Tag Archives: MRX

#MediaInsights Day 2 Recap

After co-chair Rob McLoughlin gave a recap for Day 1 and a
look at what to expect for Day 2, Amber Case, author of Design for the Next
Generation of Devices, presented our first keynote: 


Here, we got a comical look at connected devices and how the average
consumer has become dependent on them. She gave us a look at products like
PetNet, and how the Web and technology play a major role in self-development.

featured Edwin Wong of Buzzfeed and his insights on Recoding
Culture.  We got a look at Millennials
and how culture is being reshaped and where it’s headed.
76% of Gen Y say “it’s the norm to be radical” (as
opposed to 60% of Gen X).
Buzzfeed measured 4 millennial groups: Omegas, Sigmas, Cult
Kids, and Nichesters and the strong overlaps between these groups.
Wong stressed how we’re moving towards the end of
demographics, evolution of psycographics and the rise of the individual.
Tobin Trevarthen of 21st Century Narrative and author of
Narrative Generation was our next keynote speaker – BEYOND THE STORY: WHY
Tobin covered:
what is a narrative
why you need a narrative
story vs. Narrative
building a narrative
A narrative differs from a story. More directly, a narrative
is a mosaic of related, contextual stories that inform and define one’s
A story has a beginning, a middle and an end.  A story has a plot, and acts as a one-way
A narrative is endless, and has a more interactive dialogue.
Tobin showed how Tesla automotive expanded the brand
narrative to reach consumers.
Our last keynote of the morning had Mainak Mazumdar, CRO of
Recently data sets had errors and inaccuracies in station
crediting, time shifted content and missing live viewing.  Mainak addressed 2 key questions:
what is our “ground truth?
how do we understand and correct for biases?
Nielsen used RPD data along with 200,000+ high quality
person’s panel to address methodology challenges.
The first of Day 2′s Track 1 case studies (Targeting
was CHANNEL ME, presented by Jason Shalaveyus from
Starcom and Nicole Tramontano from Turner. 
Despite the industry pendulum swing away from engaged reach
towards efficiency and programmatic buying in recent years, Starcom and Turner
set out to determine:
Relative importance of contextual factors
Range of impact
Net effect
Prevalence of optimal contexts among segments
Top findings included:
easy wins where you have high control over
highly influential factors are hard to come by
content has a stable shelf life, but ads spoil
relevance is important
Armida Ascano and Gil Haddi from Trend Hunter presented our
next Targeting Viewers case study 
Trend Hunter is helping clients find the stories that
connect them to Gen Z (infants to 17) – what defines them and what they mean to
Media.  They are not as big as
Millennials, but they are just as important. 
By 2020, Gen Z will be 40% of the consumer base.
Gen Z is the most diverse generation, and they are underrepresented
in the mainstream media. As a result, they:
turn to influencers who look and speak like them
already have the tools, creativity and desire to
create, but do not enjoy passive media consumption
are swapping in aspiration for realism
As content providers, we need to choose influencers and messaging
with this in mind.
A nearly packed room showed up to see Melanie Schneider
(AMC) and Stephanie Yates (WE) present their case study VIEWER CHOICE:
TV viewership has shown downward declines over the past 5
years.  However, content is up more than
ever.  How are we able to watch all this
content?  Technology has propelled viewer
AMC Networks did a study focusing on content, taking a
deeper dive into Nielsen respondent level data exploring viewers, their habits,
and how they watch content.
Our last Targeting Viewers case study for Day 2 was THE
presented by Tamara Barber from Simmons Research.
Video consumption is not just linear and live anymore.  Simmons looked at comprehensive video
measurement across linear, SVOD, OTT and other connected devices.
OTT users are psychographically different. The Top 10 OTT
user attributes included:
more digital
more social media
While the Top 10 attributes for non-OTT users included:
use cell phone for calling only
read newspaper daily 

Simmons is hoping to use psychographics to optimize Media
planning and buying.

#MediaInsights Day 1 Recap

By: Jim Bono, Vice
President, Research, Crown Media Family Networks

MI&E Conference Director Rachel McDonald started
off the day welcoming this year’s attendees and introducing this year’s
co-chairs: Janet Gallent (NBCUniversal), Rob McLoughlin (POPSUGAR) and Bruce
Friend (Maru/Matchbox).
Bruce then sat with Turner’s Howard Shimmel for the OPENING
KEYNOTE INTERVIEW: Re-Imagining the Future of Television
.  Recently, at a Cynopsis conference, Shimmel
said “we’re at a measurement crisis.” 
Elaborating on that comment, he explained how it’s 2017 and we still do
not have a robust cross-platform solution for our industry. Advertisers want an
infrastructure that allows more exposure than just reach and frequency.  With Total Audience, we still don’t know what
to do with it.
They also discussed the Turner Ad Lab, and how people go to
Netflix, Hulu, etc., to watch content without ads. What can we do to make the
advertising experience better for the consumer? Howard believes that the industry should have a published
document that mandates what currency data research vendors should provide for
the content providers. As new platforms are emerging, we need to better
understand where those consumers are going to find content.
Bruce asked about big data and how it’s all the rage. As an
industry where do we go from here?  
Howard explained how there is an abundance of research tools out there.  We just haven’t done a good enough job
telling our clients that we have all these tools.  Big data is a component to an overall data
framework. We need to know when to use it and not to use it. Sometimes Big Data
can be wrong data.
Bruce also questioned how new companies are great with tech
but don’t understand the data they deliver. However, other great long-time
research companies are very good at analyzing data but don’t have the tech.  Howard feels that there’s nothing wrong with
using a combination of data sets like Nielsen, MRI, and panel data to come up
with the best solution. Unfortunately, there are too many companies that reach
out and don’t really understand our businesses.
He still believes that survey research is important to our
industry as data tells what, but not why.
KEYNOTE: The Importance of Race and Ethnicity in Reaching

Cathy Cohen, Professor at University of Chicago, gave us a
very entertaining look at millennials and the importance of race and ethnicity
among this group, especially regarding this year’s election. The majority of
Millennials in the US are Hispanic and African-American, and by 2060 White will
be a minority. In this past year’s election, more African-American and Latino
Millennials voted for Democrats, while there were more white Millennials voting
Republican. However, in the 2016 primary vote the choice among all Millennials (regardless
of ethnicity) was Bernie Sanders.
Cohen’s presentation covered: 

  • ??        
    The complexity of Millennials through a racial
  • ??        
    Researching race and Millennials
  • ??        
    Rise of Millennials in the workforce
  • ??        
    Importance of Millennials in the Political force
Millennials are becoming an increasingly important electoral
demographic. The share of eligible
voters that are Millennials has grown during last 3 elections:

  • ??        
    2008 – 23%
  • ??        
    2012 – 29%
  • ??        
    2016 – 36%
Cohen also addressed the six key problems with studying
 Generational frames / over-representation of
white Millennials
investigation of white Millennials
 Homogenous communities of color missing
 Segmentation of Millennials of color – pick
 Millennials as experts of Millennials -
or waves – assumes stability in taste, preferences and decisions
KEYNOTE PANEL: How Consumers Engage with Programming Across
Social Platforms
Sean Casey from Nielsen Social Guide moderated this
morning’s Keynote Panel featuring Brian Robinson (Facebook), Tom Ciszik
(Twitter), Guy Ram (NBC), Leslie Koch (HBO).
Insights focused on the evolution of social media and how
quickly it’s grown.
Consumers spend 5.5 hours per week using Social Media on
their smartphone.
64% of consumers use smartphone while watching TV. 
1.2 billion interact on Social referring to TV.
After breaking for lunch hour afternoon consisted of Concurrent
Tracks.  These case studies were broken
into three groups:

  • ??        
    Track 1 – Targeting Viewers
  • ??        
    Track 2 – Audience Insights
  • ??        
    Track 3 – Innovation in Media
Track 1 – Targeting Viewers case studies:
From Ordinary Target to Persuadable Target

David Kaplan from Bravo, along with Zach Schessel from NBCU
and Peter Bouchard from Civis Analytics, discussing how to hit the right target
audience and “swing” viewers. The presentation also looked at how to
attract casual viewers without alienating the core viewers.
Key takeaways were:
The different creative approach is often
required for on-air vs. off-channel to drive maximum impact with loyal and
casual viewers
Casual Bravo viewers may all have some affinity
for the network but only the “swing viewers” in this group can be
readily persuaded to deepen their commitment and watch more
 An ads
positive persuadability should be balanced with any potential backlash effects
to ensure a net positive effect
 Not all
swing viewers are created equal, eg. consumers in different DMAs can have a
varied response to creative hooks
Viewing Predictions & Inventory Optimization: The
Secrets to Success in Audience Targeting

Steve Schmitt of TiVo showed us how TiVo is helping clients get
from traditional linear to non-linear content, and how they improved campaign
performance using optimizers and brand targeting. His presentation focused on
consumption has undergone profound changes, especially Millennials age 18-34
video consumption continues to expand with DVR, VOD, SVOD and online/mobile
viewing extending the power of linear TV
 Linear TV
has majority share, but it is declining as on-demand options expand
Concepts on the rise are binge viewing, on-demand,
cord-cutting and cord-shaving, while things like appointment viewing and one-size-fits-all
on decline.
Online Video in the Toolbox: A Must Have

Darlene LaChapelle and Maya Abinakad from AOL talked about
the top drivers for video growth, with “social media video offerings”
and “better quality creative” leading the way, and how online video
growth is driven by mobile devices.
Online video viewing on a smartphone is on par
with that of a computer
indicate they have few technical barriers watching online video on their
smartphones, but get the convenience of watching anywhere, anytime
 62% said
I watch more online video today than one year ago
 62% said
in the next 6 months I expect to watch more online video
Laptop/desktop (70%) is still the leading device on which
online video is watch daily, just edging smartphone (67%)
How to Engage  Multicultural Millennial Influencers in 2017
and Beyond

Our afternoon continued with our only Track 1 panel.  The panel was moderated by Horowitz’s Adriana
Waterson, and we heard from Michele Meyer (Univision), Tom Kralik (Revolt) and
Lia Silkworth (Telemundo) as they discussed their key takeaways about
multicultural millennials and the importance of this audience in our business
today, as leading consumers of cross-platform media.
are leading the charge in cross-platform media consumption
 Millennial and Gen Z trends ARE multicultural
Gen Z is more diverse and multicultural and are digital
 If you
join a multicultural network, your general market skills may not “translate”
The Next Generation of Ad Effectiveness
Our first day concluded with this presentation from Chris
Kelly at Survata.

Diverse Demographics: Breaking Stereotypes

Millennials are the most diverse generation
in history ‘ only 59% are Caucasian and 27% have an immigrant background (Deloitte, 2015). Therefore, it’s no surprise that this
demographic expects brands to embrace and reflect the diversity of their lives
‘ a trend previously highlighted by Stylus Life in our report No
normal: Post-diversity marketing
. If brands are to do this successfully, they
must move beyond crude stereotyping to represent a broad spectrum of race,
gender and sexuality.
For instance, Muslim millennials offer
growing opportunities for brands
‘ the Muslim consumer lifestyle market is
predicted to reach $2.6tn by 2020. The modern yet faith-driven outlook of this
group, along with a growing disposable income, will see them buy into brands
that reflect or understand their values. Make-up brand CoverGirl is already
tapping into this lucrative demographic with its latest brand ambassador ‘beauty
blogger and hijab wearer Nura Afia
. One of a growing number of Muslim
beauty bloggers, her new role demonstrates the importance and appeal of diverse

Beauty brands are working particularly hard
to cater to often forgotten demographics. A new initiative from L’Oreal
offers free step-by-step audio tutorials
to give visually impaired women
more independence. The usability has been carefully considered to fit the needs
of this consumer group ‘ the cosmetic and skincare tutorials are concise to fit
into everyday habits, while the app’s customisable user interface features a
monochrome palette and large text.
Also targeting a currently under-catered
market, UnBeweavable
is an on-demand hair service specifically for women of colour.
On-demand beauty services, which provide a stylist straight to your home or
workplace, have been rising in popularity for some time now ‘ yet UnBeweavable
Hair is the first tailored to the specific needs of this demographic.

Created by Zina Alfa, it was inspired by her own difficulties in finding
hairdressers who understood her needs. Made by a woman of colour for other
women of colour, this case study shows that if brands want to provide products and
services that appeal to all, they must improve the diversity of their

Rebecca Minkoff recently highlighted the
need for diverse workforces, citing the lack
of female employees in technology companies
(and STEM fields in general) as
a key reason why wearables are not currently capturing female consumers. The
fashion designer also mentions examples of having to explain female
expectations and behaviours ‘ such as taking jewellery off at night ‘ that were
missed by an all-male team.
There’s a popular saying promoting better
gender and race representation that suggests ‘you cannot be what you cannot
see’ ‘ but this could easily be extended to ‘you cannot create for audiences
you don’t represent and understand’. Which is why companies with diverse workforces
are more likely to financially outperform those that are not (McKinsey,
2015). So if you want to ensure your products appeal to an increasingly diverse
consumer landscape, you’d better start with your job adverts.

Brought to you by Stylus Life, creativity and innovation news
from around the web.

Stop Listening, Start Watching. How Interest-Based Segmentation Gets to the Heart of Consumers

By: Hannah Chapple
In recent years, we’ve seen companies increase their
reliance on social data. Why? Today there are more social signals than ever.
Consumers are sharing comments, their interests, thoughts, and more online. The
result being an incredible amount of consumer-provided data at our fingertips.
The problem facing marketers is trying to make sense of the
deep end of social data. One way we’ve seen businesses and big brands try to
make sense of this data is by investing in a little something called social listening.
If we watch and listen to what consumers are saying in real-time, we’ll paint a
more accurate picture of them, right? Wrong.
Social listening is biased. Many times our online persona is
different than who we are or doesn’t show us in our entirety. And only a small
percentage of those online ever actually engage or vocalize their thoughts,
interests, and beliefs ‘ the consumer insights that companies crave.
I’ll use myself as an example. If you comb through my social
feeds (and please, don’t feel you have to) you’ll find my comments and a flurry
of articles shared on all things marketing. While I am interested in this
stuff, yes (it’s my profession after all), it is not the complete picture of
who Hannah is as a person.
So how do we get to
the heart of the consumer?
One way companies can figure out who their consumers are and
what they want is by leveraging interest-based segmentation.
Interest-based segmentation is when individuals are
clustered and segmented into naturally-occurring, unbiased clusters, by looking
at who or what they choose to follow. Instead of focusing on the vocal
minority, at Affinio we consider
following patterns and interest data to be paramount to listening or
traditional research methods. 

 Image: Interest-based
clusters generated by Affinio
Following and connecting with other people is a fundamental
property of social behaviour. It is also a silent action, whereas social biases
might keep individuals from being honest about their interests (who they
follow) or what they talk about in person. The takeaway: you wouldn’t know
everything that I’m interested in just by looking at what I say, but you would
understand my interests by looking at who I follow.
By focusing on how an audience is connected (analyzing their
shared interests and affinities), interest-based segmentation gets to the very
heart of the consumer. Instantly, companies can identify who and what their
audience cares about, even if they’ve never vocalized it. Or if they have, this
method validates that finding. This approach places focus on the honest
relationships consumers have built and maintained and lets marketers understand
their audience as human beings and not one-dimensional data points.

About the Author: Hannah
Chapple is the Marketing & Content Coordinator at Affinio, the marketing
intelligence platform. Hannah holds a Bachelor of Business Administration with
a major in Marketing from the F.C. Manning School of Business at Acadia

See Who You’ll Meet at The Media Insights & Engagement Conference

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Chadwick Martin Bailey
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Council for Research Excellence
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Selling on Emotion: Why Show Ratings and Demographics No Longer Tell the Whole Story

By Jared Feldman, Founder & CEO of Canvs

An earlier version of this article appeared in AdAge.

With upfront season just around the corner, early signs are that brands, finally, are again buying more of what networks are selling.

That’s great news for the networks, after over three straight years of declines in upfront ad-time purchases (and two years of plateaued spending before that). But as the buying season kicks off, let me suggest that brands should pay attention to some new factors this year as they lock in deals.

In the past, in making decisions about where to spend their ad dollars, buyers had only ratings and some demographic data about existing shows, plus a first peek at new ones coming in the fall. What I’d like to propose is that buyers not use, or just use, those same old methods this time around.

Oh sure, keep the ratings and demos you’re used to working with. Nielsen’s work continues to have value and it’s evolving to embrace the new TV realities.

But show ratings and audience demographics by themselves no longer tell ad buyers everything they need to know in the new universe of “TV” we now live in. The TV audience is shifting, and in lots of directions at once. With it, the business is shifting, too.

Audiences are watching TV in more ways and on more platforms than ever, and at different times and in different settings. Just as importantly, audiences are talking about the shows they’re watching, on more social media and chat and other online platforms than ever.

And when fans are talking about these shows, sharing important moments, creating content about the shows, and reacting to that, they’re also evoking and expressing a whole raft of feelings and attachments about favorite programs.

The savviest programmers realize this. They’re building shows that connect with and captivate dedicated, niche audiences who care deeply about that show. They’re sharing compelling behind-the-scenes content, live tweeting with fans, and creating other experiences that will hook and engage the superfans who care most about a program.

And those shows and networks are exactly where advertisers should be. Those fans will be a show’s best ambassadors. And the research says they’ll also be the best ambassadors for brands advertising around that show.

The shows that stir emotional reactions are the ones that also will stir reactions and buying impulses for the ads of those shows. As they say in the business, that is gold. So it’s important to figure out which companies are doing a good job reaching and holding those audiences your brand cares about most.

For instance, the two networks whose shows most often evoke the emotion “addicting” on Twitter were MTV and Freeform (then known as ABC Family), according to a Canvs analysis of tweets captured by Nielsen.

It shouldn’t be a complete surprise — both networks target millennials, who are tech-savvy and sharing-mad. They share everything they care about, including some of their favorite shows on those two networks.

“Addictive” programming isn’t the only thing buyers should look for. For instance, what networks and shows do fans find consistently “funny?” A laughing fan is one predisposed to like the brands connected to those shows.

And though the industry may not be quite ready for it, let me propose another thing. Networks and show runners will become increasingly skilled at creating compelling niche programming for ardent superfan audiences. They’re also going to get better at using the new measures of success and building to it.

At some point, as creators improve, and as brands integrate what this means for their bottom line, we’ll have new network milestones for ad sales. Expect networks to begin guaranteeing more than just ratings.

Providing a minimum level of emotional reactions that can help drive advertising success will become important. And when a show doesn’t drive that emotional response, a network will have to figure out how to make good on its promise.

By that point, the entire industry will know how much emotion matters in making a show, and its advertising, succeed. And then we’ll really see the full power and value of advertising in the new TV universe.

Related articles

How Are You Treating Your Organizational Data?

By: Anil Damodaran, Blueocean
Market Intelligence Assistant Vice President

Data fragmentation has existed for over 15 years and still does
today. However, the challenge has grown tremendously due to an increase in the
number of data sources and devices in use, at the workplace and home. Today,
data is generated and stored not only on office PCs and laptops, but on mobile
devices such as smartphones, tablets, online storage devices, and more.
Most of this data is generated in bits and pieces during
various activities like exchange of emails, feedbacks, chats, IoT feed
captures, and pilot surveys. It lies around in devices or unused drives, and
often treated as office stationery, until one day someone suddenly realizes the
cost implications of this recklessness. According to research from Salesforce,
about 53 percent of organizational data is left unanalyzed that could otherwise
have signified an opportunity for decision makers.[1]
The problem, at a grass-roots level, is leaving data unattended with disparate
sources and not implementing proper data governance.
So what can we do?
Data fragmentation can be addressed if you start considering
data generated within your organization as a corporate asset. By doing so, it
will become more instinctive to institute practices and processes of measuring
data. Once you can measure their data, it becomes easier to tag the data based
on business relevance and quality attributes.
For example, in almost all companies large and small, it is
common to take stock of infrastructure ‘ tangible and intangible ‘ and tag
them, such as company IP, laptops, mouse, and so on to the employee using it.
Similarly, are you then tagging your data generated within your organization to
its source, purpose, time, format and so on? It has been found that only 13
percent organizations have properly integrated data and predictive insights
extensively into their entire business operations.[2]
Companies that drive their businesses using data-driven strategies are five
percent more productive and realize six percent higher profits.[3]
Here are some of the traits of an organization that treat
data as an asset vs. those that do not.[4]
Organization that treats data as an asset
Organization that does not treat data as an asset
Is more innovative
Less innovative and tends to become commoditized in the long run
Is more customer-centric
Pushes products to customers, instead of developing products based on
customer needs
Harbors a culture of openness and collaboration
Politics and hierarchy based system tend to keep data in silos
Business decisions are data-driven
Run on personal experience and intuitions
Business processes and performance are measured based on feedback and
analytical models
Practices age-old business processes; no system for measuring
business performance
Risk mitigation is proactive
Risk mitigation is reactive
What kind of an organization are you and what is your
biggest challenge with the evolution? Share with us your experience and views.
Blueocean Market
Intelligence is a global analytics and insights provider that helps
corporations realize a 360-degree view of their customers through data
integration and a multi-disciplinary approach that enables sound, data-driven
business decision. To learn more, visit www.blueoceanmi.com.

3 Ways Market Researchers Approach Mobile

By: Roddy Knowles, Director, Product & Research Methodology, Research Now 

 This post was
originally published on Research
Now’s Blog

I’ve been saying (sometimes complaining or screaming) for
years that as an industry we need to wake up and approach research with mobile
in mind. I haven’t been alone here.
Several of my colleagues ‘ here at Research Now and
elsewhere ‘ have pushed hard for change. Reminders for why we need to change
are everywhere, whether that be in the statistic du jour about mobile usage, a
dataset with more mobile participants than expected, or just sitting on a park
bench watching throngs of people of all ages hunt for Pok??mon.
In spite of constant everyday reminders and the call from
many in the market research field, true change has been slow coming. So, how
have market researchers kept pace with broader mobile trends and embraced a
mobile-first philosophy?
I’ve conducted an incredibly unscientific segmentation of
researchers ‘ cute segment names and all ‘ that attempts to capture what we’re
all seeing if we look around at our colleagues.

Response 1 ‘ Meet
Response 2 ‘ Meet
Evan Tually
Response 3 ‘ Meet
Reese Istant
There is a bit of humor, a bit of shame, and a bit of truth
in these characterizations. If you are in this industry I know you know people
who look a bit like all 3 of these hypothetical folks. And I know you can call
out your friends and colleagues for being a Reese Istant, or just ask them to
be a bit more like Bill.
The simple truth in this silliness is that we know that
embracing a mobile-first mindset is the best course forward, even if we do a
good job suppressing this truth. I know that change is hard. We all know that
change is hard. But the sooner we get there, the less painful it will be. And
the good news is, we are not too late. Someday, we will have a room full of
Bills and I’ll stop my poor attempts at market research humor.

The Future of Market Research Data Collection

By: Research Now CEO Gary
S. Laben
This post was
originally published on the Research
Now Blog
The vast expansion of communications technology has
obviously sparked a dramatic change in the way our world
functions. Certainly one of the most ubiquitous and transformational
impacts is that brought on by new technologies that allow virtually everyone to
remain constantly and instantly connected; connected to one
another, certainly, but also to the growing number of systems upon which
we are growing increasingly dependent, if not addicted. Modern
communications systems have given users unprecedented access to information and
services without regard to time or location, letting them get more done faster
than ever before. Even more, the devices and systems continually monitor users’
behaviors to refine the responses to personalize the service
delivered. By providing experiences that are tailored and relevant to each
user’s expectations, this new generation of technology doesn’t just provide a
better user experience, it also preserves the user’s most
valuable resource: time.
The idea that we can use deep knowledge about individual and
groups of users’ situations, preferences, and past behavior to provide a
better, more efficient user experience applies equally well to market research.
Of course, this is not a new idea. We’ve always used profiling data to target
specific communities for research studies and minimize the amount of
information we need to collect in each study. Avoiding collecting redundant data
shortens surveys, reduces participant load, and improves data quality. What’s
changing is the vast volume of data we can mine to automatically extract and
maintain components of the user’s profile ‘ even in real time ‘ without the
need to explicitly query them. This is the realm of big data.

Applying big data to market research has tremendous
benefits to all involved in the research process. Data providers can use automation to
maintain more expansive and accurate research databases at a lower cost.
Market researchers can target research communities with greater accuracy and
know more about them in advance of fielding a study, which lets
them devote more of a survey to the core questions of the research rather than
qualifying questions. And finally, and perhaps most
importantly, the study participants benefit from reducing the number of
tedious and repetitive profiling questions asked of them, shortening surveys,
keeping them engaged, and giving them back valuable time.

The allure and promise of big data for market research is
compelling, but not without risks and issues. Technology has created
a window of opportunity for brands to know more about consumers than previously
ever thought to be possible. But, just because we can reach everybody,
doesn’t mean we should. Technology sometimes presents a facade that
can lead researchers to lose sight of the fact that they are
dealing with real people. Real people who have thoughts, feelings, emotions,
goals, dreams, and likes and dislikes. Dehumanizing a person to a set
of numbers and patterns obscures the advantages that
big data enables. Further, easy collection of data can make us
forget about the very real and important privacy interests of our participants.
If we fail to recognize, respect, and account for these concerns, we will lose
their trust and their willingness to participate.
The market research industry must
use big data as an opportunity to get smarter, quicker, so
that we are able to be more personable in our approach
to collecting information. We need to
maximize participants’ time by creating relevant engagement
for them that is also useful to the researchers. Big data presents a
new opportunity to improve our ability to accomplish both.
At Research Now, having
more data, specifically more accurate data, about
people is what defines the quality of our panels. It allows us to be
less intrusive and more in-the-moment with people who want to engage with
brands. Having more information about whom we’re talking to
permits us to put greater focus on core research by bypassing things
like screeners and
get right down to the questions our clients are interested in asking.

This improves the participant experience and gives
our research clients the ability to collect more desirable data,
which in turn fuels deeper insights and gives everyone back just
a little more of their precious time.

Image Recognition and the Future of Digital Analytics

This post was
originally published on the Kelton
Global Blog

The days of text-centric social feeds are officially long
gone. A whopping 1.8 billion images are uploaded to the Internet daily
and of those, 350 million are shared on Facebook. Instagram recently
surpassed 500 million active users, and Snapchat now has more active users than Twitter. The content that flows
into our social feeds is more heavily optimized than ever to deliver more of
what people want’less text and more visuals.
Brands have adapted their social content strategies
accordingly by delivering more visually immersive experiences. And while we’re
seeing significant shifts in branded content, this influx of visual content has
yet to herald a commensurate change in social analytics. Accordingly, few gains
have been made to measure and derive insights from the contents of images or
video. Social listening has historically focused on the challenges of
text-based analysis’specifically, the challenge of determining the context and
meaning behind posts. But as social media habits evolve, it’s clear that deriving
insights from pictures is an increasingly important aspect of understanding
consumers. That’s where image recognition comes into play.
Brands have adapted their social content strategies
accordingly by delivering more visually immersive experiences.
Simply put, image recognition is the process of translating
images to data. Photos and images can reveal a wealth of data
points’demographics, purchases, personalities, and behaviors (just to name a
few). Through next generation image recognition, a mere selfie may reveal a
person’s gender, approximate age, location disposition, and even the clothing
brands that the person is wearing. As text-centric media takes a backseat to
image and video, the opportunity to understand the contents of these formats
grows. These insights represent a veritable treasure trove of actionable data
for brands.
Tools that analyze image and video-based content are still
in development, but increased investment in research is already impacting
commercial products and how they’re advertised. One example is brand logo
recognition’scanning images for brand logos, and flagging them with the
corresponding brand names. This tool is especially powerful considering that 80% of photos shared online depict a brand logo but don’t
explicitly call out the brand’s name.
 This fact points to a sizable
opportunity for companies to measure and understand the impact of these
formerly inaccessible data points.
Photos and images can reveal a wealth of data
points’demographics, purchases, personalities, and behaviors (just to name a
As an example of how this applies to brands, Kelton’s
Digital Analytics team took a look at the scores of backyard BBQ photos that
flooded public forums, blogs, and social feeds over the recent 4th of July
holiday. We experimented to see which of two quintessentially American beverage
brands’Coca-Cola and Budweiser’netted more published images of
patriotically-themed bottles and cans (as well as other forms of branding) on
social media.

In the end, Coca-Cola branding was twice as prominent as
Budweiser’s. We found that Coke bottles and cans popped up in more diverse
settings such as public parks and inside motor vehicles, whereas Budweiser was
predominantly found in bars and house parties. Coke also aroused greater
sentiment around the theme of Americana, as many consumers
photographed vintage Coca-Cola gear and opted for bottles over cans. This might
explain why Coke captured a significantly greater share of social mentions than
This example illustrates several ways that brands can
leverage image recognition technology to build actionable insights:
Ethnographic data ‘ Identify where, when
and how often brands are showing up in people’s lives.
Updated brand health analysis ‘ We now have
a more comprehensive point of view of brands’ online footprint.
Sponsorship and Branding ROI ‘ Extend the
value of branding and sponsorships shared via online news, blogs and social
media through a multiplier effect.
Influencer identification ‘ Find authentic
brand advocates who consume and spotlight your merchandise.
Misuse use of brand iconography ‘ Surface
content that depicts improper usage of brand’s logo or other creative assets.

In today’s ever-shifting social media landscape, it’s never
been more important for brands and their partners to stay aware of the new and
emerging capabilities that can help better understand consumers’ behavior
online. Image recognition is just the beginning. From AI startups to instant
objection recognition devices
, the mobilization and fusion of research,
tech, and capital is quickly reshaping the way we think about analytics. These
new tools will add even more contextual understanding to sentiment on social
platforms, empowering brands to understand consumers like never before.