Distribution strategies for services,collaborative marketing campaign management,online marketing strategy service - For Begninners

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. A Presentation about the Distribution Strategies (Exclusive, Intensive and Selective distribution). Cooperating with Mrs.
Few years ago I started a business, which was quite competitive and it was NEARLY impossible to increase sales.
Exclusive Distribution -Few intermediaries(usually only one with more control ) -When a single outlet is given an exclusive franchise to sell the product in a geographic area, the arrangement is referred to as exclusive distribution. A channel that the manufacturers choose a certain amount ofmiddlemen (similar) to sell the products according to certainqualification.
Advantages Disadvantages Difficult for enterprises to achieve a variety of business objectives in relaxed conditions of the marketing environment,save expenses, improve marketingefficiency, control the marketing. A marketing strategy under which a company sellsthrough as many outlets as possible, so that theconsumers encounter the product virtually everywherethey go: supermarkets, drug stores, gas stations, andthe like. O SlideShare utiliza cookies para otimizar a funcionalidade e o desempenho do site, assim como para apresentar publicidade mais relevante aos nossos usuarios. Manufacturers need consumers to buy their products and consumers need somewhere to buy products from. A push strategy is where the manufacturer concentrates their marketing effort on promoting their product to the next party in the distribution chain (retailer or wholesaler), to convince them to stock their products.
A pull strategy involves the manufacturer promoting their products to the target market to create demand, so that retailers are forced into stocking the manufacturer's product.
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. What successful strategies do you employ to teach the sampling distribution (of a sample mean, for example) at an introductory undergraduate level? In September I'll be teaching an introductory statistics course for second year social science (mainly political science and sociology) students using The Basic Practice of Statistics by David Moore. I had to argue with the undergraduate administrator in our department to introduce mandatory sessions in the computer lab thinking that repeated demonstrations might be helpful (before I started teaching this course there was no computing involved).
One idea I've had is to simply not teach it at all or to not give it much weight, a position advocated by some (e.g. Use pictures (figures): I have a set of standard figures that I use every time I talk about this.
Give the students activities: The first time you introduce this concept, either bring in a roll of nickles (some quarters may disappear) or a bunch of 6-sided dice. Reinstantiate these concepts throughout the semester: I bring the idea of the sampling distribution up again each time we talk about the next subject (albeit typically only very briefly). I have had some luck with reminding students that the sampling distribution is the distribution of the test statistic based on a random sample.

I think for some (clearly not all) students this seems to help them with the idea that the sampling distribution can be very different from the population distribution.
Then when I introduce the sampling distribution I again start with simulation and say we want to develop strategies. Then when we get to inference I say that we will only be able to take 1 sample in the real world, just like we would only get to play the game 1 time (at most), but we can use the strategies we learned from simulating many samples to develop a strategy (z-test, t-test, or CI formula) that will give us the chosen properties (chance of being correct). Although I think this is about as simple a definition as I can give I realize it is not very simple and understanding of the concept will not come immediately in most cases. To convince the student that these sampling distributions really do have these shapes have the students conduct simulations genrating many samples of various sizes and compute the sample means. I think it would be good to put a 'population' of numbers in a bag ( ranging for example from 1-10). You will then be able to graphically show the variation in sample means around the population mean. Not the answer you're looking for?Browse other questions tagged distributions sampling teaching or ask your own question. Why are the number of false positives independent of sample size, if we use p-values to compare two independent datasets? Does overlapping standard deviation between null and candidate models imply statistical insignificance?
Why does one of the X-wing pilots scream "YEEEEEEESSSSSS" in the Battle of Yavin? So, after a bit of digging, I found out about intensive distribution and surprisingly, my sales increased by 12%. Products such as specially automobiles, some major appliances, certain brands of furniture, and lines of clothing that enjoy a high degree of brand loyally are likely to be distributed on an exclusive basis. Usually formed by stronger middlemen, sincethey can maintain the credibility of the manufacture of thebrand more effectively and establish a stable market andcompetitive advantage. GPs and specialists in private practice sell medicine at a price decided by individual doctors on a case-by-case basis.
This means that manufacturers not only need to persuade consumers to buy their products but also have to persuade retailers and wholesalers (the distribution chain) to stock their products. Consumer demand pulls products from the manufacturer through the distribution chain and onto the customer hence the name pull strategy. It'll be the fifth time I've taught this course and one issue I've consistently had is that the students have really struggled with the notion of the sampling distribution.
It's difficult to explain what the precise issue is other than to say they just don't get it. While I think this helps overall understanding of the course material in general, I don't think it's helped with this specific topic.

I especially think the activities and simulations are very useful for teaching this subject. No-one can understand a concept without first understanding that it is, indeed, a distinct concept. I have students think about what would happen in the sampling process itself was biased - focusing on extreme cases. I have also used the central limit theorem example that Michael Chernick mentioned with some success - especially with distributions that are clearly not normal (simulations really do seem to help). I think you nailed it with the problem being the link between what we can teach about what the sampling distribution is vs. Mark up may be as high as 200% Govt doctors get their medicine from their own hospital pharmacies. It's covered as the background for inference and follows a basic introduction to probability with which they don't seem to have trouble after some initial hiccups (and by basic, I mean basic -- after all, many of these students have been self-selected into a specific course stream because they were trying to avoid anything with even a vague hint of "math").
From a poll I conducted last semester and from exam responses, I think that part of the difficulty is confusion between two related and similar sounding phrases (sampling distribution and sample distribution), so I've don't use the phrase "sample distribution" anymore, but surely this is something that, while confusing at first, is easily grasped with a little effort and anyway it can't explain the general confusion of the concept of a sampling distribution. I don't find this particularly satisfying since it has the whiff of teaching to the lowest common denominator and more importantly denies strong and motivated students who want to learn more about statistical application from really understanding how important concepts work (not only the sampling distribution!). In an introductory course I taught a few times we used a web applet that allowed students to visualize how the statistic is calculated from the sample and how the histogram of the sampling distribution begins to take shape as you repeat this many times. For example, what would the "sampling distribution" look like if our sampling process always picked the same (special) subset.
I would guess that probably 60% leave the course with no to minimal understanding, about 25% understand the principle but not the connections to other concepts, and the remaining 15% fully understand. On the other hand, the median student does seem to grasp p-values for example, so maybe they don't need to understand the sampling distribution anyway. My plan now, as I plan my course for September, is to follow Gelman's advice and "deemphasize" the sampling distribution.
As you (and others here) have suggested, continuing to reiterate the concept over and over throughout the course is important, but not often done (at least not by me, probably because I already find it hard enough to fit in everything I want, let alone returning to concepts already covered!). Tell them that earlier you worked out the population mean, plot it on a histogram and get a member of each group to come and plot their sample mean on a historgram around this. I'll teach it, but I'll assure the students that this is a sort of FYI-only topic and will not appear on an exam (except perhaps as a bonus question?!). These are pretty simple to work out with the sample mean (especially for particular choices of "special" for the underlying population).

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