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Paper 4Introducing ‘Quintegrated’ researchLeveraging the power of qualitative and quantitative research integration

Kristin Hickey Ruby Cha Cha

MRS Annual Conference

2012

Introducing ‘Quintegrated’ research 1

Introduction

Market research has traditionally been split into silos of qualitative and quantitative research, with these streams emerging from two very distinct disciplines. This divergence is particularly noticeable in larger and more progressive markets such as the UK and Europe, where increased specialisation has facilitated this separation of skills and has left the poor insights buyer with the unenviable task of piecing these disparate insight banks together.

This landscape of the market research industry, however, is changing rapidly. The market is more competitive and clients too time-poor to pull two ‘languages’ into a single insights story. Furthermore, the landscape of research tools and respondent interactions (both consumer and B2B) is changing, fuelled by new media, technological advances and the demand for faster, shorter, more instantaneous information. These changes place new demands for smart research talent – talent that is bilingual, creative and dynamic in their approach to client challenges and can design and apply new research approaches in online settings.

As these forces come into play, we have begun to see a new era of powerful research alchemy, where left brain and right brain applications, techniques and thinking come together. We have coined this amalgamation ‘Quintegration’, and believe that the idea of Quintegration will become increasingly important as individual level data becomes more widely available and data collection methods turn increasingly towards online, digital and social vehicles.

Whilst it is recognised that many individuals and agencies are well versed at speaking both languages (qual and quant), and many have developed a unique expertise in the design, implementation and management of online communities and social media, there seem few examples of agencies that are finessing specific techniques to fully integrate qualitative and quantitative insight. This paper focuses on Quintegration as an emergent vision and not a final product. We present a series of approaches where dualism is not only advantageous, but essential. We believe each approach not only requires the aptitude and desire to combine data, but requires potentially new thinking in terms of how these techniques are applied and, in particular, embryonic ideas as to what the future of market research outputs might look like in an increasingly digital and ‘Quintegrated’ world.

In order to do this, we present six Quintegrated approaches by way of discussion. These approaches include:

1 Advertising ROI – bringing creative evaluation and econometrics together

2 Concept testing – combining linguistics and regression analysis

3 Quantification and forecasting of semiotic trends – applying forecasting tools to semiotics

4 Co-creation forecasting – using co-creation approaches to forecast the impact of experience initiatives on advocacy or promotion

7 WhiteSpace mapping – use of discourse analysis in quantitative modelling

6 Measuring broadcast and social media impact

This paper will not simply focus on approaches where two stages are brought together, but, instead, on how traditional methods can be turned on their head and explored from the perspective of the opposing discipline, how these techniques might be applied in an increasingly data intensive world and why this is likely to change the future of the market research industry.

Forces of change

Before we introduce the notion and meaning of Quintegration, let us review the key industry forces that have led to the emergence of new client and agency needs. In an ever-changing environment, it is often difficult to identify exactly which forces contribute most to disruption, however, it is undeniable that the market research landscape today is fundamentally different from that of a decade ago due, to the individual or combined effects of some of the following forces of change indicated in Diagram 1.

Diagram 1: Key forces of industry change

The key forces of change identified include the following:

1 Technological

Not only has internet penetration increased significantly to represent the vast majority of consumers in Western countries1, the degree of involvement via technological mobility (smart phones, iPads, increased availability of Wi-Fi, etc) has had a significant impact on the way we now interact and communicate. This means that online research has become more widely accepted as an industry standard, overcoming many of its original barriers of lack of population representation. In addition, it has not only become a forum for collecting information from consumers, it has become a forum for conversations with consumers as well as between them – conversations which add a richness and depth to the insights industry we have not been privy to before.


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on client-side roles and it is this phenomenon that suggests the time is right for a Quintegrated research offer.

The combination of these forces of change have led to the following phenomena when it comes to the provision of market research services:

l More online and digital mediums with strong growth in online focus groups, communities and online quantitative research as dominant methodologies.2

l Significant growth in the amount and availability of personal data collected, stored and available for use by clients, public sector, individuals and agencies. This growth has, indeed, seen the emergence of a new industry in itself – that of digital data analytics – which promises to be one of the most powerful industries of the next 3-5 years.

l Data collection vehicles and techniques becoming more involving (requiring more time and continued commitment on the part of panellists) and evolving (not only longitudinal in nature, but involved in highly developmental challenges).

l Increased pressure and demands on the client to integrate, distil and action research findings which have become specialised, but may lack continuity or the ability to truly build on previous findings

l More time pressure when it comes to research, precluding long, multi-stage projects in many instances, particularly at a global level

In summary, the result is an market research context in which clients demand research which is faster, involves deeper levels of response through continued respondent engagement and should (assuming strong data analytics and synthesis of qual and quant as we will shortly present), result in sharper and more actionable insights.

Implications of change

It is a consequence of this need for faster, deeper, sharper, that the need for stronger integration of qualitative and quantitative approaches emerges. This need extends from the capturing of data, through to its analysis and the application of insights into the client business. From this perspective, therefore, the need for Quintegration comes as both a methodological as well as an analytical need.

With any industry change of this nature (both methodological and analytic), there is also a corollary need in terms of both talent and training in the industry as well as reporting and consulting skills. These skills will become more apparent once we fully introduce the idea of Quintegration.

What is Quintegration?

‘Quintegration’ is Ruby Cha Cha’s own coined expression referring to the amalgamation of qualitative and quantitative research to better deliver to the emergent need for deeper, faster, sharper insight in today’s research environment.

If we consider the history and meaning of the two streams of

2 Social change

Social change has also affected the role of online research, as we have found that consumers are increasingly used to typing, blogging, and conversing through mobile and online communication platforms. These changes have both created a ‘sharing’ mindset, or increased willingness to actively share opinions with others, but have also supported an active engagement in terms of these interactions being able to happen anywhere, anytime. In this age where the smartphone is king and the consumer a virtual ‘victim’ to its ubiquitous engagement opportunities, we face an environment never seen before, where research respondents need little encouragement or persuasion to report their behaviour, feelings and responses in an instantaneous way.

3 Client change

There is no doubt that the financial turmoil of recent years has forced further pressure on businesses. Marketers, in particular, are vulnerable to Boards looking to validate shareholder returns and generate strong bottom line returns. These pressures have resulted in a continued downsizing pressure on client-side roles, including those of consumer insights personnel. Consequently, insights managers are being asked to cover more brands, more markets, broader scope, consumer and shopper within their portfolio of work which results in increasing time pressures within the work environment. In this pressured context, clients are requiring sharper, faster insights that are new and are able to be immediately implemented within their business. This is a new era of client demands and one which has implications for how we do research, the length of research ‘programs’ and the ability to turn around insights into actionable growth initiatives quickly and professionally.

4 Industry change

The final force is that which is happening within our industry. Here, two diametrically opposed forces are evident. On the one hand, there is increasing consolidation at the top end of the industry, with global marketing services organisations becoming bigger and broader as acquisitions continue. At the other end of the market and, potentially, as consequence of this consolidation, is the continued emergence of small, niche and specialised research offshoots which continue to spawn globally. The degree and intensity of competition that has emerged as a result of this new competitive context means that clients are increasingly ‘divvying’ up their research spend across many specialised projects including those representing specialised qualitative and quantitative components or skill-sets. As a result, there is more fragmentation than ever in terms of insights development and reporting styles, placing even greater pressure on the insights manager to consolidate these learnings and translate them into activation strategies. Never, before, has the divide between qualitative and quantitative research approaches placed such pressure


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left to right. This challenge becomes even more complex when a project stream involves multiple stages of explanation (divergence) and confirmation (convergence) – for instance in a longitudinal or an innovation-style study.

In the case of Quintegration, however, the approach is quite different. Data is collected, but could (and should) comprise both qualitative and quantitative data points, or at least observational points that can be interpreted, coded and therefore applied in either way. In doing so, the process maintains a single dimension and direction, so there is no need for additional time nor energy to be spent in integrating the two pieces of work together.

Diagram 3: Quintegration

Analytically

In terms of analytics, traditional methods are reliant on strong left or right brain thinking, skills and training. Qualitative researchers, for instance, are trained in understanding consumer psychology, observational skills, exploration of complex constructs through conversational probing and question elicitation. The models and analysis we apply in qualitative research enable us to deconstruct ideas or observations to present underlying meaning via the application of models borrowed largely from psychology. In quantitative research, however, the skills and analysis stem from the disciplines of mathematics and statistics and are more about numeric accuracy, inference and hypothesis testing than about the interpretation of meaning or reason.

In the Quintegrated world, we envisage that analysts possess both skill sets. They are able to look at a client challenge through both creative and analytic lenses and will develop new applications and techniques that deliver a sense of definitiveness/confirmation as well as a deeper explanation of meaning, interpretation and consumer understanding simultaneously.

Reporting

The implication, therefore, is that in the world of Quintegration, reporting will contain a combination of understanding and interpretative insight as well as validation of the importance, influence or role of this insight in the context of the client challenge. This may well require new platforms for reporting (i.e. beyond PowerPoint as a currency), different styles of consultancy and, potentially, new written reporting structures.

As Quintegration is only an emerging phenomenon (at

research most commonly used to deliver consumer insights, we have ‘Qualitative’ research, derived from the word, ‘quality’, and commonly associated with data collection methods such as ethnography, social anthropology and consumer/shopper phenomenology. Meanwhile, the term ‘Quantitative’ research is derived from the word ‘quantity’ or ‘to count’, drawing on the disciplines of statistics and mathematics to size and confirm patterns, relationships or phenomena of interest.

Quintegration, as we propose it, is the combining of qualitative and quantitative research methods, techniques, tools, analytics, thinking processes, consulting and reporting in a way that provides the insights industry with new types of models, answers or insights. Whilst our industry currently combines the two research styles in a number of projects or streams of research, the vision is that, given the observed changes in the industry context, we may be able to move towards a new era, where this integration becomes much tighter, or, indeed, new methodologies are developed which rely on both skill sets simultaneously.

Let us explain by exploring the key ways in which Quintegration is different from other approaches – namely methodologically, analytically and reporting.

Methodologically

Commonly, more significant research projects often combine qualitative and quantitative approaches within the same overarching project stream, however, importantly, most continue to apply these disciplines somewhat independently (albeit in a sequential way). This is illustrated in Diagram 2.

As illustrated, the combining of these two research streams is really more of a post-project component synthesis or alignment. In addition, each ‘component’ of the research stream has its own presentation or summarised findings which carry their unique style of language, representation and expression of insights or consumer learnings. Whilst these are brought together by the end of the project, the work stream itself is essentially a matrix configuration, and often a great deal of time and energy is required on behalf of the client to blend the different styles, thinking (and often agency) reports together to makes sense of the path represented from

Diagram 2: Traditional synthesis of qualitative and quantitative research


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advertisements for a market leading brand was a key driver of likeability amongst Main Grocery Buyers, whilst the use of humour had no incremental impact on either brand health measures or brand sales. The outputs from this project became instrumental in briefing new creative agencies and forming continuous learning about creative executions over the years.

Isolating the key success factors from advertising is now a practical and essential element of our planning offer. We believe this truly represents a new approach of thinking differently when it comes to research design and illustrates how the use of qualitative and quantitative skills and thinking can be aligned into a powerful communications insight and development tool.

Case study 2: Concept testing

Following similar thinking to the advertising case study is the development of a new approach applied to ‘getting more’ out of concept testing research. To date, concept testing has formed an important component of the innovation process and associated research. Traditionally, (although rapidly changing), concepts have been developed from ideating around key insight springboards and the resultant ideas put into consumer focus groups, workshops or online qual for initial feedback, consumer review, input and refinement. Subsequent to qualitative refinement and screening, progressed concepts are improved and form the input to quantitative screening methodologies. Such approaches aim to prioritise the concepts based on consumer feedback and initial volume or opportunity estimates. Subsequent stages of qualitative and quantitative research may then well be required, depending on the client and category, to further refine, package, price, target and tighten sales forecast estimates prior to launch. This traditional process is simplistically captured in Diagram 4.

Diagram 4: Existing linear innovation research

As indicated, at two points along this continuum – at the qualitative screening and at the quantitative screening stages - the pool of possible concept ideas is culled to make the process convergent and the outcomes manageable. Whilst it may be true that these ideas, “did not make the cut” and are therefore no longer meaningful to clients,3 it is of key concern that we can often get to the post screening stage in the process and find ourselves with more discards than ‘concept successes’. More concerning is the following question – are we really building cumulative learning about what makes a successful concept before we simply discard concepts and start the process afresh?

Quintegrated research takes up the challenge of addressing this question, after all, many clients will conduct hundreds of concept tests over the years and still find the need to start the

best!), it is difficult to foresee what this reporting might end up looking like, but there is no question it will need to be more virtual, digital, instantaneous, consumer-driven and actionable in nature.

LET US NOW present, in summary form, a number of case studies which illustrate the application of tools,

techniques, skill sets or unique reporting styles available through Quintegration. In each, we discuss case study examples which have been conducted by ruby cha cha consultants, however, in order to protect client confidentiality, client names and specific client information will remain undisclosed.

Case study 1: Advertising ROI

Over the years, clients invest in a vast number of advertising or communications campaigns and executions for their brands. As these are developed, they are traditionally explored via research approaches such as:

l q ualitative advertising testing (usually pre-testing or in development stage)

l quantitative ad testing (to measure cut-through, branding, take out pre and post launch)

l econometric modelling (to measure commercial impact post launch)

In the case of Quintegrated research, we are able to add value by determining the key drivers of advertising performance. This involves decoding advertisements, both in terms of content and style, into binary variables. In our case study example, working with one of the top bread and baked good manufacturers, we were able to decode a historical ad reel of key television advertisements from over the years into their constituent components. These components included elements such as:

l length of advertisement (i.e. 30 seconds or 15 seconds)l tonality (i.e. humourous or not; family oriented or not, etc)l colour versus black and whitel prominence of brandl TARPS or spend

In total, there were over 40 variables used in the analysis (each with a number of possible attribute levels). This deconstruction process requires subjective interpretation and a strong understanding of the qualitative and developmental stages of research. This qualitative interpretation is then expressed as quantitative data - either binary, or, in the case of elements such as length or brand prominence, continuous or ratio variables. Armed with this information, we then applied regression modelling, using both sales and advertising tracking measures as our key dependent variables.

This form of analysis helped us to identify key insights about what made different ads more or less effective than others. We found, for instance, that the inclusion of children in the


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While the process sounds technical and quantitative in nature, it relies heavily on the ability of the analysts to understand and be able to deconstruct concept representations in much the same way as a trained Semiotician is able to deconstruct the underlying meaning of packaging codes and their meaning. This is the case from a linguistic, visual and content perspective, so the two disciplines are heavily reliant on each other in the same process to make the modelling successful.

The project itself was extremely powerful. The findings illustrated not only how concept design or construction impacted overall concept potential (beyond existing knowledge), but the learnings allowed us to understand how an average concept could be improved into a potent concept without any need for additional consumer research. The findings were distilled into a ‘cocktail book’ which provided information about the relative attractiveness of each independent concept component, but, additionally, provided an engaging tool for thinking about how to combine these ‘ingredients’ in more powerful ways.

Case study 3: Quantitative application to semiotics and trend forecasting[Note, this ‘case study’ is more an idea than a specific case study example, although each element we mention has been undertaken across a range of studies]

A similar application of quintegration lies in the application of deconstructive, interpretative skills and reconstructive, quantitative modelling skills in the areas of semiotics and trends.

Whilst the study of semiotics has always fallen in the qualitative domain or ‘side’ of the research divide, there is no reason why we cannot support robustness in semiotic studies through sizing the prevalence of signs or signifiers, estimating the prevalence of codes and estimating the rate at which these codes are becoming more or less dominant in a market or cultural context.

Indeed, it is quite surprising that the area of trends research also seems to be dominated by qualitative researchers and social commentators, when the term ‘trend’ assumes stringent quantitative meaning within disciplines such as economics and finance. More and more frequently, as consultants, we are being asked to ‘validate’ trends as the game of trend-spotting has burgeoned remarkably in recent years. As a result, we see the application of similar Quintegration techniques as particularly useful to align qualitative foresight around social, cultural or sub-cultural change with powerful quantitative sizing and forecasting techniques which extend well beyond the count and display capabilities of online digital analytics.

Case study 4: Co-creation forecasting

One of the most significant and beneficial advances in research in the current decade is the development of online communities. Never, before, have researchers been blessed with an opportunity to interact with so many consumers or customers so quickly. More importantly, the means of

innovation process afresh with a new ideation workshop, new ideation tools and often a new ideation agency!

The process is similar to that illustrated in our advertising example, essentially involving a deconstruction of (a) the concept; (b) the qualitative responses to the concept and applying the quantitative test values as dependent variables for modelling purposes.

This approach and analysis are illustrated in Diagram 5.

Diagram 5: Quintegrated research – deconstructing and modelling for concept insight

It is probably easier to explain the process through reviewing a case study where this design has been successfully applied. The case study was for a global candy manufacturer and was designed to develop a concept development insight bible based on cumulative learning around how concepts performed in testing. Whilst this could have been achieved through a meta-analysis of existing qualitative research, we chose to apply a ‘Quintegrated approach’ through reviewing the qualitative inputs and modelling these against quantitative outcomes. We also chose to calibrate this with what we knew about resultant sales post concept launch.

The stages involved:

1 Deconstructing the concepts into the following types of elements (note, this list is not exhaustive, but will provide an idea of the process):

— Flavours, product attributes (ie. Hard versus soft chew, gum, etc), mouthfeel, size, number of serves, packaging colour, brand, price mentioned vs not, distribution, rational benefits, emotional benefits, occasions, needstates, inclusion of emotive words versus factual concept design, degree of finalisation of concept image, style of image used, etc.

2 Qualitative drivers, barriers and language was used and included in the developing list of coded attributes

3 These, in combination, then became independent variables that were used to model4 against dependent variables such as purchase intent and concept potency scores.

4 Finally, the model’s accuracy or power was calibrated by considering the existing relationship between launched concepts, their quantitative scores and their actual post-launch sales.


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In our particular approach, then next stage is then to re-introduce the adapted scenarios and test them quantitatively. We do this in a proprietary way which develops an “Experience Potency” score (EP) for each service or touch-point initiative. We also ask a question about the projected key experience metric we are trying to forecast at this stage (i.e. “if this happened to you, how likely would you be to talk about this experience to others?”). The specific questions are, of course, tailored to the needs and the KPI’s of the business in each instance. These questions are asked of consumers who have not participated in the co-creation stage, so there is no preciousness about evaluating their own suggestions. At the same time, it can reasonably be conducted within the same MROC and can work in a timely manner in terms of recycling of qualitative and quantitative information as illustrated in Diagram 6.

Diagram 6: Continuous evolution of experience ideas via MROCs

At this stage, it does not matter whether the initiatives or service delivery ideas are feasible, as the idea is that the EP’s will provide an indication of upside potential which we further measure by the reported impact on advocacy score. The business can then consider the costs involved associated with each initiative separately, to consider whether the uplift in desired outcome scores is worth the internal investment. To keep ideas reasonable, we do ‘charge’ consumers with considering the business cost implications, but we also apply our skills and learning to adapt scenarios where actual interaction may remain largely unchanged, but we are able to manipulate consumer perceptions or expectations to achieve a reasonably close outcome measure.

The final step in this process is calibrating the EP scores against actual lifts in experience measures once the service changes are made. Unfortunately, due to the ongoing nature of this work, we are not at the stage where we are able to report on these outcomes, but have confidence that the types of changes we are developing with consumers will achieve ambitious lifts in advocacy.

interaction is one that can facilitate both qualitative and quantitative learning either simultaneously, or at least, amongst the same audience.

This case study is a work in progress and is based on the concept of using online communities to help co-create brand experiences and interactions, to test them and, in doing so, to forecast the overall impact on advocacy or similar measures of key interest to the client’s business.

There are two initiatives that make the approach we propose unique. Firstly, we employ story-telling, not as a consumer methodology, but as stimulus. This borrows from previous academic research and personal experience in its application within the services arena5 which highlighted how consumers can respond quite powerfully to scenarios that are clear, build empathy and are believable/credible. This application helps us to overcome ‘experiential concept testing’ which others have often dismissed as being ‘untestable’.

The second difference is that mentioned previously – the unique context and dynamic made available through the explosion of Market Research Online Communities (MROC) and, more generally, their size. The growth in MROC popularity and acceptance as a new forum for different 6 research approaches, opens scope for us to explore these service experience ideas qualitatively and quantitatively at the same time (or sequentially, if preferred).

As such, we can apply Quintegrated research to the co-creation and forecasting of key customer experience metrics such as advocacy or brand promotion and outline the process for doing this.

First, we identify and recruit the desired participants. In our particular case study (for a financial services client), recruitment is based on consumer segments and screening conducted based on required participation in the process. We do not recommend using existing client panels or communities for this type of co-creation and testing, as pre-priming is not desirable in this instance, but may be for other types of research or continuous dialogue.

Next, we construct the scenarios. These scenarios are developed around brand touchpoint areas and their insight platforms relating either to all, or to specific segments of interest. An example of how a scenario appears to consumers is illustrated briefly below.

Example of experience scenario“Emma wakes up to find her credit card missing. Although she calls the restaurant where she last used it, they are unable to shed light on its whereabouts. She searches on the internet to find a number for her bank to call. She calls and chooses menu option ‘3’ when asked by the electronic voice to indicate she would like to speak to someone about lost or stolen cards… (scenario continues)”

We then introduce these scenarios to the community (or part thereof ), and gauge immediate qualitative feedback on the scenario content and outcome. A co-creation exercise then takes place, where consumers build on and adapt the interaction in the scenario in an attempt to improve the outcome (functional and emotional feelings Emma experiences).


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that can be illustrated in a variety of ways (a very simplified and crude coffee example is illustrated in Diagram 7.

Diagram 7 – Simplified example of multi-dimensional mind-map

Typically, these maps would contain hundreds of words, so the example above is certainly very crude as an illustration, but has been included to illustrate the following elements. Fristly, by constructing a quantified mind-map, we are able to illustrate the clustering of experiential associations and form these into ‘competitive realms’. Secondly, given existing knowledge of the market, sales data and quantified trends, we are able to overlay this information (more quintegration!) on the map to illustrate trends of interest and growth. This is illustrated in one dynamic – from ‘boring’ or everyday to exciting or ‘discovery’ as illustrated by the dimensional axis on Diagram 7. The relative growth on different ends of this dimension is then reviewed and added to the ‘war map’ such that we can identify where the growth dynamics are taking place. Next, we can size each of the identified clusters or ‘realms’ by including a question in the quantitative study which identifies the association between attributes and current brands or offers on the market.

As you can see, the overlay of qualitative, quantitative and strategic planning skills helps to finally develop a model where the following is true:

l We have a single, comprehensive and sized map of lens-specific territories that are highly resonant with consumers

l Each territory or ‘realm’, comprises a comprehensive list of features, benefits, product forms, flavours, occasions, etc, that can form a ‘pick and mix’ menu for further ideation or be used to input directly into conjoint-based concept development optimisation solutions

l We can use the map to size the current and potential strength of each of these realms, so we can pinpoint where our ideation or concept creation efforts should be based

l We can overlay where existing offers currently play and use this overlay to further test propositions which bridge realms or stretch current offers into new places

CASE STUDY 5: WHITESPACE MAPPING

Another application of Quintegrated research is evident in our WhiteSpaceTM process. The process is designed to help clients turn the traditional innovation processes on their head and offer a platform-based concept creation start point that is based on granular understanding (i.e. ground-up ideation).

Let us explain. In traditional innovation research (as previously covered), innovation processes begin with identifying potent insight platforms to use as ideation springboards. These are then taken into ideation processes and concepts are developed as an output. Admittedly, this is a crude explanation of what is a highly creative process, but for the purposes of explaining how the Quintegrated approach differs, it will suffice for now.

In the application of WhiteSpace thinking, we start with the application of psychotherapy techniques with consumers to elicit deeply held and endorsed associations about the different elements that comprise products, brands and experiences. This is deliberately taken from outside the brand or product-specific perspective in order to ensure the insights and language derived is not bounded in current category perceptions (after all, the intent behind the process is to potentially develop new categories or at least move beyond current constraints). To ensure this, we need to be careful how we define the ‘lens’ of category interest. For instance, if our category is ‘coffee’, we may choose7 to define this as ‘energy transitions’, or we might choose to define it based on ‘adult flavours’ or even ‘morning rituals’ depending on our innovation goals and objectives.

Once we have the unconstrained lens defined, we then use the skills of qualitative researchers trained in clinical psychology techniques, to invite consumers to share their most memorable and impactful (positive and negative) stories around these lenses. The story-telling process runs freely, without interruption and allowing consumers to diverge freely. The moderator continues to explore language, ideas, themes and, specifically, is looking to develop a comprehensive list of concept ingredients – i.e. lists of language around:

l Occasions (when, where, environment, etc)l User context (who is present, what relationship that person

has, etc)l Product attributes (form, flavour, etc)l Benefits and emotions l Processes (verbs, what was ‘done’ or happening)l Sensorial (textures, colours, smells, tastes, sounds, etc)

These qualitative outputs are then fed directly into quantitative research without adaptation. In the quantitative stage, respondents are asked a series of association questions which are used to construct quantified mind-maps. In doing so, each map represents a ‘cluster’ of potential product attributes, benefits, users, expectations, occasions, etc. Unfortunately, due to client confidentiality, we are unable to illustrate a specific example of these maps, but one can imagine a vast array of words mapped through associative techniques


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were supported by higher behavioural involvement, less price sensitivity, more persuasiveness as a broadcaster and, higher reported conative and behavioural impact on ‘advocees’ or those individuals they recommended to. As such, the application of quantitative measures of customer value estimation were an important build on the qualitative learnings and sparked a further idea that perhaps, if we could now separate the impact effects between online and offline media, we might be able to develop more meaningful estimates of the Return on Investment in the digital and social media space.

We have since applied this idea to a continuous study with a national tourism provider and are keenly distilling the results as we speak. This involves building a disaggregate model where we estimate the future value of each individual consumer by combining their current behaviour, intended behaviour (which is post-checked against actual future behaviour) and broadcast style and impact. To this end, the dependent variable looks something as follows:

Customer Value = f (Current Behaviour) = f (usage, frequency, spend), Conative Intent = f (intended future behaviour), Broadcast Value = f (broadcast style and impact))

Having developed this variable, we then look at the individual media consumption behavioural patterns for that particular consumer. We determine, for instance, what media they use and which they have experienced or interacted with our brand through, including, where feasible, channel relevance and evaluation. This information then becomes the independent variables in a complex statistical model, but enable us to isolate the impact of individual mediums (i.e. Facebook versus television advertising) as interaction or combined effects of various media (i.e. Facebook and television versus Facebook alone).

Although we are not at the stage of reporting on this approach yet, initial data analysis provides us with confidence that this approach will reveal rich and powerful answers to previously unanswered questions.

In reading this case, it may appear that this relies exclusively on quantitative research methods, however, it should be noted that without the ongoing qualitative work in the space of understanding motivations, benefits, language and impact of advocacy (particularly digital advocacy), we would be unable to have progressed to this stage of dynamic ROI evaluation. As such, it is a further example of the development of Quintegrated research, even though the qual and quant stages, in this instance, are sequential and were not part of a single research stream or project.

IMPLICATIONS OF QUINTEGRATION

So there we have a stream of examples of how qualitative and quantitative methods have been combined and brought more closely together to develop new and innovative ways to address outstanding client questions or challenges. It is now time to reflect on what this means for us as an industry, given the

Case study 6: Measuring broadcast and social media impact

The final application of Quintegration this paper covers is work currently underway in understanding the true impact of social media and its associated broadcast effects. This has emerged as a specific area of interest to marketers, as the strong growth of social media and internet engagement has driven a parallel explosion of investment in digital and social marketing investment. With investment increasing as a proportion of client marketing budgets, many marketers are now (reasonably) asking what the real impact and financial return on this investment actually is.

Traditionally, ROI questions have been addressed using the application of econometric analysis or quantitative measures of pre and post communications recall and reported impact. The world of online marketing, however, is very different in that it is not event bounded, but, instead, can have a continuous presence which makes such analyses less effective in measuring ROI. So what is the right approach?

A number of platform providers such as Facebook, Google and Twitter have introduced digital analytics which report key behavioural information of the end user market. Increasingly, these analytics are improving and becoming a necessary and powerful tool for marketers investing in this space. At the same time, these analytics only provide part of the answer to the ROI question which is really much deeper than simple count data. As one client mentioned to us recently, “I know I have over 50,000 ‘Fans’ on Facebook and that this number is increasing, but I have no idea whether to invest more in Facebook or not – after all, what is this fan base actually worth to me?”

With this question in mind, we have developed a new Quintegrated approach which relies on an understanding of social media user psychology, but also on specifically measuring the broadcast impact created through both online and offline investment.

To do this, we need to share two case studies – the hybrid of which best illustrates the dynamic of qual and quant ideas coming together. The first study references a project specifically looking at understanding advocacy for a mobile telecommunications business. In this study, through a combination of ethnography, qualitative and quantitative research, we were able to develop an understanding of not only why people recommended a brand to others, but how they recommended brands. By how, we do not mean simply which medium they used to make the recommendation, but what language they used, the degree to which this recommendation was persuasive, who they recommended to, under what conditions and, importantly, the impact this recommendation had.8 The inclusion of quantitative research in this project, enabled us to segment individuals on the basis of their broadcast style and, simultaneously, develop key allocation questions that allow us to identify different motivations and styles of advocacy.9

During the analysis for this project, we discovered that particular types of advocates were considerably more valuable to the client’s business, as their recommendations


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Diagram 8: From today to tomorrow – a Quintegrated vision

We acknowledge that this thinking may be controversial. It might also contain misattributions or errors of judgement (after all, we are only human!). We hope, however, it at least sparks ideas, new thinking and a perspective on what might be different in the future, as this is our ambition and our passion as researchers and planners.

Finally, we welcome any feedback, discussion or ideas to build upon this thinking from individuals, agencies and clients so that we can continue to hone Quintegrated thinking and support the continued evolution of the global market research industry.

References

1 www.internetworldstats.com UK (March 2011) internet penetration is approaching 80% in the UK according to

2 Mintel (2011), Market Research Report3 Having said this, we recognise that many clients and agencies alike keep

and recycle these ideas to test their potential in a different time, context or market at a later stage or via refinement processes

4 In this case, via Structural Equation Modelling, however, this could be conducted via regression modelling or other forms of applied statistical techniques

5 Hickey, Kristin M, (1991) The Impact of Personal Control in Services Experiences, PhD Thesis, Bond University

6 We emphasis ‘different’, as we concur with the belief that MROC’s require different methodological, analytical, interaction and data management processes and, as such, are not substitutes, but complements to traditional qualitative and quantitative research methods

7 There is a pro-forma stakeholder process for lens definition, but this is probably too detailed for the purposes of this paper.

8 This was explored through interviews with the ‘advocee’ or person receiving the recommendation.

9 The styles applied specifically to advocacy that takes place via internet, blogging or social media media is our digital persona archetypes

10 Banerji, Vivek, (2008) Do we need pragmatic polymaths to boost the qualitative research industry?, ESOMAR Istanbul

pressures towards deeper, faster, sharper insights we outlined earlier.

We see three distinct implications for our industry, both client and agency side.

1 Quintegrated contextThe first implication is that, with the rapidly expanding MROC and digital world we face, researchers will now have the opportunity, for the first time in an efficient way, to conduct qualitative and quantitative research simultaneously. As a result, we believe the methods we currently employ must adapt quickly in order that they are able to capitalise on this opportunity and turn two-stage projects into a Quintegrated approach. We foresee, for instance, an environment where we are able to both capture the richness of story-telling, language or visual metaphors AND size their role and resonance within the same forum. We are personally excited by this emergent challenge and are embracing it by rolling out a new series of tools and approaches that fit with an increasing Quintegrated world.

2 Quintegrated talentThe second implication is that, as the need for qualitative and quantitative thinking and approaches come closer together, our industry will need to find, develop and nurture individuals who are ‘Polymath’10 talented with both creative and analytic skills. This presents a new challenge for our agencies in terms of recruitment, training and inspiration, as we have prospered in an environment of specialisation for so long. It is our belief that Quintegration, in and of itself as an idea, might well become a source of specialisation in the future, but, for now, it remains a developmental need and may be best served by learning from disciplines such as management consulting, strategic planning, engineering or design.

3 Quintegrated methodsFinally, Quintegration will only take off as an emergent idea if it is supported by agencies and clients willing to take risks and trial new methodological and analytic approaches. We have presented a number of ideas throughout the body of this paper and hope there are many more ideas being currently employed that individuals and agencies are willing to share so that, as an industry, we can build credibility in the continued development of these new approaches.

Conclusion

We have now presented the idea of Quintegration in its current entirety. The notion of qualitative and quantitative research worlds colliding emphasises not the iterative nature of bouncing from one methodology to the other, but in a new world where researchers will rely on combining left and right brain thinking, qual and quant analysis within a single process, forum or study. This difference is highlighted in Diagram 8.

mrsannualconference2012...changing, fuelled by new media, technological advances and the demand for...

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