Solved: Deciding if a product is worth testing and how to test with Meta ads

Hi there,

Thanks for reaching out. Happy to give you some initial thoughts and guidance on testing that product you've been looking into.

Let's look at the product viability based on what you've seen...

So, you've done some digging and found a product with around 1,000-1,500 monthly searches. That search volume isn't massive on it's own, but it tells you there's *some* existing intent. Crucially, you've seen a main website selling it for over a year, pulling in significant monthly traffic (60k!) and running fifty-three active ads on Facebook. That's a HUGE signal right there. Businesses don't spend that kind of money and time on ads unless they are making sales and it's profitable. If they've been doing it for 18 months and scaled to that many ads, it's very likely working well for them.

The fact that 4-5 new sites have popped up recently, also running ads, further validates the market. It means others have seen the main competitor's success and are trying to get a piece of the pie. This kind of activity, while it increases competition, is actually a positive sign that there's real demand and profitability. It's much harder to find a winning product from scratch where there's no competition because you have no proof of concept. Here, your competition is showing you it works.

The Google Trends data sitting consistently between 55-65 suggests it's not a flash-in-the-pan trend but has relatively stable interest. This is good for long-term potential, assuming you can make it work initially.

And those margins sound decent! Product costing $15 and selling for $39-$79 gives you a good buffer for ad costs, shipping, potential returns, and still leaves room for profit. The original store pricing at $79 suggests there's perceived value at the higher end, which is great if you can position your offering effectively.

The Instagram followers and mixed engagement for the competitor are interesting. 43k followers is a solid audience, and even mixed engagement still means thousands of likes on some posts. It suggests social media is a viable channel, but perhaps their content strategy isn't consistently hitting the mark, or maybe they bought some followers. Either way, it shows that building a brand and audience around this product is possible.

Based on all of this, my strong professional opinion is yes, it is absolutely worth testing this product. The market validation from existing profitable competitors is probably the strongest signal you can get without running your own tests first. You've done good research here.

Okay, so you've decided to test. What about Meta ads?

If you decide to go ahead and test on Facebook and Instagram, you'll need to approach it strategically to get meaningful results without blowing a fortune. The goal of a test campaign isn't necessarily massive profit straight away, but to gather data to see if you *can* sell the product profitably.

You'll need enough budget to get enough impressions, clicks, and ideally, some conversion events (like add to carts or purchases) to allow the algorithm to learn and for you to see if people are actually willing to buy after seeing your ad and visiting your site. A common mistake is testing with too little budget, getting only a handful of clicks, and then deciding it doesn't work. That data is useless.

I'd say a reasonable starting budget for testing one product like this on Meta would be somewhere in the range of £500 to £1,000. This isn't a hard rule, and depends on your potential customer acquisition cost (CAC), but it should be enough to test a few variables properly and see some initial results over maybe a week or two.

Strategies for testing on Meta...

When setting up your campaigns, you'll want to test a few key things:

Creative: This is often the most important factor, especially for visually-driven platforms like Meta and for dropshipping products where people might not be actively searching. You need to grab attention quickly and show the product's value or solve a problem. I'd strongly recommend testing multiple different ad creatives. Don't just do one static image. Try videos (showing the product in use, benefits, different angles), carousel ads (highlighting different features or use cases), and maybe a few different image ads with varied copy. Angles to test could be problem/solution, demonstration, benefit-driven, maybe even user-generated style content if you can mock it up. We've had several SaaS clients see really good results with UGC videos, and that principle often applies to physical products too.

Audiences: For testing, you could start relatively broad or use interest-based targeting related to the product, its use case, or the lifestyle associated with it. Don't go too narrow initially. Let the platform's algorithm find users within your target group who are most likely to convert. Test maybe 2-3 different audience approaches initially. Lookalike audiences are great later once you have enough purchase data, but you won't have that starting out.

Optimisation: Even in testing, I'd recommend optimising for the lowest-funnel event you realistically expect to achieve with your budget. If you think you can get purchases, optimise for purchases. If not, optimise for add to carts or even landing page views if your budget is very small and you just want to see if people click. But ideally, you want to optimise for conversions (purchases) as soon as possible. The algorithm needs conversion data to find more people like them.

Metrics: Keep a close eye on your key metrics. Look at your Click-Through Rate (CTR) to see if your ads are engaging. A low CTR (under 1% is usually bad) could mean your creative or targeting is off. Look at your Cost Per Click (CPC) to see how expensive traffic is. Then, look at your on-site metrics: Landing Page Views, Product Page Views, Add to Carts, Checkouts Initiated, and Purchases. See where people drop off. Are lots of people clicking but not viewing the product page? Maybe your ad message doesn't match the landing page, or the page takes ages to load. Lots of product page views but no adds to cart? Could be your product photos, description, or price. Lots of adds to cart but no purchases? Could be shipping costs, checkout process issues, or lack of trust.

The importance of your website/landing page...

Honestly, paid ads won't work if you're sending traffic to a poor website. Your store needs to be slick and trustworthy. Like I've seen with other eCommerce stores, if the site feels cluttered, loads slowly, has bad product images, or lacks descriptions, people just won't buy. Think of it like a digital showroom for your product.

You'll need high-quality product photos, ideally showing the product from multiple angles and in use. Good copy for product descriptions is essential – highlight the benefits, not just the features. Make sure your pricing is clear, including shipping costs upfront if possible.

Trust is massive, especially for a new store. How can you make people feel comfortable handing over their card details? Add social proof like customer reviews or testimonials (even if you need to get some initial ones manually from friends/family who test the product). Display trust badges (secure checkout, payment methods). Make sure contact information is easy to find (email, maybe a phone number). Links to social profiles (if active) can help too. If you plan to sell on other platforms later like Etsy or Amazon, linking those could also build confidence.

Don't forget the supplier side...

While not strictly advertising, the success of a dropshipping product heavily relies on your supplier. Poor quality products, slow shipping times, or unreliable inventory will kill your business, no matter how good your ads are. Before scaling, make sure you vet your supplier. Ideally, order samples to check quality and shipping times yourself. Have a clear plan for handling returns and customer service issues.

What happens after testing?

Assuming your initial test shows promise (you get some sales, or at least good conversion rates at the top/middle of your funnel), then you'd move into optimization and scaling. This involves continuously split testing new creatives, refining audiences, improving your website conversion rate, implementing retargeting campaigns (essential for recovering abandoned carts and showing ads to people who visited but didn't buy), and potentially expanding to other platforms like Google Shopping or Pinterest if they are a good fit for the product and audience.

Scaling can be tricky, as costs per acquisition often rise as you spend more. Like we've seen with some SaaS clients who hit a scaling plateau, you constantly need to find new angles, improve your funnel, and increase customer value if possible. This often requires systematic testing and deep analysis of the data.

Summary of Actionable Solutions:

Why consider expert help?

Putting all of this together effectively can be complex and time-consuming, especially when you're trying to manage the product sourcing, website, and customer service as well. Setting up campaigns correctly, understanding the data, interpreting performance metrics accurately, and knowing how to iterate and scale requires significant experience across many accounts and industries.

For instance, knowing why a CTR is low, or whether a conversion rate from add-to-cart to purchase is 'normal' for a specific niche, or how to structure ad accounts for optimal testing and scaling are things learned through running hundreds or thousands of campaigns. Getting this wrong can quickly burn through your budget without getting the data or results you need.

An experienced hand can help accelerate the testing process, identify issues faster (whether it's ads, website, or offer), and build a scalable strategy based on proven methods rather than trial and error. This often saves you money and gets you to profitability quicker than trying to figure it all out alone.

If you'd like to discuss this further or get more specific guidance tailored to your exact setup, we're happy to book in a free consultation to chat through it in more detail.

Regards,
Team @ Lukas Holschuh