Marketing Industriale e Direzione d Impresa Lezione 20 Marketing Plan 2. Ing. Marco Greco Tel

Transcription

1 Marketing Industriale e Direzione d Impresa Lezione 20 Marketing Plan 2 Ing. Marco Greco m.greco@unicas.it Tel

2 1.1 The marketing environment Macroenvironment Demographic env. Economic env. Socio-cultural env. Natural env. Technological env. Political and legal Microenvironment Public Customers Suppliers Marketing intermediaries Competitors

3 1.1 The marketing environment Marketing intermediaries Retailers Sales representatives Logistics companies Marketing services companies Financial services partners

4 1.1 Bulsara advertising Washroom adv. for Hangover 3

5 1.2 The marketing research Make or buy Large companies often make Marketing information systems Marketing intelligence systems Often, however, also large companies buy Engaging students or professors Engaging syndicated-service research firms (gather consumer and trade information, selling them for a fee) Engaging custom marketing research firms (carry out specific projects) Engaging specialty-line marketing research firms (specialized research services) The Marketing research process

6 1.2.1 Marketing information systems Systems that provide company management with rapid and detailed data about buyer wants, preferences, and behavior. It consists of people, equipment, and procedures to gather, sort, analyze, evaluate, and distribute needed, timely, and accurate information to marketing decision makers (Kotler & Keller, 2013)

7 1.2.2 Marketing intelligence systems a set of procedures and sources used by managers to obtain everyday information about developments in the marketing environment (Kotler & Keller, 2013) The information is developed through: internal company records marketing intelligence activities marketing research marketing decision support analysis

8 1.2.2 MIS Internal company records Order-to-payment cycle: customers favor those firms that can promise timely delivery Reports on current sales: price lists, orders, vertical integration with the supply chain partners limit the stock-out risk Centralized datasets

9 1.2.2 MIS - Marketing intelligence activities Sale representatives (company s eyes and ears ) Distributors, retailers, and other intermediaries Competitors products and reports, visits to the competitors shops, trade publications, Customer advisory panel Purchase information from outside suppliers (e.g. Nielsen) Internal marketing information centres collecting and circulating information Mystery shoppers

10 1.2.2 MIS Mystery shoppers What the average customer is experiencing? Unbiased third-parties objectively record their purchase experiences As a result, you can improve customer service and customer interaction to build loyalty Mystery shoppers cannot replace customer satisfaction measures, nor customer expectations ones

11 1.2.3 Engage students or professors

12 1.2.4 Engaging syndicated-service research firms

13 1.2.5 Engaging custom marketing research firms

14 1.2.6 Engaging specialty-line marketing research firms

15 1.2.7 Marketing research process Define the problem and research objectives Develop the research plan Collect the information Analyze the information Present the findings Negative pre-test Choose the research method Sampling method Contact method Survey method

16 1.2.7 Define the Problem and Research Objectives Exploratory research: sheds light on the real nature of the problem suggests possible solutions or new ideas. (e.g. what are people feelings with respect to a specific service) Descriptive research: seeks to ascertain certain magnitudes. (e.g. how many people would pay to subscribe to your online journal) Causal research: test a cause-and-effect relationship (e.g. would an increase in free hand baggage allowances increase the retention rate of customers? )

17 1.2.7 Define the Problem and Research Objectives Retrieving data Secondary data: data collected for another purpose that already exist Primary data: specifically collected for the purpose of the research Observational research: observing and interviewing customers (E) Focus-group research: discussion among a small group with a moderator (E) Survey research: questionnaire submitted to a sample (D) Behavioral data: purchase records, path of navigation on the web site (D) Experimental research: capture cause-and-effect relationships by eliminating competing explanations (C)

18 1.2.7 Focus group research

19 1.2.7 Define the Problem and Research Objectives Criticalities Ambiguity of the hypotheses Accuracy of the measurement tools Cardinality of the sample Systematic biases Type I error: Reject H0 that it s true (false positive) rejecting a good quality lot Alpha: α=0.05 means that there is a 5% probability that we will reject a true null hypothesis Type II error: Do not reject a false H0 (false negative) accepting a poor quality lot Beta: (1-β) is the power of a test

20 1.2.7 Define the Problem and Research Objectives Source: Anderson et al., 2008 α and β are inversely proportional. The only way to improve both of them is increasing the sample cardinality

21 1.2.7 The research method Questionnaires Qualitative measures Cognitive interview (thinking aloud) Observation of the customers (e.g. purchase process) Diary (asking the customers to keep day-by-day track of their interactions with the product) Focus group Mechanical instruments Galvanometers measure the emotions aroused by exposure to a product or a ad Eye cameras study respondent s eye movements The meters trace all content watched on a TV-set

22 1.2.7 The UNITAM meters

23 1.2.7 The research method The sampling plan Sampling unit: definition of the target population Sample size: how many people should be surveyed to provide good reliability? Whole population Sub-set of the population (sample) Longitudinal (samples, repeated surveys as time passes by) Sampling procedure: how should the respondents be chosen?

24 1.2.7 The research method The sampling plan PROs Reduces costs and times of the analysis The analysis can be conducted also if some data are missing The analysis can be conducted more than once, and improved CONs The analysis returns an approximation of reality

25 Understanding sampling (Source: Anderson et al., 2008) Population of the managers in a company Sample of 30 managers Sample mean of annual salary: x = Sample proportion of training program status: p = 0.70 Source: Anderson et al., 2008

26 Understanding sampling (Source: Anderson et al., 2008) If you choose different samples, you get different results!

27 Understanding sampling (Source: Anderson et al., 2008) How different?

28 Understanding sampling (Source: Anderson et al., 2008) Like this!

29 Understanding sampling (Source: Anderson et al., 2008) Expected value of x E x = μ Population mean s = (x i x) 2 n 1 If σ is unknown, s (sample st. dev.) is used to estimate σ, the margin of error and the interval estimate for the population mean are based on a probability distribution known as the t distribution

30 Understanding sampling (Source: Anderson et al., 2008) Form of the Sampling Distribution of x Population has a normal distribution the sampling distribution of is normally distributed for any sample size. Population does not have a normal distribution if we select a simple random sample, the central limit theorem applies: In selecting simple random samples of size n from a population, the sampling distribution of the sample mean can be approximated by a normal distribution as the sample size becomes large.

31 Understanding sampling (Source: Anderson et al., 2008)

32 Understanding sampling (Source: Anderson et al., 2008) Therefore the sampling distribution of x is like this:

33 Understanding sampling (Source: Anderson et al., 2008) If you choose a sample, it is unlikely that x = μ How much unlikely? What is the probability of actually getting it? Suppose you want to know the probability that 500 x = μ ± Recall that z = x μ σ

34 Understanding sampling (Source: Anderson et al., 2008) z = z = = 0.68 = 0.68 P x = P z 0.68 P z 0,68 = 0,7517 0,2483 = 0, 5034

35 Understanding sampling (Source: Anderson et al., 2008)

36 Understanding sampling (Source: Anderson et al., 2008) If you increase the sample to 100 units, the standard error of the sample decreases σ x = σ = 400 n And p becomes 0,7888

37 The sample cardinality N n μ = x + z σ n = n n 1 with e: sampling error, with e = μ (If N<30 use student s t instead of z) In case of proportions n = Nz 2 σ 2 N 1 e 2 +z 2 σ 2 Nz 2 p(1 p) N 1 e 2 +z 2 p(1 p) x, confidence interval Confidence level Value of z 99% 2,58 98% 2,44 95% 1,96

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39 Example 1 A company wants to survey a sample of customers of a specific month to measure customer satisfaction in a 1-5 scale. The customers in the month have been 2500 (N). In a previous analysis standard deviation was estimated as 1.2. Suppose that a 95% confidence level is considered acceptable, as well as a confidence interval of +/- 0,15 points on the 1-5 scale. n = ,96 2 1, , ,96 2 1,2 2 = 159 If, making 159 interwies, the average customer satisfaction is 4.1, then we may say that the customer satisfaction of the population is 4,1 ± 0,15, with a confidence level of 95% (in 5% of cases we might be mistaken)

40 Example 2 A local government wants to estimate the ratio of citizens that required a specific document at least once. On a population of citizens, we accept a 95% confidence level and a ± 3% confidence interval. In a preliminary survey the ratio resulted 25%. We determine the sample as follows: ,96 2 0,25 0,75 n = , ,96 2 0,25 0,75 = 791 If the ratio resulting from the 791 interviews is 29% we will say that ratio of the population is 29% ± 3%, with a confidence level of 95% (in 5% of cases we might be mistaken)

41 Sample How sampling changes σ 2 = 100 e=+/-1 Population cardinality

42 Sample How sampling changes N= 1000 e=+/-1 Population variance

43 Sample How sampling changes N= 1000 σ 2 = 1 Confidence interval

44 Sampling methods Probabilistic Each element of the population has a non-null predictable probability of being included in the sample The sample may not be representative of the population The results may not be generalize Conclusive research, heterogeneous population Non probabilistic Can t estimate the probability of an element of being included in the sample The sample may not be representative of the population The results may not be generalized Explorative research, homogeneous population

45 Sampling methods Probabilistic Simple random sampling Stratified random sampling Cluster sampling Systematic sampling Non probabilistic Convenience sampling Judgement sampling Quota sample

46 Simple random sampling A simple random sample of size from a finite population of size N is a sample selected such that each possible sample of size n has the same probability of being selected. choose the elements for the sample one at a time in such a way that, at each step, each of the elements remaining in the population has the same probability of being selected Without replacement With replacement

47 Mc Donalds sampling (Source: Anderson et al., 2008) Whenever a customer presented a discount coupon, the next customer served was asked to complete a customer profile questionnaire Arriving customers presented discount coupons randomly and independently this sampling plan ensured that customers were selected independently No selection bias

48 Stratified random sampling 1. Divide the elements in the population into groups (strata) each element in the population belongs to one and only one stratum the elements within each stratum are as much alike as possible 2. a simple random sample is taken from each stratum

49 Cluster sampling 1. Divide the elements in the population into groups (clusters) each element in the population belongs to one and only one cluster the elements within each cluster are not alike Ideally, each cluster is representative of the population 2. a simple random of clusters are considered as a sample

50 Systematic sampling Selection of elements from an ordered (best if randomly ordered) sampling frame (e.g. telephone directory) E.g.: you want to extract a sample of size 50 from a population of 5000 Select randomly one element Select another element for every 5000/50=100 elements in the population

51 Convenience sampling Elements are included in the sample without prespecified or known probabilities of being selected E.g.: a small sample of student volunteers for a professor E.g.: the inspector checks haphazardly the barrels in The Hobbit It is impossible to evaluate the goodness of the sample in terms of its representativeness of the population

52 Quota sampling The researcher finds and interviews a prescribed number of people in each of several categories E.g.: 30 middle-age women and 30 middle-age man It is impossible to evaluate the goodness of the samples in terms of its representativeness of the population

53 Judgment sampling An expert selects elements of the population that he or she feels are most representative of the population E.g.: a reporter interviews only a subset of politicians that she feels may reflect the points of view of all the politicians It is impossible to evaluate the goodness of the sample in terms of its representativeness of the population

54 Sampling vs nonsampling error Sampling Error Nonsampling Error Small sample Medium sample Large sample Census % of errror