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Social Media Lead Generation
For Digital Bank
Sudarson Roy Pratihar
Data Science Evangelist and Technology Leader
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
Understanding Digital Bank
n  Young's turned to 18 n  Foreigners Relocating
n  Pillars of Digital Banking
n  Customer centric value
n  Banking anywhere
n  Disrupting and catalysing change
n  Customers are bankers
n  Go social
n  With no compromise on
n  Security
n  Privacy
n  Integrity
High Customer Life Time Value
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
Challenges
Computational and Data Collection
n  Problem
n  Diverse sources, huge data set and natural language
n  Infrastructure set up cost and time
n  Solution
n  Use cloud infrastructure on-demand to start with
n  Spot instances and EMR option of Amazon Web Services
(AWS) could be one potential way for cheaper bulk
processing of non-time critical data
Operational and Strategic
n  Problem
n  Understand customer, customers’ perception on needs and
preference
n  But finance is not so frequently discussed on social media,
except negative experiences
n  Identify and attract customer who can be brand
ambassadors
n  Convert leads to customers
n  Identify social medias to focus on
n  Solution
n  Promote discussions on social media on selected topics and
empower target segments to voice preferences in Bank’s
website through dedicated social page (and accessible
over mobile devices as well)
n  Analyze social media data to understand needs of targeted
segments, identify social media groups to discuss
n  Analyze social network to identify customer who can
influence
n  Start with Social Medias such as FaceBook, Linkedin,
Consumer Association of Singapore for major data, but fill
in the gaps using Pintrest, Google+, Slideshare.
n  Identify attributes to target influential customers and use
social media ads
n  Redesign Bank’s landing pages and mobile app
Statistical and Analytical
n  Problem
n  Measure Social Media effectiveness
n  Measure Campaign effectiveness
n  Measure Bank Website and Mobile app effectiveness
n  Solution
n  Design experiments with randomly chosen samples of
target segments
n  Draw statistical inference and significance from experiment
results
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
Process at a Glance
• Advice Marketers
• Run Campaigns
• Measure lead
conversions
• Statistical inference
• Campaign Strategy
• Product Strategy
• Marketing Incentive
Strategy
• Perception and
Preference Model
• Social network
analysis
• Field Experiments
and Identification of
main factors and
interactions
Modeling Strategy
Execution
Performance
Evaluation
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
ITERATIVE CYCLE
Modeling
Social Network Analysis
n  Data Collection
n  FaceBook ,Twitter and Linkedin Followers
n  Consumer Feedback Sites to find most influential posts
n  Technique
n  Social Media Network is analyzed to understand network
spread, geography of spread, people who influence and
people who are connected with most influential people in
target segment
n  Facebook and twitter data is analyzed to understand need
of customers. E.g. who has recently moved to singapore and
looking for local information
Perception and Preference Model
n  Data Collection
n  Secondary Data: Customer feedback from social
media about other bank’s offerings; Slideshare,
Consumer Association of Singapore
n  Primary Data: Promoted discussion on Bank’s social
page and bank initiated discussions on social media
n  Initially Digital Bank will have to rely more on
Secondary data.
n  Technique
n  Sentiment analysis of tweets, feedback to understand
customer mind and value of features in customer
mind
n  Factor Analysis to group together multiple features to
get a dimension where Bank has opportunity to
attract customer
Statistical Analysis
n  Data Collection
n  Field Experiments in terms of conducting campaign on a
selected social media on small sample of target population
along with rest of target population working as control
group
n  Technique
n  Regression Analysis to understand impact of the particular
campaign on the given social media
n  Campaign effectiveness and effectiveness of social media
can be judged
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
Strategy
Digital Strategy
n  Modify Digital Bank’s website’s landing
page and Mobile Site based on
Modeling exercise
n  A few iterations of field experiments
may be needed to test effectiveness of
landing page and mobile site
Campaign Strategy
n  Selection of Social Medias based on
Modeling exercise
n  Selection of attributes of target customers
based on modeling exercise
n  Use these attributes to select leads and
follow up through marketers using various
channels
n  Selection Campaigns and Ads based on
Modeling exercise (Field experiment)
n  Control group: A small segment of target
group would be still not exposed to direct
campaigns, so that performance can be
compared
n  Factor Analysis to group together
multiple features to get a dimension
where Bank has opportunity to attract
customer
Product Alignment / Incentive Strategy
n  Observations on Customers’ preference
and perception to be passed to product
management if there is a significant
deviation
n  Based on priority decided on banking
products, incentive for Marketers may
be changed to prioritize sell of certain
products
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
Execution
Advice to Marketers
n  Based on strategy formulated, advice
marketers to participate and watch
for selected social media discussion
n  Watch list of keywords and hash tags
n  Use software (developed on data
mining algorithms) to generate a list
of target individuals and contacts
along with ranking based on
influence
Run Campaign
n  Contract with social media for
campaign – provide keywords and
other attributes derived in strategy
phase
n  Run campaigns
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
Performance Evaluation
Performance Measurement
n  Gather metrics from social media –
such as Reach, Frequency, Cost per
Thousand, tracking of leads
n  Keyword performance
n  Conversion of leads to customer and
lifetime value
n  Conversion of referral through the
lead who became customer
Statistical Analysis
n  Regression and ANOVA analysis of
effectiveness of campaigns against
the control group
n  Analysis to determine factors of lead
conversion and effective growth of
customer base through referral
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
n  AWS EMR spot instances for low cost batch processing
n  AWS Ec2 instances for analytics system hosting
n  For real time analytics dash board, spark will be used
Alert	
  Generation Feedback	
  capturing
Event	
  Ingestion
HDFS	
  Sync
Data	
  sync
CDR	
  Data
CDR	
  Data
Query
Flume/Kafka
HDFS
Hadoop	
  Cluster
Exploratory	
  Data	
  Processing
Python
Spark
Continuous Process
Pyspark/MLLIB
Analytical	
  
adjustment	
  and	
  
pattern	
  
detection
Reporting	
  
alert/mail/feedback
MYSQL
Social Media
Data
AWS – EMR
Landing zone
Customer
acquisition data
from Bank’s
system
Copyright © 2015 All rights reserved by Sudarson Roy Pratihar

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Digital bankleadgen

  • 1. Social Media Lead Generation For Digital Bank Sudarson Roy Pratihar Data Science Evangelist and Technology Leader Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
  • 2. Understanding Digital Bank n  Young's turned to 18 n  Foreigners Relocating n  Pillars of Digital Banking n  Customer centric value n  Banking anywhere n  Disrupting and catalysing change n  Customers are bankers n  Go social n  With no compromise on n  Security n  Privacy n  Integrity High Customer Life Time Value Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
  • 3. Challenges Computational and Data Collection n  Problem n  Diverse sources, huge data set and natural language n  Infrastructure set up cost and time n  Solution n  Use cloud infrastructure on-demand to start with n  Spot instances and EMR option of Amazon Web Services (AWS) could be one potential way for cheaper bulk processing of non-time critical data Operational and Strategic n  Problem n  Understand customer, customers’ perception on needs and preference n  But finance is not so frequently discussed on social media, except negative experiences n  Identify and attract customer who can be brand ambassadors n  Convert leads to customers n  Identify social medias to focus on n  Solution n  Promote discussions on social media on selected topics and empower target segments to voice preferences in Bank’s website through dedicated social page (and accessible over mobile devices as well) n  Analyze social media data to understand needs of targeted segments, identify social media groups to discuss n  Analyze social network to identify customer who can influence n  Start with Social Medias such as FaceBook, Linkedin, Consumer Association of Singapore for major data, but fill in the gaps using Pintrest, Google+, Slideshare. n  Identify attributes to target influential customers and use social media ads n  Redesign Bank’s landing pages and mobile app Statistical and Analytical n  Problem n  Measure Social Media effectiveness n  Measure Campaign effectiveness n  Measure Bank Website and Mobile app effectiveness n  Solution n  Design experiments with randomly chosen samples of target segments n  Draw statistical inference and significance from experiment results Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
  • 4. Process at a Glance • Advice Marketers • Run Campaigns • Measure lead conversions • Statistical inference • Campaign Strategy • Product Strategy • Marketing Incentive Strategy • Perception and Preference Model • Social network analysis • Field Experiments and Identification of main factors and interactions Modeling Strategy Execution Performance Evaluation Copyright © 2015 All rights reserved by Sudarson Roy Pratihar ITERATIVE CYCLE
  • 5. Modeling Social Network Analysis n  Data Collection n  FaceBook ,Twitter and Linkedin Followers n  Consumer Feedback Sites to find most influential posts n  Technique n  Social Media Network is analyzed to understand network spread, geography of spread, people who influence and people who are connected with most influential people in target segment n  Facebook and twitter data is analyzed to understand need of customers. E.g. who has recently moved to singapore and looking for local information Perception and Preference Model n  Data Collection n  Secondary Data: Customer feedback from social media about other bank’s offerings; Slideshare, Consumer Association of Singapore n  Primary Data: Promoted discussion on Bank’s social page and bank initiated discussions on social media n  Initially Digital Bank will have to rely more on Secondary data. n  Technique n  Sentiment analysis of tweets, feedback to understand customer mind and value of features in customer mind n  Factor Analysis to group together multiple features to get a dimension where Bank has opportunity to attract customer Statistical Analysis n  Data Collection n  Field Experiments in terms of conducting campaign on a selected social media on small sample of target population along with rest of target population working as control group n  Technique n  Regression Analysis to understand impact of the particular campaign on the given social media n  Campaign effectiveness and effectiveness of social media can be judged Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
  • 6. Strategy Digital Strategy n  Modify Digital Bank’s website’s landing page and Mobile Site based on Modeling exercise n  A few iterations of field experiments may be needed to test effectiveness of landing page and mobile site Campaign Strategy n  Selection of Social Medias based on Modeling exercise n  Selection of attributes of target customers based on modeling exercise n  Use these attributes to select leads and follow up through marketers using various channels n  Selection Campaigns and Ads based on Modeling exercise (Field experiment) n  Control group: A small segment of target group would be still not exposed to direct campaigns, so that performance can be compared n  Factor Analysis to group together multiple features to get a dimension where Bank has opportunity to attract customer Product Alignment / Incentive Strategy n  Observations on Customers’ preference and perception to be passed to product management if there is a significant deviation n  Based on priority decided on banking products, incentive for Marketers may be changed to prioritize sell of certain products Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
  • 7. Execution Advice to Marketers n  Based on strategy formulated, advice marketers to participate and watch for selected social media discussion n  Watch list of keywords and hash tags n  Use software (developed on data mining algorithms) to generate a list of target individuals and contacts along with ranking based on influence Run Campaign n  Contract with social media for campaign – provide keywords and other attributes derived in strategy phase n  Run campaigns Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
  • 8. Performance Evaluation Performance Measurement n  Gather metrics from social media – such as Reach, Frequency, Cost per Thousand, tracking of leads n  Keyword performance n  Conversion of leads to customer and lifetime value n  Conversion of referral through the lead who became customer Statistical Analysis n  Regression and ANOVA analysis of effectiveness of campaigns against the control group n  Analysis to determine factors of lead conversion and effective growth of customer base through referral Copyright © 2015 All rights reserved by Sudarson Roy Pratihar
  • 9. n  AWS EMR spot instances for low cost batch processing n  AWS Ec2 instances for analytics system hosting n  For real time analytics dash board, spark will be used Alert  Generation Feedback  capturing Event  Ingestion HDFS  Sync Data  sync CDR  Data CDR  Data Query Flume/Kafka HDFS Hadoop  Cluster Exploratory  Data  Processing Python Spark Continuous Process Pyspark/MLLIB Analytical   adjustment  and   pattern   detection Reporting   alert/mail/feedback MYSQL Social Media Data AWS – EMR Landing zone Customer acquisition data from Bank’s system Copyright © 2015 All rights reserved by Sudarson Roy Pratihar