module 1 information management and analytics final
TRANSCRIPT
Real-Time Analytics & Attribution
• Noah Powers– Principal Solutions Architect, Customer Intelligence, SAS
• Patty Hager– Analytics Manager, Content/Communication/Entertainment, SAS
• Suneel Grover– Solutions Architect, Integrated Marketing Analytics & Visualization, SAS– Adjunct Professor, Business Analytics & Data Visualization,
New York University (NYU)
Video (Time: 1:20-5:00)
http://www.colbertnation.com/the-colbert-report-videos/408981/february-22-2012/the-word---surrender-to-a-buyer-power?xrs=share_copy
Module 1
Information Management and Analytics
Information Management
“There is no better place to start than data, since it is the fuel needed to make insightful decisions
that can drive your business forward.”
OtherERP SocialCRM EDW Online
Information Management
Data Sources
Information Management & Analytics
“Being able to derive insights from data is the key to making smarter, fact-based decisions that
will translate into profitable revenue growth.”
OtherERP SocialCRM EDW Online
Data Sources
Information Management
Analytics
DataQuality
DataIntegration
DataModel
Metadata
SegmentationPredictiveModeling
Social & NetworkAnalytics
Customer Profitability &
LTV
The Business Analytics Challenge
ANALYSTS
DATA DATA DATA
One Perspective…
Marketing Perspective
INSIGHTSDATA
INFORMATION MANAGEMENT
DECISIONS
ANALYTICS
Big DataWhen volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making
OUR PERSPECTIVE Big Data is RELATIVE not ABSOLUTE
VOLUME
VARIETY
VELOCITY
VALUE
TODAYTHE
FUTURE
DA
TA
SIZ
E
THRIVING IN THE BIG DATA ERA
Which Category Are You?
Data Wasters
Data Collectors
• Underperform financially
• Misalign IT and Business
• Underuse data• Mid-levels drive data
strategy
• Drowning in data• Misaligned IT and
Business• Lack resources to
leverage data
Aspiring Data Managers
• Embrace importance of data
• Allow data to inform strategic decisions
• Invest in technology enablement
• 60% put 50% of data to use
• Lack resources to leverage data
Strategic Data Managers
• Mature capabilities in data management
• Attribute data management to C-suite
• 53% outperformed peers• First to identify measurement
& data points that align with corporate strategic goals
Com
petiti
ve A
dvan
tage
Degree of Intelligence
Big Data Marketing Challenges (1)
Source: 2012 BRITE/NYAMA Marketing in Transition Study
Big Data Marketing Challenges (2)
Source: 2012 BRITE/NYAMA Marketing in Transition Study
CUSTOMER
ANALYST
EDW
CRM
BILLING
ERP
WEB
Unlocking Siloed Operational Data To Understand Customers
?
CUSTOMER
ANALYST
Ad Hoc Exploration & Analysis Can Take Weeks
CUSTOMER
ANALYST
What If We Had A Set Of Master Keys?
(Customer ID , 12345)(Name , John Smith)(Gender , M)(Age , 42)(Life Stage , FL)(HH Income , 75K-100K)(Children Ind , 1)(HH Education, College)(HH Value Score, Above Avg)(CC Propensity, 0.57)(Visit Recency, 12)(Session Count, 7)(Session Avg. PV, 4)(Engagement, High)(Content Goal, 1)(Sticky Goal, 1)(Session Affiliate, Org Search)
CRM Data Enrichment Data
Online History Data Current Session Data
Where We Want To Get To…
Integrated Marketing Data Table
Discovery and Reporting Marketing Analytic Modeling
Data Queries Acquisition Predictive Analysis
OLAP Cube Discovery CRM Segmentation Analysis
Data Visualization Churn / Attrition Real-Time Model Execution
The Integrated Marketing Table (also known as “Customer State Vector”) is an analytic approach designed for rapid retrieval of
customer-level data from any dimension.
Integrated Marketing Data Table
Why Do We Care?
YOURCOMPETITIVEADVANTAGE
Orient
Observe
Act
Act
Orient
DecideMARKET
OPPORTUNITY
Decide
Video (Time: 0:00-5:00) http://youtu.be/CrSX97elHDA?hd=1
Big Data - Why Do We Care?
ANALYTICS INSIGHTSDATA
INFORMATION MANAGEMENT
DECISIONS
Predictive Analytics
“Encompasses a range of techniques for collecting, analyzing, and interpreting data in order to reveal
patterns, anomalies, key variables, and relationships.”
OtherERP SocialCRM EDW Online
Data Sources
DataQuality
DataIntegration
DataModel
Metadata
SegmentationPredictiveModeling
Social NetworkAnalytics
Customer Profitability &
LTV
BIG DATA
Most organizations: Can’t generate the information they need.
Can’t generate information fast enough to act on it.
Continue to incur huge costs due to uninformed decisions and misguided strategies.
The opportunities afforded by analytics have never been greater!
THE ANALYTICS GAPOUR PERSPECTIVE
Domain ExpertMakes DecisionsEvaluates Processes and ROI
BUSINESSMANAGER
Model ValidationModel DeploymentModel Monitoring Data Preparation
IT SYSTEMS /MANAGEMENT
Exploratory AnalysisDescriptive SegmentationPredictive Modeling
DATA MINER /STATISTICIAN
IDENTIFY /FORMULATE
PROBLEMDATA
PREPARATION
DATAEXPLORATION
TRANSFORM& SELECT
BUILDMODEL
VALIDATEMODEL
DEPLOYMODEL
EVALUATE /MONITORRESULTS
The Predictive Analytics Lifecycle
BUSINESSANALYST
Data ExplorationData VisualizationReport Creation
IDENTIFY /FORMULATE
PROBLEMDATA
PREPARATION
DATAEXPLORATION
TRANSFORM& SELECT
BUILDMODEL
VALIDATEMODEL
DEPLOYMODEL
EVALUATE /MONITORRESULTS
Lifecycle Challenge…
“Data is the number one challenge in the adoption or use of business analytics.”
Companies continue to struggle with data accuracy, consistency, and even access.
Bloomberg BusinessWeek Survey 2011
80%20% = :*(
Data Visualization & Exploration
Information Is Beautiful
http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.htmlVideo (Time: 0:00-5:10)
Digital Channel Exploration
Geographic Exploration
Mr. Data: Talk To Me Visually!
• Which customers should be upgraded to 4G?• Which handsets should be pushed in which region?
Handset vs. Network Compatibility
• Do dropped calls contribute to churn?• Are there handsets that are more likely to drop calls?
Dropped Calls Analysis
• Which cities have the greatest handset penetration?• Which handsets have the greatest ROI in each market?
Handset Penetration Analysis
• Which markets are being hit the hardest by your competition’s iPhone launch?
• Which cities are the responding the best to your iPhone campaign?
iPhone Launch Analysis
Customer Case Study: Telco
Customer Case Study: Telco
Inner circle represents % of calls each switch type
carried.
Outer circle represents % of drops each switch
type carried.
Total number of drops that
occurred over each handset
type
Handset %s represent the distribution of handset over each
switch
% of Drops is the drop rate
for each switch.
Total calls and minutes are displayed for each individual switch by
region
© 2011, Forrester Research, Inc. Reproduction Prohibited
Vendor Independent Report: Forrester WavePredictive Analytics And Data Mining Solutions
The Forrester Wave™: Predictive Analytics And Data Mining Solutions, Forrester Research, Inc., The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.
© 2011, Forrester Research, Inc. Reproduction Prohibited
Predictive Analytic Marketing Applications
Acquisition
Retention
Ad Targeting
Content
Personalization
Experience / Engagement
© 2011, Forrester Research, Inc. Reproduction Prohibited
This Is What You Want
Probability scores are the output of predictive models, and are an essential
ingredient to making data driven decisions
© 2011, Forrester Research, Inc. Reproduction Prohibited
Why Do You Want It?
APPLICATION SCORINGBEHAVIORAL SCORINGCOLLECTION SCORING
DE
CIS
ION
AS
SE
SS
ME
NT
ANALYTICAL LIFECYCLE
© 2011, Forrester Research, Inc. Reproduction Prohibited
Is It Hard To Do?
© 2011, Forrester Research, Inc. Reproduction Prohibited
Now What?
© 2011, Forrester Research, Inc. Reproduction Prohibited
No Silly…We Bring It To Life!
Scoring is nothing more than applying a
formula created by your model to your customer records
© 2011, Forrester Research, Inc. Reproduction Prohibited
Let’s Think Bigger – What If I Could…
. . . deliver personalized offers and services to ALL customers based on up to the minute profiles
. . . gain first-mover advantage by introducing new products and services to micro market segments that haven't been identified by anyone
. . . evaluate the impact of marketing campaigns hourly & make adjustments in real-time
© 2011, Forrester Research, Inc. Reproduction Prohibited
HIGH-PERFORMANCE ANALYTICS FOR BIG DATAARCHITECTURE
ANALYTICAL INSIGHTS
OPERATIONAL DECISIONS
DATABASE APPLIANCE
ST
RU
CT
UR
ED
&
UN
ST
RU
CT
UR
ED
DA
TA
AN
AL
YT
ICS
IN-MEMORY
GRID
IN-DATABASE
BIG Data Architecture – Game Changing!
© 2011, Forrester Research, Inc. Reproduction Prohibited
Customer Case Study:
15% improvements inMarketing campaigns
DA
TAE
XP
LO
RA
TIO
N
MO
DE
LD
EV
EL
OP
ME
NT
MO
DE
LD
EP
LO
YM
EN
T
10SECONDS
11HRS
GRID enabled analytics process to improve marketing
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Copyright © 2011, SAS Institute Inc. All rights reserved.
Big Data, Analytics, & In-Database
http://youtu.be/TUHspP8irzQ
© 2011, Forrester Research, Inc. Reproduction Prohibited
Segmentation“The practice of dividing a prospect/customer base into
groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests,
spending habits, etc..”
OtherERP SocialCRM EDW Online
Data Sources
DataQuality
DataIntegration
DataModel
Metadata
PredictiveModeling
Social NetworkAnalytics
Customer Profitability &
LTVSegmentation
© 2011, Forrester Research, Inc. Reproduction Prohibited
Classic Marketing Approach: RFM
© 2011, Forrester Research, Inc. Reproduction Prohibited
Decision Trees (Supervised Learning)
Clustering (Unsupervised Learning)
Advanced Analytic Segmentation
Business Use Case
Acquisition Marketing
Business Use Case
Marketing Strategy
Decision Trees
• Decision trees are a form of multiple variable (or multiple effect) analyses
• Allow marketers to explain, describe, or classify an outcome– Use Case
1. After analyzing Dec 2011 campaign results, we use Decision Trees to calculate the classification probability of a prospect responding to the acquisition campaign
2. Score “look-a-like” prospects for Dec 2012 campaign
Decision Tree
Data Driven Segmentation Rules
Segment #1
Recency Score: HighEngagement Score: HighAge: Young Adult (25-44)Affiliate: Organic Search
Response Probability: High
Segment #2
Recency Score: HighEngagement Score: Medium
Age: Young Adult (25-44)Affiliate: Email
Response Probability: Medium
Benefits Of Decision Trees
• The multiple variable analysis capability enables one to discover & describe outcomes in the context of multiple influences
• The appeal of decision trees lies in their relative power, ease of use, robustness with a variety of data and levels of measurement, and interpretabilityBootstrap Forests CHAID / C5 / RP Boosted Trees
Clustering
• Marketing can use cluster analysis to partition prospects/customers into segments – without the bias of a historical consumer decision
• Understand the organic synergies between different groups – Use Case
1. Marketing is planning a new campaign, and historical information is not available
2. Tag prospects with cluster results for our Dec 2012 campaign, and influence creative execution
Clustering
Finding groups of observations such that the observations in a group will be similar (or related) to one another, and different from (or unrelated) to the observations in other groups
Approach: K-Means
Number of Clusters: 3
Data Table
Step 1
Step 2
Cluster #1Weight Management
Diet Focused
Cluster #2Guilty PleasuresTaste Focused
Cluster #3Health Management
High Fiber
Benefits Of Clustering
• Segmentations arise from varied business needs & demands– Marketing vs. Sales vs. Advertising
• Integrating data streams allows greater capabilities– When combined, Marketing gains an increased understanding
of customer behavior, demographics and psychographics
Centroid HierarchicalExpectation-Maximization
Customer Profitability & LTV
“Customer lifetime value (CLV) is a prediction of the net profit attributed to the entire future relationship with
a customer.”
OtherERP SocialCRM EDW Online
Data Sources
DataQuality
DataIntegration
DataModel
Metadata
Segmentation PredictiveModeling
Social & NetworkAnalytics
Customer Profitability
& LTV
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Copyright © 2011, SAS Institute Inc. All rights reserved.
Customer Lifetime Value & Influence
http://youtu.be/BRhPS0-rx6I?hd=1
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Value of Your Company = Value of Your Customers
The only value your company will ever create is the value that comes from customers–the ones you have now and the ones you have in the future.
To remain competitive, you must figure out how to keep your customers longer, grow them into bigger
customers, make them more profitable and serve them more efficiently.
By Don Peppers and Martha Rogers, Ph.D.,Founding Partners, Peppers & Rogers Group
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Perils Of Ignoring Customer Profitability
Situation
• 20% of the customers represent 80% turnover• Some customers repeatedly contact the call-center• Sales channels are incented by revenue • Identification and retention of the profitable customers is a challenge• Marketing campaigns segment customers without considering profitability
Consequence
• Profitable and loyal customers are not recognized/rewarded• It is not the profitable customers who are retained• It is not the most profitable products which are offered to the customers• Sales and call-center staff spend their time on the unprofitable customers• Sale of unprofitable products result in losses and wasted resources• Low return on sales and marketing activities
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Focus resources on gaining and retaining the most profitable customers with the most relevant offers at the opportune time.
Competitive Advantage & Profitable Growth
Customer Centric
Revenue Growth
Customer Profitability
Customer Retention
Relevant conversations:• The way the customer prefers• At the time they prefer
Predict & Execute Proactively:• Identify customers most at risk • Identify customer influence factors• Execute proactive customer retention
Positive & Negative Profit:• Many are profitable customers• Other customers reduce profits • The key is to understand which customers fall into each category
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Path to Optimized Profitable Marketing
Consolidate and Organize
Customer data
Define Customer Value
and Cost Metrics
Define Analytically
derived Customer
Segmentations
Execute Optimized Marketing Based on
Essential Insight
Harness customer insights that result in smarter more personalized marketing execution to improve customer profitability.
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Define Customer ValueChallenges Expenses are allocated with broad strokes to
customer segments
Lack of visibility into the true drivers of profitability
Solution: An advanced profitability costing and allocation engine
A full cost view of individual customer profitability to uncover profit drivers and detractors
Understanding the root causes of adverse trends for margin, revenue, and cost for individual customers and segments.
Predicting future profitability including various scenarios for customers and segments
Understand role and influence of social network
Costs Revenue
VAS
Ala Carte
Plan
Variable
Fixed
Profit
Profit Retention Potential
Lifetime Value
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Costs At The Customer Level
In order to determine customer profitability in a reliable and repeatable way, a comprehensive source of cost data at the lowest possible level of granularity is required: The data should be available on product, service and customer
level, where appropriate.
Aggregated costs need solid decomposition algorithms, accepted by business and financial analysts
Average costs might be misleading, as the same product sold to two different customers may have differing cost profiles
Customer, product and service profitability are not universal and transferable across the entire database
Other costs to serve should be calculated using a proven methodology, like Activity-Based Costing
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Define Analytically Derived Customer Segmentations
Top 20%
Bottom 10%
Middle 70%
Segment Name Description
Most Profitable
High Value
Middle
Low
Negative
• Uncover Why they are Most Profitable• High influencer/ leader? Usage? highest churn rate?
• Uncover Why they are Profitable• Is it High usage? How high is the churn rate?
• Determine which customers have potential to move up in profit.
• Learn why they have lower margins• What is the churn rate?
• Determine why they are negative value?
Create individual segmentations for each of the profit levels
Uncover profit drivers or profit detractors for each profit level
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Accumulated Profit Curve
A smaller percentage of your customer base is driving the majority of the profit.
Source: Gartner
May be some of your largest customers
Migrate / Shift to
lower cost
Keep & migrate
Spend to keep
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Customer Profitability – The Life Cycle
Acquisition Development Retention Churn/ Win-back
Net
Marg
in
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Customer Profitability – The Life CycleN
et
Marg
in
Decisions points during acquisition:
• Looking at products and offers
• Comparing pricing
• Company can be scoring - credit worthiness
Acquisition Development Retention Churn/ Win-back
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Customer Profitability – The Life CycleN
et
Marg
in
Decisions points during relationship development:
• Service & product usage
• Customer user experience
• Cross & up-sell
• Bad debt detection and collection
• Customer service
Acquisition Development Retention Churn/ Win-back
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Customer Profitability – The Life CycleN
et
Marg
in
Decisions points during retention:
• Targeted retention activities
• Complaint handling
• Renewal pricing, discounting & bundling
• Reactive retention
Acquisition Development Retention Churn/ Win-back
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Customer Profitability – The Life CycleN
et
Marg
in
Decisions points during churn/win-back:
• Win-back discount and bundle pricing
• Trigger campaigns for future reacquisition
Acquisition Development Retention Churn/ Win-back
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= CLV
(1) – Start-up of customer case
(2) + fee income
(3) – Continuing “cost to serve”
(4) + Sale of additional products, “cross-selling”
(5) – Advice
(6) – Marketing
(7) – Initiatives for retention of customer
(8) – Influence others to churn
= Customer lifetime value
OpportunitiesThroughCustomer’s “Lifetime”
- +
Examples of Elements Affecting Customer Lifetime Value (CLV)
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How Is Competitive Advantage Created?
Insight in profitability through the entire business model
Retention of the profitable customers
Realization of the customers’ potential
Pricing of products/services considering profitability
Development of new profitable products
Restructuring of organization according to the segment’s profitability
Make processes more efficient
Profitability per customer
Profitability per product and service
Profitability per market segment
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Once Profitability Metrics are calculated, the information can be leveraged across departments.
Sales/Marketing• Offer Strategies• Promotion strategies• Product portfolio management• Customer segment management• New product intro• Channel effectiveness• Marketing direction
Operations• Network optimization strategy• High cost process that needs to be reengineered• Utilization review• Infrastructure decisions• Optimize contact center strategies • Prioritize service treatments
Finance• Improved information for business
analysis• Interconnection rates• Cost control• Process improvement• Proper capital investment
Broaden Use for Profitability Metrics
• Business Issue: Needed to analyze and understand shared expenses and overhead costs such as sales, engineering, and product development and meaningfully allocate those costs to the products sold and the sales revenue generated. Lacked right information and ability to do this on a timely basis
• Results/Benefits• Created P&Ls used to hold business
leaders accountable for financial results by sales-channel segment profitability.
• Expanded model to calculate more detailed profitability information on a monthly and annual basis in:
• Channel profitability, Customer segment profitability, Product or service profitability, Cost of business processes and Cost of shared services (such as IT)
“The cost and profitability initiative at MCI, and subsequently Verizon Business, supported by SAS Activity-Based Management, provided key information in the transition of the business through acquisition and continues to provide value that only cost and profitability insight can deliver.”
Case Study: Verizon
Social Network Analytics“Social network analysis views social relationships in terms
of network theory, consisting of nodes (representing individual actors within the network) and ties (which
represent relationships between individuals).”
OtherERP SocialCRM EDW Online
Data Sources
DataQuality
DataIntegration
DataModel
Metadata
Segmentation PredictiveModeling
Social NetworkAnalytics
Customer Profitability &
LTV
81
Copyright © 2011, SAS Institute Inc. All rights reserved.
T-Mobile & Social Network Analytics
http://youtu.be/Orr5lzLul8c?hd=1
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What is Social Network Analysis (SNA)?
Overview
The practice of identifying and measuring the relationship structure that exists between individuals within a social network..
This is most commonly used in the telecommunications industry where it is used to understand the links formed through voice, text and picture messaging. Individuals can be differentiated by the number and nature of their connections to others.
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Business Value of SNA
Social Network Analysis provides both a deep and broad understanding of customer behavior. When combined with proven advanced analytics this enables the development of many powerful business focused solutions which help build strong and measurable customer advocacy.
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SNA Based Business Solutions
Below are examples of business solutions that rely on SNA:
Social Network Propensity Scores - eg. improve churn prediction, average $, or customer advocacy.
Persistent Individual Identification- Enables multi-SIM use, prepaid SIM recycling, and improved churn reporting.
Customer, Household, and Life-Stage Segmentation. Customer Value
- Understood in terms of relations and influence upon purchase behaviour of others.
Acquisition Of High ARPU Prospects - And competitor customers through referral and highly targeted viral campaigns.
Agile Campaigns- Insights and data provided which indicates when specific customer actions occur (enables a shift from monthly routine of mass campaigns).
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Better Customer Understanding Most mobile providers perform customer segmentation,
usually based upon call usage behavior or profile.
Also predictive analytics to identify churn risk customers.
Social Network Analysis reveals relationships and measures the influence customers have upon others.
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ChurnChurn
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Agile Customer Management Social Network Analysis is used to develop event-based
campaigns and customer management strategies.
Churn is an example; - contact friends immediately after a customer churns.
SNA enables a move from traditional monthly batch analytics.
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ChurnChurn
High Risk
High RiskHigh Risk
High Risk
High Risk
High Risk
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Community Detection
In addition to better understanding of individual customers SNA can be used to create or enhance household segmentation by identifying communities.
The purpose of Community Detection is to identify the strongest relationships within the customer base.
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Communities Detection The allocation of communities need not be mutually exclusive.
These can be hierarchical communities which may first represent immediate family and then extended friendships.
Supporting hierarchical communities is essential when solving conflicting business goals such household segmentation (which requires close communities) or viral marketing (which requires larger communities for optimum results).
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Household Segmentation Because Community Detection finds the natural social groupings
of all customers it is a powerful mechanism for Household Segmentation.
Using analytics to combine information about social links with, for example, customer age, gender or location it is possible to accurately infer household type and customer life-stage.
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Different Surnames Matching Address Age Group 25-30 yrs Young Couple Segment
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Male & Female Postpaid (age 40 yrs) Single Prepaid (age 19 yrs) Mature Family Segment
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Know True Customer Value Customer advocacy is critically important in today’s
marketplace. SNA is used to track adoption and spread of new services and identify key influencers.
Community detection is used to attribute $$$ value that is not visible at an individual customer level. Households that span competitor networks indicate share-of-wallet.
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I’m a highest value customer
I just boughtan Android
It looks cool, now I might
buy an Android..
I influence my partner’s purchasing decisions…
It looks cool, now I might
buy an Android..
I’m a high value customer on a
competitor network
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Not All Links Are Created Equal Customer relationships can be distinguished and
analyzed by Their strength (e.g. number of calls) Their interval class (e.g. days between calls)
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I’m a high value customer on a
competitor network
We discuss sportsscores on the weekend
We chat everydayWe chat everyday
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Identification Of Roles Customers are categorized by links and position within the entire
social network (in some cases roles are relative to the community). Leaders: Highest number of links and centrality measures. Followers: Similar to Leaders, to a lesser extent. Usually directly
connected to a Leader. Marginals: Similar to Followers, but not often connected to a Leader. Outliers: Few links and often low centrality measures. Bridges: Connect Communities and isolated individuals
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Improve Retention of “Leaders”
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Capability Marketing Action BenefitIdentify highly connected “Leaders” within customer base.
Target retention strategies to “Leaders”.
More efficient targeting of marketing spend.Reduced attrition / improved retention.Communications rapidly spread throughout the customer base.
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Improve Retention of “Followers”
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Capability Marketing Action BenefitIdentify “Followers”.Know when a “Leader” churns.
Implement highly reactive event-driven retention strategies for “Followers” at-risk
Minimise viral churn. Efficient timing & targeting of marketing $’s. Reduced attrition / improved retention.
ChurnChurn
High Risk
High Risk
High Risk
High Risk
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Use Viral Effect For Acquisition & Growth
Capability Marketing Action BenefitIdentify influential "Early Adopters" & “Bridges” to better understand viral adoption of new products.
Target cross / up-sell strategies to "Early Adopters". Leveraging viral power of “Bridges” to competitor customer bases.
Understand acquisition value of campaigns and indirect outbound communications. Improve timing & relevance of new offers.
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Persistent Customer Identification By examining a customer’s position within the social
network it is possible to infer persistent identification even after churn, mobile service number, or address changes.
This approach can, for example, also be used to identify Prepaid SIM recycling and multi-SIM use.
Accurate reporting of monthly ‘Churn & Adds’ numbers are critical to correct strategic decision making.
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Same Individual
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CLA In Banking / Financial Services Data is different and does not capture a true social network
Pseudo-social network (PSN) where consumers are linked if they transfer money to the same entities
Effectiveness of targeting network neighbors can be attributed to similarity rather than to social influence
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SNA in banking / financial services
An analytic framework that enables marketing analysts to enhance customer insight by identifying and incorporating consumer purchasing similarities and their strength in profiling and segmentation.
Use SNA derived variables to generate superior customer understanding and improve campaign effectiveness: Target those individuals that are strongly connected to key
individuals
Enhance campaign management process by introducing new consumer variables and methodology (e.g. campaign selection and response attribution).
Data can be exploited in a privacy-sensitive way, since it is not necessary to know the identities of the connected consumers or the institutions that connect them
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Copyright © 2011, SAS Institute Inc. All rights reserved.
Oi & Social Network Analytics
http://youtu.be/1O75bcTpb_M?hd=1
• Know how to gain efficiencies and boost ROI with marketing automation.
• Recognize the keys to achieve real-time relevance in both inbound and outbound channels.
• Understand how to plan, prioritize and execute to maximize profits.
Saturday Afternoon Preview
Orchestration & Interaction
OtherERP SocialCRM EDW Online
Information Management & Analytics
Data Sources
Marketing Decisions
Marketing Optimization
Real-Time Decisions
Multi-Channel Campaign Management
Case Studies
Questions?