Social Media Monitoring - Finding Relationships

Social Media MonitoringFinding RelationshipsA Pilot StudyJuly 4, 2011

Your brand is social.From toilet paperto luxury vehicles,social plays a partin the customerexperience of yourbrand.

People have alwaystalked about brandsand experiences

Only now these viewsand experiences arebeing shared online

And subsequently...Agencies and brands arestarting to take social mediaseriously.

With just abouteveryone oﬀeringsome form ofmonitoring andanalysis.

We are starting to askbetter questions.Should we be only watching for brandand product mentions?What issues matter to our brand?What is the value of our social mediaeﬀorts?

But we are ignoring akey question. ?

But we are ignoring akey question.Why are wethrowing awayall of the data?

Report 1Typically, a baseline social media audit iscreated based on an analysis of brandnames, competitors, and relevant issues.

Report 1 Report 2The next report, for a diﬀerent time period, iscreated from new data. If comparisons to theﬁrst report are made, they are aggregate ortrend-based.

Report 1 Report 2 Report 3With each subsequent social media report,the process remains the same.

We have lots ofpretty chartsand surface-levelanalysis.

Pretty charts.“More people spoke positivelyabout kittens this week.” 1500 1000 Positive Negative 500 0 Week 1 Week 2 Week 3 Week 4

…and “analysis”.“Here are our top kitteninﬂuencers 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 loversnet new people everymonth?Is this just a one time spikebecause of something in thenews?Are we building relationshipswith any of these people?

A Real-World ExampleWe decided to use Nikon (geo-ﬁltered to Canada) as a pilotcase.We explored using both Radian6and Sysomos MAP.We settled on Sysomos MAP topull the data we needed due to afeature that allowed us to pull anindividual’s raw tweets with nosearch ﬁlters applied.

Some ChartsMost of the tools can give usaccess to data and clues that leadto insights, but the built-in chartsaren’t that helpful on their own.

Inﬂuence in SysomosSysomos gives us a list of the top Twitterinﬂuencers based on an algorithmic authorityscore 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 Radian6Some tools, like Radian6,integrate Klout or other similartheoretical metrics of absoluteinﬂuence.But our attempts to generate listsof people who care about abrand with these tools generatesprimarily spammers and bigname web celebrities.

We need deeper insights.With web analytics and emailmarketing, we track unique andrepeat visitors.And traditional CRM programstrack preferences, purchases, andengagement over time.

Deeper insights in social.Why not track the same thingson social media?Who are our true brandadvocates and what can we learnabout them beyond their interestin our brand?Do the people who arepassionate about our brand haveanything 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 wonderabout the people who areconsistently talking about ourbrand or issue?What could we learn if weweren’t wiping the slate cleanevery time we run a new search?

A new approach.We believe these are people whoare passionate about your brand.We call them the consistentlyengaged.They are NOT the most inﬂuentialKlout or PeerIndex users.They are the people who have anongoing relationship with yourbrand. 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 hypothesisThe consistently engaged wholove your brand are:•  Findable•  Of value to the brand (promotions, research, reach)•  Ignored by most brands

Looking across quarters.We extracted all tweets over atwo-quarter period for Nikon andlooked 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 ConsistentThe list on the left includes @bdicroce @dorisdays88people with the most volume @caltek79 @jedgar @CFN007 @justinlee_over the past six months, in @cicychan @maryellenphotosregards to the brand in @Digital_zin @msjconnollyquestion. @dorisdays88 @neek247 @ginette4 @nortonphoto @jedgar @jonah_lewisThe list on the right includes @JohnBiehler @quotetasticcpeople who consistently @justinlee_ @RajaKalsiengaged with or mentioned @jwsutts @RDslvathe brand a minimum of four @kinematicdigit @Redawna @KingKabuz @samobeidtimes in both quarters. @Kosmatos @scottoakley @Lalalalal8 @ShaniceAshley_ @maryellenphotos @stephaniefusco @missemcee @vickiesbphoto @missﬁsh @wifewithknives @msjconnoly @ZtheWayfarer @neek247 @zeefred

Finding the consistently engaged users Volume ConsistentThe green boxes show the @bdicroce @dorisdays88people consistent between @caltek79 @jedgar @CFN007 @justinlee_the two methods. @cicychan @maryellenphotos @Digital_zin @msjconnollyIf you are using the standard @dorisdays88 @neek247 @ginette4 @nortonphotomethod (left side) then you @jedgar @jonah_lewisare ignoring up to 70% of the @JohnBiehler @quotetasticcpeople consistently engaging @justinlee_ @RajaKalsiwith your brand. @jwsutts @RDslva @kinematicdigit @Redawna @KingKabuz @samobeidWe need to focus on the @Kosmatos @scottoakley @Lalalalal8 @ShaniceAshley_users who are consistent in @maryellenphotos @stephaniefuscoorder to ﬁnd the true brand @missemcee @vickiesbphotoadvocates. @missﬁsh @wifewithknives @msjconnoly @ZtheWayfarer @neek247 @zeefred

Rethinking InfluenceThe list of influencers most socialmedia tools give us aren’t verymeaningful as they tend to usethe standard method on the leftor just find the big name webcelebrities.With our Nikon example, theyignore a huge segment of peoplewho are passionate about Nikonbut will never appear on any toplist.

The Consistently Engaged ConsistentFinding these people was just the @dorisdays88 @jedgarﬁrst step. @justinlee_ @maryellenphotos @msjconnollyNow we need to understand: @neek247 @nortonphoto1.  Who are these people? @jonah_lewis @quotetasticc2.  What are their other interests? @RajaKalsi3.  Their relationship to our brand @RDslva @Redawna4.  Their potential value @samobeid @scottoakley @ShaniceAshley_ @stephaniefusco @vickiesbphoto @wifewithknives @ZtheWayfarer @zeefred

Understanding these peopleAll social media monitoring toolsstart with a ﬁlter – a way tocross-section massive amountsof data (in this case a search for“Nikon”).Analyzing this data tounderstand who these people areis critically ﬂawed. It will onlyshow us insights based on alimited set of their conversations– those that match our originalsearch terms.

It’s akin to doing an ethnographystudy where we ignore all thethings the individual does or saysthat don’t directly involve ourproduct. Not particularly valid.

So we remove the ﬁlter.The “from:username” operator inSysomos MAP allows us to pull alluser tweets over any period oftime (sample based in somecases).This is time consuming to do butnot overly diﬃcult. But now wehave hundreds of thousands oflines of data.How do we analyze this?

Not in the SM tool, sadlyThe text analytics tools in Sysomos MAP quickly fallapart when the ﬁlter is removed and we are lookingat everything a group of users say.

So we need to export allthe activity and analyzeit using a qualitativetagging methodology

Qualitative vs. Quantitative ResearchQuantitative content analysis isabout counting content. Itprovides numbers that representfrequencies or occurrences.Qualitative content analysis isabout the quality of content. Itprovides insights into recurringthemes that can lead to thedevelopment of valuable insightsand recommendations.Source: Zhang, Y. , & Wildemuth, B. M. (2009). Qualitative analysis of content. In B. Wildemuth (Ed.), Applications ofSocial Research Methods to Questions in Information and Library

Qualitative vs. Quantitative ResearchRather than reporting how manytimes a brand has beenmentioned [quantitative], we canlook at the quality of themessage [qualitative].Rather than reportingfrequencies, the application ofqualitative tools to social mediadata will allow us to gain insightsfrom a data sample that istraditionally overlooked.

Conducting a Conducting aqualitative analysis qualitative analysis of isolated of all expressions individual from identiﬁed expressions of users of interest. interest.

Qualitative Analysis Approach1.  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 top20 consistently engaged which wereresponsible for over 20,000 socialmedia mentions in the quartersanalyzed.Only a partial analysis wasperformed to validate that the mostconsistently engaged individualsidentiﬁed would be of value to thebrand.

What did we ﬁnd?Nearly all of our consistently engagedusers sample self-identify as amateur orprofessional photographers.Most have a relationship with Nikon –either because they own Nikonproducts or discuss Nikon products andnews.1 out of 20 self-identiﬁed as aprofessional photographer and 14 out of20 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 equipmentOwns a Nikon D300and mostly uses oldermanual-focus lenses. Hobbyist looking toJust bought ﬁrst move up to pro-level“modern” Nikon lens. Nikon gear

Their other interestsUsers are more likely to talk about iPhone vs. BlackberryiPhones vs. BlackBerry devices –  74% of all iPhone mentions were positive in nature –  73% of BlackBerry mentions were positive iPhone in nature BlackBerryiPhone users more likely to talk aboutphoto related apps than BlackBerryusers –  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 interestsThe consistently engaged Nikon users Coffee Mentionsover the time period analyzed weremore likely to talk about Starbucks thanany 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 naturePeople spoke of coffee 1.7 times moreoften than tea.Of the tea mentions, DavidsTea was themost mentioned product/brand.

Quick Recap1.  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 MethodologyA)  Finding consistent users across two quarters1.  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 MethodologyB)  Determining tweet volume for consistent users1.  Merge Tweet content from both quarters into a single speadsheet column2.  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 MethodologyC)  Removing the ﬁlter and exporting all user tweets for “casually engaged” list1.  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 MethodologyD)  Running qualitative analysis to find insights about users1.  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

ColophonSean Howard is a freelancer and Eric Portelance is a Strategist at Katie Charbonneau is anAssociate at Thornley Fallis and Thornley Fallis and a regular Account Coordinator atspends his life searching for what podcaster on the show Attention Thornley Fallis with an MSc indrives 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

by Eric Portelance on Jul 04, 2011

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Social Media Monitoring - Finding Relationships — Presentation Transcript