BBDO Connect Big Data
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Transcript of BBDO Connect Big Data
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Tim Nagels, Business Lead Microsoft Dynamics
Franky Willekens, Head of Data Analytics BBDO
Forget about big data. Think big about any data.
October 2012: Franky goes to Las Vegas
Big Data. Customer Engagement. Marketing Accountability. DMA2012 Brings It All Together.
Survey participants DMA2012
91% wants to 34% is able to
What is the level of data maturity in your company? DATA
The Data Maturity Stairway
I capture customer data across different touch points
I consolidate customer data into one customer database
I analyze historical customer data (purchases, interactions, motivations)
I know what type of offer, channel and time is best for different customer segments
I uncover hidden patterns in customer data to predict what they are likely to do next
I deliver customized interactions at point of impact across touch points
Gather Data
Aggregate Data
Customer Insight
Targeted Communications
Predictive Modeling
Real-time contextual interactions
Today’s story
Relevance + Utility =
Customer Experience Leaders
+43.0% S&P 500 Index
+14.5%
Customer Experience Laggards
-33.9%
Reason 1
Reason 2
86% of customers are willing to pay more for a better customer experience
Silos in the organization
Data Silos
Marketing Sales Customer Service Billing Dept.
We live in the age of the customer. A 20-year business cycle in which the most successful enterprises
will reinvent themselves to systematically understand and serve increasingly more powerful consumers.
Forrester
• Focus of the CMO should be on creating and safeguarding the customer experience!
• Fueling innovation and new business models: CMO as firestarter...
• Become owner of customer data that will guide and enable your company strategy.
Bigger role for the CMO
Overview
Managing the data Understanding the data Acting on the data
Managing the data Understanding the data Acting on the data
Volume - Variety - Velocity - Value
• 47% of women would share their mobile phone location with a retailer in return for a $5 credit
• 83% would do so for a $25 credit
Value for Value
Research Now
Two different data sources: ‣ Own data Ask the customer (explicitly)
Auto-populate data (implicitly)
‣ External data Paid
Open
Any data
Value for value in data gathering
Managing the data Understanding the data Acting on the data
Personas are a vivid description of your customer database records.
Socio-demo
Habits
Attitudes
Consumption
Media
Technology
Socio-demo
Habits
Attitudes
Consumption
Media
Technology
Customer Behavior Data
Research data Social listing data Media reports data
Third party data …
ANALYZE
SYNTHESIZE
Analyze Customer Data
Registration Data Web Browsing Email Response User Actions
Engagement level
User action 1
User action 2
max min
Clustering Database Data
Third Party Data
Shopper Panel data
Socio-demographic & Lifestyle data
Different who?
Engaging Emily
• X% in db population • Socio-demographic profile • Shopping attitudes
• A-Brands • Coupon usage • P&G category spend
• Lifestyle data
Inactive Iris
Different what?
• Content and offers • Frequency
Site engagement • Repeat visits x2 • Time on site x9 Sales • Double-digit growth with FlavorPrint users
Predictive Analysis
“We found that 74% of the time, our model could correctly predict the exact address.”
Uber
HISTORICAL CUSTOMER DATA CLIENT ID BIRTH DATE LOCATION … # TRANS JAN # TRANS FEB # TRANS MRCH
567678 25/11/1976 3400 8 2 0
566777 23/09/1987 3245 4 8 0
567789 11/08/1945 6700 6 8 6
445566 21/03/1967 9000 8 9 3
CURRENT CUSTOMER DATA CLIENT ID BIRTH DATE LOCATION … # TRANS APR # TRANS MAY # TRANS JUNE
567898 25/08/1956 2440 6 1 0
589777 13/09/1977 3000 4 8 0
467789 11/09/1969 2431 5 2 0
445578 12/05/1988 1000 8 9 2
TODAY
CHURN FLAG YES NO NO NO
T + X
CHURN PREDICTION
YES NO YES NO
LEARN
APPLY
How it works
What’s the most likely model of interest when repurchasing?
Initial enquiry data
(date, model, method, previous car…)
Purchase data
(date, model, engine type, options, …)
Driver data
(birthdate, location, dealer…)
Satisfaction data
(survey completion, …)
Attributes
Predicted Model of Interest
DM pack Email DM pack
Es-mated Repurchase Date
Targets within buying window Non-‐responders Non-‐converted
Test Drivers
Customized customer experience
A A
B
B
C
FMCG Company
Which is the most likely coupon offer combination that will trigger redemption?
Household data
(family size, age, …)
Online response data
(email open/click behaviour,…)
Profile data
(brand consumption, …)
Redemption data
(coupon redemption, …)
Attributes
Democratization of data science
Power to the marketeer
Managing the data Understanding the data Acting on the data
Relevance + Utility =
“A pre-planned series of integrated, targeted communications, content or services designed to deliver a personal experience
for the consumer across all touch points.”
Customer Journey Planning
From campaigns (ads) to customer moments (value)
1. What are the make or break moments?
2. How can this be a positive experience?
3. What data do we need to help deliver the experience?
• Persona • Time • Place • Device • External Data { Context
5 golden rules for creating ‘contextual’ customer connections
#1 - Make it easy to interact
It’s CMR
#2 - Combine data and collaborate
Context creates new connections
#3 - Create value, not campaigns
"We are moving more and more toward service, personalization, [and] customization."
Guive Balooch, global director of L'Oréal's Connected Beauty Incubator
#4 - Make real-time data a reality
Data and technology ‘yes’, adding creativity and imagination ‘woohoo’
#5 - Think big about any data
Key takeaways
Relevance + Utility =
Requirements: • Data driven culture • People & Technology
Data-driven company culture
Strategy Data
User Experience
Optimize + Innovate
People & Technology
Next 48 hours?
1. What’s your next move on the data maturity stairway? 2. Who in your own organisation will you address and involve
in order to be able to move? 3. What value will you be offering beyond your product or
service?
Think big about any data
Thank you!
October 9th
IAB ThinkData November 20th