Effectiveness

When you run an ad, you need people to remember your brand, not just the ad. If they enjoy it but can't say who it was for, your brand probably doesn't get into their long-term memory, and the ad is unlikely to help you the next time they buy. So how you show your brand in the ad matters. Some ads leave the name out and trust a logo, a character or a slogan to do the job. That depends on people knowing the logo, character or slogan is yours, and it's easy to assume more people know it than really do. A print ad experiment tested this, and the safer bet was to keep the name in. A slogan in its place did worse for all three brands they tested, and a logo or character was a gamble, even with readers who knew it.

Three researchers, Nicole Hartnett, Jenni Romaniuk and Rachel Kennedy, tested this with print ads for three big brands in Australia: Commonwealth Bank, Cadbury Dairy Milk and Dolmio pasta sauce. They made five versions of each ad. One showed just the brand name, one just the logo or character and one just the slogan. The other two showed the name together with the logo or character, or with the slogan. Women read an online magazine at home with the ads in it. Afterwards, anyone who remembered an ad was asked which brand it was for and how much they liked it. Even for these big brands, with logos and slogans they had used for at least two years, not every reader linked them to the brand. For one of them, about a quarter didn't. The researchers counted just the readers who did know each brand's logo and slogan, so the results show those cues at their best.

The slogan result was the clearest. When an ad carried only the slogan, fewer readers named the brand than when it carried the name, for all three brands, and for the chocolate brand the drop was large. A logo or character used instead of the name came out level with the name on average, but that hid three different results. The bank's logo did better than the bank's name and the chocolate logo did worse than its name, each by about ten points, though neither gap was big enough to rule out chance. The pasta sauce character did the same as its name. Comparing across the results, the logo or character on its own did better than the slogan on its own for every brand. The study didn't test why the two logos went different ways.

Adding a logo or character to the name helped a little, as the name with the logo or character was remembered for the right brand more often than the name alone, by a small margin. Adding the slogan to the name was a little higher than the name alone too, but not by enough to rule out chance. How the ad was branded made little or no difference to how much people liked it. For each brand, the versions with the name, without the name and with both were liked about the same.

It helps to know what the study covers. It was print. The branding sat small in the bottom right corner, with one logo, character or slogan at most, and the name was set in a plain typeface rather than the brand's own. Each ad made the category obvious, and the three brands were large ones in their categories. Brand recall was measured right after reading, and the study measured memory for the brand, not buying, so it can't tell you what any of this did to sales. And because it counted just the readers who knew each logo and slogan, the researchers themselves say most advertisers won't have cues this strong, and that with less well known ones, leaving the name out is likely to do even more harm.

So what can you do with it? Don't assume people know your logo, character or slogan is yours. Find out how well known each one is, because the less well known it is, the riskier it is to let it stand in for the name. An earlier essay here covers how to measure your brand cues instead of guessing. Until you know, it's safer to keep the name in. Even with a well known logo or character, treat it as something to add to the name rather than something to replace it, because the name with the logo or character was remembered more often than the name alone. A slogan on its own is a long shot for getting the brand remembered. And if the name is being left out to make the ad more likeable, this study gives no reason to expect that it will.

Charlie

A distinctive asset is anything other than the name that brings your brand to mind: a logo, a colour, a pack shape, a character, a sound, a tagline. Many decisions about which ones to keep, which to push and which to retire are made on the judgment of the people who run the brand. A recent study tested how good that judgment is. The guesses were rarely close, and they went wrong in a way that can lead a brand to drop a good asset.

Researchers at the Ehrenberg-Bass Institute took about 400 brand elements from 50 brands, across soft drinks, snacks, supermarkets, financial services and beer in seven countries. They showed each element to people who buy in the category and asked which brand came to mind. That gave every element two scores. Fame is how many buyers linked it to the right brand. Uniqueness is how much of what buyers named went to the right brand rather than a rival. Then more than 150 marketers, mostly people who manage those brands, guessed both scores before they saw the results.

The guesses were rarely close to what buyers said. Even on a rough test, placing each element in the right box of a simple grid of strong and weak, the marketers were wrong more often than right. They were no better on their own brand's elements than on a competitor's. And the errors ran in opposite directions. Marketers thought more buyers knew the elements than really did, and they thought more of those elements got credited to rivals than really did. Elements that were already strong were the easiest to judge, and the ones in the middle were the hardest.

The study didn't follow what the marketers went on to decide, but the two errors point to different mistakes. If you overrate fame, you may think an element is already doing the branding job when many buyers don't know it yet, and lean on it without the name beside it. If you underrate uniqueness, you may think a cue points to rivals when it mostly points to you, and drop a good asset before it has had time to build. Both choices come up when a pack, a logo or a campaign is changed.

It helps to know what the study covers. The marketers had some interest or background in the subject, and some training before they guessed, which may have made their guesses more accurate than other marketers' would be. The brand owners chose and paid for which elements were tested, so the elements are not a random sample, and none of them turned out to be well known but shared with rivals, so the study says nothing about that kind. Most of the categories are packaged goods and retail, with one service category. And it measured whether buyers link an element to the brand, not whether it sold anything.

So what can you do with it? List every element your brand uses and test each one with people who buy in your category, asking which brand it brings to mind. Keep both scores, fame and uniqueness, and test again over time. Do it before you drop or change an element, because that is when a wrong guess costs most. An element that fewer buyers know but that points mostly to you is worth building rather than dropping. Where a survey is out of reach, treat your judgment as a guess until you can test it. Paul has written about how the National Lottery built and measured its assets, which is this in practice.

Charlie

Going dark means stopping your advertising altogether. The largest study of it followed brands that stopped for a year or more. On average their market share fell in the first year and kept falling in every year they stayed dark, and brands that were already losing share fell furthest.

Researchers at the Ehrenberg-Bass Institute looked at hundreds of brands that stopped advertising for at least a year. They used a large US household panel and advertising spend data across 22 supermarket categories. On average, share was lower after the first year and lower again in each year after that. An earlier study by the same group, on alcohol brands in Australia, found the same pattern. Older controlled tests described in the paper, where TV advertising was stopped in some areas and kept running in others, found lower sales after a year in about half the cases.

The average hides a wide spread. After the first year, about half the brands hadn't lost much share. More had fallen by the second year, and more again by the third. In the first year, small and big brands fell by about the same. From the second year on, small brands fell further. "Big" here means any brand in the top half of its category by share, not only the leaders. Brands that were already losing share before they stopped lost the most, whatever their size. Brands in the top half that were stable or growing barely moved over three years, though only a few of them stayed dark that long. In a handful of categories, brands held or grew share after they stopped mass media advertising. By the third year, categories people buy less often had fallen further. Even so, size and direction together explained only a small part of which brands fell.

It helps to know what the study can't tell you. It follows each brand's own history, with no group of similar brands that kept advertising to compare against, and it didn't track price, promotions or shelf space. So some of the fall may come from whatever made the owner stop. It is packaged goods in the US. It tracked mass media (TV, radio, print, outdoor, cinema and online display), so some of these brands may have kept going on search or social. It only counts brands that survived, which may make going dark look less harmful than it is. It did not look at services, durables or B2B, or at stops of less than a year. And it measured share, not why share fell.

So what can you do with it? Before you stop, look at which way your share has been moving and how big your brand is in its category. A brand that is already losing share has the most to lose by going dark. A brand in the top half of its category that is holding or growing came through best, though few of those stayed dark beyond a year or two. A one-year stop cost less, on average, than two or three. The study didn't compare spending less with stopping, because every brand in it stopped. But given how often share fell once spending stopped, keeping some advertising going is the safer bet. And if you do stop, track your share month by month alongside a measure of how many people think of your brand, so you see early if it is going the wrong way.

Charlie

Mental availability is how likely your brand is to come to mind when someone is in a buying situation, and how many buying situations it comes to mind in. The usual way to measure it is with Category Entry Points, a method from the Ehrenberg-Bass Institute. You list the situations that lead people into your category, then ask which brands they link to each one. It is one of the most useful tools a marketer has for finding where to grow. But the scores need to be read against the size of your brand, or they will mislead you.

Here is why. One of the longest-standing findings in marketing research is that people who buy a brand link it to more things than people who don't. Andrew Ehrenberg and his colleagues showed it in 1970. Researchers from the Ehrenberg-Bass Institute and Kingston University tested it again on dozens of new data sets, across services, durables, retailers and several countries, and found it still holds. So a big brand tends to score well on almost every buying situation, mostly because so many people buy it. A small brand will score lower almost everywhere.

That changes what a good score means. A high score may be telling you how big you are, more than how well your marketing is working. The more useful question is where your brand scores lower than its size would lead you to expect. Those are the buying situations where people are not thinking of you, and that is where the odds of finding growth are better.

The National Lottery in Ireland is a good example. When Paul was there, the brand already had very high penetration and people bought it often. Research turned up a buying situation where it could win more: people who had already decided to give someone a card and were wondering what to put in it. About half of adults thought of a Lottery product as a gift for a card. For most brands that would be a good number. For a brand that big, it meant half its market wasn't thinking of it at that moment. The ads running at the time were funny and well made, but they weren't about gifting. So the Millionaire Raffle campaign was rebuilt so that every scene linked the brand to the gift in a card. The raffle sold out, the advertising had to be pulled, and it kept selling out in the years after. Paul tells the full story in his case study.

So what can you do with it? List the buying situations in your category and find out roughly how many people are in each one. Measure which brands people link to each situation, then set your scores beside your penetration. Where you sit below your size, there is room to grow. Pick one or two of those situations your brand can credibly own, make the advertising about that moment, and keep the brand in every frame. Then track the same measures over time, alongside sales, so you know whether it worked.

Charlie

If you are placing a bet on where your brand's growth will come from, penetration is the better bet. Growing brands usually win more buyers. Higher purchase frequency among the buyers you already have plays a part too, but it is usually the smaller change.

The evidence for this is strong. Research from the Ehrenberg-Bass Institute looked at more than a thousand rises and falls in brand share across dozens of UK packaged goods categories. When brands grew, their penetration usually changed much more than their loyalty, for big and small brands alike.

Part of the reason is the pattern known as Double Jeopardy, which Andrew Ehrenberg's research made famous. Brands in the same category tend to have similar levels of loyalty, and smaller brands have fewer buyers who also buy them a little less often. So the odds are against a plan that relies on loyalty well above the category norm. If the average buyer in your category buys their favourite brand 12 times a year, you might get yours to 14. Getting them to 24 is a long shot. Your category's loyalty figures will show you where the ceiling probably sits.

Campaigns point the same way. When Les Binet and Peter Field analysed the IPA Effectiveness Awards cases, campaigns with a penetration objective reported very large business effects more often than campaigns with a loyalty objective. Loyalty campaigns that did work mostly worked by bringing in new customers. Their report is Marketing in the Era of Accountability, sold by the IPA. Peter Field describes every brand as a leaky bucket. Customers keep leaving, mostly to competitors and sometimes for ordinary reasons like moving house, however well you treat them. So the bucket has to be refilled all the time.

The odds shift in some places. Some brands do hold more loyalty than their size would predict, and in the UK research these were mostly market leaders and supermarket own brands. The IPA cases also suggest a loyalty objective may be more worth trying for big brands, for cheaper brands, and in categories where people buy on emotion. The buying studies here come from packaged goods. The IPA cases found the same pattern in other categories, but if you sell something else, test it against your own data before you bet on it.

So what can you do with it? Set a penetration goal alongside any loyalty goal. Track penetration every year, because a market share figure on its own won't tell you whether you grew through more buyers or more frequent buying. Plan for reach, so your marketing gets to people who don't buy you yet as well as the ones who do. And if you are a big brand, a loyalty objective may be worth a test.

Charlie

When sales rise during a campaign, it is tempting to give the campaign the credit. But that is correlation: the two happened at the same time. Causation is how much of the rise the campaign actually produced. Always measure the second, and never report the first as if it were the second.

Lidl is a good example. Its sales in the UK grew by £2.56 billion between 2013 and 2017. In 2014 it went on national TV for the first time, and growth went from 9.5% a year before the ads to 15.5% after. That timing looks like proof, but on its own it is only correlation.

But Lidl's award paper doesn't stop at the timing. The team used a statistical model to split the growth by cause: the ads, the new stores, and everything else. The ads were given about a quarter, £627 million of the £2.56 billion.

By our sum, had Lidl kept growing at 9.5% a year, it would have added about £1.4 billion anyway. So about £1.1 billion sat above the old line. The ads were given £627 million in all, a bit over half of that. So even the growth above the old line wasn't all down to the ads.

One caution. The quarter comes from Lidl's own model, and the paper doesn't show how the model was built. Its headline figure, £2.7 billion over four years, is a different sum: all the sales the ads drove each year, not their share of the growth.

The case won Gold at the IPA Effectiveness Awards in 2018. The full paper has far more and is worth reading.

Sam

When you tell your boss a campaign worked, expect one question. Was it the campaign, or was it something else? A price cut, a promotion, a new shop stocking you, a good summer.

The best answer is a number. "We didn't change the price" asks the boss to trust you. "Our price was €3 before the campaign and €3 during it" lets them see for themselves.

Does it matter? In award papers it goes with winning. We compared 96 papers that won an effectiveness award with 87 that entered and weren't awarded. 78% of the awarded papers answered three or more other explanations with a number or a chart (75 of 96). 45% of the others did (39 of 87).

So before you present a result, do three things.

1. Write down what else could explain it.
2. Put a number beside each one.
3. If you have no number for one, say so.

Marie Curie's award paper shows the habit. It lists what else could explain its result and takes them one at a time. For the weather, it went to the Met Office's records.

Two cautions. Some answers we counted point to a model or a source and show no number. Leave those out and it is about two thirds against under four in ten. And this shows what awarded papers do more often. It doesn't show that this is why they were awarded.

Sam

If a job ad asks you to use AI, show the employer the time it saved you. Nearly half the ads we read want AI for speed.

For our report The AI Ask we read 43 Irish marketing and sales job ads that ask the person to use AI tools. 20 of them say the AI is for working faster or cutting manual work. Only 7 say it is for writing or making content.

I expected writing to come first. It didn't.

Dropbox is a good example. It is hiring a sales operations manager, and the ad wants AI "to reduce manual reporting effort while maintaining strong human judgment". So it wants the speed, and it wants a person who still makes the call.

Here is what to do.

1. If you are applying, pick one task you now do faster with AI. Say how long it took before and how long it takes now.
2. Say how you checked the result.
3. If you are hiring, say in the ad what you want done faster.

One caution. 43 ads is a small number, and these are the only two reasons I counted.

Sam

If a job ad asks for "AI tools" and names none, answer it with names. Say which tools you use, what you use them for, and how you check what comes back.

We read 636 Irish marketing and sales job ads that were live on 24 September 2026. They are part of The AI Ask, our quarterly count of what these ads ask people to do with AI.

Employers name their other tools. Salesforce is named in 51 of the 636 ads. Excel is named in 43. Canva is named in 22.

With AI it is different. 56 of the 636 ads ask the person to do something with AI.

So we looked in every ad for nine well-known AI tools, among them ChatGPT, Claude, Gemini and Copilot. 6 of the 636 ads name one. In 3 of the 6 the name is not something the ad asks of the applicant. That leaves 3 ads that ask the applicant for one of those tools. Osborne Recruitment lists ChatGPT. DocuSign asks for familiarity with Gemini and NotebookLM. MongoDB asks for a deep understanding of coding tools such as Claude Code and Cursor.

I don't read this as employers not caring. I think it is early. An employer can know it wants people who use AI well and still be working out which tools it needs. That is my view, and the ads can't prove it.

This is one day of ads, and 56 is a small number. We count again in December.

Here is what to do.

1. If you are applying and the ad says "AI tools", name yours. Give the tool, the task you use it for, and how you check the result.
2. If you are hiring and you know which AI tools your team uses, name them in the ad and say what they are for.
3. If you are hiring and still working that out, say so in the ad. Then ask candidates which tools they use and why.

Sam

Some of what a campaign does you can count. Some you can only estimate. Keep the two apart, and work out your return on the part you can count. Here is a case that does it.

In February 2011 an IBM computer called Watson played the American quiz show Jeopardy! against the two finest players the game had seen, and won. IBM sells to businesses, so no consumer was going to buy a Watson. The campaign around the show was there to bring in business buyers. It was later written up as a paper for the IPA Effectiveness Awards and won Silver in 2012. What follows is from that paper.

Some sales IBM could count. It records where each sales lead comes from. Leads that came in through Watson events and materials turned into $254 million of booked revenue in North America.

Other sales it couldn't count. Watson ran on IBM's POWER servers, and sales of those servers had been falling for a long time. After the show they grew, and by more than the Watson leads explain. Did the campaign make buyers keener on those servers, even where no lead recorded it? The paper thinks so, but it can't count it.

So it compares two regions. North America saw a lot of the campaign. In Western Europe IBM ran no Watson marketing and the show wasn't on television, though news of it travelled. Before the show, sales of those servers in the two regions moved closely together. After it, both grew. The paper takes the Watson leads out of North America's growth and the fall in the euro out of Europe's. The gap that is left is $68 million of revenue.

This is the part I'd copy. The paper doesn't say the campaign brought in that $68 million. It says $68 million is the most the campaign could have brought in, because the two regions differed in other ways too: "It's an upper limit as other factors in which W. Europe and NA differed will be involved". Then it leaves the $68 million out of its return altogether. The return it claims is profit of at least 2.4 times what the campaign cost, worked out on the $254 million alone.

One caution. The paper doesn't say how much of the $254 million IBM would have won anyway. It asks the question, calls it a valid one, and says that isn't how IBM judges a campaign. So we know that revenue came in through the campaign. We don't know it was extra.

The paper holds a good deal more and is worth reading in full. You can read the IPA's summary, and buy the paper, on a public page on the IPA's site.

Here is what to do in your next results paper or board report.

1. Split your result in two: what you can count, and what you think the campaign also caused.
2. For the second part, find a market like yours that saw less of the campaign, and compare the two. Call the gap the most it could be, and say what else could explain it.
3. Work out your return on the first part only. Say what you left out.

Sam

If you are writing up a campaign, for an award or for your own board, put one figure in before the results: what you would have sold if the campaign had never run. Here is why.

I've spent the week on award papers. We took the 183 papers we hold from five effectiveness award competitions between 2011 and 2018. 96 of them were awarded and 87 weren't. AI agents on our team read every paper from start to end against the same written list of questions.

I expected the insight to be the difference. I was wrong, and it took 183 papers to tell me.

99% of the awarded papers state an insight (95 of 96). So do 93% of the papers that weren't awarded (81 of 87). The insight is so important that almost nobody enters without one. Having one puts you level with everyone else.

So where is the gap? The three widest gaps we found are all about proof, and this is the one I'd start with. 83% of the awarded papers give a figure for what would have happened anyway (80 of 96). 53% of the others do (46 of 87).

The IPA's guide to working out payback calls that figure the base level of sales: "sales that would have been made even if the work hadn't run". Take the base away from what you actually sold, and what is left is what the campaign added. Without it, sales going up while a campaign runs is correlation, and nobody reading can tell how much of the rise was coming anyway.

These counts have limits, and the first is the lesson turned on ourselves. They are correlation too. They show what awarded papers do more often. They don't show that the base figure is why those papers were awarded.

We checked the count two ways. A second agent read 32 of the papers again without seeing the first agent's answers, and the two agreed on this figure for 29 of the 32. Awarded papers are also edited before they are printed, which could make them look more complete than the others, so we compared only the papers printed in the same form. The gap is smaller there and it is still there: 87% of those awarded papers give the figure and 64% of the others.

And we counted whether a paper states an insight, not how good it is.

Here is what to do before you write your next paper or your next board report.

1. Before the results, write one line: without this campaign we would have sold this much. Say how you worked it out.
2. If you have no model, extend your own sales trend into the months the campaign ran. The IPA's guide lists that method beside econometrics and regional analysis.
3. Lead with the difference between what you sold and that line. That difference is what the campaign added.

Sam

Sales going up while your campaign runs doesn't show that the campaign did it. To show that, you have to rule out everything else that could have caused the rise, one thing at a time. Marie Curie's paper on its 2010 campaign does it better than almost any I've read. It won Gold at the IPA Effectiveness Awards in 2011.

I've spent this week reading award papers. This one is a lesson in rigour, and the discipline in it is open to any team.

Marie Curie gives free nursing care to people who are terminally ill. Every March it runs the Great Daffodil Appeal, and just over a quarter of the money comes from volunteers with collecting tins. Most charity advertising asks people to give money. In January and February 2010 Marie Curie advertised, two thirds of it on radio, to ask for something else: an hour of your time, to collect. The whole campaign cost £184,151.

On the face of it, it worked. The charity had 13,774 collectors the year before and over 20,000 that year. That is correlation: two things that happened together. Causation is the harder claim, that one made the other happen. The paper sets out to show causation, and it starts by saying what it didn't have: "Due to the low investment in the campaign, we do not have any tracking research at our disposal."

So it did three things.

First, it looked at the route people came in by. Collectors normally sign up through their local branch. The campaign advertised a national phone number, a web address and a Facebook page, and the paper says every new recruit came in through one of those three. It shows the split as a chart with no numbers on it. Most came through the website. The paper calls only the phone line dedicated, and doesn't say whether the web address was a page of its own.

Second, it set the results against where the ads ran. The radio was heavier in some regions than in others. The paper puts the radio weight in each of six regions beside the new recruits there. The more radio a region had, the more new recruits it had. One region is out of line: Wales and the West had more radio than Scotland or the North and fewer recruits.

Third, it wrote down everything else that could explain the rise, and answered each one. There are six on its list. Here are two.

• The main appeal advertising ran in March, and collectors had to register by the end of February. So the bigger campaign couldn't have recruited them.
• Compared with the March before, March 2010 was colder, less sunny and equally wet. So a fine month didn't bring the collectors out.

That last answer came from the Met Office, and it was free.

The paper is just as careful with what the campaign paid back. Collector numbers were on a rising trend anyway, though they didn't rise every year. So it works the return out three ways, each against a different base, and uses the lowest of the three from there on: £2.45 for every £1 spent, after the cost of the campaign is taken off.

Read that figure with one thing in mind. It covers two years, and the second year is an assumption: the charity believes 46% of new collectors come back the next year. On the first year alone, by my own sum from the paper's figures, the return is about £1.39 for every £1.

One limit. This is one paper, and I can't tell you which part of it the judges liked. It also won the prize for Best New Learning, and the paper's own section on what it learned is about asking people to collect, not about the proof. So the idea may have counted for as much as the rigour.

The paper holds a good deal more than this: the other four causes, the media plan, the charts and the full working of the return. It is worth reading in full. You can read the IPA's summary, and buy the paper, on the IPA's site.

Here is what to take from it before your next campaign.

1. Give the campaign its own way in: a phone number, a web address or a code that nothing else uses. Then count what comes through it. Marie Curie's paper can't say how many came by each route, because it prints no count.
2. If you can, don't run the same weight everywhere, and keep a note of where it ran.
3. Write the list of everything else that could explain a rise. Find one fact for each. Start with the weather.

Sam