Leveraging Analytics to Deliver Personalized Customer...

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30 October, 2014

Leveraging Analytics to Deliver Personalized Customer Experiences

Sejal Sura

1

© Fractal 2014 | Confidential 2

Agenda

Context: Evolution of Customer Journey

Business Implication and Challenges

Best Practices

Creating Personalized Customer Experiences

Case Studies


Past: Consumer Purchase Process and Mass Media Advertising

One message for one channel marketing to everyone in different phases of the purchase decision process

Purchase Evaluation Consideration Need

Recognition Post-Purchase

Evaluation

Audience

Brand TV network


Current State: Consumer Purchase Process

The consumer journey has become increasingly complex with a multitude of device and channel options for brand interactions at any given point of the purchase process

74% of consumers rely on social networks to guide their purchase decision


Proliferation of Devices has significant impact on the Consumer Purchase Process


Brand Marketing Evolution

Current Future

Cross- Channel

Multi- Channel Omni Channel


Agenda



Best Practices


Case Studies


Business Implication

Brands lack single view of consumer to provide holistic customer experiences


Challenges to deliver personalized experiences

Organizational Commitment Digital marketing channels operate in silos

Measurement, Test and Learn Fragmented data Channel centric reporting, analysis and optimization

Technology No single cross-channel platform to execute, evaluate and optimize media performance


Agenda



Best Practices


Case Studies


Strong senior leadership to spearhead initiative

Establish a Center of Excellence

Best Practices: Organizational Commitment

SEO

SEM

.com

Social

Email

Display

Data Storage

Analytics

Center of Excellence

Reporting


Map business objectives to KPIs and ensure alignment with strategy and tactics

This map defines what to measure, why it’s measured, and what decisions will be made based on certain outcomes

Best Practices: Measurement, Test and Learn

Vision

Objectives

Strategy

Tactics

KPI

Metrics

Overarching philosophy of the business

Brand X’s plan for success

Approach toward realization of goals

Campaigns, programs and projects

Primary success metrics for each effort

Optimization measures for every effort

Data Sources

Analysis Dimensions



Create cross-channel reports/dashboards

Dashboards/ Reports Audience Content

Reporting Cadence Format

Executive Dashboard

C-level Strategic overview of marketing performance

Monthly/Quarterly Snapshots

PowerPoint

Marketing Dashboard

Marketing/ Ecommerce

Detailed reports providing operational and tactical insights to optimize marketing performance

Weekly/Monthly Excel/PDF

Cross-Agency Dashboard

Agency Partners

Highlights performance across channels and provides learnings for future campaigns

Monthly/ Campaign Excel/PDF

Ad-hoc requests

Marketing Varies As needed Varies



Define Objective & Select Test

Metrics

Finalize Hypothesis

Develop Initial

Hypothesis

Determine Sample

Parameters

Determine Scope of Test

Select Test Dimensions

and Attributes

Develop Creative Element for Test

Conduct Test Post-Test

Analysis

Decision & Next Steps

Initiation Phase Development Phase Testing & Evaluation Phase


Create roadmap to implement analytics infrastructure in phases

− Establish Measurement Framework and Test / Learn / Optimize Cycles

− Build cross-channel measurement and data integration capabilities

− Onboard data-driven targeting and personalization technologies

Best Practices: Technology


Agenda



Best Practices


Case Studies


Solution

Integration of data across digital channels to provide holistic view of consumer to create an omni-channel experience

Andy John

Deb Kelly

Jill John

Willie Mark friends

Time

IAMS ProActive

Event 25% Off

IAMS Pet Foods

?

Trip

purchased at

Positive Review Reconstruct Jen’s profile

from her digital footprint

Jersey City


Reconstructing Jen is to understand her attitude, aspirations and values

Classifying her behavior into dimensions to develop personalized content

Connect Jen’s touch points across the consumer journey

Behavior Dimensions Intent

Purchases

Clicks

Interactions

Social connections

Attitude

Interests

Relationships

Geo-locations

Context

Events Jen


Identify Jen’s Behavioral Touch Points

Determine data sources that capture Jen’s interaction with the brand

Syndicated Data

Marketing

Campaigns

Digital Properties

Retailers

Customer

Service


Data Collection, Validation and Transformation

Obtain granular level data for every customer

Machine learning and pattern-matching algorithms


Classify a customer along different attributes to enable personalized interactions and targeting

Post Graduate

Spend

Recency

Brand Loyalty Index

Bargain hunter

Natural Diet

Price sensitive

Health Conscious

Life stage

Household Size

Education

Jen

Pet owner

Online shopper

Large pack Buyer

Home Owner

Very High High Medium Low



Very High Medium

High Medium Low


Very High Medium Low

Single

High

Married With kids

Medium Low

Very High

Low High

Under Graduate

Dog Others Cat

High

Very High Medium Low High

Owner Rental

High School

Very High Medium Low High


Delivering Personalized Experiences

Manage Interactions

Capture Responses

Analytical Modeling

Analysis & Reporting

Data Driven Smart Marketing Capability Marketing Programs

Marketing Execution

Implement Targeting Rules

Creative Offers Channel Frequency

Email Ipad Phone Web Retail

Customer Interaction Channels

Business Objectives

Single View of

Customer

Business Strategy

Customer Data

Client Data Sources

Transactions Interactions Survey data

External Data Industry data Competitive

research Market

research

Reports & Hypotheses

Optimization

Campaign Management

Campaign Management

Campaign Learnings

Campaign Optimization


Benefits

Dynamic segmentation of customers along different attributes

Utilize customized business rules to target profiles

Test, evaluate and optimize content in real-time

Deliver personalized experiences by device, channel, time of day, location


Agenda



Best Practices


Case Studies


Case Study #1

Business Challenge

Objective

Major CPG manufacturer was randomly delivering promotional offers

Minimal redemption rates

High costs to deliver and maintain many offers

No record of offer performance

Increase revenues through focused targeting of promotional programs. Identify who to target Determine which channel/device to target Define what content to communicate Track offer performance


Case Study #1: Solution

Output Available data

Profiles shoppers on various attributes based on observed online behavior

Appended Data

Self Stated Data

Derived Data

Observed Data

Social Media Data

Customer Profiles

Machine learning & pattern-matching

algorithms

Process

shopper demo & attitude

product profile

media profile

brand profile


Case Study #1: Process

Better Engagement Targeted Interaction Driving Promotional Efficiencies

Business Units Customer DNA

Campaign Strategy

• NO trips in Woman's Apparel in last 3 months but 2+ trips in last 12 months

• Currently buying Kids' Apparel. No Women's Footwear in the past 12 months

• Customers likely to move

Offer determinants Targeting determinants

Rule Offer

$10 Off $75 Apparel

$15 Off $75 Fitness Apparel

$5 Off Footwear

Rules Repository


Default offer issuance reduced from 23.2% to 4.5%

230% increase in redemption revenues

Overall redemption rates increased from 0.27% to 0.47%

Program infrastructure developed new capabilities

Customer DNA database

Offer repository with performance history

Benchmark performance metrics

Case Study #1: Business Impact

50+ Attributes for 60MM households optimizing 4000+ offers across 14 Business Units. Powering more than 75 campaigns every week.


Case Study #2

Business Challenge: Major financial services organization seeking to understand how to improve engagement with Financial Advisors to increase revenue.

FA’s end-clients

Financial Advisor (FA)

Stocks

Fixed Deposits

Bonds

Commodities

Huge competition

Mutual fund companies

• Which FA do we pursue? • When do we contact the FA? • What are the optimal channels to leverage? • What are the FA’s preferred products? • What are the messages that best resonate with each FA? • What are the triggers that impact cross-sell?

Business Objectives:


Case Study #2: Solution

Profile Financial Advisors on various attributes based on observed online behavior transactions and fund data

FA’s

inte

ract

ion

data

Fu

nd d

ata

FA’s

tran

sact

ions

Daily sales

Monthly portfolio

Sales Segments

Daily web activity

Daily social media activity

Daily emails

Daily call and visits

Daily fund NAV

Marketing campaigns

Influencer within Firm

Digital Engagement

Firm & Fund Matrix

Messaging Response

Channel Mix Preference

Social Network Activity

Redemptions

High

High

Deviant

Sales

Digital

High

High

Medium

Medium

Equity

F2F

Medium

Medium

Low

Low

Compliant

401(K)

Phone

Low

Low

Contact Sequence Call -- Email-- Call

Share of wallet Healthy Average Poor

Sensitivity to fund performance High Medium Low

Available Data

Process

Output


Attributes created for 350K advisors

Identification of optimal contact channels for sales interaction for 90K advisors

Identified 13.2K sales leads with 65% hit rate

Case Study #2: Business Impact

Leveraging Analytics to Deliver Personalized Customer...

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