Adapted from a presentation at the IIR Conference on Marketing Mix Modeling in March 2002, this article focuses on measuring the true effect of advertising. The object of marketing mix modeling is to learn the most effective ways to increase sales. Many variations of this basic problem will be encountered in marketing mix modeling, including models that are more sophisticated and complex (Figure 1). Businesspeople have developed an admirable ability to represent complex relationships mathematically. There is one prominent exception to that generalization about having inadequate measures of the various elements in the mix. There should have been warnings because what happened next, after scanner data became available, was a major change in virtually the whole package goods industry.
Price promotions increased, advertising decreased, and packaged goods switched from being one of the most profitable segments of the economy to one of the least profitable. Measures that only showed the volume of advertising were used for years during this period in marketing mix models. Almost everybody involved in advertising has experienced the thrill of a winner and the agony of a bummer. Some of the most clear-cut and conclusive evidence on this point is provided by Super Bowl commercials. Some Super Bowl commercials are aired only during the game, and never again until our interviewing is complete. The long yellow bars on the chart show the percent that recognized these 99 commercials ranged from over 50 percent to less than 5 percent, an impressively wide range for commercials that all had an identical amount of exposure.
The blue bars show the percent that passed the first two tests, and also passed a third test: they liked the commercial.
The amount of difference that can be found between the top commercial and the bottom commercial in a comparison like this can be dismissed as a rare and unusual difference. An appendix at the end of this article contains all of the data from the 99 commercials plotted on the chart in Figure 2. Among these 99 commercials that all had an equal amount of exposure under normal, real-world conditions, the top 20 percent were recognized by four times as many as those in the bottom 20 percent. When you consider this comparison is based on Super Bowl commercials, it could be understating the true difference. The participating companies pooled their results from this most expensive and conclusive procedure for measuring the effect of advertising on sales to see what could be learned.
When the testing procedure was used to see if new commercials produced greater sales than old commercials, that type of change was often accompanied by sales increases. When the same test was used to see if increased GRPs or expenditures produced greater sales, that type of change was seldom accompanied by sales increases.
There is more, but that should be enough to document a point that few researchers outside of modeling would argue with. In case the problem is not knowing how to fix the situation, we’ll move on to a review of the solutions. Subjective measures: What you hear about the advertising from friends, at home, in the office are all examples of the type of thing that should not be ignored.
Focus groups can be used the same way, as can critics’ reviews, and any awards the advertising might win. The second requirement for the best pretesting is to make respondent exposure to the material as normal as possible.
It is important to note that all pretesting is an attempt to predict how advertising will perform when it actually runs under real-world conditions. This type of testing, done after the advertising has run, is typically conducted in one of two ways. Recognition-based tracking conducted online or in malls: In this approach the material is shown to respondents and they are asked if they remember seeing it. To determine if advertising has any effect on people, you need to do the most accurate job possible segregating them into two groups: those who noticed the advertising, and those who either ignored it, or never had an opportunity to see it. I feel the reason recall has failed to show any significant relationship to sales in industry-wide studies of ad testing validity during recent decades1 is a direct result of its lack of accuracy. In short, when the question is what is the best way to measure advertising quality, the answer is recognition-based tracking. 70-80%: You have a good workable model that should help the firm improve its batting average in making marketing decisions. Returning to the question of whether there are meaningful differences in the various measures of advertising volume, the answer is yes. This is background for a key possibility: Since you tend to get what you pay for, dollar expenditures may be more closely related to advertising impact than GRPs, or TRPs (TRPs are the closely related measure of how often the commercial could be seen by those in the target group the advertiser was particularly interested in reaching). The second type of diminishing return comes into play when the opposite condition prevails. It is becoming clear both types of diminishing return should be expected and allowed for in building models. Models that use nothing but GRPs or dollars will miss the effect of both types of diminishing return.
The big advantage of the dummy variable approach is that tracking surveys to measure the quality of the advertising directly are not needed. But note the second example: a $2 million increase in advertising produced a $5 million increase in sales.
However, you cannot ignore elasticity completely because the main criteria for deciding whether one form of model fits your needs better than another is usually how they handle elasticity.
Exponential model: elasticity stays same as share approaches 100 percent AND elasticity grows infinitely as mix elements grow infinitely.
There is a moral to this: Check your model to be sure you have a good fit and one that keeps producing reasonable results when you approach extreme values outside the range covered by your historic data base.
The numbers they enter should range from zero to hundreds of times greater than the highest conceivable ad budget. The last two models are generally preferred because they never produce impossible elasticities for advertising or any other marketing input. As the volume of advertising increases does advertising elasticity increase until it reaches some maximum and then starts declining until it reaches zero, the way it does in the first chart?
Now, thanks to the more detailed data on sales and exposure to advertising that has become available from single-source research, and John Philip Jones’ work with that data5, it appears the consensus has shifted to one supporting the picture shown in Figure 6. John Philip Jones was the first to capture the industry’s attention with new single-source evidence showing advertising has its greatest impact when it first appears, not after it has been shown several times. Even though the consensus may be that the MCI (multiplicative competitive interaction) model should provide the best fit for most current advertising, that is an assumption that needs to be tested whenever enough data is available. The final issue in successfully modeling the true effect of advertising is to allow for what the competition is doing. Note that quality is included as a multiplier, just as it was in simpler versions of the model. Even though share models are more likely to account for what is going on in a market, they do have one limitation: They require more data. Modelers’ obsession with historic data can cause some in management to lose sight of what a marketing mix model can accomplish. Similarly, some ask, “Why spend the time and effort measuring the quality of past advertising? Sawtooth Software will hold its 2016 Sawtooth Software Conference on September 26-30 in Park City, Utah. New York advertising agency Young and Rubicam believes on-site interviews are one of the best ways to understand the connection between consumer and product, by seeing consumers in their natural environments. Radio advertising can be an extremely effective marketing tool, especially when selling to farmers. Before airing commercials, executives at Eye Care and Surgery Center in Baton Rouge, La., turned to marketing research as a guide. In today’s competitive, globalized world, promotions of products and services have become very important.
To understand this, the concept of advertising elasticitywhich is also known as promotional elasticity of demand is useful.
AED measures degree of change in demand brought about by change in advertising expenditure. Definition: Proportionate change in demand brought about by a unit change in advertising expenditure. It means that demand is more sensitive to the advertising expenditure and proportionately giving more than proportionate increase in demand. It means that change in advertising expenditure brings about less than proportionate change in demand. 3.The Purpose of campaigns may be to create brands, rather than only influencing size of demand. Numerical Values of Advertising Elasticity of Demand will vary from zero to infinity.It would mean that if AED is zero,advertisng expenditure has no effect on demand at all. It would mean that advertising expenditure is giving just exactly proportionate returns in terms of demand e.g. 4.Collect information about cost of advertising campaign and its returns in terms of demand or revenue. The ‘Big Style company selling T shirts increased its advertising expenditure from Rs 5 Lakhs to Rs 10 Lakhs per annum.


Content is available under the Creative Commons Attribution Share Alike License unless otherwise noted. To do that you need valid measures of every element in the mix - everything that could be affecting sales. For many elements in the marketing mix this ability is decades ahead of our ability to measure those elements. It comes from our firm’s database built from tracking the impact of every Super Bowl commercial aired during the past 11 years. We wait a couple of weeks to see how many of the Super Bowl commercials had any lasting effect on people. Those are the ones plotted on the chart in Figure 2 - and they are the ones that prove the point.
It details the commercials that were in each group, and shows how the averages for the top 20 percent and the bottom 20 percent were calculated. Advertisers were paying millions for this single airing, so they were doing everything they could, using the best and brightest in the business and giving them big production budgets, to insure they had a good commercial to run on the Super Bowl. Two key findings were relevant to the issue of whether creative quality is more important than volume. Our firm keeps track of them by publishing our Top Ten Insights, a short two-page recap of conferences that we have been sending to customers and colleagues since 1995. It is hoped at least this much can be of help, because most of the current marketing mix models our firm is aware of are still using just GRPs or dollars to represent advertising. One of the things we hear from modelers is that they don’t feel comfortable being put in the position of an expert in advertising research. Put historic information about price, advertising, and all the other elements in the model. If nothing better is available, think about the general thrust of the comments you are hearing.
Anything that can be used to help decide if the advertising is above average, average or below average is worth trying to see if it improves the fit. Often modelers feel bewildered by the many different types of pretesting they are faced with. The scores that advertising achieves in pretests are valid only insofar as they help predict its actual performance. Only then can you demonstrate with confidence that people reached by the advertising bought things, did things or believed things that the rest didn’t.
But even though we deride them, many continue to use them exclusively, and I think the rest of us will agree they are vital elements that need to be included in more complete and complex models. One hundred household GRPs means the number of homes that had their TV on and tuned to the right channel while your commercial was playing was the same as the total number of households in the area. When expenditures or GRPs are kept at the same level for a period of time, the measurable impact of that advertising will eventually start to decline.
But it is also becoming clear there are no standard decay rates that will fit all products and all types of advertising. Building an allowance for them into the model is one of the first things to try to see if you can’t get a better fit. If an adequate tracking survey had been in place, the adjustments could cover all media, all markets and all years. In our experience it is the most misunderstood element in the advertising section of a marketing mix model. If your historic data doesn’t vary much you may find all models fit because things have not been changing enough to show if any of the models fail to fit at more extreme values. To protect themselves on the advertising front I would urge them to spend a few minutes entering hypothetical numbers for advertising volume and noting the results. If it shows you could capture 110 percent of the market, or if it shows the last million dollars you add to the ad budget yields a billion-dollar increase in sales, you should question the validity of the model. His findings have been supported by other prominent media researchers such as Erwin Ephron, who uses them as the basis of his recency theory, which favors continuous, rather than pulsed, advertising6, and Collin McDonald, whose early work with single-source data 35 years ago provided much of the data cited in support of a threshold effect, but who has now come around to the view that although threshold effects exist, they are neither common nor normal7. Models that only account for what a single firm is doing may work, but only until there is a major change among competitors. They readily accept the principle that when a competitor cuts their price the firm is going to lose sales. It can tell you if doubling the sales force or investing the same amount in price promotions is more likely to increase next year’s profits. If the new advertising tests out at certain levels, it is likely to have the quality specified in the model in order to achieve the expected market share.
When the model is used to show how much was added to the bottom line by good advertising, and how little was added by bad advertising, you have what is needed for a key ROI calculation.
Cooper and Masao Nakanishi (1988), Market-Share Analysis, Kluwer Academic Publishers, Boston Dordrecht London, International Series in Quantitative Marketing. While a lot of research is involved, the process gives companies an accurate view of how consumers actually think and feel in their natural environment. After surveying both staff and patients, the company launched print advertisements and patient brochures that increased awareness of the new procedure.
The series of commercials tested very highly in focus groups and ended up appealing to people across the country.
By analyzing tracking studies, Ted Haller can create the perfect fit between advertisers and radio stations to optimize the effectiveness of radio advertisements.
What they found was information that allowed them to effectively connect with potential customers.
We wonder how much do companies spend on such aggressive marketing and whether it is worth it. When the company ‘ Cool ‘ spends 25% additional expenditure on advertising, its new product demand remains rigid or constant.
This means that if a soft drink company has increased its advertising expenditure by 25%, the demand will also rise exactly by 25%.
AED does not give us precise effect of advertisements on sales of the specific product at the specific time.
Dominick Salvatore, Principles of Microeconomics, Fifth Edition, Oxford International Student Edition. This article is adapted from a presentation at the IIR Conference on Marketing Mix Modeling in Chicago on March 21, 2002.
That is the same as saying bad advertising contributes as much to sales as good advertising. That’s why I feel an up-to-date review of the ways to measure advertising should be very productive. A good case can be made that faulty measures of advertising’s true effect, used in marketing mix models, have ruined profits in a whole sector of our economy. The latter, usually referred to as GRPs, measures the volume of advertising by adding up the rating points of each program the commercial appeared on.
But there appears to be a mindset among modelers, and the management that is responsible for them, that finds it easy to accept a different view.
When we move down a line and consider the number that both recognized the commercial and knew who it was for there was an even bigger difference. Because of this, you would hope the number of bad, ineffective commercials on the Super Bowl would be somewhat less than you usually find on TV.
This classic study showed quality was not just as important as volume, it was more important.
Modelers who say they have to stick with volume-based measures of advertising because they cannot afford anything better are not really trying. What is the main point it is trying to get across?”) Even though that has been a popular way of pretesting for decades it suffers from two near-fatal flaws. Respondents are not aware they are seeing different commercials in the normal course of watching TV at home than people in another matched panel that might be watching the same program.
Its general superiority is also emphasized in one of the best and latest books on measuring the impact of advertising.
That leaves the question of what is the best measure of the amount of advertising: dollars or GRPs? I have never found a statistics text that gave a clear-cut, unqualified statement as to what is acceptable in r2 scores, so I offer the following as my personal and somewhat subjective calibration of r square scores for marketing mix models. The time it takes to drop halfway back is referred to as the advertising’s half-life.
It takes a substantial amount of historical data to find the values in the model that will provide the best fit. In each case the expenditures would be multiplied by the same type of quality factor: a factor that shows how efficiently that advertising reached and affected people in that year, in that market.
It is given a value of 1 during weeks when it is running and a value of 0 when it isn’t.
Those half-knowledgeable MBA- types were thinking of price elasticity, where inelastic is always bad.
That second example may have been inelastic, but it showed a positive return on the additional investment in advertising. Compare the charts with the results you achieved testing your model and you will learn a lot about your model.
This is the model that sources like the Cooper, Nakanishi text, cited earlier, recommend for advertising because it reflects a threshold effect where small amounts of advertising have little or no effect.


Every firm is affected by what its competition does, so models that allow for that are the only models that should work.
Price is usually expressed as a ratio of the firm’s average price to the average price in the industry. A coordinated program of pretesting and then tracking the advertising’s actual impact after it runs can be used to calibrate pretest results so they will provide valid estimates for the quality multipliers to be used in the model.
How much can you afford to invest in additional research and additional ad development costs if it holds promise of producing better advertising?
Baack, Integrated Advertising Promotion and Marketing Communications Third Edition, Pearson Education.
It concentrates on advertising because a major reason many marketing mix models don’t work as well as they could is that advertising is only represented by dollars or GRPs.
This article stresses the importance of making that portion of a marketing mix model as realistic as possible, and reviews the best ways of doing it.
A case can be made that inadequate measures of advertising’s impact has caused serious harm in our economy.
And when we move to the last line, using all three measures, we get the biggest and most important difference. Probably the largest, most carefully conducted and most persuasive is the study popularly known as Adworks One.
With a conscious effort to be objective, this information can often be used as the basis for classifying current advertising as above average, average, or below average. But the cost of split cable pretesting is well up in the six-figure range and it takes six months to a year, so faster, less expensive methods of simulating normal exposure are more popular. Usually a little introspection on past difficulties recalling names or phone numbers is sufficient to demonstrate that is not the case with recall.
There was also one case where expenditures had been used initially and switching to GRPs improved the fit. If anything else tends to happen at the same time as the advertising (the product goes on sale, salesmen make more calls, etc.) the dummy variable will not provide a clean reading of quality. As advertising volume increases, each additional dollar of advertising has greater and greater impact until it reaches the optimal point.
The arithmetic gets more complex, but fortunately the principles are the same, formulas are available in any good modeling text, and today’s computers have little trouble handling the complexities.
There are only a fixed number of consumers who are susceptible to switching brands or increasing consumption. However, it is a need that can be met by tracking the impact of all the advertising currently being conducted in the category.
The modeler’s fixation on accounting for what happened in the past is just one critical step in building a valid tool for predicting the future.
It avoids the trap of projections inadvertently based on the assumption that unusual conditions are likely to persist, when they are not likely to persist. If your experience is like that of the Super Bowl advertisers cited previously it could mean getting a $10 million dollar ad budget to yield the impact of an $80 million budget. Young, Sreejata Banerjee, Managerial Economics – Economic Tools for Today’s Decision Makers, Fifth Edition, Pearson Education.
Belch, Advertising and Promotion- An Integrated Marketing Communications Perspective, Sixth Edition, Tata McGraw Hill. Example of as income increases, demand for services increase, such as holidays, spas, etc.But demand for food only increases a little bit. These are figures the firm already has for other purposes, so they have always been readily available to modelers. It needs to be challenged because it is now evident that a big part of the problem was that most modelers and their management did not take the rather obvious limitations of advertising volume measures seriously. It was headed by Len Lodish of Wharton and supervised by research directors of 40 major advertisers. If it does, you at least have reason to believe it may prove as successful in predicting the future as it did in accounting for the past. When a simple three-level classification like that is factored into a marketing mix model it is often enough to produce a measurable improvement in the model’s ability to account for past changes in sales. It categorizes all pretesting in terms of where it falls based on two characteristics: how the advertising is shown to the respondents, and what is measured by the questions the respondents are asked. The most common is to embed the commercials to be tested in new program material that is shown to respondents who might think they are going to be questioned about the new program.
The mind’s ability to recall things from memory is definitely not one of its strengths. If advertising starts running again before the ad stock value has dropped back all the way to its pre-advertising level, it will tend to rebound from that new level and reach levels higher than before. If the main use of your model is to optimize the annual marketing budget, don’t go through the agony of fitting your model to weekly data. Your tracking survey shows it reached and affected twice as many: 20 percent of the population. When you multiplied the $10 million for Campaign B by two that told the model it was twice as effective as Campaign A.
This indication of the ad’s effectiveness could be affected by both the quality of the advertising and the amount of advertising.
Further, they are subject to all the noise, and overemphasis on chance occurrences that happen to make the data fit better, common to most multivariate procedures. This suggests another reason support for threshold effects may be declining: Are more of today’s ads just simple reminders for well-known brands?
When a competitor captures a portion of them with their advertising, there are fewer left to capture with your advertising. A tracking survey like the recognition-based tracking survey conducted each year to track the impact of each of the 50 or 60 commercials on that year’s Super Bowl can provide precise measures of the quality of every ad and commercial running in almost any category. But their use in models ignores the problem cited earlier: The assumption that bad advertising does as much to increase sales as good advertising. That shows how well it was branded and how well it performed in getting across the most important piece of information every ad has to communicate. If it accounted for some, but not all, of the past changes, you have reason to keep working on the model to see if you can find what is missing. When these models can be fitted to a substantial amount of historic weekly data, it will show what the product’s half-life is and how much advertising needs to be conducted and at what intervals to keep the results from falling below some predetermined level.
Hopefully, advertising that had a large amount of exposure during a week when the dummy variable was given a value of 1 would have a greater effect than advertising that was given little exposure.
Still, where tracking surveys are not available, but lots of other data is, the dummy variable approach should provide a better fit than when ad quality is ignored completely. In short, your advertising will produce more sales when your competitor has a poor campaign. The investment would be likely to have a return many times greater than an investment of the same size in a greater volume of advertising.
It shows an urgent need to look at the evidence showing the quality of advertising is at least as important as the volume of advertising. The following review of opportunities for measuring advertising’s true effect should help both groups. Yet, when you buy that number of homes during the prime evening hours you pay about three times as much as if you bought the same number of homes (same GRPs) during daytime hours.
That difference in quality is best reflected with a minor addition to existing marketing mix models that only use advertising expenditures.
That made the model reflect the true conditions in the market more closely, so it fit better and achieved a higher r2. So, as long as the volume of exposure that ad or commercial was given is shown by a separate variable, the value of the dummy variable coefficient should measure the quality of advertising.
Some GRPs cost more than others because those who buy and sell GRPs agree some are worth more than others.
To measure that, we ask how well they liked it, the measure the famous Advertising Research Foundation Validity Study indicated was the one most closely related to sales.
Understanding the message isn’t nearly as important as understanding the effect that message might or might not have on a person. Those who have their TV on during the day are likely to be doing something else at the same time. The Advertising Research Foundation Validity Study showed main point recall was not closely related to sales.
During the evening they are more likely to be devoting their exclusive attention to the TV. The only difference would be that every time an advertising expenditure figure or a GRP figure appeared, it would be multiplied by a factor representing the advertising’s quality. It was actually more predictive than direct questions about future intentions to purchase, or simulated purchasing decisions before and after exposure.
However, those behavioral measures did have substantial predictive value, so we have shown both behavioral and attitudinal measures on the right side where you find the higher value pretesting.



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