Hi there,
Thanks for reaching out! It sounds like a really frustrating situation to be in – you're spending money on ads only to lose more money on deliveries that can't be completed. It's a common problem, but thankfully, one that usually has a pretty straightforward fix.
I'm happy to give you some initial thoughts and walk you through what's likely happening and how to sort it out. The issue almost certainly isn't a bug in your Ads Manager, but a misunderstanding of how Meta's location targeting works by default. Let's get it fixed.
TLDR;
- Your problem is almost certainly caused by Meta's default location setting, "People living in or recently in this location". You need to change this to "People living in this location" to stop targeting travelers and commuters.
- This one setting change is the most important thing you can do right now. It forces the algorithm to focus only on residents in the cities you can actually serve.
- You're likely losing significant money not just on wasted ad spend but also on the cost of goods and shipping for failed deliveries. I've included a calculator below to help you estimate this hidden cost.
- Restructure your campaigns with separate ad sets for each city (Islamabad, Karachi, Lahore). This will give you much better control over budget and allow you to see exactly which city is performing best.
- The algorithm is designed to find the cheapest conversions, which, with your current settings, might be people outside your major cities. Tightening your targeting is essential to guide it towards profitable customers.
We'll need to look at your Location Targeting settings...
Right, let's get straight to the heart of the matter. The reason you're getting sales from random towns and villages is almost definitely down to one specific setting inside your ad set's audience configuration. When you pick a location in Meta Ads, it gives you four options, and the one it chooses for you by default is often the wrong one for businesses that rely on physical delivery.
The default setting is "People living in or recently in this location". This sounds reasonable, but it's incredibly broad. It includes two very different groups of people:
- People whose home is within your selected area. These are the people you actually want to reach.
- People who were recently in your selected area. This includes people who were travelling through, commuting for work, or just visiting one of the cities for a day. Their actual home could be hundreds of miles away in a small village where you can't deliver.
Meta's algorithm, in its quest for the largest possible audience pool, lumps these two groups together. So, someone from a remote village visits Lahore for the day, gets tagged by Meta as "recently in" Lahore, goes home, sees your ad, and places an order. You get a sale, Meta's algorithm gets a pat on the back for finding a conversion, but you end up with a failed delivery and a loss. This is why you're seeing sales from places you've even tried to exclude – the system tagged them when they were *inside* your target zone.
The solution is to be more specific. You need to change this setting to "People living in this location". This option tells Meta to only target users whose home, according to the data Meta has on them (from their profile, device information, and connections), is within the cities you've selected. It immediately cuts out the travellers, the commuters, and the temporary visitors, massively improving the quality and accuracy of your targeting.
It might feel like a small change, but its impact is huge. It forces the algorithm to fish in the right pond. Yes, your potential audience size might shrink a little bit, but every single person within that smaller audience is now someone you can actually serve. It's about quality over quantity. You'd rather have an audience of 200,000 potential customers you can deliver to than an audience of 700,000 where a huge chunk are impossible to serve.
Default Setting: "Living In or Recently In"
Correct Setting: "Living In"
I'd say you are paying for an audience you can't serve...
The problem goes deeper than just a bit of wasted ad spend. Every one of these failed sales from a random village represents a multi-layered loss to your business. It's not just the money you paid Meta for the click and conversion; it's the cost of the product itself, the packaging, the courier fees for the attempted delivery, and the administrative headache of dealing with the return. This can add up shockingly quickly and turn a seemingly profitable campaign into a loss-making one.
Many business owners only look at their Cost Per Purchase inside Ads Manager and think they're doing well, without accounting for the real-world costs of these 'bad' conversions. You need to get a clear picture of how much this targeting issue is actually costing you. To help with that, I've put together a simple calculator. Play around with the numbers – I think you'll be surprised at how much money is leaking from your business because of this one simple setting.
You probably should restructure your campaigns...
Fixing the location setting is the priority, but while you're at it, you can improve things further by organising your campaigns more logically. Right now, you're lumping Islamabad, Karachi, and Lahore into a single audience. While this works, it doesn't give you much control or clarity.
A much better approach is to use a separate ad set for each city. So you would have:
- Campaign 1: Sales Campaign
- Ad Set 1: Targeting Islamabad (with the "Living In" setting)
- Ad Set 2: Targeting Karachi (with the "Living In" setting)
- Ad Set 3: Targeting Lahore (with the "Living In" setting)
Why is this better? First, it gives you precise control over your budget. You might find that customers in Karachi convert at a lower cost than in Lahore. With separate ad sets, you can allocate more budget to Karachi to maximise your results. If everything is in one ad set, Meta's algorithm will decide where to spend the money, and it might not always make the most profitable choice for your business.
Second, it gives you crystal clear reporting. You'll be able to see at a glance which city is driving the most sales, has the best Return On Ad Spend (ROAS), and the lowest cost per purchase. This data is invaluable for making smart decisions about where to scale your advertising efforts. You can also tailor your ad creatives if you want. Perhaps a certain product is more popular in one city than another, or you want to mention the city name in the ad copy to make it more relevant. A segregated structure like this gives you that flexibility.
It's a little bit more work to set up initially, but the control and insights you gain are well worth the effort. It's the difference between flying blind and having a proper dashboard with all the controls at your fingertips.
Before: One Ad Set for All Cities
(No control, unclear results)
After: Separate Ad Sets per City
(Clear data, budget control)
(Clear data, budget control)
(Clear data, budget control)
You'll need to work with the algorithm, not against it
Here's something a lot of people don't realise about platforms like Meta. When you give the algorithm a conversion objective like "Purchases", its primary goal is to get you as many of those conversions as possible for the lowest cost. It doesn't inherently care about your profit margins, your delivery logistics, or whether the customer is a good fit for you long-term. It just wants to hit the target you've set as efficiently as possible.
When you use the broad "Living in or recently in" setting, you're giving the algorithm a massive, messy playground to search for those cheap conversions. And it might discover that people who live in rural areas but commute to the city are far cheaper to convert. They might click more, or be less exposed to competitor ads. So the algorithm, doing exactly what you told it to, will start spending more of your budget on these people because they are statistically cheaper to turn into a "purchase" event.
This is a classic case of paying Facebook to find you the worst possible customers. You are actively funding a system that is seeking out people you cannot serve, all in the name of a lower cost-per-purchase on paper. By switching your setting to "Living in", you are giving the algorithm a much clearer, more productive instruction: "Find me the cheapest purchases, but ONLY from this specific group of people who are actually residents of these cities."
This is what effective advertising is all about. It's not about fighting the algorithm; it's about setting the right boundaries and giving it the right instructions so that its immense power works for your business goals, not against them. Your job is to define the profitable playing field. The algorithm's job is to find the winners within it.
This is the main advice I have for you:
To pull all of this together, here are the exact steps I would recommend you take right now to fix your campaigns and stop wasting money. This is a clear plan you can implement today.
| Recommendation | Action Step | Reasoning |
|---|---|---|
| 1. Fix Location Targeting | Go into each of your ad sets. Under the 'Locations' setting, click 'Edit'. Change the dropdown from "People living in or recently in this location" to "People living in this location". | This is the critical fix. It immediately stops you from targeting non-residents and ensures your ads are only shown to people you can actually deliver to. |
| 2. Restructure Campaigns | Duplicate your existing ad set twice. In the first, set the location to only Islamabad. In the second, only Karachi. In the third, only Lahore. Ensure all three are set to "People living in". | This gives you granular control over your budget and provides clear performance data for each city, allowing for much smarter optimisation decisions. |
| 3. Review Exclusions | Once the "Living In" setting is active, your issues with exclusions should mostly disappear. However, it's good practice to double-check that you are still excluding any specific areas within the cities you don't serve. | Ensures maximum precision and prevents your ads from showing up in any known problem areas, even within your target cities. |
| 4. Monitor and Optimise | Let the new campaign structure run for at least 5-7 days to gather data. Then, analyse the performance of each city's ad set. Allocate more budget to the best-performing city. | Your cost per purchase might go up slightly, but your overall profitability will increase dramatically as you'll have eliminated the cost of failed deliveries. |
As you can see, what seems like a small detail can have a massive financial impact on your business. Getting paid advertising right involves understanding these nuances and building campaigns that are not just technically correct, but strategically sound. For instance, we've helped eCommerce clients in niches from women's apparel to subscription boxes achieve returns of over 600% on Meta Ads by focusing on fundamentals like targeting the right, profitable audience, not just the cheapest one. This is where professional expertise can make a huge difference.
We spend all day, every day inside these platforms, testing what works and making sure our clients' campaigns are structured for maximum profitability. We handle all of this detailed setup, ongoing management, and strategic optimisation so you can focus on what you do best – running your business and serving your customers.
If you'd like to have a chat and see how we could apply this level of detail to your entire advertising strategy, we offer a free, no-obligation initial consultation. We can take a look at your account together and identify more opportunities to improve performance and grow your sales.
Hope this helps!
Regards,
Team @ Lukas Holschuh
