6.2 MAIN STUDY: RESEARCH RESULTS
6.2.3 Interpretation subcategory 2: The use of data visualisation in
6.2.3.1 Interpretation subcategory 2(a): The use of traditional data
Interpretation subcategory 2(a) considers the specific traditional data visualisation techniques participants use in quantitative research reports up to the specific point in time.
Participants agreed that the marketing research firm they work for uses some form of traditional data visualisation technique in quantitative research reports (refer to Figure 6.4). The main reasons participants offered for the extensive use of traditional data visualisation techniques include:
Clients have diverse reporting needs but most demand actionability
Explanation of the theme:
20 Participants agreed that clients want quantitative research reports that are actionable / indicate action steps for clients. In other words clients require marketing research firms to deliver research results that impact and guide business decisions.
Half of the participants (13 participants) indicated that client needs are diverse – their needs differ according to a few aspects.
...they want analyses that can grow their businesses...
it must give them direction, it must give them the ‘what to do’.” Participant 1
”...it’s all very well to get this big databook, what is the data actually telling them?...the action steps that will drive the clients business and help them grow.”
Participant 10
”!give me something that I can action quickly that everyone can use, that you know I paid a million bucks and I’m seeing the value in every buck spent!”
Participant 17
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20/26
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Read: 20 out of 26 participants mentioned this theme
”I think what they really want is an action plan.!”
Participant 21
”
Number of participants who mentioned the theme:
Supporting verbatim quotes:
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• Software: This involves the accessibility and ease of creating data visualisations with the aid of software like PowerPoint and Excel.
• Familiarity: Clients are familiar with the data visualisation techniques and the marketing research firm seldom needs to explain to the client how to read the data represented in the data visual.
Participants were asked to indicate the specific traditional data visualisation techniques they (the marketing research firm where they work) use in quantitative research reports out of a list of fourteen traditional data visualisation techniques (refer to Appendix I). These fourteen traditional data visualisation techniques were selected from the theory classification discussed in Chapter 4 (refer to section 4.4.3, Figure 4.2 and Table 4.3).
From the analysis, it is evident that the majority of the traditional data visualisation techniques are used regularly. Those data visualisation techniques used by participants on a regular basis include: bar charts, column histograms, pie charts, tables, column charts, stacked charts and line charts (refer to Figure 6.5). Those data visualisation techniques that are used occasionally or not at all, include: 3D area charts, waterfall charts and stacked area charts (refer to Figure 6.5). The latter data visualisation techniques are used less often because participants argued that they experience difficultly in the creation and explanation thereof.
Participants were almost in full agreement that those data visualisation techniques that are used more often, are easier for the marketing research firm to create and to explain.
In addition, and as indicated in Figure 6.4, the analysis reveals that particular traditional data visualisation techniques are used more often than other. From the in-depth interviews, differences in the use of traditional data visualisation techniques were noted – selected participants indicated that specific data visualisation techniques are more suitable for tracking research (for example line charts), whereas other techniques are more fitting for customised research (for example bubble charts).
It should be noted that participants also stated that traditional data visualisation techniques are typically complemented with pictures, videos and / or icons in
quantitative research reports (refer to Figure 6.5). Participants specified that these multi-media visual cues are used to support or enhance the message of the traditional data visualisation.
Thus the major theme evident from the empirical study for this subcategory is:
• Theme 1: Traditional data visualisation techniques are extensively used (refer to Figure 6.4 and Figure 6.5).
Figure 6.4: Interpretation subcategory 2(a) research results – Theme 1.1
Traditional data visualisation techniques are extensively used
Explanation of the theme:
All participants used traditional data visualisation techniques extensively in quantitative research reports.
• Some data visualisation techniques are used more often than others. Those more often used are easier to compile by the researcher and easier to explain to clients; and
• Some data visualisation techniques are more applicable for tracking research and others for customised (ad hoc / once-off) research.
26/26
“A lot of it would be in charts, PowerPoint based charts and again we try to keep them as standard as possible.” Participant 22
”... it’s your normal! tables, pies, stacks.”
Participant 24
” ... We use a lot of trended charts as well because we do a lot of continuous tracking so rolling the data for weeks or whatever trended charts (referring to line charts)....” Participant 13
“
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!"#$ %&$26 out of 26 participants regularly use two or more traditional data visual techniques
Number of participants who mentioned the theme:
Supporting verbatim quotes:
Figure 6.5: Interpretation subcategory 2(a) research results – Theme 1.2
6.2.3.2 Interpretation subcategory 2(b): The use of non-traditional data visualisation techniques
Interpretation subcategory 2(b) considers the non-traditional data visualisation techniques that participants use in quantitative research reports up to the specific point in time.
From the analysis, less than half of the participants indicated regular use of two or more non-traditional data visualisation techniques, while most participants indicated occasional use of non-traditional data visualisation techniques or use thereof for marketing purposes (refer to Figure 6.6) (note that due to time constraints one participant did not answer this question). Overall, participants reinforced that limited use of non-traditional data visualisation techniques in quantitative research reports exists.
Note: 1 participant did not answer this question
! Use regularly
Number of participants who regularly use each technique
The two biggest barriers to adoption are the time these non-traditional data visualisation techniques take to create, and the complexity thereof (refer to Figure 6.6). The first mentioned barrier includes several aspects of time: time to think what the data visualisation should look like, the time it takes to create it, and the time it takes to explain it to clients. Participants also indicated that these data visualisation techniques are more complex compared to traditional data visualisation techniques, as they are more difficult to create and to interpret.
Because of its complexity, participants felt that these techniques require marketing research firms to educate clients on how to read and interpret them – which adds to both the time and complexity barriers.
In addition, participants also mentioned a few other barriers that impede these data visualisation techniques’ inclusion into quantitative research reports. In descending order these barriers include: they require the researcher to be more knowledgeable, require client education, non-conducive client industries for example the financial sector, clients find confidence in the actual numbers, and non-traditional data visualisations could negatively impact on the marketing research firm’s credibility if executed incorrectly.
Participants were asked to indicate which non-traditional data visualisation techniques they (the marketing research firm where they work) used selecting from a list of seven techniques (refer to Appendix J). These non-traditional data visualisation techniques were compiled and condensed to a shorter list for the sake of the discussion from the theory classification discussed in Chapter 4 (refer to section 4.4.4, Figure 4.3 and Table 4.4).
Figure 6.7 illustrates a different situation than what was found with respect to the use of traditional data visualisation techniques. From the analysis, participants indicated that the most common regularly used non-traditional data visualisation technique is interactive dashboards. Even though participants also indicated the use of infographics, most were in agreement that it is used as marketing or sales material as opposed to visualising data in quantitative research reports. Three supporting direct quotations in this regard are provided next:
• Participant 3: “…in marketing material but not in research results presentations.”
• Participant 5: “…when we used these they were used more when we were selling a specific product.”
• Participant 21: “…these kinds of graphics and things like that could almost be viewed as a form of marketing; it’s very flashy, pretty to look at, attractive.”
Participants were also asked to add any non-traditional data visualisation techniques to the list they used that were not included on the formalised list.
Almost half of the participants (11 out of 26 participants) mentioned additional non-traditional data visualisation techniques used in quantitative reporting, which include: word clouds, heat maps and Prezi.
Nonetheless, when the creation of these non-traditional data visualisation techniques are considered, the results show that those participants with some use (regular and occasional) of these data visualisation techniques do so with the help of a specialised skilled individual or organisation (skilled in graphic design). A limited number of participants indicated that the marketing research firm for which they work has in-house graphical skills, while other participants indicated that they outsource it to a graphics organisation. Those participants who regularly / occasionally include non-traditional data visualisation techniques in quantitative research reports indicated that clients’ feedback is mostly positive and three participants mentioned that repeat business followed because of it.
From the analysis, participants were generally in agreement that the use of non-traditional data visualisation techniques is less important than advancements like interactive / real-time quantitative reporting (refer to Figure 6.8). Little importance is placed on the development of data visualisation techniques in quantitative reporting. In addition, when participants were asked to name the biggest industry challenges, only one respondent mentioned data visualisation as a challenge – instead, most challenges related to skill shortages (in terms of manpower, intellectual and resources) and restricted client budgets.
As indicated in Figure 6.8, participants also felt that traditional data visualisation techniques will always remain part of quantitative research reports – participants suggested that traditional and non-traditional data visualisation techniques should be used together with / complementary to each other. In particular, these participants indicated that traditional data visualisations will be used in the majority of the research reports, and non-traditional data visualisation techniques mainly used to highlight (draw attention to) certain research results in the quantitative research report.
Therefore, the two major themes evident from the empirical study for this subcategory is:
• Theme 1: The use of non-traditional data visualisation techniques is limited (refer to Figure 6.6 and Figure 6.7).
• Theme 2: The use of non-traditional data visualisation techniques is less important compared to other advancements (refer to Figure 6.8).
Figure 6.6: Interpretation subcategory 2(b) research results – Theme 1.1
The use of non-traditional data visualisation techniques is limited
Explanation of the theme:
Eight participants regularly use two or more types of non-traditional data visualisation techniques in quantitative research reports (15 participants with occasional use). The following two barriers are the main drivers of low usage:
• Time (19 participants): these visualisations are not yet automated and therefore require more thinking and take longer to create; it also takes longer to explain to clients;
and
• Complexity: many of the visualisations are believed to be difficult to create and to interpret (10 participants).
Because of its complexity nine participants also indicated that clients need to be educated on how to read and interpret these visualisations.
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"You could use these, I mean it’s time, I don’t know who would have the time to do them..." "I think in the real world
no one has the time to make the stuff come to life.” 8 out of 26 participants use two or more non-traditional data visualisation techniques regularly
“!it looks very fancy but it is actually really difficult to explain.” Participant 16
”
Number of participants who mentioned the theme:
Supporting verbatim quotes:
Figure 6.7: Interpretation subcategory 2(b) research results – Theme 1.2
!!!!!!!!!!! Interactive dashboard !!!!!!!!!!!!!!
!!!!!!!!!!!!!!!! Nested bubble chart !!!!!!!!!
!!!!!!!!!!!!!!!!! Infographic !!!!!!!!
!!!!!!!!!!!!!!!!!!!! Geomap !!!!!
!!!!!!!!!!!!!!!!!!!!!! Treemap !!!
!!!!!!!!!!!!!!!!!!!!!!!! Coxcomb !
!!!!!!!!!!!!!!!!!!!!!!!! Spiral !
The use of non-traditional data visualisation techniques is limited
Note: 1 participant did not answer this question
! Use regularly
! Use occasionally
! Don’t use
! Use mainly for marketing Participants who:
14
9
8
5
3
1
1 Count
Figure 6.8: Interpretation subcategory 2(b) research results – Theme 2
6.2.4 Interpretation category 3: The use and understanding of storytelling in