As well as the book I am bashing away at several conference papers. Here is a short section below relating a taxonomy of NewMR. Any thoughts or suggestions?
List of Techniques
This section briefly lists the NewMR techniques that are
currently important and describes them on three key dimensions, namely their
epistemological position, whether the research is active or passive, and what
the relationship is between the researcher and the respondent (from
Researcher->Respondent for the conventional model of research done to
respondents, to Researcher<->Respondent for research done with
respondents, and Researcher<-Respondent for those cases where respondents
are running the show). Below the table there is a quick reminder of what the
techniques are.
|
Technique
|
Epistemological Position
|
Active/Passive
|
Researcher Respondent Model
|
|
Prediction Markets
|
Post-Positivist
|
Active
|
Researcher -> Respondent
|
|
WE-Research
|
Constructionist
|
Active
|
Researcher <- Respondent
|
|
Respondent Blogs
|
Constructionist
|
Active
|
Researcher <- Respondent
|
|
Online Research Communities
|
Constructionist
|
Active
|
Researcher <-> Respondent
|
|
Community Panels
|
Post-Positivist
|
Active
|
Researcher -> Respondent
|
|
Blog and Buzz Mining
|
Post-Positivist
|
Passive
|
Researcher <- Respondent
|
|
E-ethnography
|
Constructionist
|
Active and Passive
|
Researcher <- Respondent
|
|
Other Non-Random Probability Sampling Quant
|
Post-Positivist
|
Active
|
Researcher -> Respondent
|
One interesting thing about a table such as the one above is
that they highlight areas where something might be missing. For example there
is not an entry in the table which is Constructionist, Passive, Researcher<-Respondent.
However, this does not mean the technique does not exist, it may simply be that
it is not considered part of market research. The increase use of feedback
systems, such as the rating system in eBay fits the description, but is not
considered to fall within the domain of market research.
The Techniques Outlined
This section briefly describes the techniques set out in the
table above.
Prediction Markets
Prediction markets are a research tool
where the predictions are formed by allowing the participants to buy and sell ‘positions’
on what they think will happen in the future (in a similar way to futures
markets). The most famous example prediction markets are the Iowa Electronic
Markets which have been successfully predicting the results of US elections for
many years.
WE-Research
A term coined by Mark Earls and John Kearon to describe a
phenomenon that others have described as citizen research, where the respondent
is recruited to be the researcher, providing a combination of crowdsourcing and
co-creation. The new ethnography iPhone app from EveryDayLives is a great
example of how this field is developing.
Respondent Blogs
Respondent blogs are based on recruiting respondents to work
as participants, capturing some aspect of their lives. In the early days of
Research 2.0 this tended to involve using standard blogging tools, more
recently there have a number of developments such as the mobile phone
adaptations used by MESH Planning and new platforms such as Revelation.
Although researchers set the context and may ask some questions, the process is
mostly respondent driven and at its best is a good example of WE-Research.
Online Research Communities
Online research communities, also known as MROCs, are
communities created to provide a research to companies. In a typical case these
communities have 50 to 500 members and are essentially a qualitative approach,
even though surveys and polls may be part of the engagement. In 2009 online research
communities were one of the ‘hottest’ topics, both in terms of market growth
and conference papers/presentations.
Community Panels
Community panels tend to be brand orientated and to be the
natural successor to in-house panels, albeit with more of a community feel. The
research that uses these panels tends to be quantitative in nature which is why
they tend to be larger than MROCs, typically with 5,000 to 20,000 members.
However, in line with recent trends and thinking they do not make any pretence
to be a random probability sample.
Blog and Buzz Mining
Blog and buzz mining is a passive research approach that
recognises that if people are talking about your brand then you had better be
listening to them. Blog and buzz mining are developing techniques, from web
scraping, to bots, to RSS feeds, that search the internet for conversations
from blogs to Twitter, from social networks to comments on news stories and
glean information about brands and social issues. The main limitation is that
most of the online conversation appear to be largely restricted to the top 20%
of brands, with very little being said about the 50% of brands with the lowest
salience.
e-Ethnography
e-Ethnography, along with netnography and virtual
ethnography, embraces both active and passive techniques. For example,
e-ethnography can refer to the passive process of following conversations and
interactions on the net, and the individual level (as opposed to the macro
level of blog and buzz mining). Or, e-ethnography can involve engaging with
people on the net and asking questions, or becoming a participant observer in a
community.
Other Non-Random Probability Sampling Quant
Because market research has not had access to random
probability samples for many years, and because respondents can’t answer many
of the types of questions they are asked, market research has developed a range
of techniques to provide useful answers. However, in most cases market
researchers have not really made the point that these techniques are not based
on the traditional research model. Perhaps the development if NewMR will allow
many of these approaches to ‘come out of their closet’?
Amongst the non-random probability sampling approaches that
researchers commonly use are:
Complex Weighting.
This method is based on using external data to weight a panel to make it behave
more like the population One method, made popular by Harris Interactive is
propensity scoring (Terhanian 2008), and another is to use CHAID to assign
weights (Fine et al 2009). However,these routes require time and high quality
information about the population.
Quotas. The most
common way of working with panels and other non-probability samples is to match
a small number of key demographics to the target population, for example age,
sex, and income. For example, if a new breakfast cereal is being tested via a
panel then assumption is that if young, wealthy females on the panel like it,
then young wealthy females who are not on the panel will also like it.
Benchmarking. One
approach is to keep the sample specification constant and then to assume that
changes over time amongst people who meet the specification will be matched by
changes in the target population. This is the basis of most ad and brand
tracking. The absolute values produced by the tracking may or may not reflect
the wider population, but it is the changes in the tracking lines that are monitored
and reported.
Modelling.
Modelling is a more sophisticated variation of benchmarking and seeks to link
the output from a panel survey to the real world. For example, a series of
product tests might be conducted with samples from an online panel, with the
specification of the sample held constant. Over time the company will acquire
market feedback and can attempt to model what a survey score of X means in
terms of market outcomes. When this system works it deals with both sampling
problem and the face that people can’t really answer the questions in a way
that is meaningful to their own personal outcomes.
