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How to Define the Minimum Spend Needed to Stay Competitive
A practical framework for identifying when investment stops driving advantage and starts wasting budget in the age of AI.
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For years, when clients asked how to stay competitive or build awareness, the answer was obvious – more. More content, more campaigns, more budget.
Then generative AI walked in and started quietly pulling the rug. AI-powered workflows aren’t just lowering the cost of content, they’re decoupling output from effort entirely. What used to require teams, time, and budget now takes an afternoon.
Scale is no longer something you build. It’s something you switch on.
When any organisation can produce high quality content at minimal cost, volume stops being a differentiator. Efficiency becomes table stakes. The real question becomes: what level of effort actually moves the needle, and what’s just expensive noise?
It’s not the old “more with less”. It’s not even about proving ROI. It’s about something more fundamental: figuring out what enough actually looks like.
Knowing where that threshold is, the point where effort tips from competitive to just expensive, that’s the skill now.
Sufficiency metrics are an attempt to find that line.
Not maximum effort. Sufficient effort.
The Problem with “sufficient”
Before we go any further, few things are worth saying out loud:
- Sufficiency metrics rely on trackable commercial figures
Sufficiency metrics aren’t universal. They work in channels where you can track what competitors are doing in comparable ways. Where spend can be estimated with reasonable accuracy.
This is not a flaw in the idea of sufficiency. It’s a constraint of the data environment. And it’s a fundamental one. If you can’t reasonably estimate spend, sufficiency metrics simply don’t work. - The system isn’t built for this
Comms and marketing teams hate being told to stop. Agencies especially. The incentive structure rewards activity. More campaigns. More spend. More growth.
Recommending that a client scale back because they’ve hit sufficiency doesn’t grow the account.
But if clients are asking for sufficiency metrics, they’ve already decided that infinite growth isn’t the goal. They’re looking for efficiency without losing competitive position. The question is whether you help them define the threshold or whether someone else does. - You can’t fake sufficiency with partial data
If you’re only tracking your own performance, you’re measuring in a vacuum. If you’re only tracking one competitor, you’re guessing at the category benchmark. If you’re not capturing spend signals, you can’t assess efficiency.
Sufficiency requires the full picture. That means investment in tracking infrastructure, data management, and the internal processes to actually use the metric. - “Sufficiency” only works if you know what you’re aiming for
(obvious, but often ignored)
Sufficiency needs a clear outcome. If you can’t define what you’re measuring commercial figures against, you’re just creating more metrics for the sake of it.
Building a sufficiency framework (a practical guide)
If you’re building a sufficiency metric for the first time, start with a channel where commercial benchmarking is easily accessible.
For this blog, I’ll be focusing on influencer marketing – partly because it’s one of the few channels where competitor spend is observable, and partly because it’s where my experience runs deepest. Most specialised influencer marketing tools now make competitor spend available: creator disclosure data shows who is working with whom, rate cards give you a fee range by tier, and platform estimation tools fill in the gaps. Together they’re enough to build real financial benchmarks rather than educated guesses.
But influencer marketing isn’t unique in this respect. The same principle applies to paid search, where Auction Insights makes competitor bidding partially visible, and to paid media, where tools like Nielsen or Pathmatics can estimate category spend share.
The logic holds across channels – and this framework, while built for influencer, is only a first attempt at something that could extend much further as the discipline matures and better data becomes available.
But before getting into the mechanics, a quick reminder of what Sufficiency actually asks.
THE CORE QUESTION
The question sufficiency answers is narrow and deliberate: have we spent enough, relative to competitors, to remain competitive?
Not have we spent well. Not have we maximised return. Just: are we competitive?
It won’t tell you whether your creative is working. It won’t tell you if you’ve picked the right creators. Those are important questions – but separate ones. Sufficiency asks only: are we economically in the game?
Which brings us to the most important thing to understand about sufficiency before we build a single metric.
Sufficiency is a ratio, not a number.
€500K sounds like serious investment. In a category where competitors average €200K, it’s more than enough. In a category where they average €2M, it leaves you largely invisible. The absolute figure tells you nothing on its own – what matters is what it represents relative to your category.
You don’t define “sufficient” by theory or by last year’s budget – you define it by what competitors actually do.
You can build your baseline using:
- Category median as the sufficiency floor – the point below which competitive visibility degrades
- Estimated category spend pool, derived from creator rates x posting frequency x estimated competitor roster size
- Top quartile spend as the ceiling benchmark
If you or your client’s estimated spend share meets or exceeds the category median, they are sufficient. Below it, they are not.
Once that baseline exists, the rest comes down to two dimensions: spend parity and return efficiency.
Let’s see how we can build metrics under each.
Spend Parity
This dimension answers the foundational question: are we spending at a level that keeps us in the game compared to others in our category? It’s not about spending more than anyone else, but about making sure we’re not under-investing.
Everything else is secondary to whether the investment is large enough to compete.
Metrics we look at:
- Estimated share of influencer investment vs category (%)
- Absolute spend vs category median and top quartile
- Estimated CPM for paid partnerships versus category norms
Example: Imagine the average brand in your category spends €100,000 a quarter on influencer campaigns. The top 25% of brands spend €150,000 or more. You spend €60,000. Your spend is below the category median and a well-built sufficiency framework would flag this: you’re under-investing, so your campaigns may not compete.
Return Efficiency
Spend parity tells you whether you’re putting enough in. Return efficiency tells you what you’re actually getting for it. You can be spending at the category median and still be competitively invisible if you’re overpaying for reach, under-indexing on impressions, or getting below-average engagement for your investment level.
Example metrics include, but are not limited to:
- Estimated share of total category impressions (%)
- Engagements per €10K invested vs category average
- Blended CPM vs category benchmark CPM
- Creator tier mix vs competitors (nano/micro/macro/mega ratio)
- CPM variance by creator tier vs category norm – are you overpaying for macro when micro delivers better returns in your category?
Example: Your spend is at the category median, but your mix is mostly macro influencers while the category’s most effective mix is 50% micro, 30% macro, 20% mega. You’re spending enough but buying wrong. A sufficiency framework would flag this too: you’re at parity on investment but underperforming on return because your tier mix doesn’t match what’s proven effective in your category.
Putting It Together: Your Sufficiency Score
Once you have data against both dimensions, the scoring could be straightforward. You don’t need to overcomplicate things.
Each dimension gets a score from 0 to 100 based on where you sit relative to the category benchmark. Think of it less like a grade and more like a gauge – it tells you how close to the competitive waterline you are.
- 70 or above – you’re at or above the category benchmark. Sufficient.
- 50 to 69 – you’re below where you need to be, but not critically. Worth addressing.
- Below 50 – you’re meaningfully under the competitive threshold. Action required.
Average the two scores to get your overall sufficiency number.
A real output might look like this:
Spend Parity: 62 – below category median, creator mix misaligned. Return Efficiency: 71 – impressions and engagement broadly competitive
Overall Sufficiency Score: 67 – close, but not there yet
That single number tells you something useful immediately. A 67 with a strong return efficiency score and a weak spend parity score means the problem is budget, not execution. A 67 with the scores reversed means the budget is fine but the money isn’t being spent well. Same overall number, completely different conversation.
If you want to make it instantly readable, a traffic-light system in Excel with conditional formatting does the job:
- Green (≥70): You’re at competitive parity. Hold spend. Optimise creator mix, don’t add budget.
- Yellow (50–69): Gaps exist in specific dimensions. Reallocate before you increase.
- Red (<50): Underspending relative to category. Competitors are buying the attention you’re leaving on the table.
The index won’t make the decision for you, but it tells you which conversation to have.
Your Score Has an Expiry Date
In theory, a sufficiency score gives you a point where you can pause. In reality, most teams don’t use it that way.
Most clients I’ve discussed this with haven’t used it to stop activity altogether – they’ve added it as a monthly layer to their existing measurement framework, giving them a more active handle on where their budget is going and whether it’s working hard enough relative to the category.
The more useful question sufficiency raises isn’t when to stop. It’s “how long does this position actually hold?”
This is where a decay model come into the picture. The decay model tells you how fast the competitive advantage you built starts to wear off and can turns your sufficiency frameworks into a forward planning tools.
Before getting into the mechanics, one honest caveat about the data. Decay rates aren’t published in any single authoritative place. Industry averages exist – the widely cited figure is that micro-influencer content loses around half its reach within 7 to 10 days – but these vary significantly by platform, country, and creator tier. They’re a reasonable starting point, but they’re not your numbers.
Your numbers are better. And you can calculate them yourself.
Building Your Own Decay Curve
The good news is: this isn’t complicated.
For each piece of content, you just need reach or impression data at multiple points in time. Most platforms (Instagram, TikTok, YouTube) already provide this through creator insights.
You’re looking for a simple pattern:
how much of day-one reach is still there on day 7, day 14, day 30?
Example:
- Day 1: 100,000 impressions
- Day 7: 52,000 → 52% retained
- Day 14: 28,000 → 28% retained
- Day 30: 9,000 → effectively exhausted
Run this across 10–15 pieces of content and start grouping by creator tier.
You might end up with something like:
- Micro: 50% decay around day 6
- Macro: around day 11
- Mega: closer to day 16
At that point, you’ve got something far more useful than a benchmark. You’ve got a working model based on your own campaigns.
Those figures are now your decay benchmarks. More accurate than any industry average, and more credible in a client conversation because they come from your own campaigns.
The main practical limitation is access: you need creators to share their analytics, or you need to be running campaigns through a platform that surfaces post-level time-series data. If that’s not available yet, your own brand content decay is a reasonable proxy while you build up creator-level data over time.
Final Thoughts
Sufficiency metrics reframe how budget conversations happen. Most budgets are set from last year’s number or a percentage of overall spend. It’s budgeting by habit, not by category reality.
They start from three questions instead: what does it actually cost to compete in this category, are we paying it, and how long does our current investment keep us in the game?
When you can answer all three, you’re not defending a budget anymore. You’re presenting a position.
This is a first interpretation — my attempt to bring some rigour to a question the industry hasn’t fully answered yet. I hope it’s a useful starting point and I’d love for it to spark a broader conversation across the research, analytics and strategy communities.
As AI continues to reshape how we plan and measure earned and paid media, clients will push harder than ever for efficiency metrics that actually hold up in a boardroom. How we answer that will keep evolving.