looking inside the consumer wallet key success factors for driving loyalty in a competitive...
DESCRIPTION
TRANSCRIPT
Information Services for MerchantsEnabling decisions at the speed of consumer behavior
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
Source: MasterCard SpendingPulse September 2011
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%
Source: MasterCard SpendingPulse September 2011
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.
Source: MasterCard SpendingPulse September 2011
Big Data in Action: Monitoring Key Channel TrendsExample: US ecommerce sales shift online
Source: MasterCard SpendingPulse September 2011
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
Source: MasterCard SpendingPulse September 2011
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-
Aug
2-Aug
3-Aug
4-Aug
5-Aug
6-Aug
7-Aug
8-Aug
9-Aug
10-A
ug
11-A
ug
12-A
ug
13-A
ug
14-A
ug
15-A
ug
16-A
ug
17-A
ug
18-A
ug
19-A
ug
20-A
ug
21-A
ug
22-A
ug
23-A
ug
24-A
ug
25-A
ug
26-A
ug
27-A
ug
28-A
ug
29-A
ug
30-A
ug
31-A
ug0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
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
Rank # 26 Rank # 17 Rank # 3 Rank # 6 Rank # 12 Rank # 19 Rank # 25$215.1 Online $510.0 Online $607.4 Online $580.4 Online $544.6 Online $507.8 Online $271.9 Online
18 19 20 21 22 23 24
Rank # 29 Rank # 15 Rank # 5 Rank # 8 Rank # 11 Rank # 20 Rank # 22$187.9 Online $517.6 Online $600.3 Online $561.2 Online $547.4 Online $502.9 Online $306.1 Online
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
Source: MasterCard SpendingPulse September 2011
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
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
Rank # 26 Rank # 21 Rank # 14 Rank # 10 Rank # 13 Rank # 4 Rank # 2$9,933 $11,274 $12,202 $13,279 $12,869 $15,181 $16,387
19 20 21 22 23 24 25
Christmas
Rank # 16 Rank # 8 Rank # 6 Rank # 3 Rank # 1 Rank # 11 Rank # 31$11,635 $13,882 $14,454 $15,434 $17,298 $13,127 $1,914
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
Source: MasterCard SpendingPulse September 2011
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
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
Rank # 25 Rank # 10 Rank # 3 Rank # 2 Rank # 4 Rank # 9 Rank # 17
$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
Rank # 26 Rank # 15 Rank # 11 Rank # 16 Rank # 20 Rank # 29 Rank # 31
$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
Source: MasterCard SpendingPulse September 2011
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)
Tota
l In
co
me
= T
rad
itio
na
l
Be
ha
vio
ral
vie
w=
Ne
w A
pp
roa
ch
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
ER V
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