The document outlines Freedata Labs' web monitoring process in 8 steps: 1) initial keyword analysis, 2) feeding sources, 3) URL analysis and blacklisting, 4) determining on/off target content, 5) natural language processing, 6) polarity scoring, 7) providing metrics like buzz, advocacy, topics, and semantic mapping, and 8) relating buzz to sales. It then discusses how the insights can be used to understand brand positioning, sentiment changes, marketing effectiveness, conversation topics, and competitive analyses to help optimize marketing mix budget and initiatives.

1. Freedata Labs Web Monitoring Service The Brand Race Quadrant® Pioneers in social data July 2009 Member of: London Milan Torino

2. Web Listening & Seeding Process

3. Web Monitoring Process Explained Step 0 – Initial Analysis Initial 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 – Feeding The 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 Analysis Each 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).

4. Web Monitoring Process Explained Step 3– ON/OFF Target Foreach monitoring,aspecificprofile (i.e.:asetofqueriesandrulescombined hierarchically)is defined.Eachclipisanalyzedagainstthegiven profile,andarelevanceindexisassignedtothe clip.Whentherelevanceindexisbelowagiventhresholdtheclipismarkedasofftargetandexcludedfromthe analysis. Step4–NaturalLanguageProcessing Analysis Eachrelevantcliprunsthrougha 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 Scoring Foreachtargetapolarityscoreisassignedbyevaluatingtheweightedalgebraicsumofallthepolarityrelationsfoundinagiven clip.

5. Freedata Labs Offering Cycle How 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 ?

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

8. Did I succeed to positively influence my brand ?

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

10. 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.

11. Overall & Social Buzz Buzz 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):

12. Advocacy Net 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).

13. Topics & Features Topics 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

14. 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

15. 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

16. Buzz vs. Sales Dreher Beck’s Splugen Nastro Azzurro Peroni 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.

17. Listening Marketing Listen & Act Marketing Mix Web Competitive Analysis Brand/Product Perception Communication Feed for all of the Marketing Mix

18. Get in Touch Alex Giorgi a.giorgi@freedatalabs.com