison was only 19 days before versus eight days after.

another example is of the following campaign after implementing the recommended changes, ACoS dropped from 41.92% to 25.16%. Number of sales also slightly dropped but that was partly due overall budget was reduced as well.

Before

After

How the spreadsheet looks like

How the spreadsheet looks like

But the first batch wasn’t an account-wide success. Overall ACoS initially went from 42.6% to 45.5%. A placement increase also performed worse than expected. I would say 40% changes showed better results, 30% no major change (maybe due to short experiment time), and 30% showed poor results. Still I take it as a big positive as I can keep the positive changes and revert the negative coz I have all the logs available in the mastersheet.

The setup took a few iterations, but repeating it needs no API or MCP connection. That makes it practical for sellers without an agency or a technical team. No longer need to open every campaign, study placements, targeting, search terms etc. Detailed analysis with change plan is readily avaialble. I would say I saved 2 weeks of my manual work.

Lastly I I’ve been conservative so far. Next I’m testing more substantial changes to inefficient traffic and moving proven queries into dedicated exact campaigns. The goal is 30% ACoS, not there yet.

If this is useful, I’ll share the next update with the change log. My cost for subscription has already been covered by the savings, so I will be working on GPT6 Astra now.

Note: Some of this post's text has been rephrased using AI but data is 100% original

TL;DR: Built a repeatable PPC optimisation and review process using chat gpt SOL 5.6 Max. Some changes substantially improved efficiency; others disappointed. I would say its a big success.

I’ve been working with Amazon for about five years, mostly private label now. I’m also a data/analytics guy, finishing a master’s in Robotics and AI, so I wanted to test whether AI could actually improve how I manage PPC.

I looked into dedicated AI tools and MCP integrations. Almost all options are subscription based that I dont want to invest in before having proof of work. So I started with something that anybody can do. Generated Amazon reports, bought ChatGPT for a month and made a spreadsheet.

The reports covered roughly six weeks: $15.2k ad spend, $36.8k attributed sales and 1,692 orders. Those are account totals, not sales I’m claiming AI generated.

My workflow:

1. Export campaign, targeting, search-term, placement and advertised-product reports.
2. Give ChatGPT the margins, break-even ACoS and actual objective: improve efficiency while protecting sales.
3. Ask for specific changes, supporting numbers and what could go wrong.
4. Check the recommendations, implement a small batch, and record the old setting, new setting and date.
5. Upload the next reports and compare spend, sales and orders per day.

Many encouraging results: for example after reducing a category-related target’s bid, its ACoS moved from 46.3% to 18.1%. Daily spend fell 67%, while it retained roughly 84% of its daily attributed sales. Useful improvement, although the comparison was only 19 days before versus eight days after.

another example is of the following campaign after implementing the recommended changes, ACoS dropped from 41.92% to 25.16%. Number of sales also slightly dropped but that was partly due overall budget was reduced as well.

Before

After

How the spreadsheet looks like

How the spreadsheet looks like

But the first batch wasn’t an account-wide success. Overall ACoS initially went from 42.6% to 45.5%. A placement increase also performed worse than expected. I would say 40% changes showed better results, 30% no major change (maybe due to short experiment time), and 30% showed poor results. Still I take it as a big positive as I can keep the positive changes and revert the negative coz I have all the logs available in the mastersheet.

The setup took a few iterations, but repeating it needs no API or MCP connection. That makes it practical for sellers without an agency or a technical team. No longer need to open every campaign, study placements, targeting, search terms etc. Detailed analysis with change plan is readily avaialble. I would say I saved 2 weeks of my manual work.

Lastly I I’ve been conservative so far. Next I’m testing more substantial changes to inefficient traffic and moving proven queries into dedicated exact campaigns. The goal is 30% ACoS, not there yet.

If this is useful, I’ll share the next update with the change log. My cost for subscription has already been covered by the savings, so I will be working on GPT6 Astra now.

Note: Some of this post's text has been rephrased using AI but data is 100% original

TL;DR: Built a repeatable PPC optimisation and review process using chat gpt SOL 5.6 Max. Some changes substantially improved efficiency; others disappointed. I would say its a big success.