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Deloitte Analytics Enabling a more effective, proactive marketing organization. May 2014. 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. - PowerPoint PPT Presentation
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Deloitte Analytics Enabling a more effective, proactive marketing organization
May 2014
2
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
4
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
5
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
SIN
ESS
QU
ESTI
ON
S
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
6
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
7
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
8
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
9
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
32
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
25
50
75
100
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
usto
mer
s R
emai
ning
Months after Initial Subscription Purchase
% o
f C
usto
mer
s R
emai
ning
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
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