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Social Media Monitoring
Finding Relationships
A Pilot Study
July 4, 2011
Your brand is social.

From toilet paper
to luxury vehicles,
social plays a part
in the customer
experience of your
brand.
People have always
talked about brands
and experiences
Only now these views
and experiences are
being shared online
And subsequently...

Agencies and brands are
starting to take social media
seriously.
With just about
everyone offering
some form of
monitoring and
analysis.
We are starting to ask
better questions.
Should we be only watching for brand
and product mentions?
What issues matter to our brand?
What is the value of our social media
efforts?
But we are ignoring a
key question.




               ?
But we are ignoring a
key question.


Why are we
throwing away
all of the data?
Report 1


Typically, a baseline social media audit is
created based on an analysis of brand
names, competitors, and relevant issues.
Report 1            Report 2



The next report, for a different time period, is
created from new data. If comparisons to the
first report are made, they are aggregate or
trend-based.
Report 1          Report 2           Report 3



With each subsequent social media report,
the process remains the same.
We have lots of
pretty charts
and surface-level
analysis.
Pretty charts.
“More people spoke positively
about kittens this week.”

     1500



     1000

                                                Positive
                                                Negative
     500



       0
            Week 1   Week 2   Week 3   Week 4
…and “analysis”.
“Here are our top kitten
influencers this month.”



          Twitter name     Kitten Tweets   Klout Score
          @justinbieber    618             90,194,000
          @aplusk          6               12
          @britneyspears   42              315
          @oprah           9               120
But are all these kitten lovers
net new people every
month?

Is this just a one time spike
because of something in the
news?

Are we building relationships
with any of these people?
A Real-World Example
We decided to use Nikon (geo-
filtered to Canada) as a pilot
case.

We explored using both Radian6
and Sysomos MAP.

We settled on Sysomos MAP to
pull the data we needed due to a
feature that allowed us to pull an
individual’s raw tweets with no
search filters applied.
Some Charts
Most of the tools can give us
access to data and clues that lead
to insights, but the built-in charts
aren’t that helpful on their own.
Influence in Sysomos
Sysomos gives us a list of the top Twitter
influencers based on an algorithmic authority
score of 7/10 or higher, sorted by volume.
It falls apart…
 With this chart, if we dive deeper and look at these users,
 we discover nearly all are spam or of little value to the
 brand.




 Are these truly the most passionate and
 influential Nikon brand advocates?
Influence in Radian6
Some tools, like Radian6,
integrate Klout or other similar
theoretical metrics of absolute
influence.

But our attempts to generate lists
of people who care about a
brand with these tools generates
primarily spammers and big
name web celebrities.
We need deeper insights.
With web analytics and email
marketing, we track unique and
repeat visitors.

And traditional CRM programs
track preferences, purchases, and
engagement over time.
Deeper insights in social.
Why not track the same things
on social media?

Who are our true brand
advocates and what can we learn
about them beyond their interest
in our brand?

Do the people who are
passionate about our brand have
anything in common?
What about Social CRM?
                 Some are looking at Social CRM
                 to solve this problem.

                 Most CRM tools were designed
                 with the assumption of a near-
                 perfect signal-to-noise ratio. To
                 be useful, there should be zero
                 noise in the system.

                 These systems fall apart when
                 you start throwing hundreds of
                 thousands of users and tweets
                 at them.
    Social CRM
                 e.g. There was an estimated 501,618 “Nikon” tweets
                 in the past 6 months.
A new approach.
What if we started to wonder
about the people who are
consistently talking about our
brand or issue?

What could we learn if we
weren’t wiping the slate clean
every time we run a new search?
A new approach.
We believe these are people who
are passionate about your brand.

We call them the consistently
engaged.

They are NOT the most influential
Klout or PeerIndex users.

They are the people who have an
ongoing relationship with your
brand. Good, bad or otherwise.
Looking across periods.




 Report 1           Report 2          Report 3



 Simply put, we find the individuals who are
 consistently talking about and engaging with
 your brand across time periods.
Our hypothesis
The consistently engaged who
love your brand are:
•  Findable
•  Of value to the brand
   (promotions, research, reach)
•  Ignored by most brands
Looking across quarters.
We extracted all tweets over a
two-quarter period for Nikon and
looked at two lists:

  1.  Top Twitter users by
      volume (4+ mentions)
  2.  Top Twitter users by
      volume (4+ mentions) who
      are also consistent across
      multiple quarters
Finding the consistently engaged users
                                      Volume           Consistent
The list on the left includes    @bdicroce          @dorisdays88

people with the most volume      @caltek79          @jedgar
                                 @CFN007            @justinlee_
over the past six months, in
                                 @cicychan          @maryellenphotos
regards to the brand in          @Digital_zin       @msjconnolly
question.                        @dorisdays88       @neek247
                                 @ginette4          @nortonphoto
                                 @jedgar            @jonah_lewis
The list on the right includes   @JohnBiehler       @quotetasticc
people who consistently          @justinlee_        @RajaKalsi
engaged with or mentioned        @jwsutts           @RDslva

the brand a minimum of four      @kinematicdigit    @Redawna
                                 @KingKabuz         @samobeid
times in both quarters.
                                 @Kosmatos          @scottoakley
                                 @Lalalalal8        @ShaniceAshley_
                                 @maryellenphotos   @stephaniefusco
                                 @missemcee         @vickiesbphoto
                                 @missfish           @wifewithknives
                                 @msjconnoly        @ZtheWayfarer
                                 @neek247           @zeefred
Finding the consistently engaged users
                                     Volume           Consistent
The green boxes show the        @bdicroce          @dorisdays88

people consistent between       @caltek79          @jedgar
                                @CFN007            @justinlee_
the two methods.
                                @cicychan          @maryellenphotos
                                @Digital_zin       @msjconnolly

If you are using the standard   @dorisdays88       @neek247
                                @ginette4          @nortonphoto
method (left side) then you     @jedgar            @jonah_lewis
are ignoring up to 70% of the   @JohnBiehler       @quotetasticc

people consistently engaging    @justinlee_        @RajaKalsi

with your brand.                @jwsutts           @RDslva
                                @kinematicdigit    @Redawna
                                @KingKabuz         @samobeid

We need to focus on the         @Kosmatos          @scottoakley
                                @Lalalalal8        @ShaniceAshley_
users who are consistent in
                                @maryellenphotos   @stephaniefusco
order to find the true brand     @missemcee         @vickiesbphoto
advocates.                      @missfish           @wifewithknives
                                @msjconnoly        @ZtheWayfarer
                                @neek247           @zeefred
Rethinking Influence
The list of influencers most social
media tools give us aren’t very
meaningful as they tend to use
the standard method on the left
or just find the big name web
celebrities.

With our Nikon example, they
ignore a huge segment of people
who are passionate about Nikon
but will never appear on any top
list.
The Consistently Engaged
                                         Consistent

Finding these people was just the     @dorisdays88
                                      @jedgar
first step.                            @justinlee_
                                      @maryellenphotos
                                      @msjconnolly

Now we need to understand:            @neek247
                                      @nortonphoto
1.  Who are these people?             @jonah_lewis
                                      @quotetasticc
2.  What are their other interests?   @RajaKalsi

3.  Their relationship to our brand   @RDslva
                                      @Redawna

4.  Their potential value             @samobeid
                                      @scottoakley
                                      @ShaniceAshley_
                                      @stephaniefusco
                                      @vickiesbphoto
                                      @wifewithknives
                                      @ZtheWayfarer
                                      @zeefred
Understanding these people
All social media monitoring tools
start with a filter – a way to
cross-section massive amounts
of data (in this case a search for
“Nikon”).

Analyzing this data to
understand who these people are
is critically flawed. It will only
show us insights based on a
limited set of their conversations
– those that match our original
search terms.
It’s akin to doing an ethnography
study where we ignore all the
things the individual does or says
that don’t directly involve our
product. Not particularly valid.
So we remove the filter.
The “from:username” operator in
Sysomos MAP allows us to pull all
user tweets over any period of
time (sample based in some
cases).

This is time consuming to do but
not overly difficult. But now we
have hundreds of thousands of
lines of data.

How do we analyze this?
Not in the SM tool, sadly
The text analytics tools in Sysomos MAP quickly fall
apart when the filter is removed and we are looking
at everything a group of users say.
So we need to export all
the activity and analyze
it using a qualitative
tagging methodology
Qualitative vs. Quantitative Research
Quantitative content analysis is
about counting content. It
provides numbers that represent
frequencies or occurrences.

Qualitative content analysis is
about the quality of content. It
provides insights into recurring
themes that can lead to the
development of valuable insights
and recommendations.

Source: Zhang, Y. , & Wildemuth, B. M. (2009). Qualitative analysis of content. In B. Wildemuth (Ed.), Applications of
Social Research Methods to Questions in Information and Library
Qualitative vs. Quantitative Research
Rather than reporting how many
times a brand has been
mentioned [quantitative], we can
look at the quality of the
message [qualitative].

Rather than reporting
frequencies, the application of
qualitative tools to social media
data will allow us to gain insights
from a data sample that is
traditionally overlooked.
Conducting a
                          Conducting a
qualitative analysis
                       qualitative analysis
    of isolated
                        of all expressions
     individual
                         from identified
  expressions of
                        users of interest.
      interest.
Qualitative Analysis Approach
1.       Identify your area of interest.
2.       Identify goals: e.g., understanding what common characteristics
         and interests of a specific customer population might exist.
3.       Translate your goals into a structured and hierarchical coding
         frame (either inductively or deductively).
4.       Make both categories and individual codes as mutually exclusive
         as possible.
5.       Create coding rules to ensure that multiple coders can
         consistently make the same coding decisions.
6.       Test your coding frame by calculating inter-coder reliability (or
         other fancy reliability method).
7.        Assign expressions (or whatever your agreed upon unit of
         analysis might be) to codes.
8.       Analyse the coded data by individual category and code.
         Compare codes, find relationships, uncover themes, discover
         insights and develop recommendations.
      Source: Zhang, Y. , & Wildemuth, B. M. (2009). Qualitative analysis of content. In B. Wildemuth (Ed.), Applications of
      Social Research Methods to Questions in Information and Library
What did we find?
For this pilot, we analyzed the top
20 consistently engaged which were
responsible for over 20,000 social
media mentions in the quarters
analyzed.

Only a partial analysis was
performed to validate that the most
consistently engaged individuals
identified would be of value to the
brand.
What did we find?
Nearly all of our consistently engaged
users sample self-identify as amateur or
professional photographers.

Most have a relationship with Nikon –
either because they own Nikon
products or discuss Nikon products and
news.

1 out of 20 self-identified as a
professional photographer and 14 out of
20 stated they own Nikon equipment.
Their relationship with Nikon
                                              Wants to buy a Canon
    Recently bought a                         60D but has an
    Nikon D3100 and                           interest in Nikon gear
    looking to purchase                       and often chats with
    some additional lenses                    Nikon users



               Professional Canon         Angry with Nikon
               shooter but blogged        Canada about a poor
               about a positive           warranty service
               experience shooting        experience
               with Nikon equipment



Owns a Nikon D300
and mostly uses older
manual-focus lenses.                  Hobbyist looking to
Just bought first                      move up to pro-level
“modern” Nikon lens.                  Nikon gear
Their other interests
Users are more likely to talk about                 iPhone vs. Blackberry
iPhones vs. BlackBerry devices
   –  74% of all iPhone mentions were positive in
      nature
   –  73% of BlackBerry mentions were positive                       iPhone
      in nature                                                      BlackBerry



iPhone users more likely to talk about
photo related apps than BlackBerry
users
   –  Almost half of all iPhone mentions had to
      do with a photo-related app
   –  Only 4% of BlackBerry mentions were
      about photo-related apps
Their other interests
The consistently engaged Nikon users              Coffee Mentions
over the time period analyzed were
more likely to talk about Starbucks than
any other coffee brand
                                                             Starbucks
   –  87.5% of these mentions were positive in
                                                             Tim Hortons
      nature
   –  The second mentioned brand was Tim
      Hortons, but all mentions were neutral or
      negative in nature


People spoke of coffee 1.7 times more
often than tea.

Of the tea mentions, DavidsTea was the
most mentioned product/brand.
Quick Recap
1.  The consistently engaged are
    a better way to find the
    relationships you are building
    (or not building) in social.
2.  Finding the consistently
    engaged is simply a matter of
    not throwing the data away
    and paying attention to who is
    engaging with your brand
    across time periods.
3.  Deeper analysis can lead to
    better insights into who your
    brand advocates are, their
    needs and their interests.
The “Secret” Sauce
Our Methodology
A)  Finding consistent users across two quarters

1.  Extract two time periods of data from Sysomos MAP.
    Export.
2.  Merge data into a single spreadsheet with each
    quarter in a separate column.
3.  Cross-reference columns to find users who are
    consistent across quarters.
4.  De-dupe results in the third “result” column to get a
    complete list of consistent users.
Our Methodology
B)  Determining tweet volume for consistent users

1.  Merge Tweet content from both quarters into a single
    speadsheet column
2.  Sort alphabetically and run subtotal based on
    usernames. Sort subtotals by volume.
3.  Set a threshold (we used 4 tweets across quarters)
4.  Cross-reference with list (result of part A) of
    consistent users across quarters.
5.  Manually remove any spammy users above threshold.
6.  Extract user list of top 20-30 “casually engaged.”
Our Methodology
C)  Removing the filter and exporting all user tweets for
    “casually engaged” list

1.  Using the “from:username” operator in Sysomos MAP,
    extract all top 20-30 user tweets to CSV over same
    two quarter period (Export is limited to 5000 tweets
    so generally each user must be done separately)
2.  Merge resulting CSV files for each user into a single
    spreadsheet. Discard all data except tweet content.
3.  Set up qualitative tagging system and methodology.
4.  Import data in NVivo or similar qualitative analysis
    tool.
Our Methodology
D)  Running qualitative analysis to find insights about
    users

1.  Identify an area of interest and goals. Translate these
    into a structured coding frame.
2.  Tag the data according to the coding frame.
3.  Analyze the data based on the categories and codes.
    Pull insights and findings.
4.  Put together recommendations based on key findings
Colophon




Sean Howard is a freelancer and      Eric Portelance is a Strategist at     Katie Charbonneau is an
Associate at Thornley Fallis and     Thornley Fallis and a regular          Account Coordinator at
spends his life searching for what   podcaster on the show Attention        Thornley Fallis with an MSc in
drives and identifies the most        Surplus. He’s passionate about using   Media and Communications.
passionate online and offline.         new technologies to build engaging     She’s a data junkie with a keen
                                     online experiences.                    interest in American politics.

Twitter: @passitalong                Twitter: @eportelance

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Social Media Monitoring - Finding Relationships

  • 1. Social Media Monitoring Finding Relationships A Pilot Study July 4, 2011
  • 2. Your brand is social. From toilet paper to luxury vehicles, social plays a part in the customer experience of your brand.
  • 3. People have always talked about brands and experiences
  • 4. Only now these views and experiences are being shared online
  • 5. And subsequently... Agencies and brands are starting to take social media seriously.
  • 6. With just about everyone offering some form of monitoring and analysis.
  • 7. We are starting to ask better questions. Should we be only watching for brand and product mentions? What issues matter to our brand? What is the value of our social media efforts?
  • 8. But we are ignoring a key question. ?
  • 9. But we are ignoring a key question. Why are we throwing away all of the data?
  • 10. Report 1 Typically, a baseline social media audit is created based on an analysis of brand names, competitors, and relevant issues.
  • 11. Report 1 Report 2 The next report, for a different time period, is created from new data. If comparisons to the first report are made, they are aggregate or trend-based.
  • 12. Report 1 Report 2 Report 3 With each subsequent social media report, the process remains the same.
  • 13. We have lots of pretty charts and surface-level analysis.
  • 14. Pretty charts. “More people spoke positively about kittens this week.” 1500 1000 Positive Negative 500 0 Week 1 Week 2 Week 3 Week 4
  • 15. …and “analysis”. “Here are our top kitten influencers this month.” Twitter name Kitten Tweets Klout Score @justinbieber 618 90,194,000 @aplusk 6 12 @britneyspears 42 315 @oprah 9 120
  • 16. But are all these kitten lovers net new people every month? Is this just a one time spike because of something in the news? Are we building relationships with any of these people?
  • 17. A Real-World Example We decided to use Nikon (geo- filtered to Canada) as a pilot case. We explored using both Radian6 and Sysomos MAP. We settled on Sysomos MAP to pull the data we needed due to a feature that allowed us to pull an individual’s raw tweets with no search filters applied.
  • 18. Some Charts Most of the tools can give us access to data and clues that lead to insights, but the built-in charts aren’t that helpful on their own.
  • 19. Influence in Sysomos Sysomos gives us a list of the top Twitter influencers based on an algorithmic authority score of 7/10 or higher, sorted by volume.
  • 20. It falls apart… With this chart, if we dive deeper and look at these users, we discover nearly all are spam or of little value to the brand. Are these truly the most passionate and influential Nikon brand advocates?
  • 21. Influence in Radian6 Some tools, like Radian6, integrate Klout or other similar theoretical metrics of absolute influence. But our attempts to generate lists of people who care about a brand with these tools generates primarily spammers and big name web celebrities.
  • 22. We need deeper insights. With web analytics and email marketing, we track unique and repeat visitors. And traditional CRM programs track preferences, purchases, and engagement over time.
  • 23. Deeper insights in social. Why not track the same things on social media? Who are our true brand advocates and what can we learn about them beyond their interest in our brand? Do the people who are passionate about our brand have anything in common?
  • 24. What about Social CRM? Some are looking at Social CRM to solve this problem. Most CRM tools were designed with the assumption of a near- perfect signal-to-noise ratio. To be useful, there should be zero noise in the system. These systems fall apart when you start throwing hundreds of thousands of users and tweets at them. Social CRM e.g. There was an estimated 501,618 “Nikon” tweets in the past 6 months.
  • 25. A new approach. What if we started to wonder about the people who are consistently talking about our brand or issue? What could we learn if we weren’t wiping the slate clean every time we run a new search?
  • 26. A new approach. We believe these are people who are passionate about your brand. We call them the consistently engaged. They are NOT the most influential Klout or PeerIndex users. They are the people who have an ongoing relationship with your brand. Good, bad or otherwise.
  • 27. Looking across periods. Report 1 Report 2 Report 3 Simply put, we find the individuals who are consistently talking about and engaging with your brand across time periods.
  • 28. Our hypothesis The consistently engaged who love your brand are: •  Findable •  Of value to the brand (promotions, research, reach) •  Ignored by most brands
  • 29. Looking across quarters. We extracted all tweets over a two-quarter period for Nikon and looked at two lists: 1.  Top Twitter users by volume (4+ mentions) 2.  Top Twitter users by volume (4+ mentions) who are also consistent across multiple quarters
  • 30. Finding the consistently engaged users Volume Consistent The list on the left includes @bdicroce @dorisdays88 people with the most volume @caltek79 @jedgar @CFN007 @justinlee_ over the past six months, in @cicychan @maryellenphotos regards to the brand in @Digital_zin @msjconnolly question. @dorisdays88 @neek247 @ginette4 @nortonphoto @jedgar @jonah_lewis The list on the right includes @JohnBiehler @quotetasticc people who consistently @justinlee_ @RajaKalsi engaged with or mentioned @jwsutts @RDslva the brand a minimum of four @kinematicdigit @Redawna @KingKabuz @samobeid times in both quarters. @Kosmatos @scottoakley @Lalalalal8 @ShaniceAshley_ @maryellenphotos @stephaniefusco @missemcee @vickiesbphoto @missfish @wifewithknives @msjconnoly @ZtheWayfarer @neek247 @zeefred
  • 31. Finding the consistently engaged users Volume Consistent The green boxes show the @bdicroce @dorisdays88 people consistent between @caltek79 @jedgar @CFN007 @justinlee_ the two methods. @cicychan @maryellenphotos @Digital_zin @msjconnolly If you are using the standard @dorisdays88 @neek247 @ginette4 @nortonphoto method (left side) then you @jedgar @jonah_lewis are ignoring up to 70% of the @JohnBiehler @quotetasticc people consistently engaging @justinlee_ @RajaKalsi with your brand. @jwsutts @RDslva @kinematicdigit @Redawna @KingKabuz @samobeid We need to focus on the @Kosmatos @scottoakley @Lalalalal8 @ShaniceAshley_ users who are consistent in @maryellenphotos @stephaniefusco order to find the true brand @missemcee @vickiesbphoto advocates. @missfish @wifewithknives @msjconnoly @ZtheWayfarer @neek247 @zeefred
  • 32. Rethinking Influence The list of influencers most social media tools give us aren’t very meaningful as they tend to use the standard method on the left or just find the big name web celebrities. With our Nikon example, they ignore a huge segment of people who are passionate about Nikon but will never appear on any top list.
  • 33. The Consistently Engaged Consistent Finding these people was just the @dorisdays88 @jedgar first step. @justinlee_ @maryellenphotos @msjconnolly Now we need to understand: @neek247 @nortonphoto 1.  Who are these people? @jonah_lewis @quotetasticc 2.  What are their other interests? @RajaKalsi 3.  Their relationship to our brand @RDslva @Redawna 4.  Their potential value @samobeid @scottoakley @ShaniceAshley_ @stephaniefusco @vickiesbphoto @wifewithknives @ZtheWayfarer @zeefred
  • 34. Understanding these people All social media monitoring tools start with a filter – a way to cross-section massive amounts of data (in this case a search for “Nikon”). Analyzing this data to understand who these people are is critically flawed. It will only show us insights based on a limited set of their conversations – those that match our original search terms.
  • 35. It’s akin to doing an ethnography study where we ignore all the things the individual does or says that don’t directly involve our product. Not particularly valid.
  • 36. So we remove the filter. The “from:username” operator in Sysomos MAP allows us to pull all user tweets over any period of time (sample based in some cases). This is time consuming to do but not overly difficult. But now we have hundreds of thousands of lines of data. How do we analyze this?
  • 37. Not in the SM tool, sadly The text analytics tools in Sysomos MAP quickly fall apart when the filter is removed and we are looking at everything a group of users say.
  • 38. So we need to export all the activity and analyze it using a qualitative tagging methodology
  • 39. Qualitative vs. Quantitative Research Quantitative content analysis is about counting content. It provides numbers that represent frequencies or occurrences. Qualitative content analysis is about the quality of content. It provides insights into recurring themes that can lead to the development of valuable insights and recommendations. Source: Zhang, Y. , & Wildemuth, B. M. (2009). Qualitative analysis of content. In B. Wildemuth (Ed.), Applications of Social Research Methods to Questions in Information and Library
  • 40. Qualitative vs. Quantitative Research Rather than reporting how many times a brand has been mentioned [quantitative], we can look at the quality of the message [qualitative]. Rather than reporting frequencies, the application of qualitative tools to social media data will allow us to gain insights from a data sample that is traditionally overlooked.
  • 41. Conducting a Conducting a qualitative analysis qualitative analysis of isolated of all expressions individual from identified expressions of users of interest. interest.
  • 42. Qualitative Analysis Approach 1.  Identify your area of interest. 2.  Identify goals: e.g., understanding what common characteristics and interests of a specific customer population might exist. 3.  Translate your goals into a structured and hierarchical coding frame (either inductively or deductively). 4.  Make both categories and individual codes as mutually exclusive as possible. 5.  Create coding rules to ensure that multiple coders can consistently make the same coding decisions. 6.  Test your coding frame by calculating inter-coder reliability (or other fancy reliability method). 7.  Assign expressions (or whatever your agreed upon unit of analysis might be) to codes. 8.  Analyse the coded data by individual category and code. Compare codes, find relationships, uncover themes, discover insights and develop recommendations. Source: Zhang, Y. , & Wildemuth, B. M. (2009). Qualitative analysis of content. In B. Wildemuth (Ed.), Applications of Social Research Methods to Questions in Information and Library
  • 43. What did we find? For this pilot, we analyzed the top 20 consistently engaged which were responsible for over 20,000 social media mentions in the quarters analyzed. Only a partial analysis was performed to validate that the most consistently engaged individuals identified would be of value to the brand.
  • 44. What did we find? Nearly all of our consistently engaged users sample self-identify as amateur or professional photographers. Most have a relationship with Nikon – either because they own Nikon products or discuss Nikon products and news. 1 out of 20 self-identified as a professional photographer and 14 out of 20 stated they own Nikon equipment.
  • 45. Their relationship with Nikon Wants to buy a Canon Recently bought a 60D but has an Nikon D3100 and interest in Nikon gear looking to purchase and often chats with some additional lenses Nikon users Professional Canon Angry with Nikon shooter but blogged Canada about a poor about a positive warranty service experience shooting experience with Nikon equipment Owns a Nikon D300 and mostly uses older manual-focus lenses. Hobbyist looking to Just bought first move up to pro-level “modern” Nikon lens. Nikon gear
  • 46. Their other interests Users are more likely to talk about iPhone vs. Blackberry iPhones vs. BlackBerry devices –  74% of all iPhone mentions were positive in nature –  73% of BlackBerry mentions were positive iPhone in nature BlackBerry iPhone users more likely to talk about photo related apps than BlackBerry users –  Almost half of all iPhone mentions had to do with a photo-related app –  Only 4% of BlackBerry mentions were about photo-related apps
  • 47. Their other interests The consistently engaged Nikon users Coffee Mentions over the time period analyzed were more likely to talk about Starbucks than any other coffee brand Starbucks –  87.5% of these mentions were positive in Tim Hortons nature –  The second mentioned brand was Tim Hortons, but all mentions were neutral or negative in nature People spoke of coffee 1.7 times more often than tea. Of the tea mentions, DavidsTea was the most mentioned product/brand.
  • 48. Quick Recap 1.  The consistently engaged are a better way to find the relationships you are building (or not building) in social. 2.  Finding the consistently engaged is simply a matter of not throwing the data away and paying attention to who is engaging with your brand across time periods. 3.  Deeper analysis can lead to better insights into who your brand advocates are, their needs and their interests.
  • 50. Our Methodology A)  Finding consistent users across two quarters 1.  Extract two time periods of data from Sysomos MAP. Export. 2.  Merge data into a single spreadsheet with each quarter in a separate column. 3.  Cross-reference columns to find users who are consistent across quarters. 4.  De-dupe results in the third “result” column to get a complete list of consistent users.
  • 51. Our Methodology B)  Determining tweet volume for consistent users 1.  Merge Tweet content from both quarters into a single speadsheet column 2.  Sort alphabetically and run subtotal based on usernames. Sort subtotals by volume. 3.  Set a threshold (we used 4 tweets across quarters) 4.  Cross-reference with list (result of part A) of consistent users across quarters. 5.  Manually remove any spammy users above threshold. 6.  Extract user list of top 20-30 “casually engaged.”
  • 52. Our Methodology C)  Removing the filter and exporting all user tweets for “casually engaged” list 1.  Using the “from:username” operator in Sysomos MAP, extract all top 20-30 user tweets to CSV over same two quarter period (Export is limited to 5000 tweets so generally each user must be done separately) 2.  Merge resulting CSV files for each user into a single spreadsheet. Discard all data except tweet content. 3.  Set up qualitative tagging system and methodology. 4.  Import data in NVivo or similar qualitative analysis tool.
  • 53. Our Methodology D)  Running qualitative analysis to find insights about users 1.  Identify an area of interest and goals. Translate these into a structured coding frame. 2.  Tag the data according to the coding frame. 3.  Analyze the data based on the categories and codes. Pull insights and findings. 4.  Put together recommendations based on key findings
  • 54. Colophon Sean Howard is a freelancer and Eric Portelance is a Strategist at Katie Charbonneau is an Associate at Thornley Fallis and Thornley Fallis and a regular Account Coordinator at spends his life searching for what podcaster on the show Attention Thornley Fallis with an MSc in drives and identifies the most Surplus. He’s passionate about using Media and Communications. passionate online and offline. new technologies to build engaging She’s a data junkie with a keen online experiences. interest in American politics. Twitter: @passitalong Twitter: @eportelance