ASC 2016. Are We There Yet? Where Technological Innovation is Leading Research
Edited by T. Macer et al.
Compilation © 2016 Association for Survey Computing
Are Social Analysis Tools Holding Back Social Media
Intelligence as a Market Research Method?
Jillian Ney
Abstract
As an emerging market research method, social media intelligence has many unique challenges and
nuances for researchers to navigate. These challenges include the inability of researchers to guide
research questions and a lack of frameworks or models to effectively analyse social media
conversations. Such challenges are further compounded by the social tool domination of the industry
and their limited analytical functionality for research purposes. The very tools that researchers must
use to analyse large volumes of social media conversations are stalling the growth of social media
intelligence as a market research method.
The industry has been plagued by a flood of underwhelming insights reports because of the promise
from social tools to ‘find insight in the noise’, and it could be argued that this marketing promise is
degrading the perception of what an insight should be. The tools have been developed for marketers as
end users, not researchers, and the focus on quantitative measures and fuzzy metrics are problematic
for a researcher looking to generate deep customer insight. Currently social tools are not attempting to
change their measurement approach and it is left to the Researcher to overcome the limited
functionality to effectively answer traditional market research questions.
It can be difficult for researchers to achieve the depth of analysis required from social intelligence
research. However, by setting clear objectives for research, designing a bespoke analysis framework
and using multiple social tools you can answer research questions that may have been missed by other
research methods. This paper outlines the approach taken to answer two complex research questions:
•
What is our sponsorship awareness for the 18-24 year old target demographic?
•
Why is the age demographic of our customer falling?
The paper covers the approach to the research, the tools used and the results of the analysis, the future
of what social media analysis tools should look like and the considerations they should address to be
used in mainstream market research will also be discussed.
References
Social Media Intelligence, Social Media Analysis, Social Listening, Social Media Research, Advanced
Analytics>
1. Introduction
In the western world, the ubiquity of the internet is being realised with faster connections, access to
non-technical communications and social networking sites. It was recently reported that 64% of adults
Jillian Ney
in the UK use social media on a weekly basis, specifically looking at the 16 to 24 age demographic,
this figure increased to 99% active weekly social media users (Ofcom, 2016). If we compare the
number of social media users to global populations, it is reported that Facebook would be regarded as
the largest country and Twitter would be regarded as the 7th largest (World Economic Forum, 2016).
The rise of social networking has changed the fabric of society, with a virtual layer of every facet of
our lives are played out online. Our collective discussions, comments, likes, dislikes and networks of
social connections are now all data, and their scale is massive (Finger and Dutta, 2014). For the first
time in history, it is now possible to study human behaviour and conversations at depth; any human
interaction imaginable can now be studied in real-time from social media.
Researchers have responded with a series of studies exploring whether Facebook can predict
relationship survival rates (State, 2014), if social media can control emotions (Kramer et al, 2013) and
whether the collective wisdom of the internet could predict the location of Osama bin Laden (Leetaru,
2011). Social media intelligence SOCMINT has risen as an excepted method of security intelligence
(Omand, Bartlett and Miller, 2012). However, the potential power of social media as a market research
method for business is currently overshadowed by criticisms of data quality, and challenges in data
analysis of large sets of unstructured conversations. Practitioner approaches to social media research
has been led by the rise of social media analytics (for example, SimplyMeasured) focusing on content
performance, and social media listening (for example, Brandwatch and Crimson Hexagon) focusing on
the measurement of brand buzz. Instead of a methodological led approach to the analysis of social
media conversations, industry use of social media research has been driven by the functionality and
capabilities of commercial social analysis tools.
This paper will explore the challenges of social media research; what industry practitioners commonly
refer to as social media intelligence (Harrysson, Metayer and Sarrazin, 2012), and questions, if the
industry focus on social analysis tools, is holding back social media intelligence as a credible business
market research method. The paper will introduce two recent social intelligence research projects and
demonstrate the need for theoretical models and to go beyond the functionality of commercial social
analysis tools.
2. Challenges of Social Media Intelligence
While social media offers an unparalleled opportunity to study human behaviour and an unlimited,
evolving data set, many challenges have seen social media intelligence initiatives dubbed as
‘underwhelming’ by business leaders attempting to use social media intelligence in business decisionmaking. For example, Caroline Morris, Sky IQ has been quoted as becoming increasing frustrated
with data initiatives: ”I’ve lost count of the times I have been presented with some amazing fact the
data has told us through the use of some incredible new technology, to be left thinking, “so what” or
“isn’t that obvious”. Indeed, while it is believed that 70% of marketers use social listening tools
(Falcon Social, 2016), 60% of data projects will fail by 2017 (Bain and Company, 2015). Why is it
that social data intelligence can predict the location of Osama bin Laden but businesses struggle to
find value from the data?
Focus on Marketing Measurement
Since the rise of social media in 2007, marketing functions have held primary responsibility for social
media initiatives, including social media intelligence. This has created a dichotomy between what
business leaders require of social media intelligence and the focus of current measurement practices.
2 ASC 2016. Are We There Yet? Where Technological Innovation is Leading Research
Are Social Analysis Tools Holding Back Social Media Intelligence as a Market Research Method?
The idea that ‘vanity metrics’ (Ries, 2011), those metrics that measure likes, shares and comments can
assist organisations to find business value beyond a measure of active engagement is senseless. The
gulf between the vanity metrics currently being measured and what intelligence is required for
business decision-making is vast.
It is understandable that marketers wish to focus on measuring the performance of content because it is
directly related to their performance as a business unit. However, it is short-sighted to think that
measurement should be constrained to content marketing and active customer engagement alone. The
very nature of customer engagement is: “actively involving customers in generating intelligence on
their changing needs and helping the organisation respond to those needs” (Sashi, 2012, pg253). Even
the analysis and measurement of brand buzz in social listening initiatives can fall short on
understanding customers’ future needs in favour of measuring the perception of marketing initiatives
or identifying service failures and customer service issues.
By sandboxing measurement to task driven practices, organisations only receive insight on their
performance; they fail to activate their audience in the spirit of customer engagement. The
organisations who focus on marketing metrics fail to generate any intelligence that may help in future
business decision-making. The insight generated is incomplete and this can impact on the perceived
value of social media initiatives.
It is clear that the industry has been focused on helping marketers understand their marketing
performance but if we consider the famous adage by Akio Morita “we don’t ask consumers what they
want. They don’t know. Instead, we apply our brain power to what they need and will want”. There
are indications that social media intelligence could be something more powerful to organisations if the
focus of analysis is extended beyond marketing measurement, and that the need for this new analysis
approach may still be unknown to the market. The idea that the market does not know what they need,
are not aware of what is available, or do not understand how a given technology or service can benefit
them (Tillmann, 2008) is plausible given that social media intelligence is a relatively new concept. To
date, Social analysis tools have been developed to assist in analysing large volumes of unstructured
data in respect of the tasks or ‘jobs to be done’ (Bettencourt and Ulwick, 2008) of marketers. Meaning
that the analysis of social media is both led and constrained by social analysis tools on the market. The
focus of marketers on measuring brand content, engagement and performance and the need for social
analysis tools to provide the means to achieve this may be perpetuating the problem of
‘underwhelming’ insights and slowing the use of social media intelligence in market research.
Social Tool Domination
The abundance of social analysis tools on the market is something of a phenomenon, with social
analytics combining a $160 billion market value in 2015 (MarketsandMarkets, 2016). These tools
claim to provide ‘insight in the noise’ (Ney, 2016), but they provide little more than a route to gather
and analyse data. The most important stage of data analysis, the interpretation stage, has largely been
excluded from discussion and has seen a change in what an insight is perceived to be. The control of
social media intelligence initiatives by marketing functions, the need to focus measurement on
marketing output and active engagement, and the emphasis of social tools on pursuing marketers are
their target customer has led to professionals interpreting the data who are inexperienced in developing
customer or market insight. This inexperience has directly impacted on the type of insight generated,
and the quality of this insight because their inexperience fails to recognise that the marketing claims of
social analysis tools are false. Social analysis tools do not provide insight – the user must interpret the
data from the tool to generate actionable business intelligence.
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The domination of social analysis tools is further compounded by the lack of indistinguishable
functionality and benefits of each solution. The ubiquitous nature of social analysis tools functionality
increases purchase difficulty. Moreover, the evident ‘hyper-choice’ (Mick, Broniarczyk and Haidt,
2004) in purchasing a social analysis tool is evident with the rise in specialist review platforms like G2
Crowd and Trust Radius to help purchasers make an informed decision between the thousands of
social analysis tools and their long lists of functionality. To achieve the scale required to analyse
social media conversations, researchers are reliant on social analysis tools. However, the current remit
on functionality over methodology is limiting to the market researcher. There is also a reliance on
automation of measurement and insight, and a growing focus of automated, standardised reporting.
While this capability appeals to time-poor marketers, a market researcher is driven to segment, model
and analyse data to answer specific questions, not report on marketing performance alone.
The tool domination of the social media intelligence market changes the conversation to ‘what tool are
you using?’ Instead of ‘what are you measuring?’. The solutions focused view is slowing the adoption
of social media intelligence in market research because the social analysis tools focus on quantitative
measures and fluffy metrics do not provide the depth to answer the questions of rigorous research.
Lack of Measurement Metrics
Social media intelligence is unlike any other research method in two distinct ways. The first is that
there is a lack of established practices and journals to help design good social intelligence research
(Ney, 2016). The second is that the researcher cannot guide the research questions; the research
challenge is to extract value from social media conversations without asking any direct questions and
without anyone knowing the research is being conducted. Unless researchers use the predefined
functionality or metrics of social analysis tools, there is little guidance on how to develop effective
social media intelligence research practices.
The lack of research development guidelines can decrease the quality and consistency of social media
intelligence research, with research being conducted on ‘gut-feel’. Moreover, the ability of the
researcher to use social data to shift between an individual-level view and an aggregate view of a
target audience means that analytical approaches no longer fit into traditional categories of qualitative
versus quantitative (Procter, Voss and Lvov, 2015). Social media intelligence research transcends
traditional connotations of ‘mixed methods’ research, where you may use qualitative studies to add
depth to quantitative analysis (Tashakkori and Teddlie, 2003; Manson, 2006; Procter, Voss and Lvov,
2015) as the same data can be analysed both qualitative and quantitatively (Mackay and Tong, 2011).
The challenge becomes the development of research methods and metrics which can extract value by
combining these two perspectives (Procter, Voss and Lvov, 2015).
The contrast between the measurement metrics in social analysis tools and the typical methods of
analysis of researchers is stark. For example, the automated share of voice in social analysis tools,
compared to perceived quality, purchase intent or customer journey analysis in research terms. Many
social analysis tools are also ‘black boxed’, allowing little flexibility to conduct analysis not already
available in the system (Procter, Voss and Lvov 2015). The functionality of social analysis tools is
generally developed on ‘keyword’ analysis to assist researchers to segment data by matching identical
words. However, when you consider the vast differences in human speech and regional lexicons, the
task of keyword analysis can become difficult and without knowing which keywords to search. The
research may become inconsistent, and it may become difficult, if not impossible to reproduce
(Halfpenny and Procter, 2015).
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Human Behaviour
The unstructured nature of social media data is driven human participation, thus rendering content and
communication subject to the psychology of human behaviour. This has lead to arguments to suggest
that social media many not be a reliable data source because (1) social media has created a world of
narcissists (Bergman et al, 2011; Leung, 2013); (2) serial complainers (BT, 2012); and (3) that many
individuals are developing fictional online personas (Beninger et al, 2014; Lenhart, 2015). The
implication is that the underlying themes in the content are subject to fiction, overt exaggeration and
therefore should not be a trusted source of business intelligence. While social media data is subject to
the human condition, these studies fail to interpret the patterns in behaviour, thoughts and experiences
from some individuals who share a similar experience or viewpoint.
In a business sense, social data is the artefact of communications that are making or breaking brands.
Experience has become the new cultural capital, with an increase in individuals ‘lifecasting’ (Miessler,
2008) insights about their daily life. The actualized self is overtly promoted, and it is this self that has
the strongest motivation to purchase (Wolfe and Sisodia, 2003). In every interaction, individuals leave
behind a series of virtual markers about their lifestyle, interests, thoughts, feelings and mood. These
virtual markers, for the most part, are free from a high level of self-rationalisation and provide subtle
insights into what drives real-life behaviour, when analysed at scale.
For example, in studying emotions, Chris Hansen has found that Twitter data can accurately predict
the mood of the nation, and can correlate the influence of external events upon changing emotions
(Hansen, 2015). Hansen (2015) also found that Twitter provides researchers with a representative
sample of the US population. As a market research method, social media intelligence can offer a new,
effective approach to the study of human behaviour when the associated challenges of analysis have
been overcome.
3. The Solution for Effective Social Media Intelligence
There is a growing body of literature to suggest that the analysis of social media should encompass a
multi-disciplinary approach, that is part data science and part human science (for example, Whitehead,
2015). Certainly, the human aspect in the creation of social media conversations and the need for
customer engagement initiatives to ‘understand future customer needs’ suggests that social media
intelligence research has a larger opportunity than what is currently provided from social media
analysis tools. There is a definite need to go beyond traditional marketing metrics and to develop
solutions that can extract quantitative and qualitative value from the data (Procter, Voss and Lvov,
2015).
The study of behaviour, rather than marketing metrics also suggests that research professionals should
take ownership of social media intelligence initiatives. To overcome the challenges of consistency and
scalability, the focus should be on developing research methodologies and then the use social analysis
tools that fit into the chosen methodological approach, not ‘shoehorning’ research to fit into social
analysis tool functionality. To highlight the potential of social media intelligence, two recent social
media intelligence studies that adopted a methodological approach will be presented and the
relationship between research methodology and social analysis tool selection will also be discussed.
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4. Example Project I: Sponsorship Awareness
The spend on sponsorship in the European market is expected to reach a value of $15.9 billion (US
Dollars) in 2016 (Statista, 2016). Considering the huge amount of money spent on sponsorship, return
on investment should be a key concern. However, it is reported that about one-third of companies do
not have a sponsorship measurement system in place (Jacobs, Jain and Surana, 2014). It has been
argued that there is neither a specific or unique way to measure the contribution of sponsorship
(Theofilou et al, 2008). Past research has found that sponsorship can be effective in generating brand
awareness and purchase intent. For example, most spectators of a tennis tournament could identify
official sponsors (Bennett et al, 2006). Ngan et al (2011) found that a winning team with a “star”
produced higher purchase intent amongst fans and Tomalieh (2016) found that attitude towards the
event moderated audience purchase intentions.
By focusing on recognition these studies identified the impact of sponsorship on awareness and intent
to purchase, however, they fail to understand how sponsorship drives audience/sponsor relationships
in the digital world. The studies also fail to explore the relationship between sponsorship activation
and sponsor benefits (Plewa and Conduit, 2016). Broughton (2010) suggested that interaction on
social media can increase a fan’s level of identification with the sponsor organisation, but little is
known about what drives online engagement for event sponsors or how sponsors can engage event
audiences online. A headline sponsor for a large UK event wanted to explore their sponsorship
awareness and online engagement as a result of sponsoring the event.
Research Objective
The headline sponsor for a large UK event wanted to use social media intelligence to understand the
impact and awareness of their event sponsorship on the target demographic of 18-24-year-olds.
Social Media Intelligence Research Challenge
Segmentation of the data for the 18-24-year-old demographic was the primary research challenge.
Social media data is generally segmented on keywords, demographic segmentation, particularly by age
is uncommon and posed a challenge to the research. Due to the segmentation constraints, multiple
social analysis tools were required, and therefore the tools had to be compatible with each other.
Research Approach
Twitter was chosen as the data source as the Twitter platform arguably provides the most open access
to data (Housley et al, 2014), and has previously been used to analyse reactions to events (Procter et
al, 2013). The analysis was conducted both qualitatively and quantitatively. The qualitative research
sought to understand the context of sponsorship awareness and the impact of sponsorship association.
While quantitative analysis sought to understand the size of association and impact from qualitative
analysis. A further quantitative analysis was undertaken to determine the crossover between fans,
followers of the event and the sponsor(s) to determine if sponsorship of the event drove customers to
follow the sponsor(s) in social media..
The research was conducted in six stages.. Within the qualitative analysis, the first stage encompassed
the development of a ‘search query’ to gather all relevant Twitter conversations. The second stage
focused on the cleansing of the data to remove any inappropriate comments. The data was segmented
in four ways by, age, customer journey stage, sponsor and product category. This analysis focused on
understanding sponsorship awareness and customer perceptions of sponsorship at each stage of the
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customer journey for the target age demographic. The final interpretation stage focused on the analysis
of the data in relation to behavioural theories to explain the findings.
The quantitative stage of analysis was used to understand the size of associations that emerged from
the qualitative research. The secondary quantitative analysis used pivot tables to identify the crossover
between the target demographic talking about the event in social media and the followers of the
sponsor, the event and other event sponsors. This quantitative analysis was used to determine the
volume of event consumers who self-segmented their affinity (Ney, 2016) to both event and sponsor.
This indicated a level of active and long-term involvement with the event sponsor which is believed to
be as a result of event sponsorship.
Social Analysis Tools Required
This project required the use of three social analysis tools and Microsoft Excel. Due to their advanced
segmentation functionality that can be fully controlled by the researcher, Brandwatch was used to
gather all event conversations. Once all conversations were gathered and cleansed for any
inappropriate conversations, all individual Twitter handles were exported and then re-analysed for
demographic and psychographic markers with Demographics Pro. Demographics Pro
(www.demographicspro.com) is social analysis tools that helps users to: “understand and grow social
audiences, by using demographics to analyse, influence and target the consumers who matter most for
your brand”. Once the macro-analysis was completed, all Twitter handles of the 18-24-year-old
demographic were exported and then imported into Brandwatch to act as the basis of demographic
segmentation. Brandwatch (www.brandwatch.co.uk) is regarded as a social listening tool that: “allows
users to get deep insights into consumer opinion on any topic from across the social web”. The
Brandwatch platform was then used for all other qualitative analysis and qualifying quantitative
analysis.
To understand the relationship between the event audience and sponsors online followers Birdsong
Analytics was used to gather all records of the sponsors followers. Birdsong Analytics
(www.bridsonganalytics.com) is a social reporting tool that allows user to: “run reports on any public
Twitter, Facebook, Instagram or YouTube account”. Using the individual Twitter handles of the 1824-year-old demographic discussing the event on Twitter and the output of the Birdsong Analytics
data, Microsoft Excel was used to create a pivot table to understand the crossover between fans and
followers. This was repeated for the event and all other event sponsors to identify which account has
the highest proportion of followers actively discussing the event on Twitter.
Research Findings
The findings of the research indicated a low level of overt sponsorship awareness from the target 1824-year-old demographic. 1.1% of all event conversations acknowledged the headline sponsor and
analysis highlighted that only 0.1% of these conversations contained overt sponsorship awareness.
While engagement with the headline sponsor was low, they generated 32% more engagement than
other event sponsors. The customer journey analysis highlighted that the event audience engaged with
the sponsor immediately before and during the event. However, the relationship between event and
sponsor was not overt or widespread.
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5. Example Project II: Falling Demographic Audience
The development of customer personas has risen as an important business function to increase the
ability of organisations to encourage empathy towards customers (Merholz, 2009). Customer personas
are said to increase empathy as the brand is put into the customer's shoes to understand “what the
buyer is trying to achieve” (Allbee, 2013). While customer personas have been successful in product
design and customer experience design, a common complaint is that customer personas are not based
on real customer data (McGinn and Kotamraju, 2008) and fail to recognise that individuals are fluid in
the social identities (Champniss et al, 2015). Social media intelligence has been introduced as a datadriven approach to developing ‘real-life’ customer personas (for example, Siarto, 2013). In response,
specialist social analysis tools focusing on the development of customer personas are being
commercially sold (for example, People Pattern and Affinio). However, these tools focus on the
analysis of followers of a brands social profile, they do not consider those people who engage with the
brand but do not follow.
In the following example study, a leading FMCG brand was concerned about the growing negative
sentiment about their products and customer experience. The brand hypothesised that their online
audience was not their target customer personas and sought to employ social media intelligence to test
their hypotheses.
Research Objective
A leading FMCG company wanted to understand their online audience and to confirm their hypothesis
that their online audience was younger and less affluent than their pre-established customer personas.
Social Media Intelligence Research Challenge
Segmentation of social data based on age and income demographics posed the primary challenges of
this research. The segmentation of social data based on age and income demographics is uncommon as
these markers are not widely available from social media data, particularly on Twitter.
Research Approach
This research was also focused on Twitter analysis due to the volume of publically available data
compared to other social data sources (Housley et al, 2014), and previous success in using Twitter data
to understand how brand fans engage and interact online (for example, Bore and Hickman, 2013). The
research adopted a quantitative and qualitative methodology to understand the volume of followers
based on demographic profiles and qualitative analysis to understand the context of their engagement
and interaction with the brand.
The research was completed in five stages. The first stage quantitatively segmented the brands
followers into their respective age and income demographics to understand the characteristics of the
brands followers on Twitter. The second stage, comprised of a ‘search’ query to gather all relevant
brand conversations. The data gathered was then cleansed and any inappropriate conversations were
removed from the sample. Following this, stage four, encompassed the exportation of Twitter handles
to be analysed by age and income demographics. This analysis highlighted the demographic
composition of the individuals engaging with the brand on social media. The final stage was the
qualitative analysis of social media engagement and context of this engagement for the different
demographic groups.
The quantitative analysis was used to understand the characteristics breakdown of the brands Twitter
followers. Whilst the quantitative research indicates the age and affluence of the individuals self-
8 ASC 2016. Are We There Yet? Where Technological Innovation is Leading Research
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segmenting and displaying an active interest in the brand, it does not provide any insight into how
active the groups are in engaging with the brand. To overcome these limitations, the qualitative
analysis sought to explore the frequency of engagement from the demographic groups and the
reason(s) for engagement.
Social Analysis Tools Required
This project required the use of two social analysis tools, one to profile demographics and the other to
analyse the context of engagement with the brand. Demographics Pro and Brandwatch were both
deployed on this project. The reason for this was previous experience of these tools and the
understanding of their compatibility. Demographics Pro was used to identify the demographic
composition of the brands Twitter followers and then subsequently used for the demographic
composition of the individuals engaging with the brand. Brandwatch was used to segment and analyse
the context of the conversations and ascertain their corresponding volumes.
Research Findings
The results of this research indicated that approximately 70% of the brand's online followers were not
the target customer persona. While the brand believed that their target customer personas were older in
age and high in affluence, results of the demographic breakdown of their Twitter audience indicated
that 70% of their followers were between 17 and 24,of a lower economic background, and
predominantly students. Qualitative analysis of the conversations of this new customer group
highlighted that they drove approximately 84% of the online conversation and that this group were
responsible for decreasing the luxury perception of the brand. The research indicated that the brands
most active audience was very price sensitive; while they aspired to purchase the product they were
highly vocal that the cost was too expensive, drowning out conversation and engagement from the
target customer groups.
6. The Future of Social Media Intelligence
The research discussed in this paper indicates that social media intelligence can be a viable market
research method for organisations. Social media intelligence should focus on assisting organisations to
identify their strengths, weaknesses, opportunities and threats as well as provide insight to strategically
plan for future decisions. To successfully capture the market research industry, social analysis tools
must consider the ‘jobs to be done’ (Bettencourt and Ulwick, 2008) of the researcher and not shoehorn
these tasks into the tool configuration for marketing measurement. Although social media intelligence
can offer a new avenue for market research, it’s widespread adoption in business is being held back
because the social analysis tools are focused on the measurement of marketing performance. The
example projects presented in this study demonstrated that simple sentiment, fuzzy metrics and high
level quantitative analysis measured as standard in social analysis tools do not provide enough rigour
or flexibility for market research.
The example projects presented in this paper indicate that while advanced social media intelligence
research is possible with current tools, multiple tools are required to adequately answer business
related questions. However, in an industry where tools dominate the market, many organisations may
fail to recognise that multiple tools are required to answer questions because of marketing claims that
the tools provide organisations with strategic insight. Social analysis tools have reduced the barriers to
entry for market research because of their ease of access to customer data and perceived low cost.
However, the easy entry into market research has impacted the quality of the insight generated and
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how actionable this insight is to business decision-making. Social analysis tools are controlling what
‘insight’ is perceived to be, and a wave of inexperienced professionals are perpetuating
‘underwhelming’ insights reports that are holding back the credibility of social media intelligence as a
market research method.
Social analysis tools are a necessary evil in the analysis of social media conversations, and their focus
on marketers as their primary market are holding back widespread adoption in market research. To be
a successful instrument for market research, the tools themselves must learn more about the research
to be conducted and develop functionality and provide solutions to enhance this process. The market
researcher must to have more control over the segmentation and analysis of the data; automation is
good for the time poor marketers, but it does not provide the flexibility or rigour required in research.
The future of social analysis tools should seek to help researchers understand customer behaviour,
needs and fears, as well as provide easy insight into the customer experience and robust brand
tracking. It is necessary the social analysis tools increase rigour and functionality to segment data
properly. Furthermore, the tools need to be more transparent about their ‘black boxed’ algorithms so
researchers can identify any potential limitations or weaknesses.
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About the Author
Dr Jillian Ney is the UK’s first Dr of Social Media and a Digital Behavioural Scientist. She helps
ambitious organisations find opportunities for growth by using digital data. Her proprietary analysis
frameworks advocate for People Science for your Digital Data, and focus on quality of insight not
quantity of data.
Dr Ney offers consultancy services in the analysis of social media conversations. The two project
examples she presents in this paper are part of her work in this area.
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