Module 1 Information Management and Analytics Final

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


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.”


Data Sources


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?

http://youtu.be/CrSX97elHDA?hd=1

http://youtu.be/CrSX97elHDA?hd=1

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.”


Data Sources

DataQuality

DataIntegration

DataModel

Metadata

SegmentationPredictiveModeling

Social NetworkAnalytics


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)

http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html



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.


Predictive Analytic Marketing Applications

Acquisition

Retention

Ad Targeting

Content

Personalization

Experience / Engagement


This Is What You Want

Probability scores are the output of predictive models, and are an essential

ingredient to making data driven decisions


Why Do You Want It?

APPLICATION SCORINGBEHAVIORAL SCORINGCOLLECTION SCORING

DE

CIS

ION

AS

SE

SS

ME

NT

ANALYTICAL LIFECYCLE


Is It Hard To Do?


Now What?


No Silly…We Bring It To Life!

Scoring is nothing more than applying a

formula created by your model to your customer records


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


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!


Customer Case Study:

15% improvements inMarketing campaigns

DA

TAE

XP

LO

RA

TIO

N

MO

DE

LD

EV

EL

OP

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NT

MO

DE

LD

EP

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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




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..”


Data Sources

DataQuality

DataIntegration

DataModel

Metadata

PredictiveModeling



LTVSegmentation


Classic Marketing Approach: RFM


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.”


Data Sources

DataQuality

DataIntegration

DataModel

Metadata

Segmentation PredictiveModeling

Social & NetworkAnalytics

Customer Profitability

& LTV

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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



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et

Marg

in

Decisions points during relationship development:

• Service & product usage

• Customer user experience

• Cross & up-sell

• Bad debt detection and collection

• Customer service



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et

Marg

in

Decisions points during retention:

• Targeted retention activities

• Complaint handling

• Renewal pricing, discounting & bundling

• Reactive retention



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et

Marg

in

Decisions points during churn/win-back:

• Win-back discount and bundle pricing

• Trigger campaigns for future reacquisition



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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).”


Data Sources

DataQuality

DataIntegration

DataModel

Metadata

Segmentation PredictiveModeling



LTV

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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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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


Information Management & Analytics

Data Sources

Marketing Decisions

Marketing Optimization

Real-Time Decisions

Multi-Channel Campaign Management

Case Studies

Questions?

Documents

Module 1 Information Management and Analytics Final