Looking Inside the Consumer Wallet Key Success Factors for Driving Loyalty in a Competitive...

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Looking Inside the Consumer Wallet Key Success Factors for Driving Loyalty in a Competitive Environment

Top Themes for Today:

1. Big Data delivers key macro and micro business insights

2. New world realities require new models

3. Full-wallet, 360o view is the key to marketing success

Bring it All Together – A Roadmap for Success

Big Data Delivers Business Insights

Real Consumer Behavior Based Data Sources

Products & Tools Deliver Quick Insights

Accelerant for growth

Effective Marketing Starts with Big Data

Member Reported Data

MasterCard Network Data

Third Party Data

Survey Based Data

• Early indicator of Sector and Channel trends• Clear understanding of consumer behavior

• Competitive view at a location level

Economic trends Competitive benchmarksCustomer insights

Big Data in Action: Understanding Economic DriversExample – 2011US Retail PerformanceOverall retail sales (ex auto) showing resilience through August of 2011

The growth rates may decelerate into Q4 with a more difficult comparison environment and poor consumer confidence.

Source: MasterCard SpendingPulse September 2011

Big Data in Action: Understanding Economic DriversExample – US retail sales

One of the largest domestic opportunities is to capture $ migrating from brick and mortar to the online sales channel

• In Apparel alone over $8 billion will move from brick and mortar to online over the next three years

• Online Apparel sales are approaching 20% share of Apparel market

Economic conditions remain unfavorable for an aggressive expansion

• Elevated total retail sales growth rates may moderate as we move through the rest of 2011

• Employment, housing and confidence have to improve for sustainable retail sales growth in 2012


Big Data in Action: Understanding Economic DriversExample – US Sector Performance

Consumer Discretionary US

Apparel + 6.1%

Luxury ex. Jewelry + 7.4%

Department Stores - 0.7%

Jewelry + 8.9%

Consumer Durables

Electronics & Appliances - 7.2%

Furniture & Furnishings - 1.6%

Auto Parts, Service (ex. Dealers) + 2.0%

Hardware + 4.9%

Travel

Airlines - 1.0%

Lodging +4.3%

Restaurants +7.1%


Big Data in Action: Understanding Key Channel TrendsExample – Online Sales

Electronics was marginally above zero growth to halt a two month string of negative growth rates.

Online sales accounted for over 22% of Apparel sales on Tuesdays in August!

Online sales only represent 7% of Apparel sales on Saturdays in August.

Online Apparel sales had year-over-year growth of almost 20% in August 2011.


Big Data in Action: Monitoring Key Channel TrendsExample: US ecommerce sales shift online


Online retail sales growth has accelerated in August to almost 17% compared to August 2010.

Big Data in Action: Monitoring Key Channel TrendsExample: US Apparel had 16.4% of sales online in 2010


16.7% of Jewelry sales are now occurring online this is up from 12.8% in 2007.

2007 2008 2009 2010

Non-eCommerce Sales 0.891203340145694 0.87315854376269 0.855811420669269 0.836236541350694

eCommerce Sales 0.108796659854307 0.12684145623731 0.144188579330731 0.163763458649306

5.0%

15.0%

25.0%

35.0%

45.0%

55.0%

65.0%

75.0%

85.0%

95.0%

Big Data in Action: Monitoring Key Channel TrendsExample - US Online Apparel sales as a share of Total Apparel sales has dramatic shifts throughout the week in August

• Tuesdays are consistently the busiest online day of the month.

• Weekends show the lowest penetration for online sales.

Source: MasterCard SpendingPulse September 20111-

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Big Data in Action: Daily ViewDaily online retail sales forecast for September 2011

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

1 2 3

Rank # 9 Rank # 21 Rank # 24$558.0 Online $501.6 Online $274.5 Online

4 5 6 7 8 9 10Labor Day

Rank # 30 Rank # 28 Rank # 14 Rank # 4 Rank # 7 Rank # 16 Rank # 23$180.6 Online $203.4 Online $534.5 Online $604.8 Online $568.4 Online $516.0 Online $303.2 Online

11 12 13 14 15 16 17


18 19 20 21 22 23 24


25 26 27 28 29 30

Rank # 27 Rank # 18 Rank # 2 Rank # 1 Rank # 10 Rank # 13$209.8 Online $508.0 Online $607.8 Online $620.6 Online $548.5 Online $542.8 Online

September U.S. Retail Sales: $312.0 Billion / $13.7 Billion OnlineAll figures in Millions of US Dollars Data Not Seasonally Adjusted

September 2011 - Anticipated



1 2 3 4

Rank # 19 Rank # 20 Rank # 12 Rank # 9$11,403 $11,309 $13,089 $13,825

5 6 7 8 9 10 11

Rank # 25 Rank # 23 Rank # 22 Rank # 18 Rank # 15 Rank # 7 Rank # 5$10,391 $10,839 $11,115 $11,486 $11,961 $13,985 $14,975

12 13 14 15 16 17 18


19 20 21 22 23 24 25

Christmas


26 27 28 29 30 31

Rank # 29 Rank # 28 Rank # 27 Rank # 24 Rank # 17 Rank # 30$7,402 $8,597 $9,527 $10,515 $11,520 $6,002

December U.S. Retail Sales: $366.8 Billion / $23.0 Billion OnlineAll figures in Millions of US Dollars Data Not Seasonally Adjusted

December 2010 - Actual

Big Data in Action: Using Trends to Plan Ahead Example - US Daily Total Retail sales during holiday season



1 2 3 4

Rank # 1 Rank # 6 Rank # 14 Rank # 19

$1,134.3 Online $991.6 Online $891.5 Online $647.8 Online

5 6 7 8 9 10 11

Rank # 24 Rank # 13 Rank # 5 Rank # 8 Rank # 7 Rank # 12 Rank # 18

$514.5 Online $906.2 Online $1,031.4 Online $985.6 Online $990.7 Online $928.5 Online $697.4 Online

12 13 14 15 16 17 18


$509.8 Online $949.9 Online $1,093.3 Online $1,098.0 Online $1,044.1 Online $968.6 Online $710.3 Online

19 20 21 22 23 24 25

Christmas


$506.0 Online $880.8 Online $933.3 Online $787.2 Online $584.7 Online $345.2 Online $130.7 Online

26 27 28 29 30 31

Rank # 30 Rank # 27 Rank # 22 Rank # 21 Rank # 23 Rank # 28

$186.5 Online $466.9 Online $572.9 Online $578.1 Online $541.1 Online $393.1 Online

December U.S. Retail Sales: $366.8 Billion / $23.0 Billion OnlineAll figures in Millions of US Dollars Data Not Seasonally Adjusted

December 2010 - Actual

Big Data in Action: Using Trends to Plan Ahead Example - US Online daily total retail sales during holiday season


Insight into Action: Apparel Case StudyFollow the Trends.

The Challenge• A multi-channel apparel retailer seeking to better understand fluctuations in sales,

including the trends impacting overall demand. • In addition, the retailer needed a more strategic way to predict future shopping trends.

The Solution• Provide insight into sales trends across sectors trended over time, by month and

by year on a monthly basis. • Forecasting reports predict key shopping days for total Apparel by channel.

The OpportunityInsight into channel sales and trends pertaining to key shopping days used to inform future marketing initiatives and promotional calendars.

New World Requires New Models

•Fewer resources to drive higher returns

•Proliferation of noise at consumer level

Acknowledging the New World RealityDespite recent retail sales growth, we are in a more challenging time for acquisition and loyalty marketing

Corporate ConsultingServices

ManagedServices

InformationServices

Consumer

•More choices and channels than ever

•More devices (research and buying)

•New global options

•More retail options

•More ways to access (social, etc.)

An incomplete picture

• In-store spend does not show category opportunity or competitive positioning

• Demographics only takes segmentation so far

• Survey data is inferred behavior but does not equate with real behavior

Current Models are Limited Use of demographic data and in-store spend is no longer enough given the marketplace changes

Spend with you:$35.44

Spend with you:3 times per year

Success Requires A Whole Wallet ViewA whole wallet view and near real-time insight complete the picture

Transaction data builds on your existing customer insight and provides a 360o, whole-

wallet view to consumers

Combined intelligence delivers: Early Industry trends

Near real-time channel trends 360 degree behavioral spend view

Competitive performance at a local level

Spend with you:$42.17

Total Wallet:$16,273.81

Spend with you:3 times per year

Spend in industry:

19 times per year

Likely to spend in category in the next 3 months:

4x your average customer

The Challenge

A multi-channel retailer wanted to understand if customers shopping in-store exhibited different characteristics than those shopping online, and whether the two segments should be marketed to differently..

The Solution

Analytics using transaction data to compare customer activity, by channel:• Identify unique purchase patterns filtered by seasonality to account for

gift / holiday buying periods. • Purchase Cluster scoring included to enhance insights at the channel

level.

Insight in Action: Retail Case Study Dive into the Channel.

The Opportunity

Merchant’s marketing team can leverage full wallet view to better tailor their marketing messages against retention and acquisition strategies by channel.

Whole Wallet, 360o View is the Future

Looking Inside the Consumer WalletDo You Know?

Canada is the top cross border country for US based cardholders when purchasing Home Furnishings, Italy is #2.

New York, Chicago, Los Angeles and Philadelphia drove 20% of total retail spending in Q2 2011 (these top 4 drive 26% of Apparel spend).

68% of restaurants guests visited 5 or more restaurant merchants in Q2 2011.

US: 6 sectors drive 45% of card spending with Restaurant, Apparel, & Home Improvement ranking as top 3.

Source: MasterCard anonymized data warehouse 2011

Looking Inside the Consumer WalletDo You Know?

International Business Coming to the US – InboundSpend has declined marginally year-over-year, but Brazil, being less impacted by global economy, is growing +34% on average across industries

Due to the proliferation of restaurants and frequency of visits, 84% of industry customers visited at least 5 different restaurant merchants from July ’10 – June ‘11

International Competition and Opportunity - OutboundUS merchants are competing with the UK for Men’s and Women’s Apparel purchases, but with Canada and Italy for Children’s Apparel and Home Furnishing

Philly is the #4 DMA driving retail sales, ranking ahead of Dallas andSan Francisco

Source: MasterCard anonymized data warehouse 2011

Total Spend Customer Accounts

Average Spend

Average Transaction

Size

Average Purchase

Frequency

Track Key Performance Indicators vs. CompetitionIdentify opportunities to focus on customer acquisition or basket size efforts

Increase Average Transaction Value and/or Average Purchase Frequency to Drive Average Spend UP

Increase Customer Acquisition and/or Average Spend to Drive Total Spend UP

Discretionary Income and Purchase BehaviorRevisiting the thought around the correlation between income and spend

Discretionary Spend

Traditional View

Customer “B”% Total Spend Allocated to

Discretionary Spend

Customer “A”% Total Spend Allocated to

Discretionary Spend

Traditionally: Discretionary Spend = F (Total Income) But: Higher income does not imply different spending preferences

Now: Discretionary Spend = F (Actual Consumer Spending Behavior) Because: Discretionary spend in relation to total spend can reveal general spending preferences irrespective of other allocation of income (i.e. savings)

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Enhanced Targeting & SegmentationMapping the customer journey for more relevant offers, engagement and response

Improve segmentation and lift overall customer engagement by adding another dimension to the equation

Recency & Frequency

Increased Engagement

& Sales

Purchase Sequence Spend Value

Optimal time to reach customer segment with offer

Recent purchasing behavior and how often customer segment purchases

Customer segment average transaction size

Purchase with You

Where else are they engaged and how can this

be leveraged?

What up-sell/cross-sell opportunities exists?

Purchase Sequence

Whole Wallet: Achieving Sales LiftA whole-wallet customer view based on transaction data can dramatically improve marketing ROI

• Append transaction data to existing customer profiles to identify high likelihood segments

• Use transaction data insight to identify strong shopping days for targeted offers

• Leverage transaction data insight to identify shopping behavior outside your store for high-value customers

CUST

OM

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ALU

E

ACQUISITION GROWTH & RETENTION CUSTOMER LIFE CYCLE

In-store, demographic and survey data

Lift from addition of near real-time transaction data

The Challenge

• Lack of insight on the sequencing of actual customer online purchases across different industries

• Identify correlations that can drive insights to inform advertising strategy and planning

The Solution

A Customer Analytics purchase sequencing exercise to:• Illustrate the time elapsed distribution between purchases across

two different industries in the online channel. • Day and week- part analysis to help inform merchant’s internal

marketing planning and external advertising strategies

Insight In Action: E-Commerce Case StudySequencing analytics help determine customer promotions

Insight In Action: Retail Case StudyUnderstanding customer migration helps refine site selection

The Challenge• Lack of insight on the impact that new locations might be having on existing

ones• Insights were needed to drive and influence future site selection and format

decisions

The SolutionA Customer Analytics solution that:

• Analyzed customer migration from existing to new stores • Impact on spending across merchant locations over time • Correlation between new location cannibalization and proximity measures

The Opportunity

Creation of more stringent site selection guidelines around the proximity of new stores vs. existing ones.

The Challenge

• Lack of insight into “best customer” definitions for marketing targets

Insight in Action: Fuel Case StudyCustomer segmentation helps grow high-value customers

The Solution

A Customer Analytics segmentation that included:• Engagement-level measures with the merchant • Spending outside the merchant franchise that could fuel customer

development strategies company-wide

The Opportunity

Provide a clearer understanding of purchasing dynamics across high value customer segments

Holistic Customer View Drives New World SuccessAchieving effective acquisition and loyalty marketing

Customer• Whole wallet view• Transaction-based purchasing

trends

Competition• Competitive share• Performance benchmarks

Market• Industry trends• Economic influences

Opportunity• Underpenetrated segments,

zip codes• Share shift based on potential

customer purchasing behavior

Realizing More Effective Marketing in the New World

1. Big Data delivers business insight from macro economic to micro consumer purchasing trends

2. Understand and acknowledge today’s new world realities and limitations of current data models

3. Enhance current customer intelligence with transaction data for full-wallet, 360o understanding of the customer, market, competition, and opportunity

Information Services Product SuiteTools, that range from macro to micro insights, to address your business challenges

Merchant Need

Product

My Industry:Understand sector trends and outlook

My Markets:Measure competition and identify specific market challenges/opportunities

My Segments:Reveal customer loyalty trends and spending behaviors

My Customers:Improve customer acquisition and retention marketing

Actions

Insights

Spending Pulse

Benchmark Analytics

Customer Analytics

Customer File Enhancement;Acquisition Targeting

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Looking Inside the Consumer Wallet Key Success Factors for Driving Loyalty in a Competitive...

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