Solved: Manual vs. Meta Advantage+ Audience Results?

Hi there,

Thanks for reaching out!

I've had a look at your question about manual detailed targeting versus Meta's Advantage+ audience. It's a common crossroads for advertisers, and the answer isn't as simple as one being universally better. To be blunt, you're asking the wrong question. The real issue isn't which button to click in Ads Manager; it's whether you've done the strategic work *before* you even open the platform. A powerful algorithm is useless if you're pointing it at the wrong problem.

I'm happy to give you some initial thoughts and guidance. Below, I’ve outlined the framework we use that moves the conversation from "which audience is better?" to "how can I build a system that predictably acquires high-value customers?". It's a bit of a mind-set shift, but it's the only way to scale profitably without just burning cash.

We'll need to look at your ICP, not just your audience...

Let’s be honest. The way most businesses build a "customer persona" is a complete waste of time. "Companies in the finance sector with 50-200 employees" or "mums aged 30-45 who like yoga" tells you absolutely nothing of value. It's sterile, demographic-based guesswork that leads to the kind of generic, wallpaper ads that everyone ignores. This is why your manual audiences might feel "okay" but never truly exceptional. You’re targeting a description, not a person with a real problem.

To stop burning cash, you have to redefine your customer by their pain. You need to become an obsessive expert in their specific, urgent, expensive, career-threatening nightmare. Your customer isn't just a job title; she's a leader terrified of her best developers quitting out of frustration with a broken workflow. He's a small business owner who hasn't slept properly in weeks because he doesn't know if he can make payroll next month. Your ICP isn't a person; it's a problem state.

When you understand the nightmare, your targeting transforms. You stop looking for "interests" and start looking for watering holes. Where do these people go to solve their problems?

• -> What niche podcasts do they listen to on their commute, like 'Acquired' or 'My First Million'?
• -> Which industry newsletters do they actually open and read, like 'Stratechery' or 'The Hustle'?
• -> What SaaS tools do they already pay for, like HubSpot, Salesforce, or Xero?
• -> Are they members of specific Facebook groups like 'SaaS Growth Hacks' or niche subreddits?
• -> Who are the key influencers they follow on Twitter or LinkedIn, like Jason Lemkin or Sam Parr?

This intelligence isn't just data; it's the blueprint for your entire targeting strategy. These watering holes become your interest targets. The language they use to describe their pain becomes your ad copy. Do this work first, or you have no business spending a single pound on ads. The difference in outcome is stark, moving from a low-probability guess to a high-probability connection.

I'd say you need to know your numbers before you test anything...

You mentioned being concerned about CPA and ROAS. This is good, but it's only half the story. The real question isn't "how low can my CPA go?" but rather "how high a CPA can I afford to acquire a truly great customer?". The answer to that lies in its often-ignored counterpart: Lifetime Value (LTV).

Without knowing your LTV, you're flying blind. You might be killing ad sets with a £100 CPA that are actually profitable because those customers are worth £5,000 to you. Or you might be scaling campaigns with a £20 CPA that are losing you money because those customers churn after one month. LTV is your strategic north star. Here’s how you calculate it, simplified for clarity:

1. Average Revenue Per Account (ARPA): What do you make per customer, per month/year?
2. Gross Margin %: What's your profit margin on that revenue? (Revenue - Cost of Goods Sold).
3. Monthly Churn Rate: What percentage of customers do you lose each month?

The calculation is straightforward:

LTV = (ARPA * Gross Margin %) / Monthly Churn Rate

Let's run a quick example. Say you run a SaaS business:

• -> ARPA = £200/month
• -> Gross Margin = 85% (0.85)
• -> Monthly Churn = 5% (0.05)

LTV = (£200 * 0.85) / 0.05 = £170 / 0.05 = £3,400

In this example, each customer is worth £3,400 in gross margin to your business over their lifetime. Now you have the truth. A healthy benchmark for many businesses is a 3:1 LTV to Customer Acquisition Cost (CAC) ratio. This means you can afford to spend up to £1,133 (£3,400 / 3) to acquire a single customer. If your sales process converts 1 in 10 qualified leads into a customer, you can afford to pay up to £113 per qualified lead.

Suddenly, that £80 lead from a perfectly targeted Advantage+ campaign doesn't seem so expensive, does it? It looks like a bargain. This is the maths that unlocks aggressive, intelligent growth and frees you from the tyranny of cheap, low-quality leads. Use the calculator below to get a feel for your own numbers.

You probably should rethink how Meta's algorithm actually works...

Your fear of "letting Meta control the targeting" comes from a common misunderstanding. Here is the uncomfortable truth about many campaign types on platforms like Meta. When you set your campaign objective to something vague like "Reach" or "Brand Awareness," you are giving the algorithm a very specific, and very unhelpful, command: "Find me the largest number of people for the lowest possible price."

The algorithm, in its infinite wisdom, does exactly what you asked. It seeks out the users inside your targeting who are least likely to click, least likely to engage, and absolutely, positively least likely to ever pull out a credit card. Why? Because those users are not in demand by other advertisers. Their attention is cheap. By choosing these objectives, you are actively paying the world's most powerful advertising machine to find you the worst possible audience for your product.

This is where the magic of Advantage+ and conversion optimisation comes in. When you set your objective to "Sales" or "Leads", you give the algorithm a completely different command: "I don't care about cheap impressions. Go and find me people within this broad audience who have a history of doing what I want them to do - buying things, filling out forms, booking calls."

The algorithm then uses its trillions of data points to find patterns and seek out users who behave like your existing customers. It's no longer about just reaching people in your interest group; it's about reaching the *buyers* in that group. This is why you shouldn't fear giving up manual control. You're not ceding control to randomness; you're leveraging a system designed to find you money. Awareness is a byproduct of having a great product that solves a real problem, not a prerequisite for making a sale. You must optimise for the action you want.

You'll need a proper testing structure...

So, how do you actually test Advantage+ against your manual audiences? You need a structured, scientific approach. Don't just duplicate a campaign and switch the audience type. You need to isolate the variable you're testing.

When I audit new client accounts, I often find a chaotic mess of campaigns with dozens of ad sets, all competing against each other with overlapping audiences. The structure should be simple and reflect your sales funnel. Here’s a basic but effective framework for an eCommerce business, which can be adapted for lead gen or SaaS.

META ADS AUDIENCE & CAMPAIGN PRIORITISATION

Campaign 1: Top of Funnel (ToFu) - Prospecting

• -> Objective: Sales / Leads
• -> Budget: Campaign Budget Optimization (CBO) enabled. Give Meta the budget and let it allocate to the best-performing ad set.
• -> Ad Set 1: Advantage+ Audience. Provide your pixel data, customer lists, and some broad interest suggestions if you wish, but let the algorithm do the heavy lifting. This is your primary test.
• -> Ad Set 2: Manual Detailed Targeting. This is where your best, most well-researched manual audience goes. Build it based on the pain-point "watering holes" we discussed, not vague demographics. For instance, if you're targeting eCommerce store owners, don't just target "Amazon". That's a trap. It includes millions of shoppers. Instead, target interests like "Shopify", "WooCommerce", or followers of eCommerce-focused publications. Layering interests can sometimes help, but often keeping them distinct is better for testing.
• -> Ad Set 3: Broad Targeting. Only use this once your pixel has thousands of conversion events. This is just age, gender, location. You're telling Meta, "You have enough data, go find my customers anywhere." Sometimes this can outperform everything else.

Campaign 2: Middle/Bottom of Funnel (MoFu/BoFu) - Retargeting

• -> Objective: Sales / Leads
• -> Budget: CBO enabled, but with a smaller budget than prospecting (e.g., 20-30% of total spend).
• -> Ad Set 1: Warm Audience. People who have engaged with your ads, watched videos, or visited your website in the last 30-90 days, but haven't added to cart or purchased.
• -> Ad Set 2: Hot Audience. People who have added to cart or initiated checkout in the last 7-14 days but haven't purchased. This is your highest-intent audience and should recieve your strongest offers (e.g., reminding them of their cart, maybe a small incentive).

Here’s what that looks like in a table:

By structuring your test this way, you'll get a clear signal on what works. After a few days (or once an ad set has spent 2-3x your target CPA without a conversion), you can confidently turn off the losers and reallocate budget to the winners. More often than not, for our clients, Advantage+ ends up being a consistent performer once it has enough data to learn from. I remember one SaaS client where we reduced their CPA from £100 down to just £7 simply by restructuring their account this way and letting the algorithm do its job, after we'd fed it with the right high-intent conversion data first.

And honestly, your offer is probably the real problem...

We can talk about algorithms and audiences all day, but here's the brutally honest truth: the number one reason advertising campaigns fail is the offer. You can have the most perfectly targeted audience in the world, but if what you're offering them is weak, irrelevant, or high-friction, they will ignore you.

The "Request a Demo" button is perhaps the most arrogant Call to Action ever conceived. It presumes your prospect, a busy decision-maker, has nothing better to do than book a 45-minute slot in their calendar to be sold to. It screams, "I value my time more than yours." It's high-friction, low-value, and instantly positions you as a commoditised vendor. You have to do better.

Your offer’s only job is to deliver a moment of undeniable value—an "aha!" moment that makes the prospect sell themselves on your solution. You must solve a small, real problem for free to earn the right to solve the whole thing.

• -> For a SaaS product: The gold standard is a free trial or a freemium plan (no credit card required). Let them use the actual product. Let them feel the transformation. A Product Qualified Lead (PQL) who has already seen the value is infinitely better than a Marketing Qualified Lead (MQL) who just downloaded a PDF.
• -> For a service business: You must bottle your expertise into a tool or asset. A marketing agency could offer a free, automated SEO audit that finds their top 3 keyword opportunities. A financial consultant could offer a "Cash Flow Projection" template. For us, as a B2B advertising consultancy, it’s a free 20-minute strategy session where we audit failing ad campaigns. We give away real value upfront.

Once you have an irresistible offer, your ad copy needs to articulate it. Don't just list features. Speak to the "Before-After-Bridge".

Before: "Your AWS bill just arrived. It’s 30% higher than last month, and your engineers have no idea why. Another fire to put out."

After: "Imagine opening your cloud bill and smiling. You see where every dollar is going, and waste is automatically eliminated."

Bridge: "Our platform is the bridge that gets you there. Start a free trial and find your first £1,000 in savings today."

This structure connects directly with their pain, paints a picture of a better future, and presents your (high-value, low-friction) offer as the clear path to get there. This is what compels a click, not a list of your product's technical specs. No amount of audience tuning can fix a broken offer.

This is the main advice I have for you:

To sum up, moving from manual targeting to Advantage+ isn't just a tactical switch. It's an opportunity to level up your entire advertising strategy. Below is a summary of the actionable recommendations I've detailed for you to implement.

As you can see, the question is much bigger than just manual vs. Advantage+. This is a strategic process. Getting it right involves a deep understanding of your customer, your business metrics, and the advertising platform itself. It takes time, expertise, and constant testing and iteration. Many businesses we work with come to us after spending months or even years trying to figure this out on their own, often wasting thousands in the process.

An expert partner can help you bypass that painful learning curve. We can help you nail your ICP, calculate your LTV, craft an irresistible offer, and build a scalable advertising system from day one. In our experience working with dozens of software and eCommerce clients, like the one for whom we generated over 5,000 software trials at just $7 per trial, a solid foundation is everything.

If you'd like to discuss how these principles could be applied specifically to your business, we offer a free, no-obligation 20-minute strategy session. We can take a look at your current setup and provide some concrete, actionable advice you can use right away.

Hope this helps!

Regards,

Team @ Lukas Holschuh