SAP Predictive Analytics Wissen, was morgen läuft Rebekka Ketterer, Presales Business Intelligence & Predictive Analytics 23.04.2015
Informationen sammeln und nutzen Was wir sammeln Was wir davon nutzen 3
Wettbewerbsvorteil Erweitern Sie Ihre analtischen Fähigkeiten Wahrnehmen und reagieren Vorhersagen und agieren Optimization Predictive modeling Was kann im besten Fall passieren? Raw data Cleaned data Standard reports Ad hoc reports and OLAP Generic predictive analytics Warum ist es passiert? Was wird passieren? Was ist passiert? Analytische Reife This presentation and SAP s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement 4
Predictive Analytics: wie funktioniert das? Historische Daten Typisches und wiederkehrendes Verhalten/Muster Prognosen und Initiativen Kunden Produkte Transaktionen Anlagen/ Gerätschaften Nachfrage Risiko Betrug Marketing Finanzen Unterhalt/ Wartung Iterativer Prozess This presentation and SAP s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement 5
Die Strategie: Einfach - Schnell - Überall VORHERSAGEN, SCHNELL, EINFACH UND GENAU Automatisierte Prognose-Prozesse CHANCEN ERKENNEN IN ECHTZEIT Echtzeit-Prognosen in Big Data Umgebungen mit SAP HANA MIT MEHR ENTSCHEIDUNGSSICHERHEIT AGIEREN Nutzung von Prognosen und Analysen in Geschfätsprozessen und applikationen 6
Im gesamten Unternehmen anwendbar Keine Geringe Ausgeprägte Analytische / Data Mining Kenntnisse Business Users & LOB Business Analysts Data Scientist 97 % ~ 3 % < 1 % Embedded Analytics SAP Lumira Automated Analytics Industry & Business Process Analytics SAP Predictive Analytics 2.0 Expert Analytics SAP ADVANCED ANALYTICS Custom Analytics R Integration PAL & APL This presentation and SAP s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement 7
SAP Predictive Analytics Expert Mode ehemals SAP Predictive Analysis Statistische, individuell erweiterbare Algorithmen für den Data Scientist Flexible Modellerstellung, Modell-Finetuning HANA-native Algorithmen Offen für R-Algorithmen In-memory Processing und Management für Big Data Grundgesamtheit Moderne, visuelle Aufbereitung und Weiterverarbeitung der Ergebnisse This presentation and SAP s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement 8
SAP Predictive Analytics Automated Mode ehemals SAP InfiniteInsight Vollautomatisierter Prozess für den Business Analysten und den Data Scientists Datenvorbereitung / - encoding Automatische Estellung analytischer Datensets Predictive Modeling / Data mining Regression / Classification Segmentation Forecasting Association rules Social Network Analysis Model deployment und Management Funktionen statt Algorithmen This presentation and SAP s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement 9
Methodologie zum Trainieren von Modellen Übertrainiertes Modell /nicht robust (Kein Fehler beim Training, hohe Fehlerrate beim Test) Untertrainiertes Modell / sehr robust (Hohe Fehlerrate beim Training = hohe Fehlerrate beim Test) Robustes Modell (geringe Fehlerrate beim Training geringe Fehlerrate beim Test) Modellbildung Bekannte Daten Neue Daten 10
In-memory Modellerstellung/ -ergebnisse mit SAP HANA SAP Lumira SAP Predictive Analytics R Partner Tools & Solutions Association Analysis Cluster Analysis Classification Analysis Time Series Analysis Outlier Detection Link Prediction Data Preparation Automated Classification Automated Clustering Automated Regression In-Memory Processing Engine SQL Engine Calculation Engine Text Engine PAL & APL 1 R-Scripts 2 SAP HANA In-memory In-database Predictive Analysis R-Engine HANA Studio Modeler 1 Predictive Analysis Libraries (PAL & APL) Accelerate predictive analysis and scoring with native in-database algorithms delivered out-ofthe-box. Graphical Modeling HANA Studio for app developers. Pre-built commonly utilized business & predictive algorithms to facilitate a faster and easier development. 2 R Integration Execute R scripts via high performing parallelized connection. Embed R scripts as part of overall query plan. This presentation and SAP s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement 11
Anwendungsfälle aus der Praxis
Wie erhöhen wir Kundenbindung? Customer Churn und Retention Wer? Vertrieb, Marketing, Service Tut was? Kunden, die zur Abwanderung tendieren erkennen Zielgruppen identifizieren und untersuchen Individuelle Kampagnen und Angebote erstellen Nutzen Kundenzufriedenheit erhöhen Gezielte Kundenbindungsprogramme Kündigung von Kunden in einigen Kundensegmenten um etwa 12 % pro Jahr reduziert Improve performance of retention programs Scoring von mehreren Millionen Kunden in wenigen Stunden, statt Monaten
Wie verkaufen wir mehr? Cross-sell and Upsell Wer? Vertrieb, Marketing, Service Sparkasse Bei gleicher Vertriebszeit können 70% mehr Kampagnen- Abschlüsse generiert werden (z.b. durch 3 parallele, gleichartige Kampagnen) Tut was? Nutzen Findet Profile mit hohen Abschlussaffinitäten Kundensegmentierung, welche Kunden sprechen positive auf eine Kampagne an? Verbesserte individuelle Kundenansprache Verbesserter Kampagnenerfolg Verringerter personeller und monetärer Kampagnenaufwand
Wie verkaufen wir mehr? Cross-sell and Upsell Wer? Vertrieb, Marketing, Service ALLEGRO Seitenabruf pro Besuch um 30 % erhöht Klickrate um 500 % erhöht Conversion Rate 40x so hoch Tut was? Nutzen Erstellung von Link Analysen / Social Graphs Personalisierte Produktempfehlungen Personalisiertes Kundeneinkaufserlebnis, Kundenzufriedenheit Cross-/ Upsell Potentiale entwickeln Umsatzsteigerung
Wie können wir Ausfälle vermeiden? Predictive Maintenance Wer? Produktion, Service Kaeser Verkauf von komprimierter Luft, statt Kompressoren Analyse aller Sensordaten nahezu in Echtzeit Reduzierung Wartungskosten um 2% / Jahr Tut was? Nutzen Potentieller Ausschuss /Ausfall frühzeitig erkennen Wartung-/ Servicetätigkeiten vor dem Ausfall durchführen Selbststeuernde Fertigungsprozesse Service- und Reparaturkosten sparen Ausfallzeiten vermindern Neue Geschäftsmodelle
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Danke Rebekka Ketterer Presales BI & Predictive Analytics rebekka.ketterer@sap.com 2015 SAP SAP AG AG or or an an SAP SAP affiliate company. All All rights reserved.
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