brand report

ANALYSING DATA & PRESENTING

RESULTS

2

Brand Management 24752

Data coding • This is the process of grouping and assigning

value to the responses from the survey instrument.

• Incorporate coding into questionnaire design where possible

• Assign a coded value to each response • Use numeric codes • Assign codes to missing data • Open-ended questions need to be coded for data

entry

3

Brand Management 24752

Data coding continued

4

Brand Management 24752

Data coding continued

• Coding open-ended questions • Open-ended questions provide greater insight into the

topic than forced-choice questions or rating scales • Open-ended questions do not allow for an exact list of

potential responses

• Developing codes for open-ended questions • Generate a list of as many potential responses as possible • Consolidation of responses into categories • Assign values to data which has been captured by the survey

instrument • Assign a numerical value as a code to each of the consolidated

categories

5

Brand Management 24752

Data description • The process of describing the data sample so that general patterns

of responses and respondent profiles are revealed

• Why is it required • Almost all data sets are disaggregated (just rows and columns) • Every set of data needs some summary information developed that

describes the numbers it contains

• Statistical techniques used • Data tabulation (frequency distribution, cross-tabulations, etc.) • Measures of central tendency (mean, median, mode) • Measures of dispersion (range, standard deviation)

6

Brand Management 24752

Data tabulation continued

• The process of counting the number of observations and cases that analysts classify into certain categories

• The purpose of tabulations range from further validation of the accuracy of the data to the communication of results

• Two common forms of tabulation are: • one-way tabulation • cross-tabulation

7

Brand Management 24752

One-way tabulation • When a research team performs a ‘one-way’ tabulation they

focus on a single variable operating in the research study • Research analysts use one-way tabulations to:

• determine the degree of non-response to individual questions • locate simple blunders in data entry • calculate summary statistics on various questions, for example

averages, standard deviations and percentages • communicate the results of the research project

• Profile sample respondents • Distinguishing characteristics between groups • Establishing percentage of respondents who respond differently to

different situations

• Frequency table is constructed to illustrate one-way tabulations

8

Brand Management 24752

One-way tabulation continued • One-way tabulation can be illustrated through

constructing a one-way frequency table

9

Brand Management 24752

One-way tabulation continued

One-way frequency tables provide: • indications of missing data

• one-way frequency tables indicate the absolute number of missing responses for each question

• valid percentages • the establishment of valid percentages is based on

removing incomplete surveys or particular questions • summary statistics

• One-way frequency tables can illustrate a variety of summary statistics relevant to the question being analysed

10

Brand Management 24752

Cross-tabulation • This simultaneously treats two or more variables in the

study by categorising the number of respondents who have responded to two or more consecutive questions

• Helps to analyse relationships among and between variables

• To quickly compare how different groups of respondents answer survey questions

• Provide a valid description of both aggregate and subgroup data

• Merging of frequency distributions of two or more variables

11

Brand Management 24752

Cross-tabulation continued

• Two key elements of cross-tabulation are: • how to develop the cross-tabulation • how to interpret the outcome

• It is widely used • Normally the main form of data analysis in most

marketing research projects • Easily understood and interpreted by managers • Simple to conduct and appealing to less

sophisticated managers

12

Brand Management 24752

Cross-tabulation continued

13

Brand Management 24752

Cross-tabulation continued • Challenges

• The analyst should take care to construct cross- tabulations that accurately reflect information relevant to the objectives of the project

• Certain survey approaches can lend themselves to the construction of an endless variety of cross-tabulation tables

• More than three variables can be cross-tabulated but interpretation complex

• Cross-tabulations are not efficient when examining relationships among several variables

• Cross-tabulations can interpret associations not causation

14

Brand Management 24752

Measures of central tendency • Mean

• The arithmetic average of the sample • Survey data should display some central tendency with

most of the responses distributed around the mean

• Mode • The most common value in the set of responses to a

question; that is, the response most often given to a question.

• The value (item) that occurs most frequently • For nominal data, researchers generally use the mode.

15

Brand Management 24752

Measures of central tendency continued

• Median • Middle value in the data set when the data are arranged in ascending

or descending order

16

Brand Management 24752

Example – mean brand performance on attributes (Contd.)

Ratings ( out of 5 points)

Convenience (Brand A)

Convenience (Brand B)

R1 2 1 R2 3 5 R3 3 2 R4 2 1 R5 5 4 R6 4 3 R7 1 2 R8 2 4 R9 2 3 Average Rating 2.67 2.78

17

Brand Management 24752

Example – mean brand performance on attributes (Contd.)

Average Ratings Brand A Brand B

Convenience 2.67 2.78 Quality 3.45 3.21 Affordability 2.10 3.67 Service 2.92 2.27 Staff 3.33 2.94 Variety 4.12 3.78 Cleanliness 3.65 3.23

18

Brand Management 24752

Example – mean brand performance on attributes

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Convenience Quality Affordability Service Staff Variety Cleanliness

Brand A Brand B

19

Brand Management 24752

Results • Focuses on data analysis and findings

• The challenge for researchers is to summarise and present the analysis in a way that makes it easy to understand for non-specialists.

• Key contents – Results corresponding to each aspect of brand

equity – Corresponding charts and tables with comments

20

Brand Management 24752

Results continued

• Best practice suggests that tables, figures and graphs be used when results are presented.

• Graphs and tables should provide a simple summary of the data in a clear, concise and nontechnical manner.

21

Brand Management 24752

Results – sample display • Sample display of bar charts

22

Brand Management 24752

Conclusions • Descriptive statements which generalise and summarise the

results

23

Brand Management 24752

Recommendations • Generated by critical thinking • Must address how the client can solve the problem at hand • Researcher must critically evaluate each conclusion and develop

specific areas of applications for strategic or tactical actions

24

Brand Management 24752

Appendixes

• An appendix can include details which are not possible to insert in the main report

• Most appendixes should be treated as points of reference in the report.

• Key contents • Complex or detailed information (e.g. ads) • Questionnaire • Detailed statistical analysis and tables

25

Brand Management 24752

Common problems encountered

• Some of the more common problems encountered when preparing a report are:

• lack of data interpretation • unnecessary use of statistics • too much emphasis on packaging/the look of the

report. • lack of relevance • too much emphasis on a few statistics.