Posts Tagged ‘Distribution’
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
Colgate in India
Posted on: November 29, 2010
- In: FMCG | Marketing
- Leave a Comment
Colgate has come up as one of the most resurgent brands in the Indian market. The brand covers massive market in terms of volume in toothbrush and toothpaste sales. Colgate is one of the top performers in its category. But with new players entering the segment, Colgate has been resurgent and is ready to face all the challenges. The toothpaste sales of the brand rose by 11.4 percent in the past year and the that of toothbrush by 14 percent. Even as other players in the market hiked prices owing to inflation, Colgate showed restraint and focused on volume growth without increasing prices.
From a modest start in 1937, when hand-carts were used to distribute Colgate dental cream toothpaste, Colgate Palmolive (India) today has one of the widest distribution networks in India – which is a logistic wonder which makes Colgate available in around 43 lakh retail outlets in the country. The company dominates the Rs 3,500 crore Indian toothpaste market with more than 50 percent market share.
Since 1976, Colgate has worked very closely with the Indian Dental Association (IDA) to spread the message of oral hygiene to children across the country. In 2004, as an additional effort to create awarenesss for good oral hygiene ‘Oral Health Month’ (OHM) was introduced. The strong relationship and the trust of generations of consumers, trade and the dental profession built over the decades of operations in India has made Colgate a trusted household name.
The Government of India has allotted special zones called Free Trade Warehouses to promote trade. The advantage with these warehouses is companies can store there stock in these warehouses and can pay the customs duty only when they actually pull the stock out of the warehouse. For example, the a major petroleum corporation in Rajahmundry needs to replace its oil rigs immediately after damage. Any delay of more than 4 to 5 hours will result in huge loss in production. These oil rigs are imported and will take atleast a week to be imported. So the petroleum corporation actually will import the oil rig and store in the FTWZ near to Visakhapatnam. The moment they need the replacement, they will immediately pay the customs and bring it to Rajahmundry.
These are some of the advantages of these FTWZs promoting industrialization. However, there are some regulations and restrictions in the kind of stock that can be stored.
Similarly, with the coming of the GST tax system, the whole country becomes a level playing field. Earlier because of different tax structure in different states, the warehouses are constructed in every state to avoid inter-state sales tax, etc. So even if there is no necessity to put a warehouse in Kerala, companies will still have warehouses in Kerala. Similarly, the whole distribution system is established in a way to minimize the taxes which is the reason why most companies have their warehouses in Pondicherry, and other union territories where taxes are low.
The Government of India helps the industries in this regard, as the percentage of taxes paid in India is one of the highest in the world. With the new GST system, the distribution will be well connected and will be set up for efficiency in the distribution to the consumer rather than reducing taxes. This helps in the better service to the customer and also cheaper services to the customer.
India is one of the most challenging markets in the world which fooled big marketers and companies across the world. We have over 1500 Gods segmented into 350 broad categories, and we have a God for every single day of the week. There are 9.5 lakh pan shops, 638,667 number of villages, 612 districts, and 28 states in India. This is the country where you see a pan shop and Haagen Daz together, and a bullock cart and a Mercedes in the traffic jams. Indian market is very challenging and it really fascinates me as a marketer.A very large part of Indian population still lives in villages defining the Rural India.
Rural India is very important for many companies and there is tremendous increase in investments and strategies surrounding the rural markets. Rural India Market buys:
- 45% of all soft drinks
- 50% of motorcycles, TVs, cigarettes, washing soap, fans, blades, and a lot others.
Rural Market Opportunities
Few of the companies that are going bullish in the rural markets:
- HUL with its Project Shakti has already has a reach of 1.7 lakh villages, and aspires to reach 5 lakh villages by 2020.
- Indian Tobacco Company (ITC) has a lot of penetration in the rural markets and the eChoupals are a big hit in the rural market.
- Airtel is planning to reach around 2 lakh villages.
- Marico with its most famous brand Parachute has a reach of 1 lakh villages.
- Pepsi and Coke, the Cola giants, have a reach of 70,000 villages.
- Dabur, known for its Lal Dant Manjan and Hajmola, has a reach of 60,000 villages.
- Colgate with its Operation Jagruthi has a reach of over 60,000 villages.
- Mahindra & Mahindra sells most of its SUVs in the rural market.
Mahindra Shubhlabh is India’s largest exporter of fresh produce. Mahindra Shubhlabh engages with farmers in the production of export quality grapes, pomegranates, and apples aimed at delivering to domestic and international markets. It has a huge R&D facility in Pune to research on various modern seeds and saplings.
- Nokia 1100 with its torch is a very big hit in the rural market. It is a perfect example of understanding the needs of the consumer. Nokia realized the need for a torch in the mobile for the rural people as they walk in the dark streets and fields of the village. Nokia is set to release some low cost phones to tap more from the tier-3 and tier-4 markets.
There are other companies like Godrej, ParleG, Asian Paints, Yes Bank, Royal Enfield, ITC and Revlon.
Marketing Challenge
Delivering to the rural markets is a real challenge to many companies. In fact, the whole dynamics of these markets are so different that you need to look at a different product mix containing the 4A’s instead of the traditional 4P’s of marketing:
Acceptability – Build what the consumer wants
Affordability - Make an affordable product
Availability - Distribution plays a key role in the rural markets
Awareness - Don’t promote the brand, demonstrate the product.
Top Media in Rural Markets
Dainik group is the leading newspaper in the rural markets. In the realm of television, we have the following in the descending order of penetration in the rural markets.
- Doordarshan has a reach of 97% of the rural markets in India.
- Zee Cinema which carries with the image of movies being the favourites of rural people.
- B4u movies
- MTV
- Discovery Hindi
One of the key trends in the rural markets is people changing very quickly from cable to satellite TV. This is because of the hassle-free dish connection of the satellite TV. Most of the dish TV companies like Tata Sky, BIG TV, and Airtel are selling good in the rural markets too. Similarly, Revlon has come up with a lipstick for the rural markets and it is doing very good as against Lakme. This shows that there is huge potential in these markets and it is interesting to see how these trends will transform the lives of the rural people and in turn impact the Indian markets.
Hindustan Lever and its strengths
Posted on: March 14, 2010
- In: Business | Marketing
- Leave a Comment
Hindustan Lever Limited (HLL) is the largest detergent manufacturer in India. HLL is one of the few companies that targeted and delivered to the bottom-of-the-pyramid (BOP) markets very effectively. One of the key strengths of HLL is their distribution and marketing penetration in the BOP markets. HLL products are manufactured at around 100 locations around India and distributed via depots to almost 7500 distribution centers. HLL has the reach to all villages with atleast 2000 people.
Pricing for the BOP markets
HLL is known as the company that comes up with innovative products for which the poor are willing to pay for. In fact they base their product on peoples’ willingness to pay for the product. For example, let us consider the case of Lifebuoy. HLL does some initial market research and comes up with the requirement that the Indian rural population need germ kill soap. Now, HLL experts immediately don’t go to the laboratory and come up with the most sophisticated germ kill soap. Rather, they have a bottom-up approach which works well for the mass markets. HLL does market research to understand how much are people willing to pay for a benefit like germ killer. Considering the price to be the retail price, it evaluates its target margins it gives a challenge cost. Then it comes up with a business model which delivers that challenge cost.
Marketing and Communications Strategies
Everything seems good on paper, but how does HLL manage its competitors. The key strengths of HLL is its distribution channels available to deliver Lifebuoy at the price the market dictated. The sales and marketing strategies of HLL are based upon microfinance institutions, micro-credit lending, and rural entrepreneurship. One such project is the Project Shakti which started in Andhra Pradesh and expanded to 12 states in India.
HLL tied up with the Self-Help Groups (SHGs) and offered them products which are relevant to the rural population. A member of an SHG is selected as a Shakti entrepreneur, also called ‘Shakti Amma’ will receive stocks from HLL rural distributor. With some training from HLL, the Shakti entrepreneur sold those goods directly to the local village population. HLL witnessed 15% increase in sales from the villages of AP, which accounted for 50% of total sales of HLL products in AP.
An important reason for the success of this integrated marketing strategy for rural India is the consistencies of goals between HLL, the government units, and the NGOs. As Lifebuoy is targeted for socially desirable improved health goal, the other parties are happy to cooperate with HLL. This kind of integrated positioning, targeting, sales, marketing, and distribution strategy has given HLL a real edge over its competitors.
Why Oranjolt failed?
Posted on: February 7, 2010
Rasna Limited is one of the leading soft drinks companies in India. The Rasna advertisement is as mouth-watering as the drink itself. We will discuss about Rasna’s experiment of a new drink outside its niche segment.
Given the threat of foreign competitors, Rasna was pressed to do something new. Rasna launched a drink called ‘Oranjolt’. Very few people must have heard about it because it died even before it came to the store.
Oranjolt is a fruit drink in which carbonation is used as a preservative. So why did it fail? Oranjolt requires continous refrigeration. Many Indian retailers switch off their refrigerators during night time. As a result, Oranjolt faced quality problems.
The other problem Oranjolt has is its shorter shelf life. Oranjolt has a shelf life of three to four weeks, whereas most soft-drinks have a shelf life of around 10-12 weeks. This demanded quick replenishment at the stores. Unfortunately, the distribution structure in India is not very efficient to replace the product every three to four weeks. Even FMCG giants Pepsi and Coke replenish once in 10 weeks.
Rasna’s first experiment was not that successful. However, it looks like Rasna has some big plans in this sector as it reinvents Oranjolt.
Comments