Using linguistic analysis to break through the noise - #CLZSF - Benjamin Spiegel

transcript

August 11–14, 2014

#CZLSF | @ClickZLive

The Global Conference Series Designed by Digital Marketers, for Digital Marketers

The Language of DiscoveryUsing linguistic analysis to break through the noise

Benjamin SpiegelManaging Director, Strategy@nxfxcom / @GroupMWorldWide

August 11–14, 2014 | #CZLSF | @ClickZLive

THINGS USED TO BE SIMPLE

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THINGS HAVE EVOLVED

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DEVICES HAVE EVOLVED

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DIGITAL WAS SIMPLE TOO

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THINGS EVOLVED, AGAIN

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I NEED CAR INSURANCE

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GOOGLE EVOLUTION:I NEED A TRUCK

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NNGROUP.COM/BOOKS/EYETRACKING-WEB-USABILITY/

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MISSCONCEPTIONS

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(I AM A HAPPY CONSUMER)

@nxfxcom(I AM A HAPPY CONSUMER)

WWW.LUMAPARTNERS.COM/LUMASCAPES/

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LOTS OF NOISE

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FIGHTING FOR THE CONSUMERS ATTENTION

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1. Who are our consumers2. Where are our consumers3. Are they ready to buy

Now we know things like

WHO – WHERE - WHEN

WE ARE MISSING THE MOST IMPORTANT THING.

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WHAT TO SAY!

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OUR ADVANTAGE(OVER TRADITIONAL)

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UNIQUE CONSUMER EXPERIENCES

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WE HAVE THE LARGEST CONSUMER PANEL IN THE WORLD

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THE INTERNET

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WHERE CONSUMERS LOVE TO TALK

REDEFINE CONSUMERUNDERSTANDING

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SOCIAL LISTENINGSEARCH DATA

PANELSSURVEYS

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READING…

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…WHAT THEY THINK & SAY@nxfx

NUMBERSVS

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LANDING PAGE METRICS

/COUPONS 1,000 VISITS 17% BR

/FEATURES 3,000 VISITS 27% BR

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WOULDN’T YOU RATHER HAVE

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“I like the smell”

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“very efficient”

“great price”

USING LINGUISTICAL ANALYSIS TO BETTER CONNECT WITH

OUR CONSUMERS

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WHERE TO START(2 EXAMPLES)

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#1SOCIAL MEDIA

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#CLZSF

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#1 GET DATA

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https://dev.twitter.com/docs/api/1.1/get/search/tweets

#2 PICK A TOOL

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http://rapidminer.com/products/rapidminer-studio/

#3 BUILD A FLOW

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Its easier then it looks

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SAMPLE FLOW

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POS FILTER PATTERN OF SPEECH

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POS?WHAT ARE PATTERNS OF SPEECH

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AMAZING RESULTSIN TEXT BASED ADS

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#2COMPETITIVE INSIGHTS

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https://snap.stanford.edu/data/web-amazon.html

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pyOut = {}

for str in kIn['Col0']: tokens = str.split(':', 1) if len(tokens): if tokens[0] in pyOut: column = pyOut[tokens[0]] else: column = [] column.append(tokens[1].strip()) pyOut.update({tokens[0] : column})

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COMPETITOR ATTRIBUTES

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NEGATIVE ATTRIBUTES

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LEARNERS / PREDICTORS

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1. Keyword tagging by intend2. Engagement by condition3. Most Likely outcomes4. Ranking Factors

Machine learning

CURRENT PROJECT

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*NON BRANDED, EQUAL EQUITY ETC. VERY OBJECTIONABLE

WHERE SHOULD YOU LOOK FOR YOUR DATA?

- CRM SYSTEM- CUSTOMER SUPPORT EMAILS

- SUPPORT FORUM- KNOWLEDGE BASE (BY PV)

- RATINGS AND REVIEWS

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THANK YOUQUESTIONS? @NXFXCOM

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Using linguistic analysis to break through the noise - #CLZSF - Benjamin Spiegel

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