Watson Calling: Can Cognitive Computing Improve Ad Effectiveness?

May 28, 2013

The following post first appeared in the IBM “Building a Smarter Planet” Blog on May 21, 2013.


It’s often said that marketers are drowning in data, but here at Nielsen, we have an idea for turning that harrowing experience into an insightful deep sea exploration.

For decades, Nielsen has been in the business of empowering brands and agencies with the information they need to understand and build connections with consumers.  We do this by measuring the advertising and media content people watch, the goods and services people buy – and very importantly, by spotting connections between the two. Why are these connections important? If you know what ads people are exposed to, and you know what those same people buy, you can get a lot smarter about how well your advertising is working and how to make it work better.

IBM Watson Calling - Can Cognitive Computing Improve Ad Effectiveness?

IBM Watson Calling – Can Cognitive Computing Improve Ad Effectiveness?

However, despite vast amount of data at their fingertips—and often because of the complexity of the data at hand—our clients’ ability to form these vital connections is oftentimes hindered. In fact, our advertiser clients tell us that despite the ever increasing amounts of data they have access to, they still have problems answering basic questions such as: How much should I spend on advertising? How should I allocate my spending across media platforms and within them? And, how do I measure my advertising performance “in-flight,” and make course corrections to improve results?

To help answer these questions, Nielsen created a simple “end-to-end” framework for measuring advertising and media effectiveness: the 3R’s. The 3R’s try to answer three simple questions: First, is my advertising reaching my intended audience (Reach)? Second, is it breaking through, being remembered and changing consumer opinion about my brand (Resonance)? And third, is it driving a behavioral reaction – e.g. sales (Reaction)?

We’ve been working hard to create the data sets and tools to help brand marketers answer these questions. We’ve gotten much better at answering the fundamental questions about advertising effectiveness, but we still have work to do.

Like never before, we and our clients face a continuous and, sometimes overwhelming stream of data generated by consumers and our digitally enabled measurement tools.  Just envision terabytes of structured and unstructured data pouring into our offices – trends, metrics, sentiments and perspectives – that express who consumers are, which programs and ads they’ve seen, how those ads perform by TV program, genre, web site, placement, number of times they’ve seen the ad, exposure to social media, where they’ve shopped, what they’ve bought, etc.

Within this data, there exists a range of opportunities to better understand consumers. But the challenge is clear, and for many, overwhelming – due to the data’s volume, scope and growing complexity, it’s almost too much to decipher.

A new form of technology is needed to dive deep into this sea of data, and come up for air with actionable advertising and media insights that can help brands understand their campaigns’ reach, resonance and reaction, and in turn, improve their advertising campaigns.

That’s why we were so intrigued when we began discussions with IBM about an entirely new and unique category of technology known as cognitive computing.

Cognitive computing systems can understand the nuances of human language, process questions akin to the way people think, and quickly cull through vast amounts of data for relevant, evidence-based answers to their human users’ needs. And very importantly, they learn from each interaction, to improve their performance and value to users over time.

Seems like a custom fit for the data-driven challenges today’s advertisers face.

Building on this excitement, we’re embarking on a new collaboration between the Nielsen Innovation Lab, which we founded in 2012 to advance research in advertising effectiveness, and IBM to harness the power of Watson, the sole player in this exciting new era of cognitive computing.

IBM Watson

IBM Watson

As part of our own brand’s continuous effort to advance understanding around advertising effectiveness, we’ll be exploring ways to use Watson for helping our agencies and their client brands engage more effectively with consumers across all devices – from TV to tablet to smartphone – while improving the impact of their advertising and media plans.

We believe this collaboration will open up a realm of possibilities for our clients, so they too can uncover value from new and exciting data. It’s time to stop drowning, and start exploring.

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How to Make Digital Advertising Work Harder for Your Brand

October 2, 2012

As the world moves inexorably digital, Marketers increasingly want to understand how to improve the effectiveness of their digital ad spending.

Digital Metrics and Ad Effectiveness

In many ways, the digital revolution has been its own worst enemy—at least for brand advertisers. Supposed great advances beyond TV metrics like the focus on direct click-thru rates and other digital specific measures were anything but.  I would argue that they’ve sometimes slowed the move of ad spending to the digital medium.

Buying Behavior is Predictive of Advertising Responsiveness

Let’s take click thru rates. For example, it’s now been well documented that for CPG brands click thru rates do not correlate with off-line sales. So, why do so many marketers still talk about click-thru?

Digital Advertising Objectives – Building Brands and Increasing Sales

What do brand advertisers want ? They want to build their brand and improve their sales and advertising ROI. How do they do this in the digital world ?

One new advance is the use of buyer behavior based targeting. New research shows that CPG advertisers can generate about a +20% improvement in average sales lift when their on-line advertising is delivered to consumers using purchase based information to better identify the most responsive consumers.

Now CPG brand marketers know two things with certainty:

  1. Brand focused on-line advertising drives off-line sales.
  2. Buyer behavior based media buys generate even stronger Sales lifts.

Purchaser Based Data – What is it ?

So, what is “purchaser based data”? Simply put, it’s using consumers past purchase behavior to identify consumer groups which are most responsive to your ads, and then targeting future advertising against these kinds of consumers.

For example, the CMO of Brand X discovered that heavy category users who were light buyers of her brand were much more responsive to Brand X advertising: they accounted for only 20% of the impressions delivered, but 80% of the sales lift due to the advertising.

Other buyer groups received 50% of the impressions, but accounted for almost no sales lift—e.g. Brand X’s spending on ad impressions against this group was a complete waste.

The central insight was this:  Brand X’s media plan was comprised of many TV shows and web sites that were under represented by these highly responsive heavy category/light brand buyer consumers. In addition, there were many TV programs and sites that were over represented with these highly responsive consumers that were nowhere to be found in the brand’s media plan. Read this as ” big opportunity.”

Brand X’s CMO worked with her agency to plan and buy against programs and sites that had heavy concentrations of the heavy category/light brand buyer target—and generated a much higher sales lift by spending against those consumers who are most responsive to the advertising. Buyer behavior based targeting works– just like that.

Additional Buyer Behavior Based Targeting Learnings

So, what else have we learned about buyer behavior based targeting in the on-line space?

  • Sales lift differs by category – OTC and health and beauty categories are even more responsive to buyer behavior based targeting than other categories.
  • New products get an even larger sales lift – new products averaged about +30% sales lift using buyer behavior based targeting, vs. about +20% for existing products.
  • Both small and large brands benefit from buyer behavior based targeting – this is especially good news for small brands which often find TV unaffordable.

With $1 of every $10 ad dollars now being spent on-line, and expectations of even more in the future, CMO’s can get a lot smarter about how they spend their on-line dollars by using buyer behavior based targeting.

So, if you’re a brand based advertiser, throw out the clicks and bring on the buyer behavior based model. After all, it’s only about 20% better. Go tell that to your CFO.


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“Paid, Owned and Earned Media” Interview Part 2 with Market Edge’s Glenn Engler

May 8, 2012

The following post first appeared as “Paid, Owned and Earned Media with Randall Beard” on Market Edge on Webmaster Radio FM. This is Part 2 of a 3 part series. Part 1 covered Advertisers biggest pain points, end-to-end advertising measurement, and real time optimization.

Host Glenn Engler is a Fortune 500 industry figure and has worked in the marketing and communications world for more than 25 years. He focuses on perspectives on social media and digital marketing, that will help you gain insight into the unique opportunities and challenges facing marketers and thought leaders today.


Glenn:  Hi and welcome to Market Edge. I’m your host Glenn Engler, CEO of Digital Influence Group. A full service digital market agency that helps companies unlock the social potential of their brands and amplify its impact to drive results. 

Today I’ll be talking about paid, owned and earned media with Randall Beard, the Global Head of Advertiser Solutions for the Nielsen company – a Global leader in media, marketing and consumer information.

Randall is an award winning marketing executive, speaker and author with more than 25 years of global experience across consumer package goods, financial services and high-touch service brands, including Procter and Gamble, American Express and UBS.  

It’s great to have you on Market Edge Randall, welcome.

Randall:  Thanks, Glenn.

Glen: So it’s early February, I know you’re a Giants fan and I’m a Pat’s fan so we won’t talk about the game, but advertisers spent an average of $3.5 million or so for 30 second spots, and I know many this year there were 60 or more spots during the Super Bowl.

More and more the ads are integrating social in, whether it’s a twitter hashtag , an online channel or some drive to. How do you see that integration of social media impact the way brands are thinking about using traditional television spots?  

Randall:  Yeah, it’s a great question and there are a lot of interesting things happening in that space. First of all, let me just comment on the Super Bowl because we do measure a lot of things in the Super Bowl and I’ll share three things with you that I think are pretty interesting.

Number one, If you look at the audience at the Super Bowl, it starts out at the beginning of the game and the audience just grows gradually across the first, second quarter, half-time, third and fourth quarter and then peaks pretty much right at the end of the game. That’s when the audience is largest.

If you look at TV ad effectiveness, it actually declines throughout the game. The TV ads in the first quarter, on average are the most effective and they become less effective in the second quarter, less effective in the third quarter and they’re least effective in the fourth quarter.

I was on MSNBC and they asked me about this and I said, I think part of it is people are very hyped up and looking for the new ads because people watch the Super Bowl just as much for the ads as they do for the football. The other thing is the Super Bowl is a social event and people are standing around drinking so it’s harder to remember things later.

The third point, and it relates to social, is if you look at buzz, if you look at online buzz it’s fairly flat in the first couple of quarters and then there is this huge spike during half-time and then it comes back down again in the third and fourth quarter.

So one of the very simply things we said to our clients is, look, if you’re going to build a program around both paid advertising in the Super Bowl plus social, you want to be advertising in the first two quarters and driving people to talk about your brand at half-time. Because naturally when we look at the dynamics in the game that’s when ads work the best in the first half of the game and people do a lot of the online conversation, socially during half-time.

And yet we had clients that had been traditionally been advertising in the third or fourth quarter and tried to drive conversations. So that’s a really simple example. I have some other examples but I’ll stop and see if you have any thoughts.

Glen:  No, share them all. This is great stuff. People love hearing these.

Randall: So another example is, we have a partnership with Facebook and we’ve done a lot of work with Facebook to understand the impact of social on paid advertising.

One very simple example is when brands advertise on Facebook they can just run a standard ad, so when you’re on Facebook, over on the side you might see an ad for Virgin America. You can also buy a version of that same ad, which is Virgin America, but below it, it might say, Alex K, Randall Beard, and three other of your friends like Virgin America.

If you just look at the performance of those two ads, side by side, where the only difference is one ad underneath says these friends of yours like this brand we can see that the ad with the social layer to it performs significantly better than the one without social. Which makes sense, right, because we all trust our friends and family and people we know.

So, I think what you’re going to start to see is more and more examples of where social and paid come together in that way.

The last example I’ll give you is a lot of times when we’ve look at paid advertising metrics, historically, we might look at two ads and say they perform pretty much the same.

Now, what’s really interesting is at Nielsen we have a panel that can not only look at what people watch on TV, but with their permission, we can also look at what they do online. And so we’re able to look at people who have seen an ad and what percentage of those people talk about the brand online, search for the brand, and go to the (brands) Facebook fan page, those kinds of things.

Very interesting, in some cases you will see two ads that perform exactly the same on TV metrics and yet one of them will drive much more desirable social behavior online than the other ad. So I think what you’re going to start to see is more and more clients, more advertisers, figure out how do I use paid advertising to drive earned media and social, and how does earned media and social reinforce and also improve the performance of paid advertising?

Figuring out that dynamic I think is going to be a really important challenge for marketers over the next few years.

Glen:  And how do you start to see the whole concept of social search, if you will, impacting that customer process?

Randall:  I think first of all, all the research to the point we’ve just been talking about shows that people trust friends and family members recommendations about brands more than they trust any other kind of advertising or marketing for that matter.

So I have to believe as social comes to search you’re going to see the same thing. You’re going to see that search behavior is going to differ and change based on whether or not people see that social layer and how that influences what they do.

Glen: Yeah I think that’s right. So the phrase of the year seems to be “big data”. And if there is a place that has a point of view and experience with “big data” I’ve got to believe it’s your would at Nielsen.

You had a recent blog post called “driving higher ROIs without big data” and a really interesting point-of-view where you talked about how CMO’s now  have this explosion and wealth of data at their fingertips, yet because the explosion of data is so overwhelming they feel they can’t adequately, necessarily, measure ROI on their initiatives.

Talk a little bit about the three concepts you recommended using to simplify this assess the ROI. You mentioned the reach, resonance and reaction but talk a little about the forces that are facing the CMOs.

Randall:  So earlier on, as you said, I talked about having an end-to-end measurement framework across reach, resonance and reaction.

A really simple way I think about this is, reaction is the outcome but it’s a function of reach and resonance. It’s almost like reach times resonance gives you reaction, so you really have to understand all three.

I think one of the disservices the research and analytics industry has done is to create all these different metrics that are different across platforms because it just increases complexity and makes it really hard to do an apples to apples comparison.

What I hear again and again from CMOs and heads of media and heads of research is comparability, “I want comparability across platforms so I can do an apples to apples comparison.”

I’ll give you an example:  historically the way people have measured earned media or buzz online is just volume.  So there were 20 million impressions, buzz about your brand. You look at that and you say some may be positive and some may be negative. You look at that and you say, what does that mean? Is that twenty million people each saw one impression or is that one person saw twenty million impressions?

For me it’s a much more effective way to think about that buzz volume if you translate it in to reach. So we can look at TV reach and say “your TV reach among your target consumer group was 83%. Your digital media reach was 23%. If you look at the buzz reach and it’s 1%, then it’s probably not that important, right? Or if your buzz reach is 50%, it’s probably pretty important.”

And so bring the metrics in line across the platforms:  TV, digital print, mobile, earn media as well as owned media, right? Comparability of metrics is one of the best things we can do to simplify this kind of big data phenomenon. Because what’s happening is big data is overwhelming people, particularly, when you’ve got a multiplicity of different metrics by platform.

Glen:  And add to that, what’s the average tenure for a CMO? Are we down to 20 months or something like that? There’s even more stuff going on, so I love the concept of comparability, that’s huge.

Randall: Comparability doesn’t mean you can’t also have some unique measurements across platforms, because there are some differences. But I think at the core of it you need comparability.

Next in Part 3:   Retail, Mobile, QR Codes and more

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“Paid, Owned and Earned Media” Interview Part 1 with Market Edge’s Glenn Engler

April 9, 2012

The following post first appeared as “Paid, Owned and Earned Media with Randall Beard” on Market Edge on Webmaster Radio FM.

Host Glenn Engler is a Fortune 500 industry figure and has worked in the marketing and communications world for more than 25 years. He focuses on perspectives on social media and digital marketing, that will help you gain insight into the unique opportunities and challenges facing marketers and thought leaders today.


Glenn:  Hi and welcome to Market Edge. I’m your host Glenn Engler, CEO of Digital Influence Group. A full service digital market agency that helps companies unlock the social potential of their brands and amplify its impact to drive results. 

Today I’ll be talking about paid, owned and earned media with Randall Beard, the Global Head of Advertiser Solutions for the Nielsen company – a Global leader in media, marketing and consumer information.

Randall is an award winning marketing executive, speaker and author with more than 25 years of global experience across consumer package goods, financial services and high-touch service brands, including Procter and Gamble, American Express and UBS.  

It’s great to have you on Market Edge Randall, welcome.

Randall:  Hey Glen, thanks very much.

Glenn:  So let’s start if you can describe and share a little bit for the listeners what your role is as Global Head of Advertiser Solutions at Nielsen.

Randall: Sure Glen thanks. As you said, I’m the Global Head of Advertiser solutions at Nielsen.  If you think about Nielsen simplistically, we measure what people watch and what people buy. And what Advertiser Solutions does is bring those things together on behalf of clients, be they advertisers or  media companies to help them optimize their advertising and media return on investment.

Glenn: So you joined Nielsen in 2009 and before that you were on the marking side with a number of world class leading brands like P&G, American Express and UBS. What inspired you to shift from the brand marketing side to working in Advertising Solutions at Nielsen?

Randall: It’s a great question. I actually never thought about working on the other side of the table if you will. A recruiter called me and when she started talking about the opportunity at Nielsen and what the company was trying to do in bringing what people watch and what people buy together to deliver much more advanced and proven analytics for clients around advertising effectiveness, I started getting excited.

It’s a really exciting space to be in because I think right now there’s more change happening at any time that I can think of since the invention of brand management or TV advertising. It’s just a really interesting space to be in right now.

Glenn: So you have the luxury of getting to work with a number of CMOs across a variety of industries and media heads. What are some of the biggest challenges they share with you?

Randall: Yes, when I came to Nielsen a couple of years ago we partnered with one of the major consulting firms and went out and interviewed a large number of c-suite executives at major advertisers, agencies, media companies, and even some technology disruption companies and we asked them a really simple question, Glen, we said: what are your biggest pain points in the advertising and media effectiveness space?

There are about ten common themes that we heard but there were three that bubbled up to the surface as being the most important for them.

And the three were, number one:  how do I figure out how much I need to spend next year? I have a business plan, I have a revenue target, a profit target but figuring out how much I really need to spend on marketing communications is still really hard, right?

Second is:  how do I allocate that spending? If I know how much I need to spend, how do I allocate it? That is becoming more and more difficult as media fragments. It’s more complicated than simply, how much in TV, how much in digital and how much in print? Within each of those mediums where do you put your money and then there are all kinds of new things like earned and owned media as well.

Then the last pain point is everybody now wants to understand how their advertising in media is working in real time. So they can sense what’s happening and they can respond to that by optimizing inflight as opposed to waiting until after the fact.

I’ll tell you a little story, when I was in Japan years ago, working at P&G, we launched Charmin in Korea. We launched into the market and initially the first couple of months were pretty good in terms of sales and market share, but things started going a little slower after that.

By the time we got our tracking results and modeling results in month six to month twelve– it was too late. Whatever problems we had, it was too late, because we had already spent almost all of our money launching the product. That’s no longer good enough. What people want to know is within days or a week is how am I doing, what can I do about it, and how do I optimize inflight?

Glenn: Wow, so, how much should I spend next year, how do I allocate it and then how does it work real time? So give a sneak peak for the listeners some of the solutions and products your group delivers on behalf of or with your clients.

Randall:  One of the things we hear from clients, beyond the three pain points is people are looking for an end-to-end measurement system for advertising and media that’s common across platforms. So first let me explain what I mean by end-to-end.

Traditionally when people think about advertising effectiveness they think of brand tracking or what’s the sales impact of the advertising, what’s the return on the investment. What we’ve done is we’ve created an end-to-end model that includes but expands on those notions. We have a framework we call the Three “R’s,” Reach, Resonance and Reaction.

Let me start with Reach. When we think about reach the first question is, who are the consumers who are most responsive to my advertising? There are tools now to identify those consumers who are most likely or have in the past responded best to your advertising. Then importantly, where do I reach them? What TV do they watch, what do they do online, and how do I build a media plan around that.?

So if you’ve figured out who the most responsive consumers are and how to reach them, the second question is, are you actually reaching them in the market?  In TV that’s something that’s been done pretty well over the years but in digital there’s a lot to be desired. So how do you know that you’re actually reaching the desired target you want to reach, and we can now measure that on a daily basis, not only for a campaign in total but by individual website, to drive accountability and optimization opportunities. So all of that is reach.

So let’s say you’re reaching the right people. The second question is, are your ads breaking through, do people know the advertising is about your brand and is your advertising changing consumer’s attitudes and opinions about your brand so they’re more likely to buy? We call that resonance. Again, the news in this space is you can now measure this in real time. And you can make decisions in-flight to optimize how you get better results.

And then the last R is reaction. And reaction is, is the advertising driving behavioral change? Usually, that’s sales but it could be is the advertising driving people to go to your Facebook fan page or search for your brand. Or other desirable behaviors like that.

Glenn: It’s interesting; I’d heard that, I love the phrase reaction because in the past everyone would talk about optimization. Which sounds like a very mechanical, let’s start all over again. And your answers in the last couple have really been about real time, urgency and immediate, things like in flight just has to completely transform not only the marketing but from your stand point the measuring systems.

Randall: Yeah, absolutely, and I’ll give you a simple example. We measure every TV ad in every show, every day in the U.S. across about 70% of the audience. So we have about a million TV ads in our data base over the last 7 years.

So as we measure every TV ad in every show, every day we’re reporting to clients, typically on a weekly basis how their ad is performing. And importantly it’s not just how their ad is performing overall but how much of their performance is due to the quality of the creative, how much is due to media weight, how much is due to programming and how much is due to placement. And by disaggregating the performance into those areas you can actually make decisions in those areas to improve your performance.

So here’s an example, a typical engagement might look like this:  A client may put a new advertising campaign on the air. Week two we come in and say, you’re running two ads, A and B. A is performing really well it’s well above norm. B is below norm. Even when we factor everything else out A is much better than B. Take B off the air and reallocate your weight to A.

Two weeks later we might walk in and say, your overall break through is trending up because you’ve moved your weight to your best ad, but brand linkage is a little soft. So in other words, the percentage of people who remember the ad, who know it’s for your brand is soft, so why don’t you work on editing the spot to improve branding. So the brand group and the agency might do that over the weekend, they put it back on air.

Two weeks later we walk in and say, ok, you’re break through is going up, your branding is now better, what we’re now seeing is that your ads are performing better in sitcoms than reality shows. But you have 30% of your weight in reality shows. Look at whether you can reallocate some of your media weight to the other show that performs better.

And lastly, maybe two weeks later we walk in and say, hey you’re running 15’s and 30’s, the 15’s are performing just as well as the 30’s — why don’t you move out of the 30’s altogether and move into 15’s, which is a more efficient use of your media dollars.

So, those are the kinds of things we work with clients on and those are examples of what real time optimization might look like.

Glenn: That’s amazing. Gone are the days of, to your point, every three months let’s huddle in a conference room and share the brand reports. You’re sitting there, literally, as you mentioned with the CMO with the media agency, with the creative agency, obviously an integral part of that leadership team.

Randall: Yeah, and I think what you’re going to see is an even more extreme versions of this in the future.

For example, today there are firms that will optimize digital advertising based on click through rates and so forth in a very highly automated way, where there’s not even a finger print from the client on it. It’s all being done on an automated basis.

Now that’s interesting for direct marketers but I think what’s equally interesting is very soon you will see the same capability in digital based on branding metrics, as well. So imagine you’re running a digital campaign and you’re doing constant polling to measure the general recall, the branding, the messaging and all those metrics by individual creative unit by individual websites and then that data is being fed back into the placement of new ads, so you’re figuring out which sites are working best and it’s completely automated, so the system is actually moving inventory around and placing your ads in the places they are working best.

That’s pretty much right in front of us, that’s going to be happening. TV it’s a little harder because the backend systems to do those kinds of things aren’t as sophisticated as digital.


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How to Make “In-Game” Adjustments to Improve Advertising ROI

March 26, 2012

Imagine a coach who only shows up after the game to find out the final score. No half-time adjustments, no in-game adjustments based on what’s working and what’s not.  Is this good coaching? Of course not.

Advertiser as Coach -- Why Advertising ROI Isn't the Final Score

The final score is just that–a final score. It doesn’t say anything about what caused the game to be won or lost, and more importantly, what adjustments could be made throughout the game to win.

Measuring advertising ROI only is like a coach coming to the game after it’s over to learn the final score, and then declaring victory–or defeat.

Coaching After the Game is Over

In my last post, Driving Higher Advertising ROI Without Big Data, I described the apparent disconnect between the growing avalanche of “big data” and CMO’s continuing frustration with their inability to measure marketing effectiveness.

Given this, the 3R framework of Reach, Resonance and Reaction is a simple, yet powerful framework for evaluating advertising effectiveness. As important as it is to measure sales impact, measuring advertising ROI is not the be all and end all.

The 3R framework illustrates why this is the case. Reaction is the outcome, the end product. It is the product of Reach and Resonance–they are both drivers of Reaction. Even when Reaction is strong, Reach and/or Resonance aren’t always optimized. And this is the opportunity for the advertiser to coach–during the game.

A Simple Case Study — The Power of In-Flight “Coaching”

A client invested a significant amount in a broadscale digital campaign:

  • Target (disguised):  males, aged 21-29
  • 100 million impressions
  • Mix of banner ads, rich media, on-line video
  • Multiple creative units
  • Multiple web sites

Now, let’s work backwards:

Reaction — How well did the campaign drive sales ?

The campaign delivered +22% sales lift among those exposed to the ads.

How do we know this? The digital ads were tagged, and sales lift was measured via off-line purchase panels among those people exposed to the ads versus those not exposed.

This looked like a huge victory: what’s not to like about a +22% sales lift ? Let the celebration begin…

Reach — How well did the campaign reach males, aged 21-29?

Of the 100 million impressions delivered, only 60 million hit the target. So, 40 million impressions hit women, or men older than 29, teens, or some other group other than males aged 21-29. Obviously, the campaign was not well targeted. In my experience, this is the norm, and not an exceptional case.

But the campaign also wasn’t well planned: the reach was <10% and the frequency was 50+. Most consumers exposed to the campaign were almost certainly exposed way too much. And way too few target consumers were not exposed at all.

This kind of data is available now on a daily basis. Now, if it was just before halftime and you were coach, what would you do with numbers like these ?

Resonance — How well did the advertising break-thru and change consumer attitudes ?

Overall, the campaign performed slightly above norm. Ad recall was well above norm and branding was generally solid–good news and consistent with the sales results.

However, performance varied tremendously across creative formats, creative units and web sites. On-line video performed best, followed by rich media and banner ads. Of the 10 creative units, 3 performed well below norm. And of the 8 web sites, 2 performed well below norm.

Now, if was the end of the 3rd quarter, and your we looking at results like the above, what would you do ? Most likely, you’d move spending out of low performing ads, ad formats and sites, and reallocate them to higher performing ones.

Optimizing Advertising Performance “In Game”

It’s clear in hindsight that the campaign wasn’t optimized. The campaign clearly drove a sizable sales lift among those people exposed to it. If the client had measured results in flight and made adjustments along the way, the sales lift would have been higher, and among a much larger group of target consumers.

As the example above illustrates, it’s not enough to  just measure the sales lift of the advertising. This is like measuring the game’s outcome, without coaching throughout the game.

There are other important factors which are critical for you as the advertising “coach” to do to optimize advertising performance in-game:

  • Understand whether you are reaching the right consumer with the right reach and frequency.
  • Make sure that your campaign breaks thru and changes consumer opinions–across all creative units and all sites.
  • Measure all of the above in flight, in real-time, so you can asses what’s happening and quickly make decisions to change your plan to optimize the campaign and generate the best possible result.

So, what are you waiting for? Don’t be like many advertisers and be content to simply measure the final ROI score. Or, just take Yogi Berra’s famous advice to constantly coach, adjust and optimize during the game, because:

“It ain’t over till it’s over.”

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