The six month figure is a crossover point in self-graded award entries, not a delay. The one randomized experiment on the question says something harder.
No published source gives a number for how long it takes to build a brand. Not one. Searching for it returns agency posts offering four to six months, six to twelve months, or “at least five years”, none of which cite anything.
What does exist is a single figure, repeated everywhere, that is not what people think it is: the claim that brand effects take about six months to appear. Its actual source is a 2013 report by Les Binet and Peter Field for the Institute of Practitioners in Advertising, and the sentence reads:
“long-term advertising effects on sales uplifts only start to dominate short-term effects after six months. That is to say brand building takes over as the primary driver of growth from sales activation after six months.”
Read that carefully. It says one aggregated curve crosses another at month six. It does not say anything becomes visible at month six that was invisible at month five. It is a crossover point, not a latency.
The distinction matters enormously if you are deciding whether to keep funding something.
Where the data comes from
The IPA Databank is the most cited evidence base in marketing effectiveness, and its provenance is stated plainly in the reports themselves. From Media in Focus (2017), page 8:
“the data source is the IPA Databank - the confidential data submitted alongside entries to the biennial IPA Effectiveness Awards competition”
So the population is campaigns that an agency chose to enter into a paid effectiveness awards competition. That is a selected sample by construction, and the authors do not hide it.
The second feature is less often quoted and matters more. From the same report, page 9:
“Case study authors assess these measures on a four-point scale of magnitude: only top-box scores (i.e. ‘very large’) are used to identify high performers.”
The people submitting the case grade the size of their own result, on four levels, and the analysis keeps the top level. The researchers did not measure the effects. The entrants rated them. As a partial counterweight, 58 percent of the 2014 and 2016 cases were backed by a full econometric model.
The authors defend the sample directly, and the defense is worth reading in full because it is more honest than most of what gets built on top of it:
“Although this still means that we have a biased sample of campaigns that tend to be successful, there is a wealth of variation in the effectiveness data, including some very mediocre performances. We know from comparisons of efficiency metrics with non-biased samples from other studies that our results are not greatly different, which leads us to believe that our sample is more average than many think.”
Not everyone accepts that. Byron Sharp of the Ehrenberg-Bass Institute put it bluntly in 2022: “they analysed a very weird data set, which is award submissions. If you wanted to solve this issue, you would never use that data.” A strategist at BBH London made the same point in 2018: “every one of those 996 campaigns is a success in its own right. Nobody’s entering dross into the IPA.”
Taken as what it is, the pattern is consistent and worth knowing. Across cases from 1998 to 2016:
Very large market share effects appear in 3 percent of cases evaluated over zero to six months, against 38 percent of cases evaluated over more than thirty months.
Cases running longer than six months generate about 4.6 times more market share growth than short cases.
The relationship inverts for activation: 65 percent of short cases produce very large activation effects, against 33 percent of cases of three years or more.
The advantage of emotional over rational campaigns on very large profit effects widens with time: 13 against 10 percent at one year, 30 against 20 at two years, 43 against 23 at three years and beyond.
Now the circularity problem, which nobody who quotes these numbers mentions. A campaign evaluated over three years is, by construction, a campaign that survived three years. Its budget was renewed. Someone judged it good enough to write up and enter. The correlation between long duration and large effect is partly a mechanical consequence of how cases arrive in the dataset, not a discovery about how brands work.
The randomized evidence says something harder
There is one large body of genuinely experimental work on this question, and it is thirty years old and rarely cited in brand strategy decks.
In 1995, Marketing Science published a summary of 55 in-market split-cable experiments. Households in the same market were matched and randomly assigned to receive different television advertising weights, for established consumer packaged goods, in real stores. This is randomized assignment, not a sample of self-reported success stories.
Two findings, and the second one is the uncomfortable one:
Where a weight increase produced a significant effect in year one, the cumulative impact over the next two years was about double the year one effect. Long-term effects are real and roughly triple the first-year figure in total.
Where there was no significant effect in year one, there was none in years two and three either.
The second finding directly contradicts the most common defense of an underperforming brand campaign. “Brand effects take time, be patient” is not supported by the only large randomized experiment on the subject. In that data, patience paid off exclusively where something was already measurable.
The 60:40 rule does not survive contact with your sector
The companion claim to the six month figure is that roughly 60 percent of budget should go to brand building and 40 percent to activation. In 2018 the authors refined it to 62:38, with a footnote reading “Unlikely to be statistically significant from 60:40.”
The problem is not the precision. It is that the same report publishes a table of sector optima, and the spread destroys the single number:
Sector
Brand to activation
Financial services
80:20
Non-automotive durables
69:31
Retail
64:36
Packaged goods, non-food
65:35
Packaged goods, food
56:44
Durables
58:42
Telecoms and internet providers
58:42
Other services
51:49
Perishable services
48:52
From 80:20 to 48:52. And the authors add that “the IPA data cannot reliably be cut at the individual category level”, which is an unusual thing to publish next to a table of category-level numbers.
For business to business specifically, the same authors published a separate estimate in 2019: “Efficiency appears to be maximised when around 46% of the budget is allocated to brand, with around 54% allocated to activation.” Note that this is 46:54, not the 50:50 that circulates in summaries.
Their own caveat on that number is the most important sentence in the report:
“There are still relatively few B2B cases in The Databank, so sample sizes are small, at less than 50 cases. And those cases may not be typical of B2B marketing in general, because the IPA Databank is biased towards effective campaigns… Geographically, they are skewed towards the UK… They also tend to have relatively big budgets.”
Fewer than fifty self-selected cases, mostly British, mostly well funded. That is the entire evidentiary basis for the B2B budget split repeated across the industry.
In 2020 the National Bureau of Economic Research published a study built specifically to avoid the selection problem that afflicts everything above. It covers 288 consumer packaged goods brands, uses an explicit and replicable brand selection protocol, estimates effects brand by brand under two separate identification strategies, and, critically, publishes every result regardless of its sign, size or significance.
The findings:
Mean long-run advertising elasticity: 0.025. Median: 0.014.
More than two thirds of the brand-level estimates are not statistically different from zero.
Median weekly return on investment: negative 79 percent, and negative for more than two thirds of brands.
And the authors’ own interpretation of why their numbers are smaller than everyone else’s:
“the magnitudes of the estimated advertising elasticities are considerably smaller compared to the results in the extant literature. This is consistent with both publication bias and over-estimated elasticities in the literature due to confounding factors.”
A peer-reviewed counterweight from 2010 puts the same point differently. Analyzing five years of advertising and scanner data across 25 categories and 70 brands, it estimated total long-run elasticities of 1.37 for product, 0.74 for distribution, 0.13 for advertising and 0.04 for promotion. What you sell and where people can buy it weigh roughly ten times more on long-run sales than what you say about it.
That is not an argument against brand building. It is an argument against expecting brand building to do the work that product and distribution have not done.
Three gaps matter for anyone applying these numbers to their own company.
Nothing here is about a brand starting from zero. The IPA cases, the split-cable experiments, the 288-brand study and the 70-brand elasticity study all cover established brands adjusting their spending. Extrapolating a timeline from them to a company nobody has heard of yet rests on nothing.
Almost nothing here is business to business. The rigorous causal work is exclusively in packaged goods, and the authors of the largest study say why: demand data is not available elsewhere. Packaged goods represent roughly a tenth of United States household consumption spending. Everything applied to B2B is extrapolation, plus fewer than fifty self-selected cases.
None of it assigned campaigns randomly to a duration. That is the whole reason no source can tell you how long brand building takes. To answer the question you would need to run the same campaign for six months, eighteen months and thirty-six months across comparable randomized markets, and nobody has published that.
Strip out what the evidence does not support and four practical statements survive.
Set a first checkpoint inside the first year, and mean it. The randomized evidence says an effect that is going to appear is already detectable in year one. A plan whose first honest read is at month thirty is not a patient plan, it is an unfalsifiable one. A checkpoint is only readable if the work under it was sequenced, and positioning first, then the platform, then the identity, then the guidelines is the order we run it in.
Expect the total to exceed what you see early, if you see something. Roughly double the first-year figure across the following two years, in the experimental data. That is the real argument for continuity, and it applies only after the first checkpoint clears.
Do not import a budget split from another sector. The published spread runs from 80:20 to 48:52, and the authors say their data cannot reliably be cut at category level. Use the direction, which is that activation alone underperforms over time, not the number.
Fix what product and distribution are doing first. Long-run elasticity of 1.37 for product against 0.13 for advertising is the least fashionable finding in this entire literature and the most consequential for a company deciding where next quarter’s money goes.
And when someone tells you brand building takes eighteen months, or three years, ask what that number measured. The honest answer, every time, is that it measured how long somebody’s campaign happened to run before someone wrote it up.
Frequently asked questions
How long does it take to build a brand?
No credible source gives a number. The published figures describe when certain effects were observed in campaigns that happened to be evaluated over certain windows. None of them assigned campaigns randomly to a duration, so none of them measures how long building a brand takes.
Where does the six month figure come from?
From a 2013 report for the Institute of Practitioners in Advertising, which states that long term effects on sales uplifts only start to dominate short term effects after six months. That is a crossover point between two aggregated curves, not a latency. Nothing becomes visible at month six that was invisible at month five.
Is the 60:40 brand to activation rule reliable?
Not as a single number. The same authors publish sector optima from 80:20 in financial services to 48:52 in perishable services, and note that their data cannot reliably be cut at the individual category level. Their B2B estimate is 46:54, based on fewer than fifty cases.
If a brand campaign shows nothing after a year, should I keep going?
The only large randomized evidence on this says no. Across 55 split-cable experiments, where there was no significant effect in year one there was none in years two and three either. Patience was rewarded only where something was already measurable.
Does advertising work at all?
On average, weakly, and with enormous variation. A 2020 study of 288 brands with a preregistered selection protocol found a median long-run elasticity of 0.014 and more than two thirds of estimates not statistically different from zero. Product and distribution weigh roughly ten times more than advertising on long-run sales.