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Big data im B2B Umfeld

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BIG Data Anakyse im B2B Umfeld.

Nutzung von Internet-Profilen für ein erfolgreiches Marketing

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Big data im B2B Umfeld

  1. 1. Bonn  im  Oktober  2012  Bewertung  von  Neukundenpotenzial     Folie  1  
  2. 2. Die  Vorteile  der  Nutzung  von  Big  Data-­‐Analyse  für   Vertrieb  und  MarkeEng   Keine  verpasste   Durch  OpEmierung     Umsatzchancen     der  Prozesse  die     mehr   Erfolgreich   Kosten  senken   Umsatz-­‐ ins  Ziel  –  mit   wachstum   Gezielt   treffsicheren   umsatzstarke   Prognosen!   potenzielle   Neukunden   ansprechen   Arbeitszeiten  der   Erschließung   Vertriebsmit-­‐ neuer  Bereiche  &   arbeiter  effekEver   WeNbewerbs-­‐ gestalten   vorteile  Bewertung  von  Neukundenpotenzial     Folie  2  
  3. 3. Big  Data  –  Zusammenwirkung  dreier  großer   Technologietrends   Big  Data  aus   Big  Data  aus   TransakEonsdaten   InterakEonsdaten   OLTP   Daten  aus  Sozialen  Medien   OLAP   Andere  InterakEonsdaten:   DW-­‐ApplikaEonen   E-­‐Mail,  Webtexte  etc.   Qualifizierung   der  Adressen   für  Vertrieb   und  MarkeEng   Bereich   Webcrawling   Scoremodell   PrognosEsche  Analyse   Verarbeitung  der     Big  Data  Bewertung  von  Neukundenpotenzial     Folie  3  
  4. 4. Prozessablauf:    Big  Data  Analyse  für  Qualifizierung  der   Adressen  im  Vertrieb  und  MarkeEng  Bereich   101 110   Daten-­‐   Daten-­‐ Keyword  – Score  –     PrognosEsche   Auslieferung   Crawlen   Lieferung   auswertung   Analyse   Modell   Analyse   Lieferung  der   Verarbeitung   Big  Data  aus   Verarbeitung   Verarbeitung   Übergabe   Big  Data  aus   der  Big  Data:   Bewertung   InterakEonen:   der  Big  Data:   der  Big  Data:   von   TransakEonen:   Einstufung   neuer   Extrahieren   Analyse  der   Erstellung   potenziellen   Kundendaten,   der  Bestands-­‐ Firmen-­‐ von  Web  – relevanten   eines  Score  –   Neukunden   Umsatz,  Reak-­‐ kunden  nach   adressen   Seiten   Keywords   Modells   –  Adressen   Eonsverhalten   Umsatz     KLEINER  AUFWAND  –  GROSSE  CHANCEN  Bewertung  von  Neukundenpotenzial     Folie  4  
  5. 5. Möglicher  Dateninput   Lieferung  der  Daten  von  Bestandskunden     Email  – en   Web  – Umsatz/ en nam n   Adresse   Telefon-­‐ Firm dresse nummer   Adresse   ReakEon   -­‐A und   h?p:// www.  Bewertung  von  Neukundenpotenzial     Folie  5  
  6. 6. Anwendungsszenario:  Score-­‐Modell-­‐Erstellung   anhand  von  Bestandskunden  zur    Bewertung  neuer   Firmenadressen   Lieferung  der  Daten  von   Datenauswertung   Bewertung  neuer   Bestandskunden   Extrahieren  der  Webseiten   Firmenadressen   Keywords-­‐Analyse   Daten  Auslieferung   h?p://www.   h?p://www.   h?p://www.   h?p://www.     h?p://www. 110010 101101   Big  Data  aus  TransakEonsdaten   Big  Data  aus  InterakEonsdaten   Verarbeitung  der  Big  Data  Bewertung  von  Neukundenpotenzial     Folie  6  
  7. 7. Exemplarisches  Analyse-­‐Ergebnis   Auswertung  neuer  Firmenadressen     miNels  Scoring   Prognose     Firmenname   Adresse   URL   Umsatz   Max  Mustermann  GmbH   Musterstraße  54   hNp://www   158.749,45   TOP   Max  Mustermann  AG   Maxweg  7,  12345     hNp://www   73.612,89   Max  Mustermann  GBR   Am  Musterweg  1   hNp://www   1.200,74   MiNe   Max  Mustermann  OHG   Max  Muster  Str.  9   hNp://www   983,18   Max  Mustermann  E.V.   Mustermannweg     hNp://www   88,57   Flop   Max  Mustermann  KG   Max  Straße  3,  123   hNp://www   0  Bewertung  von  Neukundenpotenzial     Folie  7  
  8. 8. Gezielte  Neukundenansprache   ZeitopEmierung   Kostenersparnis   Tieferes  Kundenwissen  Bewertung  von  Neukundenpotenzial     Folie  8  
  9. 9. Kontaktdaten   MarAnCon   GesellschaJ  für  MarkeNng,  Analysen  und  ConsulNng  mbH   Königswinterer  Str.  418     53227  Bonn     Geschälsführer   Meinert  Jacobsen   Kontakt   T:  +49  228  338300-­‐00   M:  +49  151  15675483   E:  meinert.jacobsen@marancon.de  Bewertung  von  Neukundenpotenzial     Folie  9  

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