Auswirkungen von Big Data Analysen auf das Online-Marketing

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1 Auswirkungen von Big Data Analysen auf das Online-Marketing e-marketingday rheinland - IHK Initiative Rheinland - Wuppertal, 13. April 2016

2 Heutige Unternehmen verdursten in einem Meer aus Daten Bis zu 90 % der heutigen Daten sind unstrukturiert Nur 20 % der Daten können mit herkömmlichen Systemen nutzbar gemacht werden

3 Arbeitsthese Der immer mobilere Mensch soll schnellere und bessere Entscheidungen zu unstrukturierteren Problemstellungen treffen, wobei ihm immer mehr Daten in vielfältigerer Form zur Verfügung stehen.

4 Herausforderungen von Big Data Big Data na und? Big Data beschreibt keinen Nutzen Big Data formuliert kein Problem Beschreibung des Business Case = Herausforderung TROTZ fachlich formulierter Frage: zielführende Auswertung verschiedener Quellen aufwändig bestehende IT-Umgebung nicht Big-Data-fähig

5 Liest Du noch oder analysierst Du schon? Es scheint, also ob Sie alle Daten hätten was ist also das Problem?!

6 Ihre Entscheidungsunterstützung soll sich systematisch, strategisch weiterentwickeln können Reifegrad Cognitive Computing Predictive Analytics Planung & Forecast Reporting descriptive predictive prescriptive Zukunftsorientierung

7 Die besondere Kombination von drei Eigenschaften unterscheidet Watson von anderen Technologien Verarbeitet natürliche Sprache und versteht Komplexität unstrukturierter Daten Wendet fortgeschrittene Analysemethoden an, um Antworten logisch zu gewichten und auszuwerten Lernt mit jeder Iteration und Interaktion der Anwender

8 Google Search Approach

9 Watson Jeopardy!

10 A travel services company uses cloud-based social media analytics to detect the desires of customers and drive more sales

11 A fashion brand uses cloud-based social media analytics to successfully expand into foreign markets 72% reduction in time to market for new products Pricing strategy aligned to local market conditions Differentiated brand to established retailers in the market Brand values which resonate with the target customers Powered by Real time Social Media data IBM SMA 1.3 (DB2, Cognos) IBM SPSS Modeler IBM Psycholinguistics Next Best Action Business challenge: Although the fashion brand had a good understanding of its home markets, it realised that the fashion trends, attitudes, values and market structure in a foreign market were different. Social conversations provide clues but how can the company reveal the underlying insights and successfully expand into a new foreign market? The socially aware solution: The fashion brand uses social media conversions to understand consumer preferences and the market landscape to build brand equity in a new market. And in the fast moving world of fashion, real time social conversations provide the insights needed by product designers to bring new wears to market quickly and boost profits. By using advanced analytics and real time social media postings, the fashion brand positioned itself for success in a new market. Trend alerts enable product designers to bring new lines to market at speed. The outcome, social intelligence driving real financial results.

12 A Telecom company uses cloud-based social media analytics to proactively retain customers before they have decided to leave 30% reduction in customer attrition rate Increased revenues 10% increase in Call Centre Agent revenues 25% increase in cross sell and upsell opportunities Higher Customer Satisfaction Rating improved from 1.5 to 2.7 on a five point scale Powered by Real time Twitter feed and other Social Media data IBM SMA 1.3 (DB2, Cognos) IBM Big Match IBM SPSS Modeler IBM Psycholinguistics Next Best Action Business challenge: Customers continue to shift to mobile and social channels in the way they converse about brands. Social channels are often the first way to express grievances and doubts. In real time, negative sentiments can quickly proliferate and influence existing and prospective customers. How can we leverage these social channels to connect with customers in a way that suits their preferences in order to build loyalty and reduce attrition? The socially aware solution: The Telco uses customer sentiment derived from Twitter postings along with other social data and internal company records to understand customer preferences and predict customers at risk of attrition. Resource is being shifted from staffing call centres to social media engagements in order to empower customers and respond to them via their preferred communication medium.

13 A Health Insurance company uses cloud-based social media analytics to improve marketing effectiveness through one-to-one engagement 25% reduction in time to market for new products Increased revenues 10% increase in Seller revenues 25% increase in cross sell and upsell opportunities 20% reduction in the cost of acquiring new customers Powered by Real time Twitter feed and other Social Media data IBM SMA 1.3 (DB2, Cognos) IBM Big Match IBM SPSS Modeler IBM Psycholinguistics Life Event Detection Next Best Action Business challenge: Customer engagement is limited by what is captured within the internal company data repositories. And yet customers are publicly conversing about their products, services and market via social media. How do we tap into this wealth of social insight in order to better engage with our customers individually and increase market share? The socially aware solution: The Health Insurance company now combines enterprise customer data with insights from social conversations to enhance how they engage with customers more individually, and feeds market intelligence insights about the market, competitors and segment needs into their key messaging and product development to increase market share.

14 Moderne Plattformen erlauben einen schnellen, kostengünstigen, einfachen Einstieg z.b. IBM BlueMix Plattform as a Service Benötigte Apps können schnell und kostengünstig per drag+drop erstellt werden Beinhaltet Watson-Angebote und mehr als 100 Cloud- Services

15 Ohne Strategie geht es nicht Was will ich erreichen? Was will ich mit den gewonnenen Daten und Informationen machen, wie will ich sie nutzen? Bsp. Sentimentanalyse: will ich Positives verstärken oder Negativem entgegenwirken? Will ich individuell antworten, z.b. in Social Media? Auf den richtigen Ton kommt es an

16 Wo stehen die Firmen heute? 5% STEP 1: Ad-hoc 65% STEP 2: Foundational 21% STEP 3: Competitive 9% STEP 4: Differentiating Manual, slow, error prone, cumbersome, fragmented data quality concerns Automated, instant, accurate, seamless, converged Data governance is in place

17 Sie verdursten nicht mehr im Datenmeer

18 START SMART SCALE FAST

19 Haben Sie Fragen? Stephan Multhaupt Geschäftsführer gmc² gerhards multhaupt consulting GmbH Adenauerallee 136 D Bonn Main Tel Fax s.multhaupt@gmc2.de Website