Optimising Ad Targeting
If you have ever watched your ad spend climb while your enquiries stay flat, you already know the problem. The targeting is off. The algorithm is showing your ads to people who will never buy from you, and you are paying for every wasted impression.
I have spent 18 years running paid campaigns for small businesses across Derbyshire and the East Midlands, and ad targeting is the single biggest lever for cutting wasted spend. Get it right and the same budget brings in more of the right customers. Get it wrong and you are funding someone else's curiosity.
This post covers how ad targeting algorithms actually work, the practical steps to sharpen them, and the testing habits that keep your targeting improving month on month rather than drifting.
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Get your free scorecardWhy algorithms matter more than your ad creative
Most business owners obsess over the ad itself. The image, the headline, the offer. All of that matters, but it counts for nothing if the algorithm is showing your ad to the wrong person.
Platforms like Meta and Google use machine learning to decide who sees your ad, when, and how often. They are constantly analysing browsing history, purchase patterns and engagement signals to predict who is most likely to convert. Your job is to feed that algorithm clean, specific signals so it can do its job properly.
I worked with a kitchen showroom in Matlock that was running broad, untargeted Facebook ads to "anyone interested in home improvement" within 15 miles. Once we narrowed the audience to homeowners actively researching renovations and layered in a lookalike audience built from past customers, cost per enquiry dropped by 41 percent within six weeks. The ad never changed. The targeting did.
Define your audience before you touch a targeting setting
Audience segmentation has to come first. Break your audience down by demographics, behaviour and purchasing patterns, then build out two or three detailed buyer personas. These personas are not a branding exercise, they are the foundation every targeting decision sits on.
For a Derbyshire SMB, this often means thinking beyond "homeowners aged 35 to 55" and getting specific: recently moved into the area, browsing planning permission content, engaging with local social media groups. The more specific the persona, the easier it is to give the algorithm something useful to work with.
Putting algorithms to work on your targeting
Once your audience is defined, the next step is using the platform's own tools to refine and extend that targeting automatically.
Predictive analytics and machine learning models
Predictive analytics looks at historical behaviour to forecast what a customer is likely to do next. Most ad platforms now build this in by default, but it only works well when you give it enough conversion data to learn from. If your CRM data and ad platform pixel are not properly connected, the algorithm is working half blind.
Machine learning models inside the platform then continuously test and refine which audience segments perform best, shifting budget towards the people most likely to convert. This is the engine behind features like Meta's Advantage+ and Google's Performance Max. They work, but only when the inputs are clean.
Behavioural targeting and lookalike audiences
Behavioural targeting uses signals such as browsing history and past interactions to deliver more personalised ads. Lookalike audiences take this further, identifying the characteristics of your best existing customers and finding new people who share them.
This is where a tight customer list pays off. A landscaping business we worked with near Ashbourne uploaded 18 months of completed job customers and built a one percent lookalike audience. It outperformed every other audience in the account within the first month, simply because the seed data was accurate and specific.
Client result
41% lower cost per enquiry from targeting alone
A Matlock kitchen showroom cut its cost per enquiry by 41 percent by refining audience targeting, without changing a single ad creative.
See how we approach paid adsOptimising ad delivery, not just audience
Targeting is not only about who sees your ad. It is also about when and where, and how much you bid to get the placement.
Dynamic ad placement and timing
Algorithms can identify when your specific audience is most active and adjust delivery accordingly. A tradesperson's audience might be most responsive early morning before the working day starts, while a retail audience might engage more in the evening. Let the data decide rather than assuming.
Automated bid optimisation
Automated bidding strategies adjust your bids in real time based on performance signals, pushing spend towards the moments and people most likely to convert. This removes the guesswork of manual bidding, but it needs at least a few weeks of consistent data and budget to learn properly. Switching strategies every few days starves it of the learning period it needs.
Monitoring performance and testing continuously
None of this is a one-off setup. Algorithms drift, audiences change and platforms update their models constantly, so targeting needs ongoing attention.
Track the metrics that actually matter
Click-through rate tells you whether your ad is relevant. Conversion rate tells you whether your landing page and offer follow through. Cost per acquisition tells you whether the whole funnel is profitable. Watch all three together, not in isolation, because a high CTR with a poor conversion rate usually points to a targeting and landing page mismatch.
A/B test deliberately, not randomly
Test one variable at a time, whether that is the audience, the creative or the offer. Running multiple changes together makes it impossible to know what actually moved the result. This is the same discipline we apply across test and measure work for every client, not just paid ads.
For SMBs in Derbyshire and the wider East Midlands, the businesses that win with ad targeting are rarely the ones with the biggest budgets. They are the ones who feed the algorithm good data and review performance properly every month.
How ad targeting fits into the bigger BIG12 picture
Ad targeting is one piece of a much wider system. It sits within the Algorithms pillar of our BIG12 framework, but it only performs well when your website, CRM and brand positioning are pulling in the same direction. Sharp targeting that lands on a slow, confusing website wastes the budget you just optimised.
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Not sure how your marketing measures up right now?
The BIG12 Scorecard shows you exactly where ad targeting and the other 11 pillars stand for your business today.
Take the scorecardThe challenge is never learning. It is doing.
Most business owners I meet already know they should be reviewing their targeting, testing their bids and cleaning up their customer data. The information is not the gap. The time and consistency to actually do it every month is the gap.
That is the role we play. After 18 years working with SMBs across Derbyshire and the East Midlands, we know which targeting levers move the needle for a specific industry and budget, and which ones are a waste of time for a small operation.
You do not need to become an ads specialist to get this right. You need someone keeping an eye on it consistently, who understands your business well enough to make the right calls when the data changes.
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