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Mieke is de Customer Intelligence Lead South-West Europe bij SAS, waar ze verantwoordelijk is voor alle “Integrated Marketing Management” projecten. In het verleden heeft zij bij een aantal multinationals (PWC, SAP, Microsoft en Selligent) gewerkt aan alle aspecten van klantrelaties. Ze is gespecialiseerd in het helpen van klanten bij het creëren van klantervaringen, customer intimacy en customer life cycle management. Later ging zij zich focussen op de waarde van Big Data in marketing context. De komst van de datastromen (online & social data) aan de ene kant en de rekenkracht aan de andere kant, levert aan marketeers interessante real-time inzichten die, als ze slim worden gebruikt, de klantervaring en customer experience naar een hoger niveau kunnen brengen. Vanuit haar wetenschappelijke track-record (PhD onderzoeken in Artificial Intelligence en in Advanced Marketing) en praktijk gaat Mieke vertellen hoe Big Data kan worden ingezet om belangrijke management beslissingen te maken.
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Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
THE BIG DEAL BEHIND BIG DATA MARKETING MAKING SMARTER CUSTOMER CENTRIC DECISIONS WITH BIG DATA
MIEKE DE KETELAERE, SAS, SWE CI LEAD, APRIL 2013
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
ABOUT MYSELF
• Customer Intelligence Lead at SAS for the SWE region • Worked for several multinationals on CRM
• Transactional CRM • Analytical Customer Insight • Interactive Marketing • Marketing Accountability
• Main focus on Telco, Retail, Finance and Utilities • Development of roadmaps towards customer centricity • Strong interest in Digital Intelligence
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
JANUARY 2007
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CHALLENGE 1
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CHALLENGE 2
On top of the mind of most CMOs
• Trusted Brand • Customer Experience • Customer Intimacy • Relevant Interactions
CMO CFO
On top of the mind of every CXO: • Revenu • Cost • Margin
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
Objectives Value/ Results
Data Management
People and Processes
DATA DRIVEN CUSTOMER CENTRIC MARKETING
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
BIG DATA DRIVEN CUSTOMER CENTRIC MARKETING
DATA TYPE
DATA
SO
URCE
Inte
rnal
Ext
erna
l
Structured Unstructured
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
BIG DATA DRIVEN CUSTOMER CENTRIC MARKETING
DATA SIZE
BIG ANALYTICS
BI BIG DATA BI
BIG DATA ANALYTICS
ANAL
YTIC
AL C
APAB
ILIT
Y
Rea
ctiv
e
Pro
activ
e
Large Big Data
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
Analytical
Social
Who They Are
What They Receive
How They Respond
How They Interact
What They Say
Who They
Influence
Their Loyalty
Their Value
Their Potential
360 Customer Insight
Contact History
Socio-demographic & transactional info
Online Behavioural
CUSTOMER DNA BIG DATA INSIGHTS
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EXISTING INTERNAL STRUCTURED DATA BIG DATA INSIGHTS Who They Are
Creditcard/Payment Transactions Loyalty Card Information
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
Referring domain Campaign ID Affiliate Natural search Search keywords Direct/bookmark
Referrer Variables
Customer/prospect New/return visitor All click data Tools usage Previous Product interests Searches Previous online purchases Previous Campaign exposure Previous Campaign responses
Site Behaviour Variables
IP address Country of origin Time zone Operating system Browser type Mobile type
Environment Variables
Temporal Variables
Time of day Day of week Recency Frequency Length of visit
Email interactions Social Media interactions
Channel Variables
NEW (UN)STRUCTURED ONLINE DATA BIG DATA INSIGHTS How They Interact
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O
Psychographic/IAO (Interest/Activities/Opinions) variables Attributes relating to personality, values, attitudes, interests, or lifestyles.
Explicit data (directly given by user)
–Movies/Books –Sports
Implicit data (indirectly given by user)
–Context - Relationship analysis
Content Categorisation/ Context Extraction
Sentiment Analysis Network Analysis Mood Detection
BIG DATA INSIGHTS NEW (UN)STRUCTURED SOCIAL DATA Who They
Influence
What They Say
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BIG DATA INSIGHTS NEW DATASOURCES – NEW IDEAS
RFID Tags Sensors
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Response Modeling
Customer Lifetime Value
Market Basket Analysis
Cross and Up Selling
Web Mining
Customer Link Analytics
Churn Prediction
Credit Scoring
Social Media Analytics
Customer Segmentation
Fraud Detection
Location Analysis
KPI Forecasting
Marketing Optimization
Marketing Mix Analysis
OVERVIEW CUSTOMER ANALYTICS BIG DATA ANALYTICS
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THE MARKETING CHALLENGE: CUSTOMER ANALYTICS BIG DATA ANALYTICS
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CASE RETAIL – NEW INSIGHTS BIG DATA ANALYTICS
QUESTIONS
• Do we understand how our customers decision making process is changing?
• How well are our merchant strategies aligned with our customer expectations and corporate objectives?
• Are our customer insights leveraged across our organization?
• Who are my most profitable customers?
• What drives purchasing behavior?
• What messages or promotions have my customers responded to?
• What can I influence my customers to buy in the future?
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CASE RETAIL – NEW INSIGHTS BIG DATA ANALYTICS
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CASE RETAIL – MARKET BASKET ANALYSIS BIG DATA ANALYTICS
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BIG DATA ANALYTICS CASE TELCO – LIFESTYLE ANALYSIS ON IPDR
QUESTIONS • Explore consumer web surfing behavior in IP data records (IPDR)
• IPDR describes who (IP), what (URL), when (timestamp) of web surfing
behavior
• Categorize web surfing behavior via a deep rooted taxonomy • E.g. Top/Entertainment/Video Games/Music;
• Based on standards in International Press Telecommunications Council
• Visualize the results over various dimensions to discover online
lifestyles of consumers • Visualize interests against customer segments/ profile, events, location, etc
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
BIG DATA ANALYTICS CASE TELCO – LIFESTYLE ANALYSIS
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
BIG DATA ANALYTICS
• Consumers • Being able to monitor their energy usage • Cost saving • CO2 reduction • Remote access
• Suppliers • Avoided meter reading/ site visit • Price optimisation • Debt handling • Fraud detection
• General • Reduced losses • Micro-generation technologies
CASE UTILITIES – SMART METER READING
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CASE UTILITIES – SMART METER READING BIG DATA ANALYTICS
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BIG DATA ANALYTICS CASE BANKING – ONLINE CUSTOMER LEAD MANAGEMENT
Bad Leads
Leads for nurturing
Sales Leads
Call center Leads
Marketing
Closed Sales
Qualified Leads
Sales Ready Leads
Conversions
Inquires/Suspects
Nurture
Touchpoints: Email, Online, Contacts, etc
Sales
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BIG DATA ANALYTICS CASE BANKING – ONLINE CUSTOMER LEAD MANAGEMENT
When is my customers online and what is he doing?
How is the evolution of interest in products?
Where is my customers located and what is his behaviour?
What is the relation between certain online variables?
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WHITEPAPERS: BIG DATA @ MARKETING NEXT STEPS
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THANK YOU! [email protected]
