Archive for the ‘Market Research’ Category
Choice based Conjoint
Choice based Conjoint (CBC) is a research technique based on the observation that consumers always choose products among a set of products in the marketplace, and a simulation of it is the closest to the real consumer behaviour. CBC is a technique wherein the respondent is shown a set of concepts (with specifications) and is asked for his/her preferences. This technique hopes to simulate the tradeoffs that consumers make in their daily buying experiences; the tradeoffs could be among the attributes of the product or among the products and brands listed. This technique is generally used to understand the interaction among the attributes, and for pricing studies.
One needs to list down the attributes and the levels for each of the attribute. For example, to conduct a CBC to understand the importance of the features of a smartphone; an example of an attribute could be “RAM Size” and the levels could be 512MB, 1GB, 2GB or whatever options you would like to present to your consumers. The options should be as close to the actual product as possible and the attributes and the levels should be given an extra-ordinary amount of thought. CBC should ideally be done on a sample of around 300-600 respondents who are aware of the products and the category.
One of the issues I faced while deciding on the attributes and the levels is that it is a little on the easier side for a very functional product like a smartphone or a car, where you can easily distinguish between different engines or processors, (different features like power steering, windows, etc…). The features and levels in functional products are easily distinguishable and conceivable. On the other hand, for products such as biscuits, toothpastes, sanitary napkins, etc. I am not sure how well people can distinguish and conceive different product benefits in such categories where you know the product only by experiencing it.
History of CBC
Limitations of CBC
- Not all brands are equally known to the consumers, and there is a risk of popular brands mostly being preferred in a CBC study.
- CBC doesn’t take promotions and distribution into consideration, and it assumes that all brands are available and have enough media spends.
- It assumes that the consumer has the ability to buy the product.
- The number of questions involving different choice sets could easily increase, causing respondent fatigue.
Brand Price Trade Off
BPTO is a simpler version of a conjoint analysis where a set of brand/price combinations are shown to the respondent. As the respondent choses a particular brand, the price of that particular brand is increased and the consumer is again asked to choose among the new set of brand/price combinations. This technique helps us understand how the consumer trades off the brand and price, and what is the best price point or price band for your product.
The one biggest advantage of this method is its simplicity, while it has quite a few critics in the market. One of the disadvantages of BPTO is that consumers may become conscious and may start playing around with the lowest price, or consumers may be protective of their brand and may always prefer a brand and take it to unrealistic pricing levels.
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Significance Testing
Posted on: July 8, 2012
Significance Testing lies at the heart of all the inferences that we do from a sampling exercise. We always start with a ‘Null Hypothesis’ in the jargon. A test of significance is a test of that hypothesis. We analyse the data from the sample and try to estimate what would be the probability of getting that data if the hypothesis were true in the universe.
For the below reading, it is first important to understand the difference between accuracy and precision.
ACCURACY
Accuracy is the proximity to truth. If we knew the truth we would totally not estimate it altogether. So, accuracy of an estimate is a totally useless concept altogether for population statistic estimation.
PRECISION
Suppose you have the task of adding up long list of numbers – perhaps your daily expenditures over a month. You do your sum and get a particular result. But you’re not sure whether you got it right. You may have made a mistake in adding or punching in the numbers if you were using a calculator.
What do you do? You do the sum again. And if you’re a cautious accountant you might even do it a third time. If you get the same result every time you feel you have got it right.
Lesson: When in doubt, repeat. Repeatability of the result generates confidence in it. Repeatability is reliability.
Actually, our example of adding up a list of numbers is not a good one. Because, in this case there is only one true answer and we shall get it every time we do our sum correctly. But, the real life situations that we are interested in are the results that we get from measuring a sample of people from some universe. Again, we are not sure if the results are true. So, in line with our commonsense philosophy, we should be repeating the sampling exercise. If we did, it is highly unlikely that we would get exactly the same result, because different people would be included this time. In fact, if we repeated the sampling exercise many times and measured the same thing on different samples of people, we would find that most of the results fall within a range.
We would be entitled to come to a conclusion that, most probably, the truth that we are trying to estimate must lie somewhere in that range.
If we had a method of being more precise and if we could say, for example, that after repeating the sampling exercise many times, 95 percent of the results would fall within a certain range, then there would be a 95 percent chance that the truth would lie in that range.
The width of this range is a measure of the precision of our estimate - narrower the range, higher the precision. Our objective is to narrow this range as much as possible, because that would bring us closer to the elusive truth. Precision replaces the concept of accuracy. We will never be able to say how accurate is our estimate of the truth, but we can say how precise it is.
But how do we get a fix on this range? Taking just one sample in real life is problematic and costly enough. Repeating the exercise many times may be conceptually brilliant, but completely undoable in practice.
Actually, you don’t have to repeat the sampling exercise. This is where the science of inferential statistics comes in. By analysing the data in one sample that you have taken, specifically the variation contained in it, and by making some assumptions about the pattern of variation in the total universe, it can calculate the 95 percent or 99 percent or any other precision range that would actually come to pass if you did take the repeated samples. The whole purpose of inferential statistics is to save you the trouble of actually repeating the sampling exercise by inferring what would happen if you did.
It sounds like magic, but it is only logic. This logic completely depends on a crucial aspect of reality, namely the ‘Laws of Chance’, more commonly known as ‘Probability’.
So, the whole stuff is all about how precise are we in our estimate of a population statistic. After all, we all know the statistics of the sample. The problem is to understand the average height of the population in India, if you have a sample whose average height is known. This is where it all starts, and this is the role of the Central Limit Theorem (CLT). CLT assumes the population to have a normal distribution, else the ‘n’ value has to be a minimum of 30.
CLT says that if you have a sample mean (x-bar) and the standard deviation of the sample is σ, then the probability that the population mean(µ) lies between the confidence intervals for a desired confidence level (z) (read it as a confidence level for now, I will come back to it later)
which is nothing but
For now, understand that CLT will provide with a confidence limit for the population statistic if you know the sample statistic and the standard deviation. Understanding the nuances of how CLT works and what its details are decently complicated and I will come back to it later.
Let us take a practical requirement for our understanding. Take for example, we have done a product test among men and women in a population and we asked the purchase intention of a product. Let us say, the results look as follows: (the numbers quoted are just for understanding the concept and may not hold the law of statistics)
Since we want to examine the differences in scores between men and women, we formulate the ‘null’ hypothesis that ‘there are no differences in the real scores in the population among men and women’ implying that the differences in the scores in the sample have come about by chance, and if we had repeated the sampling exercise, the differences would have disappeared.
The first thing to do is to calculate the confidence belts for both the scores by analysing the ‘variance’(using CLT) in the sample scores among men and women. Various situations can arise as follows:
Situation 1
Sample Size: 100 each
95% of range of scores of men is: 4.5———-4———–3.5
95% of range of scores of women is: 3.4———–3————2.5
There is only a small chance that men’s scores will be lower than 3.5 and women’s scores higher than 3.4. Therefore, the statement that ‘Men score higher than women’ has only a 5% chance of being wrong. Scores are significantly different at 5% level.
So if the 95% confidence belts don’t overlap much, then we can say that the scores are significantly different and cannot come by chance. So, we reject the null hypothesis. Here the degree of risk in rejecting the hypothesis is 5%.
Situation 2
Sample Size: 100 each
95% of range of scores of men is: 6———-4———–2
95% of range of scores of women is: 5———–3————1
No evidence for believing men score higher.
Scores are not significantly different at 5% level. We therefore don’t reject the null hypothesis.
Scores are not significantly different at 5% level. We therefore don’t reject the null hypothesis.
We can make this case to be significantly different by taking decreasing the confidence level or increasing the sample size as follows:
90% range of men: 4.6———–4———–3.6
90% range of women: 3.5———-3———–2.5
Scores significantly different at the increased risk level of 10%
We can also increase the sample size
Situation
Sample Size: 200 (As we increase the sample size, the range for confidence level will decrease which may lead to significant difference even when the confidence level)
95% range of scores of men: 4.2———-4————3.8
95% range of scores of women: 3.3———–3————2.7
Scores significantly different at 5% level
Therefore, increasing sample size will make smaller differences significant.
Interpretation of Significant Results
The fact that a survey result is found to be significant, by carrying out a statistical significance test, often leads to confusion when such a result is presented to people unfamiliar with recent methodology. The layman, when told that something is significant, often assumes that the researcher considers the result to be “important”. Always remember when the researcher says significant he means that the result is statistically significant. In statistical terms, if, for example, a difference between two percentages is declared significant, it simply means that this difference, no matter whether it is a large or small difference, cannot have occurred by chance.
References:
http://dsearls.org/courses/M120Concepts/ClassNotes/Statistics/530_conf_int_mean.htm
http://dsearls.org/courses/M120Concepts/ClassNotes/Statistics/530G_Derivation.htm
http://lssacademy.com/2007/07/16/explaining-the-central-limit-theorem/
http://www.southalabama.edu/coe/bset/johnson/lectures/lec16.htm
AdStock GRPs
Posted on: June 30, 2012
AdStock is a simple mathematical model of how advertising builds and decays. It is invented by Simon Broadbent as he studied Milward Brown’s ad awareness data.
AdStock helps to:
- Optimize your advertisement scheduling
- Used in marketing-mix modelling to come up with advertising ROIs, etc.
- Helps you decide when to be off-air and when to be on-air
- Helps you understand the advertising decay behaviour
How advertising builds and decays?
Let us take awareness as a parameter to understand the concept of AdStock. As a consumer watches an advertisement for the first time, let us assume that consumer gains certain awareness of the brand, category, etc. Now, when the same consumer watches the advertisement for the second time, the advertisement builds on the awareness. The advertisement hopefully will strengthen the awareness, recall, preferences, etc. So, advertising builds on itself and that is why we call it as a campaign building.
Similar to the way it builds, an advertisement also decays in similar fashion. If a consumer has seen an advertisement A1 10 times in a week and the same consumer has seen an advertisement A2 only once in a week, then the way the consumer forgets the advertisements is very different. The decay rate of an advertisement depends on various parameters such as: the strength of the advertisement itself, media plan, media vehicles chosen, category involvement of the consumer, etc.
The normal GRP data doesn’t take into account the build and decay rates. So it doesn’t take into account the residual effect of advertising, though a company doesn’t advertise in a specific period. AdStock is nothing but the GRP data taking into account of the build and decay of advertising, which is more sensible in marketing applications.
Optimize your advertisement scheduling
As explained, the AdStock GRPs are the GRPs weighted for the advertising build and decay rates.
Let us look at case to optimize the scheduling strategy for an advertisement. For this case, the advertisement is assumed to have a half-life of 6 weeks (hypothetical). This will come out for a decay rate of 12.24% as shown in the table below.
We have four options of scheduling, each using roughly the same (1200-1500 GRPs) amount of GRPs. Once we translate these raw GRPs into AdStock GRPs, it will help us decide which scheduling strategy is the most optimum as explained below.
The AdStock GRPs are adjusted based on the decay rate. For example, the number 469 in Wk 2 is arrived by: (250 of Wk2) plus (250*87.8) (decayed GRPs of Week 1) = 469.
Similarly, 662 in Week 3 is arrived by: (250 of Wk 3) + (250*87.8) (decay of Wk 2) +(250*77.0)(decay of Wk 1)= 469
From the above, it is clear that Option 1 gives the maximum ROI. The other parameter important for selection of an option is the off-air time. Which of the above options gives me the maximum off-air time (when you don’t air the advertisement)?
From the above table, it is clear that Option 1 gives the maximum off-air time for the advertisement by still maintaining more than 500 GRPs. In the above example, 500 GRPs is considered as the threshold and if it goes below, then the advertisement has to come on-air.
To sum it up, AdStock helps marketers understand ‘When to advertise‘? AdStock is commonly used in scheduling, marketing-mix modelling, etc.
Any comments on this regard are most welcome.
Thank you.
Before reading this article, just close your eyes zeroing your mind for a moment and recollect three television advertisements. Write down a few details of each of the television advertisements you could recollect.
Of all the numerous advertisements I’ve watched, I could recollect only three advertisements:
- The old Nescafe advertisement
- The recent Flipkart’s advertisement of office-going children
- The JK Cement advertisement
These are the only three advertisements I could recollect instantaneously. It is strange to think that I hardly could recollect any other advertisements. Now, close your eyes and recollect a few brands. I recollected a few brand names listed the following:
- Dairy Milk (chocolate)
- Kellogg’s
- Dettol
- Coca-Cola
- Pepsi
- ICICI Bank
- Samsung
- McDonalds
- Ford Figo
- Flipkart
Also, if one wants to understand which brands do consumers associate with a category, then we have to ask the consumers to recollect advertisements w.r.t those categories. The above shouldn’t be mixed with this.
Clearly, the top of mind (TOM) set of brands are the above. I read through the list and tried to recollect the last seen advertisement in each of these brands. I could recollect the advertisements of all the above brands. Now, why couldn’t I recollect most of these advertisements in the first question? It is because the first question lacked a context.
This shows that a television advertisement on its own is generally of not much use. But if you provide a context to the consumers, then the television advertisements will help the consumers connect the brand with the context. Consumers going to the shop will subconsciously recognize the brand that they’ve watched it on television. This means if you are investing in television advertisements, you have to provide sufficient contextual support such as in-shop presence, BTL, distribution etc.
Until now, we spoke about two things: Advertising your product on television and creating a context offline. This helps the consumers connect the brand with the context. But what actually helps the consumer receive your communication in the first place.
To communicate something you need to first command the recipient’s attention
Consumers, as human-beings, switch on and off in various situations based on different factors. One of the key factors that make the consumers decide to switch on or off is Relevance. A consumer who is about to buy a car will suddenly switch on (becomes attentive) while watching an advertisement of a car. The same consumer 2 years back might be passive and switched off to advertisements of cars.
Also, anything different from routine generally catches the attention of people. For example, the Flipkart advertisement having elderly looking kids. Another example is the use of celebrities. Because consumers become attentive when they look at celebrities, usage of celebrities and other unique elements commands attention.
I’ve put celebrities and unique creative elements under one category because they are good in commanding attention. But they are not enough. Only uniqueness in the creative will help consumers remember the advertisement, but consumers will not remember the brand of the advertisement.
To communicate something you should be relevant to the recipient
The presence of unique elements or celebrities doesn’t make a communication relevant. But the problem is relevance is something that has to come from the consumers. I cannot shout in the media that I am relevant to you, hear me! For example, a consumer considering to buy a car finds the advertisements of cars relevant. Does this mean that to communicate to a target audience I have to wait for the consumers to feel my category relevant to them? No, in such cases you have to build category relevance to the consumers. You have to give them reasons why they have to use the category.
But, how does one build relevance? Relevance is a recurring theme. You build relevance to a category by relating the category to what is relevant to the target audience. For example, if you want to communicate something on conditioners, you have to make consumers relevant to the category. But the category is very nascent and consumers don’t feel a relevance to the category. So, in such cases you build relevance for conditioners by understanding what is relevant (1-level below) to the prospective buyers of the conditioners and connecting that (1 level below relevance) with the category. So, you come up with elements like softened hair, strong roots, etc which are relevant to the consumer and connect that to the category. This is building category relevance for effective communication. The recent TVCs on Colgate Sensitive Pro-Relief is also an example of the category relevance.
But what if the category is already a well penetrated category like shampoos or toilet soaps? As the category is already relevant, all brands clutter the consumer confusing him and he switches off to the category. This is where brand relevance comes into play. This means you have to make the consumer feel relevant, not by talking about the category, but by talking about the brand. Here you don’t talk about the category elements like softened hair, but you try to build relevance by distinguishing your brand such as natural, herbal, seeds of some plant etc. You have to give reasons to buy your brand and make your brand relevant. This is the true test of marketers on how well they can create the brand relevance – the brand associations, the aura of the brand, brand values, brand differentiation etc. Consumers have to feel a specific brand in the category relevant to them.
Most television advertisements today fail because they are not relevant to the audience and they failed to build relevance. The media is so cluttered today that advertisers struggle to draw attention first, and the very few that draw attention fail in being relevant to the target audience. So, television advertisements are an effective tool to build brand awareness and recognition. But it is a difficult task to build brand relevance using TVCs, because consumers are not ready (and too much clutter) to receive the differentiating factors that should make this brand relevant to them.
In my next post, I will write about how to build effective relevance and how we can connect relevance with the consumer decision making process.
No ‘One India’ in Advertising
Posted on: December 6, 2011
- In: Advertising | Market Research | Marketing | Psychology
- 3 Comments
Not many people would argue about the diversity of the culture in India. This is the country where the language and the taste of water changes every 100 km. The question is – how does this diversity affect the marketers to communicate their products? How does an advertiser look at this diversity? Is there a magic mantra that cuts through all these linguistic and cultural diversity?
Research done by Milward Brown suggests that there is no single mantra in advertising that works for the whole country. There are very few advertisements that worked well across the country. Some of the observations from the research are:
1. Celebrities can be the glue that binds markets
Celebrities can work across markets, but that definitely doesn’t guarantee the success. Using a celebrity reduces the risk of failure of the advertisement across markets. Particularly in the South, the use of local celebrities is more followed as it is observed that people connect well with the local celebrities. Cricketers can certainly help communicate across the markets. It is true that every Indian lives on Bollywood and Cricket.
2. Children can certainly help
Children can help raise empathy and likingness quotient in the ad. However, an interesting stat is one-third of all the ads that don’t do well have kids in them. So, the mere presence of a child doesn’t increase the likingness of the ad. Generally speaking, it is observed that people tend to be active watching ads of children. Children, if used properly, can help in breaking the clutter.
3. Effect of humour is variable
Humour in India works well when it is based on visuals and music. Humour which is based on regional situations and wit may not do well across the markets. While some of the ads have worked well in some regions, the ads based on regional humour caused some serious trouble to the brand in other markets.
4. Product demonstrations are less likely to do well
Advertisements that focus on product demonstrations are less likely to do well. Differing expectations from advertising are at the root of this difficulty. There are clear differences across regions and town tiers in this regard. South India tends to be driven more by the need for information, as does small-town India.
Regional differences in receiving ads
First, something that all of us (in India) suspected: North is North and South is South, and never the twain shall meet. These two regions show the poorest creative transfer; an ad that does well in the South is unlikely to do well in the North, and viceversa.
Second, the West is a poor receiver of ads from the North. Looking at ads tested in those two regions, only 34 percent of ads that were highly enjoyable in the North did well in the West, while close to half the ads that did well in the West also performed well in the North. Therefore, if we need to prioritize between the two regions, the West provides a better litmus test of likely performance.
Third, the South is neither a borrower nor a lender. Ads that do well in the South transfer poorly to the North, moderately well to the West, and well to the East. Therefore, if the South is relevant for a brand, it must automatically be selected as a test centre.
Fourth, successful ads in the East transfer well to other regions, though within a specific and limited context. To clarify, the East is an important market for relatively few brands and categories, so any principle of travel would apply to this rather specific set of brands. The East also tends to be more critical of advertising compared to other regions. Hence, purely from the perspective of creative transference, strong performance in the East is an indication of good performance in other zones.
Most of the research above is done based on the Link scores, a evaluative tool of advertising by Milward Brown. Whether your objective is to raise awareness, promote trial, develop rational or emotional brand associations, or to convey a very specific message, Link will tell you how your ad will perform.
Thank you.
- In: Business | IT | Market Research
- 3 Comments
According to the latest Gartner reports in Apr 2011, the worldwide operating system(OS) market revenue is $30.4 billion by the end of 2010. The OS market is divided into two sub-segments: Client OS and Server OS. The Client OSes outperformed the Server OSes as the former grew 9.3 percent, and the latter grew 5.7 percent in 2010. Following are the OS market shares released in April 2011 by the market research firm Gartner:
Microsoft leads the OS market as it is a leader in both client and server OS sub-segments. However, Gartner says that Mac OS is the fastest growing operating system in the Client segment and Linux is the fastest growing in the Server segment, with Red Hat dominating the Linux market. Microsoft leads the server OS market with 48.5% market share followed by Linux at 16.9%. On the other hand UNIX has 38% share but it is dying as a server operating system. Industry experts say that the phenomenon shows that the market has accepted Linux as a viable alternative to UNIX in mission critical environments. It is good to hear that Linux is growing at an 18% growth rate with a 20% share of the server OS segment.
Coming to the smart-phone market, the Linux kernel based Android continues to dominate. Following are the smart-phone OS market shares from Gartner:
A to B and B to A marketing
Posted on: July 31, 2011
In marketing, there are two fundamental principles called Attitude to Behaviour (A to B) and Behaviour to Attitude (B to A) marketing.
In A to B marketing, you target and change the attitude of the consumer first, so that the change in the attitude may result in a desired change in the behaviour. For example, you tell the consumer the toothpaste whitens your teeth, so that this attitude may result in the change in behaviour of buying the product. This is what most ATL activities do.
In B to A marketing, you target and change the behaviour of the consumer first, so that the change in the behaviour may result in a desired change in the attitude. For example, the whitening toothpaste gives a promotion of 1+1 free that makes you buy the product. After using the product (behavioural change), you liked it (favourable attitude) and you changed your earlier inimical attitude towards the product. This is what most BTL activities do.
Positioning
Posted on: June 11, 2011
Positioning of a product is how do you intend the consumer to perceive your product. It is to understand where and for what do you want to stand in the consumer’s mind. After segmenting a market and then targeting a consumer, next step will be to position a product within that market. It refers to a place that the product offering occupies in consumers’ minds on important attributes, relative to competing offerings. How new and current items in the product mix are perceived, in the minds of the consumer, therefore re-emphasizing the importance of perception.
With big companies involved in multiple categories, multiple brands, different sets of competitors in each category positioning can become extremely complex. For example, there are mother brands like Dettol, Lifebuoy, Cinthol, Palmolive, etc in the toilet soaps category. If the consumers are increasingly becoming health and hygiene conscious and let us suppose it is observed that there is huge potential in the market. Now, immediately Cinthol cannot launch a variant saying Cinthol Germ Kill, because it doesn’t go with its existing brand positioning (freshness, aroma) within the category and across other categories. May be the alternative is to launch a new brand like ‘Godrej Protekt’ where the brand is positioned towards germ-kill and at the same time to gain the equity it is endorsed by the ‘Godrej’ brand. These things become extremely complex and requires excellent understanding of the markets, categories, consumers, brands, etc. It is not only research, but the marketer has to have the intuition about the workings.
As said, Positioning is more to deal with the perceptions and it spans across different parameters. Positioning can be based on product characteristics, price quality perceptions, usage, culture, geography, symbols, product class, competition. Sometimes you being excellent at something itself becomes your rival of not being able to position yourself as something else. I personally believe that this is one of the most trickiest parts of Marketing and Brand Development. It involves understanding the category, competition, customer attitudes and perceptions, product life cycle, distribution, positioning of different players in different ways, growth and opportunity areas, strengths and weaknesses, finding out the gaps in the positioning and trying to develop a proposition towards developing a perception in the consumer’s mind. Once we develop a position in the consumer’s mind, it is important to monitor the positioning and understand how it has to be shaped in future w.r.t the category stage. This is what I call as Positioning Life Cycle, as your positioning strategies differ a lot based on the category stage.
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