Freedata Labs Web Monitoring ServiceThe Brand Race Quadrant®Pioneers in social dataJuly 2009Member of:London Milan Torino

Web Listening & Seeding Process

Web Monitoring Process ExplainedStep 0 – Initial AnalysisInitial analysis identifies keywords that are used for Web crawling. Multiple keywords are necessary to ensure Web search errors are minimized. This step also identifies where on the Web people discuss about products or brands object of the analysis.Step 1 – FeedingThe feed counts about 45K distinct sources and 300K clips per day. Additionally, the feed includes more than 1M blog posts per day. Sources are as in the picture above. Queries against each search engine are scheduled appropriately (at least once a day) in order to ensure the highest incremental coverage.Step 2 – URL AnalysisEach source collected is already typed (i.e.: Web, blog, forum). We define a blacklist of sites that shall be excluded from the analysis. We also classify the sources based on its geographical origin (where possible).

Web Monitoring Process ExplainedStep 3– ON/OFF TargetForeach monitoring,aspecificprofile (i.e.:asetofqueriesandrulescombined hierarchically)is defined.Eachclipisanalyzedagainstthegiven profile,andarelevanceindexisassignedtothe clip.Whentherelevanceindexisbelowagiventhresholdtheclipismarkedasofftargetandexcludedfromthe analysis.Step4–NaturalLanguageProcessing AnalysisEachrelevantcliprunsthrougha multi-stagepipelineof analysis.Theanalysisincludes (notnecessarilyinthis order):− Target Identification:mentionsofagiventargetandotherreferencesare identified;− Linguistic Analysis:morphsyntacticanalysisofgiven clip. Sentences, lemmas, part-of-speech (i.e.: nouns, verbs, adjectives, etc.)andsyntacticroles (subject, object)are identified;− PolarItems Identification:polaritems (i.e.:termsandphrasesthatconveysomekindofpolar orientation)areidentifiedandapolarstrengthisassignedtothe item;− ResolutionofPolarity Relations:polaritemsareassociatedtothecorresponding target,inaccordancewiththeroleofthetargetinthesentence (parsetree analysis).Step5–Polarity ScoringForeachtargetapolarityscoreisassignedbyevaluatingtheweightedalgebraicsumofallthepolarityrelationsfoundinagiven clip.

Freedata Labs Offering CycleHow much do they talk of my brand ?How can I act to change my brand perception (Social Media Marketing) ?How do they talk about my brand ?Which are the main topics and features mentioned about my brand ?Where is my brand positioned against my competitors and how can I act ?Which are the most used words “attracting” my brand ?

Some Questions Answered How is my brand positioned against competitors ? Did the sentiment against my brand change in response to my actions ? Did I succeed to positively influence my brand ? Which are the subjects of on-line conversations, can I act somehow ? How can I use more efficiently my marketing mix budget ?It is now possible to know and measure what consumers say about my brand and products and act consequently.

How is my brand positioned against competitors ?

Did the sentiment against my brand change in response to my actions ?

Did I succeed to positively influence my brand ?

Which are the subjects of on-line conversations, can I act somehow ?

How can I use more efficiently my marketing mix budget ?

Overall & Social BuzzBuzz generates two different metrics. Buzz Overall comprises both consumers’ conversations on the Web as well as corporate communication and measures how much a brand or product is talked about on the Web. Social Buzz concentrates only on how much consumers talk about a brand or product on social networking sites. Both metrics can be compared to sales data and help predict future sales trends. And example (beers):

AdvocacyNet Promoter ® Score (a metric developed by Bain) is used as a measure of the sentiment (advocacy) found on the Web towards a brand or product (from forums, newsgroups, blogs, …).Conversations are analysed using semantic instruments to assign a score in a range from 0 to 10 (from extreme negative to highly positive).

Topics & FeaturesTopics and features tracking on brand and products as they merge from conversations on the Web helps marketing to better address initiatives and communication as well as better position the brand and products against competitors. An example (beers – in Italian):Powered by

Semantic Map Semantic map, generated thanks to a AI specialised software, shows the “attraction” of multiple words towards brands observed. An example (beers – in Italian):Powered by

The Brand Race Quadrant®Buzz & Advocacy (Net Promoter® Score) are reported in a quadrant(The Brand Race Quadrant®) where brand or products can be easily compared to thoseof competitors. Marketing initiatives can leverage on this mapping and set the appropriate next steps. An example (beers):Buzz

Buzz vs. SalesDreherBeck’sSplugenNastro AzzurroPeroni and Dreher seem to have a bigger market share even if with a lower buzz (communication). Moretti and Carlsberg instead seem to have a higher communication compared to their market share.

Listening MarketingListen & ActMarketing MixWebCompetitive AnalysisBrand/Product PerceptionCommunicationFeed for all of the Marketing Mix

Get in TouchAlex Giorgia.giorgi@freedatalabs.com