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

Transcription

1 Marketing Industriale e Direzione d Impresa Marketing Plan 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 Socio-cultural environment Views of God Views of themselves Views of others Views of nature Views of companies Views of society

4 1.1 Mattel s Barbie Source: zilla308.buzznet.com

5 1.1 The marketing environment Technological environment Technology as creative destruction Very fast innovation rate Many International, European, National and Local grants for innovation Universities and public research institutions Patents Other forms of innovation (more or less protected by the law)

6 l 1.1 The marketing environment Natural environment Renewable energy Pollution Climate changes Epidemics

7 1.1 The marketing environment Political and legal environment Business legislation (e.g. Safety, workers rights, quality standards, competition, intellectual property) Special interest groups Lobbying Public administration bureaucracy

8 1.1 The marketing environment Public Shareholders Human resources Media Government, Parliament, Local government Associations and groups of citizens Public opinion

9 1.1 The marketing environment Customers Business to consumer market Business to business market The resellers market The Public Administration market The global market The web market Monopsony: only one customer

10 1.1 The marketing environment Suppliers Suppliers of natural resource, renewable or not Suppliers of capitals (Ex. Banks, venture capitalists, ) Suppliers of work Is our company monopsonist?

11 1.1 The marketing environment Marketing intermediaries Retailers Sales representatives Logistics companies Marketing services companies Financial services partners

12 1.1 Bulsara advertising Washroom adv. for Hangover 3

13 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

14 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)

15 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

16 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

17 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

18 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

19 1.2.3 Engage students or professors

20 1.2.4 Engaging syndicated-service research firms

21 1.2.5 Engaging custom marketing research firms

22 1.2.6 Engaging specialty-line marketing research firms

23 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

24 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? )

25 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)

26 1.2.7 Focus group research

27 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) recommending a solution that does not work 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) push aside a solution that could have worked Beta: (1-β) is the power of a test

28 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

29 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

30 1.2.7 The UNITAM meters

31 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?

32 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

33 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

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

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

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

37 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

38 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 x can be approximated by a normal distribution as the sample size becomes large.

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

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

41 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 x = μ ± 500 Recall that z = x μ σ

42 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

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

44 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 n = 400 And p becomes 0,7888