Deloitte Analytics Enabling a more effective, proactive marketing organization

May 2014

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Marketers today are looking to improve performance and reduce churn through an enhanced customer experience

The competitive advantage:Real-time actions, tailored to each customer

Real-time offers to

encourage conversion

Unique interactions to build loyalty

Customizedmessaging to connect with customers

High value customer segments

Exhibiting attrition behavior

Customer interaction

preferencesAutomatic action allows you to proactively own the customer relationship

Case Study 1

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At Client X, member retention was a strategic priority and a predictive model helped to close the gaps between member renewal targets and attainment

Project Objectives

1. Identify members most likely to attrite

2. Understand attrition drivers by member

3. Of these members, identify most likely responders to intervention strategies

Background Increasing membership retention is a strategic priority; Revenue from membership represents

approximately 53 percent of Client X’s net income

In recent years, the membership base has stagnated and renewal rates for new members are low

There is a gap between the forecasted member revenue plan and actual member revenue growth

Understanding membership renewal patterns and reasons will help close the gap

Key Business Objectives

Short Term: Identify gaps by membership type that can be filled to bring membership income to plan

Long Term: Inform development of a membership renewal strategy

Additionally, provide valuable information to the Winback team to optimize budgets

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Insights and tools generated by the project enabled Client X to improve membership retention

Tools to enable

Base data exploration Univariate analysis Churn predictions exploration Interventions targeting

Insights by member

Renewal scores Renewal Reason codes Intervention lists

Frameworks to enable actions from insights

Periodically score members Periodically recalibrate Test plan

BU

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DRIVERS (EXPLANATORY VARIABLES) DELIVERABLES OUTCOME (RESPONSE)

Member Characteristics Age Gender Income Tenure Distance To Club Cohort Education Level Investor Likelihood Dwelling Type Family Type Occupation Yrs. at Residence Donates Money Acq. Month Card Type Upgrade Ind Downgrade Ind Renewal Type

Member Behavior Promo Participation Promo Response Number of renewals Pct. on-time renewals Pct. late renewals

Member Interactions Purchase Amt 6/12 mos Upgrade in 6/12/24 mos RFM Decile RF Decile (50/50) WRFM Decile (39/60/1) R / F / M Decile # Unique cat Shopped in last

6/12/24 mos Cat Shopped in last 6/12/24

mos Activity by Channel

Store Characteristics Location Type Store Size, Tenure Number of Employees SIC Segment, Micro / Metro Comp in 10 miles Restaurant in 10 miles FIC – FY 11/12 Renewal Micro / Metro Portfolio–2010/11 Renewal Proto Size Remodeled in 2010/11 Client X Combo

Member renewal indicator (Prediction target variable)

Member renewal likelihood score

Member renewal reason Codes

Intervention Responsive-ness scores

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Definitions and assumptions within the modeling approach

Definition of Attrition

For the purposes of the models, attrition was defined as a failure to renew by 60 days following a member’s DTR date

Normalization of Member Year

Member behavior was examined by quarter and normalized for each member’s unique member year

Q1 for each member refers to the 1st through 3rd months of their membership (not Q1 of the calendar year)

Model Scope

Five models were built:o 1 model that predicts attrition for the entire member populationo 4 sub-models differentiated by member type (Advantage vs. Business) and

tenure (first-year members vs. tenured members) used to explain reasons for attrition risk

“Category Groups”

For the purposes of examining category purchase behavior, five “category groups” were looked at:

o Consumables excluding Snacks/Candy

o Snacks/Candy/Tobaccoo General Merchandise

o Gas/Car Washo Miscellaneous/Other

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The suite of deliverables allowed Client X to both act upon the insights generated from the analysis, and re-run the models in the future

Attrition Models

Model Outputs

Use Cases

1 2 3

WMS

Model Formulas

Model Code

Tableau Visuals of Scores and Reason Codes

Member Scores and Reason Codes

Model Usage Examples

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1 2 3 4 5 6 7 8 9 100

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

Decile1 2 3 4 5 6 7 8 9 10

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

DecilePredicted to renew Predicted to attrite

The attrition model was used 6 months before a member’s DTR date – enabling Client X to identify potential attriters and intervene early

1.55 2.24 3.13Decile Gains: 1.55 2.13 2.65Decile Gains:

2012 attrition rate = 26%

Predicted to renew Predicted to attrite

Mem

bers

hip

(Tho

usan

ds)

Run for members at DTR date Run for members 6 mo. prior to DTR

Renewed Attrited

The model predicts well in the highest risk groups (deciles 8, 9 and 10) when run at the DTR date When run at 6 months from DTR, the model retains a large portion of its predictive power

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The model also explained why high risk members are at risk. Client X used this insight to develop intervention options

Member shopped very little (in terms of $ of purchases) during year

Member shopped a lot in membership Q1 but very little in other quarters

Member does not buy much general merchandise

Member purchases low quantities overall, comprised primarily of high margin items

Member does not exhibit behavior of primarily purchasing high quantities of low margin items

Member did not shop a lot in Q4 of member year

Member purchases high quantities overall, comprised of mostly low margin itemsMember is not assigned to a club that has one more more of BJs / Costco / Restaurant Depot in the

areaMember is assigned to a club that has one more more of BJs / Costco / Restaurant Depot in the area

Member is likely not a minority small business owner

Member does not exhibit behavior of primarily purchasing high quantities of low margin items

Member is likely a minority small business owner

Member shopped a lot in membership Q4

Member does not make a lot of misc. item purchases

Member did not make a lot of visits before 11 or after 6

Member shopped a lot of general merchandise

Member had downgraded from PLUS in the past

Member makes a lot of visits before 11am and after 6pm

Member likely has a Sams credit card

0% 5% 10% 15% 20% 25%

Top Reason Codes for High Risk Members in 2013 (Deciles 8, 9, 10)

Percent of Members in Deciles 8-10

All Member Population• Among the most at-risk members, lack of

shopping (from a $ perspective) or a decrease in shopping across quarters are the most common signals of attrition

• Lack of purchases in General Merchandise also is a common factor behind the at-risk population

• The next-most-common signal is a tendency to buy very few items, but relatively high-margin ones (i.e., spend high $ on a big-ticket item, then attrite)

1

3

21

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This graph depicts how often each factor appeared within the top 5 most significant predictors for each member in the three ‘at risk’ deciles (i.e. top 30% most likely to attrite)

What interventions can Client X take 6 months prior to the member’s DTR date to: Re-engage shopping frequency and

spend? Drive purchases / spend in the General

Merchandise category-group? Introduce ‘one and done’ members to the

rest of the club experience?

Case Study 2

Case Study 2 – Customer Churn Management

Topics Segmentation analytics, Cost-to-serve analytics, Pricing Analytics

Key Issues • How is customer lifetime value measured under new a business model (retail subscription)?• What would be optimal offers, prices, bundles, promotions, and payment model for customers?

Data• Analyzed 100K+ customer purchase and online gaming usage data points• Conducted 55+ customer interviews in US & EU• Enhanced input data points captured to enable analysis (e.g., added new fields in systems)

Analysis• Identified groups of customers that displayed differentiated valuation for game benefits and their price sensitivity to establish different

customer segment types• Determined likelihood of drop-rates in subscriptions and tailored marketing messages by segment

Decisions• Utilized churn rates to determine inflection points in customer lifecycle that triggered different targeted marketing programs (e.g., optimizing

subscription models, driving micro-transaction content revenue)• Used insights to drive development of new offerings and pricing strategy

Impact • Reduced customer churn, improvement in SKU uptake at more optimal prices

LARGE GAMING COMPANY

DISCUSSION TOPICS

Customer Lifetime Value Example

Potential insights to be gained from analytics:• How do you formulate consumer segments based on customer value?• How do consumers’ purchase paths relate offline and online, and how can

marketing influence them at appropriate stages with right messages?• What type of engagement is most relevant for consumers at certain

deflection points (e.g., price, offer, brand promotion)?

Potential operational considerations:• How do companies need to restructure the way customer data points are

captured in order to be able to perform customer lifetime value and cost-to-serve analytics?

Case Study 2 (cont.) – Reduce Customer Churn with Lifetime Value AnalysisCHURN RATE ANALYSIS

EXAMPLE

0

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 300

25

50

75

100

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Starting with a 1 month Online subStarting with a 1 year Online sub*Starting with a 1 year non-online sub

Starting with a 1 month non-online sub

% o

f C

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Months after Initial Subscription Purchase

% o

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INSIGHTS

• Churn is higher for customers who purchase via retail

• Non-online buyers can be moved online by driving awareness of the benefits of the online options

• Moving non-online customers to online subscription increases Customer Lifetime Value by $100 on average

ILLUSTRATIVE