best practices in intgr loyalty programs into cust value mgmt strategy of comm and media...
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Loyalty best practiceTRANSCRIPT
SUW 20 April 2005 © 2005 IBM Corporation
Best Practices in Integrating Loyalty Programs Into the Customer Value Management Strategy of Communications and Media Providers
Christine Wyatt, IBMOnil Gunawardana, Siebel Systems
© 2005 IBM Corporation2 SUW 20 April 2005
Agenda
Issues facing telcos, utilities and media service providers in managing Customer Lifecycle and loyalty
Optimized Loyalty Management: An integrated approach from IBM and Siebel
Cross-industry case study examples
© 2005 IBM Corporation3 SUW 20 April 2005
Market responsiveness is a key competence required by Communications, Media and Energy Service Providers in the competitive market of today
Situation
Implications
Problems
Needs
Customer development as opposed to acquisition has become increasingly important in a saturated market
Telcos need to increase revenue through the sell-on of new products and services
Increasing competition from other channels – banks, supermarkets, insurance
Objectisation of marketing budget to deliver customer lifetime value
Synchronisation with back end systems Need a competitive intelligence capability Need to obtain true information and
insight
Need to consider use/appropriateness of Customers Loyalty Programmes
Need to maximise use of marketing spend
Need to implement Multi-Channel strategy
Need to boost non-SMS data usage Need to meet strategies set by “Group” Legacy systems Need to match subsidies and marketing
to customer value
© 2005 IBM Corporation4 SUW 20 April 2005
A variety of approaches aimed at generating deeper understanding of the customer have been implemented in Telcos
Customer Segmentation Transaction analysis Purchase pattern
understanding Churn prediction and
management
How have campaigns affected a customer’s value or loyalty?
What characteristics do customers within a segment share?
What is the lifetime value and risk (volatility) of my customers?
What proportion of each type of promotion should be offered to which customers?
How to exploit the correlations between marketing actions for better budgeting?
How much to invest in each marketing action?
How do the most cost-intensive marketing activities contribute to overall performance?
Approaches But too often key questions from Executives remain unanswered:
© 2005 IBM Corporation5 SUW 20 April 2005
Almost 60% of communications companies think their Campaign Management initiatives have led to either failure or have generated limited success, while only 11% has implemented CM successfully
Success of CRM Initiatives
Question: How successful have you been at the following CRM and customer-focused initiatives in your company? (Failure = project stopped, on hold, Complete success = project implemented according to plan, on time and within budget.)
Number of Responses: Meta Analysis, 64 communications sector companiesSource: IBM Institute for Business Value survey and analysis, 2004.
Perc
ent o
f Res
pond
ents
Complete Success
Failure
8% 5%
17%11% 11% 13% 9%
27%
9%
6% 9%
9%
5% 6%8%
3%
22%
3%
8%16%
11%
13% 14%16%
17%
11%
22%
38%
42% 27% 42%47% 39%
45%
23%
50%
23%
14%23%
19%11% 16% 17%
13% 11%17% 14% 13% 11% 11% 9% 8% 5% 5%
0%
20%
40%
60%
80%
100%
Customerservice and after-
sales support
Strategic brandmanagement
Loyalty andretention
programs
Campaignmanagement
Sales programeffectiveness
Channelintegration and
optimization
Cost reduction CRMOutsourcing
Productoptimization and
management
Not Applicable
© 2005 IBM Corporation6 SUW 20 April 2005
Why have these approaches been unsuccessful?
Strategically, churn management is a re-active approach reliant on success in interpreting behaviours as it happens, versus a pro-active approach of loyalty management
Inability to develop a single view of the customer – data has to come from many sources with all the data quality issues inherent in this
A lack of tools to use to optimise marketing dollars, eg. Within a particular segment being confident in a sequence of marketing actions that will increase long term customer value
A lack of integrated business processes and technology to execute Customer Lifecycle Management activities across the enterprise at every customer touchpoint
© 2005 IBM Corporation7 SUW 20 April 2005
Agenda
Issues facing telcos, utilities and media service providers in managing Customer Lifecycle and loyalty
Optimized Loyalty Management: An integrated approach from IBM and Siebel
Cross-industry case study examples
© 2005 IBM Corporation8 SUW 20 April 2005
Tools and approaches from IBM and Siebel to address these customer management pain points
Single view of the customer
Pro-active approach of loyalty management
Tools to use to optimise marketing dollars
Integrated business processes and technology to execute Customer Lifecycle Management activities
Siebel’s UCM
IBM’s CELM tool
IBM’s CELM tool
Siebel CRM
….Focusing today on CELM and how to use it with Siebel’s Marketing suite
© 2005 IBM Corporation9 SUW 20 April 2005
An integrated, iterative implementation approach we call Optimized Loyalty Management (OLM)
Siebel Analytics• Churn rate• Customer Value Index• Offer acceptance rate• Loyalty Index IBM CELM
Understand customers Customer Value Index Loyalty Index
Siebel UCM Consolidate Customer Information
IBM CELM• Identify series of actions for each customer segment for a given budget
Strategy
Analyze
Execute
CustomerCustomer
Gather Data
Measure
Siebel CRM• Across ALL customer touch points• Marketing
• MRM• Campaign Management• Upsell / Cross sell• Lead Management
• Loyalty Program Management• Service
• Priority Service• Self-service
• Sales• Complex Order Management
© 2005 IBM Corporation10 SUW 20 April 2005
Marketing managers can leverage their customer information for optimal modeling and control of Customer Dynamics
Loya
lty In
dex
Value index
Customer
Campaign planIBM
CELM Revenue
State: Medium loyalty & Medium valueSequence of actions: ExtraP campaign, Cash campaign, Accrual campaignRevenue: 2000 $Next state: High loyalty & High value
1 - A customer is in a given state
2 – CELM recommends a
sequence of specific campaigns
3 – The customer brings a benefit whe she responds to the
campaign
4 – The customer moves to a better
state
CC
XP
AC
© 2005 IBM Corporation11 SUW 20 April 2005
CELM allows marketing managers to dynamically optimize marketing budget allocation per Customer/Segment
Given:
a time horizon for future planning
a (sub)set of customers with associated value/risk profiles
a (sub)set of eligible customer offerings (e.g. campaigns)
a limited marketing (targeting) budget
Determine the optimal portfolio of customers and offerings which maximize the value/risk ratio (ROI) and minimize Saturation over the given time horizon under the budgeting constraint.
© 2005 IBM Corporation12 SUW 20 April 2005
Modules Functionalities
Prediction of Customer Dynamics (based on Markov Decision Processes(MDPs) and Reinforcement Learning Algorithms
• Identification of customer dynamics (probability to move to a higher/lower value state)
• High accuracy evaluation of customer lifetime value/risk (volatility) over variable time horizons
• Prediction of impact of future marketing action- sequences on customer lifetime value
• Generates optimal future customer targeting policies because of the consideration of optimal associations of future actions per customer vs. next action only
• Simulation of change from current to optimized marketing policies (cost / benefit analysis)
• High accuracy of estimated customer Financial Profile due to consideration of value and risk (volatility)
• Better customer targeting policies because of the consideration of optimal association of future actions per customer vs. next action as it’s usually the case with campaign management tools.
Value Proposition
Portfolio Optimization
• Optimally allocate restricted marketing budget to maximize return on investment and minimize risk (uncertainty) in customer response
• Optimized resource allocation per customer segment in a way which maximizes the Value/Risk ratio of the whole customer portfolio.
Segmentation
• Definition of customer groups based on demographics and transactional behavior (e.g. value/ loyalty/ recency/ frequency) or customized (business) segmentation rules
• Highly flexible state of the art segmentation algorithms
The functionality and value proposition of CELM can be grouped into three key parts: segmentation, prediction of customer dynamics and portfolio optimization
© 2005 IBM Corporation13 SUW 20 April 2005
So how does the CELM solution differ from existing products on the market?
Key difference is the dimension of time– CELM marketing plans consider best treatment for the customer
segment to optimise the eventual value and loyalty of that segment to the company, rather than the immediate value (and the actions therefore could be different)
Second key difference is the calculation of the optimal budget allocation for a marketing campaign within a segment relative to the likely response and result from that segment of the campaign
© 2005 IBM Corporation14 SUW 20 April 2005
An integrated, iterative implementation approach we call Optimized Loyalty Management (OLM)
Siebel Analytics• Churn rate• Value-Loyalty migration rate• Offer acceptance rate IBM CELM
Understand customers Customer Value Index Loyalty Index
Siebel UCM Consolidate Customer Information
IBM CELM• Identify series of actions for each customer segment for a given budget
Strategy
Analyze
Execute
CustomerCustomer
Gather Data
Measure
Siebel CRM• Across ALL customer touch points• Marketing
• MRM• Campaign Management• Upsell / Cross sell• Lead Management
• Loyalty Program Management• Service
• Priority Service• Self-service
• Sales• Complex Order Management
© 2005 IBM Corporation15 SUW 20 April 2005
Siebel Analytics
Customer Value Customer lists and associated campaigns
Siebel CRM
IBM CELM
Value-Loyalty cell migration rate Offer acceptance rate
IBM CELM and Siebel exchange critical information about customers to provide a closed loop solution
© 2005 IBM Corporation16 SUW 20 April 2005
A few sample stories emphasize the tangible business benefits that CELM can deliver to improve KPIs
Sample Story 1
Increasing Customer Profitability
Problem: 80% of the customer base consists of customers with a negative or low profitability. Those customers with a negative profitability should be presented attractive campaigns to move them to better profitability levels.
Solution: Based on the marketing budget available for Q1 and Q2, and the risk profile that is preferred for moving the customers to a better profitability level, CELM predicts and suggests 5 next subsequent campaigns for each value/loyalty subsegment
Sample Story 2
Retention Program Enhancement
Problem: Customer churn rates are high, and retention programs are costly with limited success rates on retention offers that are provided.
Solution: Based on historic transactional data, CELM predicts trajectory paths that newly acquired customers will follow towards the end of their predicted customer lifetime. Sequences of campaigns are identified to treat customers in the most effective way to prevent churn behaviour and make customers loyal, already in the early stages of their lifetime.
Sample Story 3
Segment Value Enhancement
Problem: The ´youngsters´ segment is underperforming in terms of uptake of new services. No ideas exist as to better promote new products/services and increase campaign response rates.
Solution: CELM re-segments the ´youngsters´ segment into different value/loyalty subsegments. Based on this new, enhanced segmentation, CELM proposes 3 targetting campaigns for the next quarter to drive the ´youngsters´ segment to a better value/loyalty state
© 2005 IBM Corporation17 SUW 20 April 2005
LegacyUCMHR DataMartERP SCMOrderMgmt Service
Process & Data Integration
Marketing Provisioning Service
Employee Alignment
Business Intelligence & Analytics
Sales, Marketing, Service Best Practices
Web, Call Center, SMS, IVR, Email, Partner
Customer
Siebel provides an integrated set of tools for executing Loyalty Management
© 2005 IBM Corporation18 SUW 20 April 2005
ODSSiebelOLTPPSFT Data
WarehouseSAP Data MartsXML Other
Siebel DW Server
with pre-built ETLPrograms
SiebelRelationshipManagementWarehouseLegacy/
Host
Siebel Marketing Suite 7.7 is the industry’s most comprehensive solution
Enterprise Analytics Platform
Industry Specific, Best Practice Enabled Marketing Applications
Planning and
Resource Management
Segmentationand
Targeting
MultichannelCampaign/
DialogManagement
EventsManagement
PartnerMarketing/
Trade PromoManagement
Email& Web
Marketing
LeadManagement
LoyaltyManagement
Field Sales
Web/eMail Partners Call
CenterDirectMail WirelessPOS/
ATMsBills &Stmts
BranchesStores
CustomerCustomer
Intelligent Interaction Across Customer Touchpoints
Customer and Business Insight
© 2005 IBM Corporation19 SUW 20 April 2005
Partners
• Enroll members• Send transactions to the
host organization• Approve joint loyalty
promotions
• Manage service requests• Approve transactions• Manage products• Collaborate on servicing the
customer
MembersCarrier
• View complete member profile
• Define tiers
• Enroll members
• Reward behavior• Create targeted promotions• Define accrual and
redemption rules
• Service a member’s request
• Join program
• Keep profile up to date• Conduct web transactions• Enroll in loyalty promotions
• Redeem rewards• Refer friends• View statements• Create Service Requests
• Set contact preferences
Rules Rewards Tiers Member Profiles Eligibility Promotions Transactions Point Expiration
Loyalty Manager Loyalty Member Portal Loyalty Partner Portal
Loyalty Engine
Siebel Loyalty Program Management
© 2005 IBM Corporation20 SUW 20 April 2005
Siebel Loyalty supports all the key loyalty program management business processes
• Business and customer analytics• Proactive alerts• Fact-based loyalty planning
• Loyalty promotions• Define rules, criteria and actions• Partner approvals
• Engine updates tiers automatically• 360 degree member profile • Order history and transaction
history is maintained
• Measure success of promotions• Analyze member transactions• Rate partners, products, etc.
Business and Customer Insight
Create a Personalized Experience
Update Member Tier & Behavior Profile
Closing The Loop andMeasuring ROI
• Personalized web site• Multi-channel campaign• Reward behavior
Define Targeted Loyalty Promotions
Collaborate w/ Partners Online
• Partner submits transactions• Partner services members
© 2005 IBM Corporation21 SUW 20 April 2005
Adopting and integrating the Optimized Loyalty Management in your organisation
Scenario 1 (Proof of Concept), Provisioning of a pilot study: one-off study performed jointly with IBM
experts on a limited scope to demonstrate value of CELM Study iterations require involvement of IBM experts on a case-by-case
basis
Scenario 2 (Planning and Set-up)Provisioning of methodology and a desktop application (e.g. Excel based) or
as a stand-alone tool Client enabled to perform analyses independently on a day-by-day
basis, input data needs to be imported by user Planning OLM approach
Scenario 3 (OLM Implementation)Provisioning of simulation method and (system-) tool which integrates the
modeling with other marketing applications in use or required Multiple users/processes enabled to use CELM independently on a day-
by-day basis to support CRM operations, input data is automatically imported from appropriate systems and can automatically link to execute and manage campaigns
Project/ integration effort
Operational practicability
low
high
© 2005 IBM Corporation22 SUW 20 April 2005
Agenda
Issues facing telcos, utilities and media service providers in managing Customer Lifecycle and loyalty
Optimized Loyalty Management: An integrated approach from IBM and Siebel
Cross-industry case study examples
© 2005 IBM Corporation23 SUW 20 April 2005
25/01/04
“Last spring, IBM's Services and Research Labs started working with FinnAir on a project to use mathematical modeling and optimization algorithms to try to increase customer loyalty, reduce marketing costs and improve response rates among members of its frequent-flier program...
FinnAir is pleased with an initial project involving half of its frequent fliers. Eero Ahola, Senior Vice President for Business Development and Strategy, says the technology has reduced marketing costs by more than 20 percent and improved response rates by up to 10 percent... "That can be huge money in the airline business," Mr. Ahola said. "And it's done with mathematical modeling. We could never do it ourselves." Such work, he added, shows another step in the evolution of FinnAir's relationship with IBM. "They've gone from being a supplier to our data center to a partner," Mr. Ahola said. "It's a totally different relationship."
CELM In The Press …
© 2005 IBM Corporation24 SUW 20 April 2005
Finnair FFP optimizes targeted marketing activities
Finnair FPlus, as most of existing loyalty programs, was lacking advanced value and loyalty metrics to quantify, plan, and optimize targeted marketing activities efficiently
CELM was introduced to optimize marketing planning and budgeting of targeted marketing activities in Finnair FPlus in order to:
Enhance existing segmentation with customer value/loyalty metrics Estimate customer lifetime value and risk (volatility) over variable time horizons Identify customers’ different life cycle phases and dynamics (e.g. Track the value of a family) Optimize planning of sequences of campaigns per segment (avoid saturation!) Optimize marketing budget allocation to maximize the Value/volatility ratio Deliver quality consistently at all customer touch points (call center, check-in, gates, etc.)
Benefits Reduced marketing costs by 20% while improving response rates by 10% Achieved 80% accuracy rate for predicting eventual customer value Helped improve customer satisfaction rate by 10%
© 2005 IBM Corporation25 SUW 20 April 2005
Nestle/Nespress optimizes their promotion policies toproactively promote up-/ cross-selling and avoid churn
Identification of optimal marketing policies per customer segment to maximize marketing efficiency across the entire customer portfolio and decrease churn rate
Solution
Sophisticated approach for using analytics on both a strategic and operational level
CELM tool for multi-staged marketing campaign planningBenefits
Optimized customer equity over lifetime and increased revenues due to actionable plan to minimize defection and/ or transition to low value segments and to optimally promote and leverage up-/cross selling opportunities
Enhanced marketing ROI due to optimal resource allocation to maximize value/risk ratio across the entire customer portfolio
SUW 20 April 2005 © 2005 IBM Corporation
Backup
© 2005 IBM Corporation27 SUW 20 April 2005
Scalability for integrating large multi-channel data Optimization of generated rules across channels and over time Mapping rules into channel-specific actions
DemographicData
Transaction Data(Channel 1)
Transaction Data(Channel 2)
Transaction Data(Channel 3)
EstimationModule
Data PreparationModule
Virtual Join
ValueIteration
ChannelOperational CRM Rules
Selective Sampling
Model 1
Model 2
Model 3
Scalability
Optimization Mapping
CELM is highly flexible and scalable against typical customer requirements and needs
© 2005 IBM Corporation28 SUW 20 April 2005
An integrated, iterative implementation approach we call a loyalty management lifecycle
Loyalty Marketing Lifecycle
Measure Promotion
Results and Effectiveness
Create and Execute
Marketing Campaign
Create Proactive Marketing
Strategy and Plans
Create Flexible, Targeted Loyalty
Program
Segment Members and Create value
Profiles
Design Reactive,
Event-based Marketing
Respond to Customer Questions
about Promotion
Process Members
Transactions and Accruals
Siebel LoyaltySiebel Analytics
Siebel Marketing IBM CELMOptimize Marketing
Budget Allocation
© 2005 IBM Corporation29 SUW 20 April 2005
Screenshot (1): Data Management – Raw Data visualization
Raw Data: dynamic information (transaction
and marketing activities).
Raw Data: static information
(demographics)
Raw Data: temporal information visualization
© 2005 IBM Corporation30 SUW 20 April 2005
Screenshot 2: Customer Histories – build customer profile and track them over time
Customer profile/events over
time
Customer temporal data computed every
time period
Static information (demographics)
© 2005 IBM Corporation31 SUW 20 April 2005
Screenshot 3: Customer Dynamics – use customer histories to model customer response
Searchable list of marketing actions with statistics
(revenue/profit, coverage)
Transition probabilities over time from one segment to
another
Customer path along the market segments (path is
highlighted in red and tagged with individual marketing
actions)
Market dynamics over time with some statistics (number
of customers in each segment, transition probabilities, etc.)
Market dynamics can be restricted to only a sub-set of marketing actions. Allows to
assess the impact on the value and the dynamics.
© 2005 IBM Corporation32 SUW 20 April 2005
Screenshot 4: Marketing Action Planning – use the customer response model to infer the best marketing strategy that maximize the profit under budget constraints
Revenue distribution per state
Revenue distribution per action
Number of customers to target in each state with the selected marketing actions at specific times
Marketing budget constraint (user input)
Risk aversion factor (risk averse = do as before)
Simulate what will be the customer distribution over
market segments if this marketing plan is
implemented
Expected revenue and cost of the current
marketing plan
© 2005 IBM Corporation33 SUW 20 April 2005
CELM software components are all Java-based and by result can be easily integrated with a customer´s existing CRM software components (e.g. campaign management software, call center software)
SQL Scripts (*.sql) : SQL command files to perform:– Data model creation and import of core data from files/tables– Basic OLAP functionalities for quick data diagnosis– Data preparation (pre-processing) for CELM algorithms
Discretization, Segmentation and Clustering (CELM.discretization, CELM.segmentation, CELM.clustering): discretizes customer features and creates segments based on business rules or on advanced clustering algorithms. Classifies new customers into one or several segments given the value of their features.
Markov Decision Process (MDP) and Reinforcement Learning (RL) (CELM.mdp & CELM.rl): models the inter-state dynamics (trajectory) of customers as they react to actions (targeting policy) and generate value (profit). Predicts the best series of actions to take in order to maximize probability to move to higher value states and minimize the volatility of that value over given time horizon T.
Defector Transition Model (celm.defector): uses the Markov Chain modeling to analyze historical customer dynamics and produce transition patterns which lead to customer defection
Portfolio Optimization (celm.portfolio): Models customer segments as financial assets in a portfolio. Finds the best marketing budget allocation strategy which would maximize the portfolio Return/Risk Ratio (ROI).
© 2005 IBM Corporation34 SUW 20 April 2005
IBM’s CELM allows CME companies to understand and optimize customer lifecycle dynamics across value and loyalty states
r1 $ r2$ r3 $
BargainHunter
Repeater
LoyalCustomer
ValuableCustomer
One Timer
Repeater
Defector Defector
Repeater
LoyalCustomer
PotentiallyValuable
Campaign A
Campaign B
Campaign C
Campaign D
Campaign E
Present Future
The customers states are represented by the customer/segment features Actions are represented by the marketing events (e.g. campaigns) Each transition has a probability (p), generates a revenue (r)
CELM answers the key question: what are the optimal sequences of actions which would maximize Customer value and loyalty over some given time horizon?
© 2005 IBM Corporation35 SUW 20 April 2005
Step1: Find targetting policy ( sequence of campaigns) which maximizes customer value/risk ratio over specified time horizon T (weeks, months, quarters).
Step2: Determine when to target, and how much to allocate to, each customer to implement the policy within horizon T.
... ...
t
t
t
$
$
$
Customer Portfolio Discounted cash flows Value
distribution Cost
... ...
Staged Relationship actions and options
...
periodstateactionsoptions
p1S1A1O1
p2S2A2O2
pnSnAnOn
periodstateactionsoptions
p1S1A1O1
p2S2A2O2
pnSnAnOn
periodstateactionsoptions
p1S1A1O1
p2S2A2O2
pnSnAnOn
How does it work: The CELM solution generates its actionable business intelligence based on a predefined two-step approach
PortfolioManagement
•Resource Allocation•Portfolio ROI optimization
Simulation
Historical Policy Assessment•Campaign Mix•Product / Customer Mix
Model
Optimization
Optimized Policy•Optimal Mix Design•Optimal targeting policies over variable time horizons
Understanding
Optimizing
Customer Behavior•Value-based Segmentation•Lifetime Value/Risk profiling•Customer Transition model
© 2005 IBM Corporation36 SUW 20 April 2005
Our team built a model based on the German market data to analyze the impact of marketing selections on customer dynamics
Goals:– Analyze the impact of the marketing selections on customer dynamics.
Methodology:– We analyze the German market for a period of 11 months: June 2003 to May 2004.
– We started by defining customer histories. A customer history is modeled as a list of (state, selection, revenue) triplets computed every month as follows:• State: Client position in the segmentation stages given by client. The state value
is computed the first of every month.• Selections: the sequence of marketing actions that have been assigned to a
customer during the whole month.• Revenue: the revenue generated by the customer during the whole month.
– Looking at (state, selection, revenue), (next state,…), we build the following transition model:
New Member
Active Member
EKS06/03 + NACC06/03
9.7 Euros
State Selection Revenue New state