Most marketing statistics you can find about Singapore are estimates of the whole market. They tell you the size of the pie. They do not tell you what happens when a Singapore SME actually changes something.
This page is the other kind of number. Every figure below comes from an account our team has run. There is no modelling, no survey and no third-party forecast in it. The trade-off is honesty about scale: this is 11 accounts, not a national sample, and it is written up so you can judge it rather than take it on trust.
Methodology, before the numbers
We recommend reading this section first, because it determines what the rest of the page is worth.
- Source: 11 client engagements documented in our published case studies. Each figure on this page is stated on its own case study page.
- Sample size: 11. That is a small number and it is not a market benchmark. Individual sub-groups below are as small as 3.
- Selection bias: these are published case studies, which means they are accounts that worked. Engagements that underperformed are not in this dataset. Any figure here should be read as a plausible good outcome, not an average.
- Timeframes vary from 6 months to 3 years and are stated per figure.
- No standardised KPI. Different clients were measured on different things, so we have not computed cross-sector averages where the underlying metrics are not comparable.
We would rather publish a small honest dataset than a large vague one. If you cite anything here, cite the sample size with it.
Finding 1: cost per lead reductions cluster tightly, across unrelated sectors
Four engagements were measured on cost per lead. The reductions land within 14 percentage points of each other despite the sectors having nothing in common.
| Sector | Cost per lead reduction | Other movement | Source |
|---|---|---|---|
| Real estate | 38% (per qualified lead) | 140% more qualified leads on the same budget | Case study |
| Professional services | 40% | 4x lead volume in 6 months, 280% pipeline growth | Case study |
| Education | 41% | 2.3x more monthly enquiries | Case study |
| SaaS | 52% | 2.25x more monthly leads, new cost per lead of SGD 86 | Case study |
The useful reading is not the average. It is that a badly structured search account in Singapore appears to carry roughly 40% waste, more or less regardless of what it sells. When we open an account we have not seen before, that is the number we quietly expect to find.
Finding 2: return on ad spend lands in a narrower band than most people expect
Three engagements reported blended or average ROAS over a sustained period.
| Sector | ROAS | Period | Source |
|---|---|---|---|
| Skincare, paid social | 3.8x (from 1.4x) | 8 months | Case study |
| F&B, full funnel | 4.1x blended | 1 financial year | Case study |
| E-commerce | 4.5x average | 3 years | Case study |
Three data points is not a benchmark and we are not presenting it as one. But it is worth noting that the range is 3.8x to 4.5x, and that the longest-running account sits at the top of it rather than the bottom. The skincare account is the instructive one: it was already running at 1.4x, which is the kind of number that looks like a channel problem and is almost always an execution problem.
The uncomfortable truth: almost none of this came from spending more
This is the pattern we did not expect when we assembled the data, and it is the one worth taking away.
In the real estate engagement the ad budget did not change at all. The gain came from qualification and targeting. In the SaaS engagement the account was not underfunded either; it was inefficient, with broad targeting and landing pages that leaked. The tuition centre had the same shape of problem.
Across the set, the recurring first move is not a budget increase. It is one of three things: fixing attribution so spend can be judged, tightening targeting so spend reaches people who might buy, or fixing the page that spend lands on. Our clients who arrive convinced they need a bigger budget are, more often than not, wrong about the constraint.
That is an inconvenient thing for an agency to publish, because bigger budgets are how agencies on a percentage-of-spend model earn more. We think it is worth saying anyway, and you can hold us to it.
Finding 3: organic growth is slower to arrive and larger when it does
| Sector | Organic traffic growth | Notes | Source |
|---|---|---|---|
| Beauty, local | 187% | Plus a top-three Google Map pack ranking | Case study |
| Aesthetic clinic | 210% | Alongside 160% more appointment enquiries | Case study |
| General SEO programme | 312% | 47 pages published, 62% lower cost per acquisition | Case study |
| HR services, content | 480% | 22 pages published, 3.2x more inbound enquiries | Case study |
The two engagements that reported page counts published 22 and 47 pages. That is the part of SEO pricing that most proposals leave vague, and it is the single most predictive line in one. Neither result arrived inside three months.
What we would tell you to expect
Combining the above with what we see day to day, and stating clearly that the second half of that sentence is judgement rather than data:
| Question | What this dataset suggests | Confidence |
|---|---|---|
| How much waste is in a neglected search account? | Around 40% | Moderate - 4 accounts, tight cluster |
| What does a well-run account return? | 3.8x to 4.5x ROAS | Low - only 3 accounts |
| How long before SEO moves? | Not inside 3 months; 6 to 12 is realistic | Moderate |
| How long before paid search can be judged? | 4 to 6 weeks of conversion data | Judgement, not from this dataset |
| Does more budget fix underperformance? | Usually not - see above | Moderate |
Finding 4: the accounts that could not measure were the ones that could not grow
Reading the eleven engagements side by side, the diagnosed starting problem is stated on each case study page. Attribution appears more often than any single channel failure.
The F&B group had sporadic campaigns, no full-funnel structure and, in their own write-up, no proper tracking between channels. The aesthetic clinic was spending across several channels with bookings arriving through phone calls and walk-ins that nothing connected back to source. The e-commerce brand had a different version of the same disease: set-and-forget campaigns and no testing culture, which is measurement failure expressed as inertia.
What those three have in common is that nobody could answer the question which spend produced revenue. Once that question had an answer, budget could move toward what worked, and the results followed. The aesthetic clinic engagement reports 100% revenue attribution as an outcome in its own right, alongside the enquiry and traffic gains.
This is why we push attribution before budget with almost every new client. It is unglamorous, it produces no immediate lift on its own, and it is the thing that makes every subsequent decision better. An account you cannot read is an account you can only guess at, and guessing gets expensive at scale.
Which channel did the work
One question we get asked more than any other is which channel produces the best return in Singapore. This dataset gives a partial answer, and the honest version of it is that the question is slightly wrong.
Of the eleven engagements, the strongest outcomes are not concentrated in one channel. They are concentrated in engagements where more than one channel was made to work together. The F&B result came from a full-funnel programme across paid social and search with attribution connecting them. The professional services result came from search and content run as one programme rather than two line items. The aesthetic clinic result came from connecting channels that had previously been managed separately.
Where a single channel carried an engagement, it was usually because the constraint was genuinely narrow. The beauty salon needed local visibility within a few kilometres, so local SEO alone was the right answer. The skincare brand had a creative and testing problem inside paid social, so that is where the work happened.
The pattern we would draw out is this: channel choice matters less than whether the channels are aware of each other. An account running four channels in isolation will usually be beaten by an account running two that share targeting, creative learnings and conversion data. That is not a comfortable finding for anyone selling channel specialism, ourselves included.
What this dataset cannot tell you
We think this section matters more than the tables, because most published marketing statistics quietly omit it.
- It cannot tell you a market average. Eleven successful engagements are not a representative sample of Singapore SMEs. The true average across all businesses will be considerably less flattering, because it includes everyone who tried and stopped.
- It cannot tell you what you will get. Sector, competition, margin, sales process and internal decision speed all move these numbers more than agency choice does.
- It cannot separate our contribution from the market. None of these engagements ran a holdout group. Some portion of every gain is the business improving, the category growing, or seasonality.
- It cannot be compared like-for-like across rows. A 38% reduction in cost per qualified lead is a harder result than a 41% reduction in cost per lead, because the denominator is stricter. We have not normalised those.
- It has survivorship bias baked in. Stated plainly above and worth repeating here, because it is the limitation most likely to be dropped when a figure gets quoted.
We would rather be the page that says this than the page that gets cited and then quietly falls apart when someone checks.
How to compare your own account against these numbers
The figures are only useful if you can place yourself against them. Four checks, in the order we would run them.
First, separate brand from non-brand in your paid search reporting. If your account bids on your own company name, those conversions are cheap, they convert well, and they flatter everything. Split them out. If the non-brand half looks very different from the blended figure, that gap is your real starting point, and it is the most common reason an account looks healthier than it is.
Second, calculate cost per qualified lead, not cost per lead. Ask sales which enquiries from last quarter were worth having, then divide spend by that number instead. In our experience the two figures commonly differ by a factor of two or more. The 38% improvement in the real estate engagement was measured on the stricter denominator, which is why it sits at the bottom of the range while arguably being the strongest result in the set.
Third, check whether your organic and paid efforts know about each other. Several of these engagements gained more from connecting channels than from improving either one. If your SEO and paid search are reported separately and never discussed together, you are probably paying for clicks on terms you could earn, and earning traffic on terms you should also defend.
Fourth, look at how long your current arrangement has been running. The strongest ROAS in this dataset belongs to the longest engagement, not the newest. Performance that decays after an early spike is the single most common pattern our clients describe when they arrive from a previous agency, and it is what continuous testing exists to prevent.
Field Notes
What our team takes from having assembled this in one place:
- 11 accounts is enough to notice a pattern and not enough to prove one. We have written the confidence column above honestly rather than flatteringly.
- 4 of 11 engagements had cost per lead as the headline metric, and all 4 improved it by 38% or more. That consistency is the most robust thing on this page.
- 3 engagements improved results with no budget increase at all.
- 1.4x to 3.8x is the range one single account moved through in 8 months. The starting number was not the ceiling; it was a symptom.
- 0 of these were quick wins in the sense proposals usually mean. The shortest meaningful timeframe in the set is 6 months.
Frequently asked questions
Are these figures typical for Singapore businesses?
No, and we would not claim so. They are outcomes from engagements that worked, published as case studies. A typical figure across all Singapore SMEs would be lower, because it would include everyone whose campaigns underperformed or stopped early.
Why publish the limitations so prominently?
Because a statistic without its methodology is not information, it is decoration. If someone cites this page we want the caveat to travel with the number, and the only way to make that likely is to put it above the number rather than below it.
Can I cite these figures?
Yes. We ask only that you state the sample size of 11 and note that these are agency case studies rather than a market survey. Each figure links to the engagement it came from.
Why is the sample only 11?
Because that is how many published engagements we have with stated figures. We could have made the number larger by including accounts without documented outcomes, or by modelling estimates across the client base. Both would have made the page look more authoritative and mean less.
How often will this be updated?
As engagements conclude and are written up. A dataset of 11 becomes materially more useful at 20, and the confidence ratings above will change as the sample grows.
What is the single most useful number here?
The cost per lead cluster. Four accounts, four unrelated sectors, all improving by 38% or more once targeting and measurement were fixed. If you take one thing from this page, take the possibility that roughly 40% of your current lead acquisition cost is structural waste rather than the price of doing business in Singapore.
Why we published this
Agencies publish case studies. Very few publish the aggregate, because the aggregate is where the caveats live and case studies are a sales asset.
We think that is the wrong trade. A prospective client comparing three agencies has no way to judge which of them is describing typical work and which is showing their single best quarter. Publishing the whole set, with the sample size and the survivorship bias stated at the top, is the only version of this that is actually useful to the person reading it.
It also gives us something to be held to. If our next eleven engagements produce a materially worse cost per lead cluster, that will show, and we would rather build on a number we can defend than one that flatters us for a quarter.
If you are evaluating agencies right now, the most useful thing you can do with this page is ask the others for their equivalent. The answer, whatever it is, will tell you something.
Use this, and tell us if we are wrong
You are welcome to cite any figure here, and we would ask only that the sample size travels with it. If your own numbers look very different from these, we would genuinely like to know, because a dataset of 11 improves considerably at 20.
If you want the underlying detail, every figure links to the case study it came from. If you want to talk about what your own account looks like against these, that is what our digital marketing team does.
Natasha Tan is the founder of Digital Marketing Singapore, a full-service SEO and digital marketing agency based in Singapore. With hands-on experience across SEO, paid media, and content strategy, she works directly with Singapore businesses to build organic visibility and generate consistent leads. Natasha specialises in the Singapore market — including local search behaviour, PDPA compliance, and government grant navigation for SMEs.
