Social Dr. Marco Hetterscheidt. Zürich, März 2013

Transcription

1 Social Dr. Marco Hetterscheidt Zürich, März 2013

2 Deutsche Telekom profile. 2011

3 Germany. 35 m mobile customers Leading (V) DSL-provider in Germany 12 m broadband connections 1.8 m IPTV customers EUR 24.0 bn revenue EUR 9.6 bn Ebitda 76,028 employees 97,522 employees (incl. Headquarters/GHS) Data basis financial figures: DT annual report 2011

4 Deutsche Telekom.

5 Modern Broadband & Mobile experience.

6 Television & Cloud Services: Cloud for everyone.

7 CRM In focus

8 CRM acts as an enabler for Sales and Service. For Each Customer Customer Insight; e.g. analyses, segmentations, customer affinities Marketing The Right Offer Selection of relevant Sales and Service offerings At the Right Time Management of sales and loyalty as well as inbound and outbound measures In the Right Channel Display of individual offer recommendations for each customer Sales and Service IT systems and processes

9 Heart of scoring and value prediction: Mining Factory. It ensures the right offer at the right time in the right channel for each customer. What happens in the Mining Factory every month? 100+ processes/programs Value Prediction Model Prediction Target Scoring 50+ input tables on 40 million contracts and other characteristics different variables from the Jewel-Box Thousands of lines of program code Jewelbox Processor Mining Team Approx. 100 affinity/retention models Over 200 different product recommendations Mining Manager Mining Quality Model Report 800 Million Pearls (are used for steering)

10 Telekom and Social Web. Telekom Erleben Telekom hilft Liga total! Facebook: >150k Fans Facebook: >50k Fans Facebook: >250k Fans Youtube: >7.7 Mio. Views Youtube: >3k Views Youtube: >500k Views >600k fans in several facebook channels

11 Social Media first steps. Command Center: Shitstorm alert Like and Share count Identification of Shitstorms and rapid response functionality Sentiment Analysis: Tag Clouds Identification of service topics Proactively inform customers about solutions product improvement processes improvment + Network Analysis: In-, Out-Degree Hub-& Authority score Communities Indentification of multipliers & communities for special sales & service offers

12 Processing unstructured data of internal & external sources. KNIME Proccessing unstructured data with KNIME to find golden nuggets example flow

13 General Social Media network definitions. Author network analysis undirected, weighted digraph 1 4 The authors of a social media page form an undirected, weighted digraph The number of authors to whom a given author has incoming/outgoing connections are given by the in-/and out-degrees The authority and hub scores represent the leaders and followers of a network The adjacency matrix is asymmetric and is 0 where no connection between authors exists 3 Author 2 has two out- and one in-degree Link between author 1 and 2 is directed and weighted A ij Adjacency Matrix:

14 Degree characteristic of a fan page. In- & Out degree distribution exemplary Authors Only a few authors have several inand out degrees Most authors have one 1 or two in- or out degrees Sparse network! Out-degree In-degree

15 Likes and posts of a fan page Post with Likes Posts Likes per post of a fan page ( Likes - in total) Authors vs. posts vs. likes Likes Authors Example: 1 Author who received in total 6 likes for 11 posts exemplary

16 Network of a fan page. network analysis of authors nodes and links: identification of multipliers exemplary Author with a strong leader characteristic - multiplier: 1 Post with Likes 76 In-Degree & 3 Out-Degree 111 Total-In-Comments Authority Score = 1; Hub Score = 0,01 Author with a strong follower characteristic 1 Post with 0 Likes 0 In-Degree & 45 Out-Degree 57 Total-Out-Comments Authority Score = 0; Hub Score = 0,013 Sparse network with little cross linking few leaders but with high influence

17 Social Media & Textmining. The process of sentiment analysis with KNIME Message Transf. into documents Extraction of sentences Breakdown into terms Detection of sentiments Classification Challenges: every community has its own language every topic has its own taxonomy identification of irony, sarcasm sentiment-determination of single messages with high viral potential

18 Social Media & Textmining. sentiment score week zoom-in Development of sentiment score over time sentiment score Telekom competitor VODAFONE 1 competitor YOURFONE 2 days weeks Positive Storm on a Telekom fan page on one day

19 Thanks for your attention!