Customer Intelligence & Analytics - Part IV: An Evolutionary Process: Moving Toward Analytically Driven Marketing

3.1 Introduction

3.2 Marketing optimization

3.3 The art and science of the marketing mix

3.4 Real-world, success case studies

3.5 Questions

Module 4: An Evolutionary Process - Moving

Toward Analytically Driven Marketing

• Debbie Mayville – Sr. Solutions Architect, Communications & Marketing

Analytics, SAS

• David Kelley – Sr. Solutions Architect, Customer Intelligence, SAS

• Suneel Grover – Solutions Architect, Integrated Marketing Analytics, SAS

– Adjunct Professor, Integrated Marketing Analytics,

New York University (NYU)

3.1 Introduction




3.5 Questions



The Marketing Process

Risk Customer Service

Corporate Affairs

Merchandising

Finance

Operations

Online Mobile

In Person

Call Center

Direct Mail

Social

Campaign ERP Social CRM EDW Online

Optimization

Marketing Strategy

Marketing

Marketing Processes

Marketing Campaigns

Analytics

Data Integration

Analytics


Corporate Affairs

Merchandising

Finance

Operations

Online Mobile

In Person

Call Center

Direct Mail

Social



Marketing

Data Integration

Campaign Management

Real-Time Decisioning

Marketing Operations

Management

Marketing Performance Management

Optimization Marketing Mix Analysis

Online Customer Behaviour

Social Media

Optimization

A computational problem in which the objective is to

find the best of all feasible solutions

Optimization Defined

• Many customers, offers, channels

• Managing the contact strategy

• Looking ahead and behind

• How do you allocate offers effectively to maximize return?

The Relationship Marketing Context

• Many constraints impact decisions

Budgets, resources, policies

• How to respect constraints?

• How to reconcile competing goals?

• How to plan effectively for change?

Intelligence

Busin

ess V

alu

e

Marketing Optimization

Predictive Modeling

Marketing Simulation

Marketing Dashboard

Data Quality, Integration

Data Access

Predictive Reactive Proactive

“What would happen?"

“What should I do to achieve the best

results?“


“What measures are available to better understand our

business?”

“How can we trust analysis if we don’t trust the data?”

“How many new customers did we get last

month? How much customer attrition?"

“How likely are my customers to respond to

an offer?”

Strategic

Product A

Product B

Product C

Massive Problem - Potential Choices

Marketing Optimization Applications

• Financial Services

– Insurance policy offers

– Credit line increase/decrease

– APR to offer on balance transfer offers

• Telecom

– Complex cell phone plan offers

– Bundled services

– Cross channel offers with different execution costs

• Hospitality (Hotels, Casinos)

• Loyalty offers

• Retail

• Personalized coupons (POS)

• Offer prioritization and collisions

• Contact stream optimization

Do All Marketing Approaches

Yield The Same Results?

10–100+ %

Optimization

- Solves by holistic approach

- Factors all constraints

- Determines the best result

Prioritization

- First In, First Out

- Prioritized by Campaign

- Does not provide best combination

Customer Rules

- First In, First Out

- Prioritized by Customer/Campaign

- Fails in the face of constraints

5-10 %

?

Campaign A Campaign B

Campaign C

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Camp’ CCamp’ BCamp’ AClient

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Optimization Techniques Example

• Lines of business = 3

• Return = expected value (probability*expected revenue)

• Business objective = maximise value

• Constraints: Each customer is assigned to at most 1 campaign

Each campaign can have at most 3 customers


Campaign C

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Optimization Techniques –

Campaign Prioritization

• Campaigns assigned a priority

• Customers allocated to campaigns by expected customer value


Campaign C

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Expected Return: Constraints: 1 customer - 1 campaign 1 campaign - 3 customers

Cross-channel Optimisation


Campaign C

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485 Expected Return:

Constraints: 1 customer - 1 campaign 1 campaign - 3 customers

260


Campaign C

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655 Expected Return: Constraints: 1 customer - 1 campaign 1 campaign - 3 customers

485


Campaign C

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Optimization Techniques - Customer Rules

• Customers assigned a priority

• Campaigns allocated to customers by expected customer value


Campaign C

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Customer Rules

120 Constraints: 1 customer - 1 campaign 1 campaign - 3 customers

Expected Return:


Campaign C

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75110809

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Customer Rules


Expected Return: 195


Campaign C

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195

Customer Rules


Expected Return:


Campaign C

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270

Customer Rules


Expected Return:


Campaign C

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Customer Rules




Campaign C

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425

Customer Rules

500 Constraints:

1 customer - 1 campaign

1 campaign - 3 customers

Expected Return:


Campaign C

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5060755

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75110809

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500

Customer Rules

580 Constraints:



Expected Return:


Campaign C

75110809

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5060755

7580554

6575603

7570502

901201001


75110809

6060658

7570807

6065756

5060755

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Customer Rules

Constraints:





Campaign C

75110809

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7570807

6065756

5060755

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6575603

7570502

901201001


75110809

6060658

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5060755

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Customer Rules

+60 Constraints: 1 customer - 1 campaign 1 campaign - 3 customers



Campaign C

75110809

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5060755

7580554

6575603

7570502

901201001


75110809

6060658

7570807

6065756

5060755

7580554

6575603

7570502

901201001


Optimization Techniques - Optimization

Business objectives, constraints, contact policies define ‘priority’

Optimization decides allocation


Campaign C

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Optimization

+30 Constraints:




Planned Campaigns

Eligible Customers

Model Scores

Contact Policy

Define Optimization

Scenarios


Identify & Execute Optimal Outcome

“What-If Analysis”

Review Optimization

Results

Optimization Parameters: • Objective • Suppression Rules • Constraints:

• Budget • Capacity

• Contact / Blocking Policies

Marketing Optimization: Process Flow

Case Study: Commerzbank, Germany

+407% ROI

Business Impact

• POV: Up to 80% ROI improvement

• Production: 50% yield with the

same budget

• ROI increased by 407%

"We have compared SAS intensively with other manufacturers offerings. The result was impressive: SAS Marketing Optimization is exactly the solution we were looking for. We are setting an industry Benchmark”

Heiko Güthenke, Department Director Customer & Business Analysis

Challenges

• 4 million customers, 20 offer types

• Optimize utilization of consultants

• Optimize Yield vs. Budget

• Optimize Marketing ROI (revenue /

cost)

http://www.adexchange.co.uk/media/63969120/sms-icon-on.gif

More Case Studies…

Client Name Benefits

Vodafone (Australia) • 3-10x Response Rate increase

• Improve campaign ROI by 4x

• 30% reduction in campaign costs

Scotiabank • 50% Campaign ROI improvement

Major Insurer • 12% increase in revenue; 52% in earnings

• Savings of >$4 million per year

U.S. Regional Telco • $6 million incremental LTV in the 1st month

Global Telco • Reduced call center contacts by 25% without

decreasing effectiveness

#1 Market Share European

Retailer

• Individualized targeting of monthly coupon

mailers

• Increased offer response rates

• Decrease mailing costs

3.1 Introduction




3.5 Questions



Analytics


Corporate Affairs

Merchandising

Finance

Operations

Online Mobile

In Person

Call Center

Direct Mail

Social



Marketing

Data Integration

Campaign Management

Real-Time Decisioning


Management

Marketing Performance Management

Optimization Marketing Mix Analysis

Online Customer Behaviour

Social Media

Interactive Direct 1:1 Email & Mobile

Advertising &

Promotions

Social Marketing

Retail Marketing

Web (Corp)

Web (eCommerce)

Social Media

Media & Ads

Direct Mail

Word of Mouth

Sales Customer

Service

How do you decide the right mix across all channels?

Increased Complexity With Marketing

Above The Line…Below The Line…

» Above the Line » Below the Line

Above the Line…Below the Line…

» Above the Line

» Below the Line

• How did we perform across products, geographies, campaign types?

• What marketing activities drove our new sales?

• What if we move funds from traditional to online marketing?

• What actions/decisions to we make for various scenarios?

• MOST of my marketing data is in silos…can I leverage it for analysis?

Media Planner/Buyer

Brand Manager

Interactive Marketing

Marketing Planning

• How can I get the right offer, to the right person via the right channel?

• Can I coordinate my multi-channel campaign efforts?

• Can I be relevant with EVERY interaction, every time?

IT

Interactive Marketing

Director of Database Mkt

Campaign Planner/Designer


• Aggressive corporate goals & objectives

• Increased accountability and scrutiny

into marketing budgets

• Reductions in budgets

Marketing Challenge: Financial Pressures

Questions Marketing Mix can Address

• How can I still achieve my marketing goals while facing

budget cuts?

• I am below target, how do I re-allocate my marketing

budget to hit targets?

• How do I decide where to invest my marketing budget to

support a product portfolio?

• How and where do I invest in social media to maximize

business impacts?

• Where do I increase marketing investments to achieve

higher returns?

A data driven analytic process that quantifies the

relationship between drivers/influencers of sales and the

resulting sales across channels

• Understand the past performance of sales & marketing activities

• Analyze and assess average ROI and marginal ROI

• Evaluate marketing investment among ever increasing media options

• Compare and assess different future marketing spending plans

What is Marketing Mix Modeling?

Marketing Mix Technology

Analytic Dashboards

Technology Capabilities

Analytic dashboards

• Analytic data warehouse surfaced through

interactive dashboards

• All media and promotions display in one location

with prebuilt reports delivering summary and

detailed results

Powerful analytic tools

• Understand the impact of advertising on sales

and incorporate into response models

• Ability to explore product interactions to

understand and uncover halo and

cannibalization effects across your product

portfolio

Analytic Dashboards

Halo / Cannibalization Analysis

Adstock Analysis


Elasticity Reports

Sensitivity Report

Diminishing Returns

Response Model Diagnostics

Econometric response models

• Build and test time series and causal models

Elasticity reports

• Objectively quantify the relative responsiveness

of each driver of sales

• Decompose sales into its various components.

Diminishing returns

• Capture changes in marginal ROI as spending

levels increase through diminishing returns

curve for each channel

• Determine the threshold point beyond which

marketing expenditures would not yield any

additional benefits


Report Dashboard

Decomposition Reports

Simulate/Forecast

Optimization Marketing Mix Analytics

“Leave less up to chance and make data

driven, evidence-based decisions”

Compare scenarios

What‐if analysis & scenario planning

• Ability to simulate expenditures over different

media and analyze the impact on

products/brands/channels/geo’s

• Compare competing spending plans to

understand the differences in sales

Marketing mix optimization

• Optimal media expense allocation for selected p

roduct, channel & geography combination over a

defined period of time.

• Define different sets of business constraints to

explore the impact on the optimal solution

Case Study: Large Insurance Company

“The technology help us develop a “strategic” tool that enables us to lower risk in decision-making as we integrate all

marketing disciplines with an eye toward better forecasting, budgeting, and collaboration.”

Director of Strategy

Quantify effectiveness of all marketing mix elements

• Direct-response • TV • Direct marketing • Web marketing • Retail channel

communications

Marketing mix analytics allows them to share assumptions about marketing analysis across all types of marketing Data is integrated from multiple sources and analyzed to ensure accurate short-term and long-term forecasts across marketing and operations

Even though they are consistently outspent by their competitors, became more competitive by determining which media and channels worked the best across products and regions.

Business Issue Solution Benefits

3.1 Introduction




3.5 Questions

