Application of Decision Sciences

to Solve Business Problems

Customer Relationship Management

Acquisition Usage & Loyalty

Val

ue

Time

Retention

Activation

Make targeted decisions for effective relationship management with the customer through every phase of

their lifecycle

CRM

Analytics

Acquisitions

Prospect Targeting

Prospect acquisition is specifically concerned with issues like acquiring the right prospect at an optimal cost,acquiring as many prospects as possible, optimizing across channels, etc. The main objectives are ensuringhigh profitability of new customers and acquiring them at a low cost. By analyzing prospect demographics,predictive modeling techniques are employed to identify their propensity to respond. Profitability models arethen built for different segments.

It helps in answering business questions like: How do we proactively acquire new customers? Who will be the most profitable customers? And in which channels do we target them? Can the varied data sources be leveraged to expand prospect universe and implement efficient direct

marketing campaigns? How can direct marketing spend be lowered while maintaining results?

Response models to optimize Acquisition budgets

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Random; 10.9% leads bought a new car

Predictive Model

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Predictive Model Deciles; Each decile has 10% of Leads

Cold Leads

Who are my Customers?

Customer Segmentation

In today’s competitive business scenario with customers having a multitude of options, their preferences andbuying patterns have been constantly evolving. For retaining the profitable and loyal customers, it istherefore necessary to keep track of changing customer trends and accordingly tailor the offerings.

Segmentation is the practice of identifying homogenous groups of customers based on their needs, attitudes,interests and purchase behavior. It enables identifying profitable customer segments and customizingproduct and service offerings and marketing campaigns to target them effectively. It is typically done using acombination of transaction data, demographic data and psychographic information.

It aids in answering critical business questions like: Which are the most profitable and loyal customer segments and how do we have tailored offerings for

these segments? How do we have special promotion campaigns, specifically to reach the high value customer segments? What are the revenue and profit contributions by different customer segments?

Platinum: Current investment > 50KGold: Current investment > 5K; < 50KSilver: Current investment < 5K

Tenure<12mo

All Customers1,889

1,637 MM EAD87k AED/Customer

New Customers4,568 (24%)

433 MM EAD (27%)95k AED/Customer

Existing Customers11,573 (76%)

1,203 MM EAD (73%)

84k AED/Customer

Savers2,944 (16%)

39 MM EAD (2%)13k AED/Customer

Investors7,316 (38%)

812 MM EAD (50%)111k

AED/Customer

Redeemers871 (5%)

60 MM EAD (4%)69k AED/Customer

Revolvers3,190 (17%)

292 MM EAD (18%)92k AED/Customer

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Segmenting customers based on their revenue contribution

Who are my Profitable

Customers?

Profitability & Loyalty analysis

For the sustainable growth of any enterprise, it is very important to identify the most profitable and loyalcustomers. Having special schemes for these customers in form of offers and discounts, can help in realizingthe long term goals of increasing profits and expanding customer base.Organizations use customer profitability and loyalty analysis to identify the most valuable customer segmentsto prioritize marketing, sales and service investments. Transactional behaviour is analysed for creating aCustomer Value Score (C-score) for each customer, which explains their engagement levels. The C-Score canbe leveraged for proactive action to defend, retain and grow the customer base.

This can help answer key business questions like: Which are the most profitable and loyal customer segments and how much they contribute to the firm’s

profit? Which are the customer segments to be targeted for marketing programs and special offers? Which are the customer segments that can have a negative impact on the company’s profitability?

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Focus:Customers who

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Sustain:Highly profitable customers

Invest & Sustain:Profitable customers

%Customers

Invest :Highly profitable

customers40%

Cross Selling & Upselling Strategies

It is not just enough to retain the profitable and loyal customers, but it has become a necessity to increasethe revenue contribution from the existing customer base. Cross Sell involves the sale of additional items inorder to increase the wallet share from the customers.

Market basket analysis is the technique used to evaluate customers’ purchasing behaviour and to identify thedifferent items bought together in the same shopping session. It uses transactional data and employspredictive modelling techniques to identify customers’ preferences based on the associations between theproducts recently purchased. It helps to determine which products are to be offered and which are thecustomer segments most likely to be receptive to these cross selling propositions.

It aids in strengthening relations with customers by: Customizing layouts, product assortments and pricing so that it appeals to the customers Designing effective affinity promotions

• Stimulating trials and increase customer awareness during launch of new products and variants• Handling excess stock by designing offers among associated products

Deepening Engagement

CONFIDENCE Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product8

Product 1 100% 25% 9% 6% 18% 2% 28% 31%

Product 2 42% 100% 7% 8% 22% 6% 29% 22%

Product 3 31% 16% 100% 5% 10% 4% 18% 17%

Product 4 35% 29% 8% 100% 28% 7% 26% 12%

Product 5 47% 35% 8% 12% 100% 3% 37% 24%

Product 6 37% 66% 18% 19% 21% 100% 25% 21%

Product 7 45% 28% 8% 7% 23% 2% 100% 25%

Product 8 57% 24% 9% 3% 17% 2% 29% 100%

Probability that Product 8 is purchased given that Product 1 is bought is 31%

Probability that Product 1 is purchased given that Product 8 is bought is 57%

Increasing sales by creating cross-selling opportunities using MBA

Campaign Management

Promotion dashboard to track the effectiveness of different campaigns

Campaign Effectiveness

Campaigns include a variety of short term programs directed at consumers to stimulate product awareness,trial or purchase. The most commonly implemented programs include sampling and free trials, free gifts,couponing , loyalty reward programs, special pricing, promotional contests and so on. Advanced econometricmodeling techniques are used to help companies refine their promotion strategies, to understand the liftgenerated by various campaigns and the associated ROI.

This information is then used by marketers to: Identify the impact of different campaigns and find out the most effective one Optimally allocate budget among different campaigns while increasing sales and maximizing ROI Design campaigns specific to a category instead of following “one-size fits all” approach Measure the campaign effectiveness for continuous improvement and track the profitability of retained

customers

Customer Lifetime Value

Customer Lifetime Value

Customer lifetime value (CLV) represents how much a customer is worth in monetary terms and is based oncustomer’s expected retention and spending rate. It can be defined as the present value of the total profitexpected from the customers during the entire period they do business with the company. CLV analysis usescustomers’ past transaction data and employs predictive modelling techniques to forecast how much eachcustomer would contribute to the company’s revenues and profits till they remain with the company and donot attrite. The analysis can also be extended to estimate the lifetime values of new customers. CLV analysistakes into account estimated annual profits from customers, duration of business relation of the customer,and the discount rate to assess the net present value of the customers.

CLV analysis is used for: Forecasting the expected revenue from new customers and weighing it against the acquisition and

retention cost for them Deciding how much to spend on marketing programs for different customers Identifying the high value customer segments that can contribute the maximum to company’s revenue

and have special offers for them Identify the prospects who can become profitable for the company

Computing CLV for a Cards Portfolio

MonthlyExpenses

MonthlyNet Revenues

CustomerTenure

Net Margin

AccumulatedMargin

AcquisitionCosts

Customer Lifetime Value

Predict monthly Revenues

Predict Customer Attrition

Predict Response RatesFrom existing P&L’s

Customer Retention

Churn Management

To retain customers, it is very essential to keep tracking customers’ activity regularly — their frequency ofshopping, evolution of their shopping patterns, how often do they shop and so on. Customers attrite on adefinite path to inactivity which can be identified and therefore managed. Also, acquiring new customers hasbecome far more expensive than retaining existing ones and hence customer retention has become a majorcorporate priority. By employing attrition analysis, customers whose engagement levels have lowered andwho are likely to attrite can be identified and appropriate retention strategies can be formulated.

Churn analysis helps answer key business questions like: Which are the customer segments, to be targeted for retention programs? How do we identify the factors which are most likely to drive customers to remain:

• Creating segments based on preferences and buying patterns so that right offers can be made to theright people

• Understand the variables that make high-value customers most likely to purchase and offerincentives and personalized service

Attrition rate by customer tenure

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Business Situation:The client, a leading retail chain offering various products across categories, wanted to understand its customers to better plan customized campaigns and promotions with the objective of increasing customer engagement and overall revenues.

The Task:Identify appropriate customer segments based on various factors such as purchase patterns, promotion response and demographics of the customers.

Framework:

Customer Personas:

Analytics in ActionIncreasing Revenues by better Understanding Customers

Client: A Leading Retail Chain

Define & Build customer segments

Segment analysis Customer profile

Identified an appropriate customer base based on the #

of visits and days on books

Built customer segments using clustering algorithms after

treating the outliers

Analyzed the segments and identified the customer

personas in each segment

Got a detailed profile of customer in a segment to

target for promotion

Who?

What?

When?

70% salesfrom FMCG &Staples

Early morningWeekend

Early Morning Weekend Shoppers

Large family High visits

60% salesfrom FMCG &Staples.Multi-categoryshopping

Afternoon to Evening

High sales, large family shoppers

Salariedstaplesshoppers

70% sales from StaplesShops in rice, oil, pulses and flour

Morning to Afternoon1st 10 days

Salaried, Health conscious,

staples shoppers

SalariedLarge family

50% sales-staples, 30%-FMCG. Multi-category shopping

Morning to AfternoonWeekend 1st

10 days

Salaried, large family, weekend shoppers

Low visits Low sales and high margin

45% sales from ApparelsShops in Men’s casual and formal, ethnic wear

Morning to Afternoon Weekend

Weekend, apparel buying shoppers

Single familywith kidsHealthconscious

70% sales-FMCGHigh proportion of baby care and health SKUs

Morning to Afternoon

single/small family shoppers

Discountseekers

50% sales from Home needs.Shops in utensils, bed and luggage

Afternoon to EveningWeekend

Discount seekers

70% sales from Staples & FMCG

Evening

Evening shoppers

The Result:

• Developed relevant Customer personas like discount oriented, large family, weekend specific category shoppers,impulsive buyers, high end buyers, etc

• Customer personas helped the business to appropriately target customers based on the day, time, affinity and categoryof purchase with appropriate promotional offers, leading to incremental revenues

Identify an appropriate

Customer Base

Small to Medium size families

Large families shops mostly in FMCG and Staples

Business Situation :The insurance provider generated leads through cross-selling. Potential customers were targeted in a 4-stage process, and they generallydisplayed 4 possible outcomes:

Resources were being wasted on pursuing unlikely Prospects classified in red boxes above. The insurance provider wanted to determine whichmembers were more likely to complete at each stage , and then fast track the application through the approval process. By reducing theproportion of declined and incomplete applications, operating costs could be optimized.

The Task :- Develop a framework of predictive models to calculate the probability of a prospect purchasing the insurance product

- Get a more targeted base of Prospects, and hence reduce costs by removing prospects with least probability of buying the Product

Analytical Framework :- Historical data , which contained information from both, internal and external sources, was analyzed

- Logistic models were built for identifying separate probabilities for each stage of the approval process

- Testing was done if Oversampling or Undersampling would improve the performance of the predictive models

- All the models were then combined to identify the ‘best’ leads

- The models were validated and implemented as SAS Macros to enable real-time scoring

The Result :• The framework of 3 models provided useful insights on probabilities associated with approvals and important factors affecting it

• Up to 25% of total applications were removed with a loss of just 5% of Paid customers

• With costs going down by 25%, we were able to achieve an increment 14% net profits.

Analytics in ActionTargeted Prospecting. Increasing Profits by 14%.

Client : An Insurance Provider in the US

Targeted

ProspectsApproved

Completed Forms

Paid Premium

Didn’t PayDid notDeclined

Target : 100

Approved : 85

Completed: 68 Paid : 58

Predictive Models

Target : 75

Approved : 70

Completed: 62 Paid : 55

Business Situation :The Automotive OEM, with dealerships across the US, was receiving almost 30,000 leads every month from various lead aggregator sitesacross the internet. Individual leads came with limited information – name, address, email, time frame of purchase, vehicle of interest andtrade-in type. The auto retailer wanted to put in place a ranking system so as to classify each incoming lead into hot, warm or cold; dependingon the leads propensity to buy a new car in the next 30 days. This ranking system would enable the OEM to be the first to reach out to a Leadand convert him into a Customer.

The Task :- Develop a predictive model that will tag each incoming lead as hot; warm or cold depending on the leads propensity to buy a new car in the

next 30 days

- Implement the predictive model in a real-time system so that hot leads get scored and automatically routed to the appropriate dealershipdepending on the location of the lead and the dealer

Analytical Framework :A 4-step analytical process was used:

1. Lead information along with auto purchase status over the past 2 years was analyzed. It was found that on average, 10.9% of leadsconverted and bought a new car within 30 days.

2. Lead information variables like name, address, email, time frame of purchase, vehicle of interest and trade-in type, etc were transformedinto derived variables. Text data entered online by leads as ‘comments’ was also considered.

3. A predictive model was built to classify each lead into hot, warm or cold.

4. The model was validated and implemented as a SQL Stored Procedure to enable real-time delivery of hot leads to the right dealerships.

The Result :• The predictive model was able to segregate each incoming lead into hot, medium or cold.

• ‘Hot’ leads had an auto purchase rate of 19%; almost twice that of an average lead. These hot leads were instantly routed to theappropriate dealership for immediate follow-up by their best salesmen. ‘Warm’ leads had a purchase rate of 11% and were actioned uponin the usual manner. ‘Cold’ leads were not actioned upon.

• After 3 months of using the lead rating system, auto sales went up by 12% across dealerships.

Analytics in ActionIdentifying Hot Auto Leads. Increasing Sales by 12%

Client : An Automotive OEM in the US

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Predictive Model Deciles; Each decile has 10% of Leads

Hot Leads Warm Leads Cold Leads

Random; 10.9% leads bought a new car

Predictive Model

Business Situation:The client, a US based technology corporation with a Global presence, has hundreds of partnerships across verticals and solutions. Recently they noticed that some partners dilute their brand, are not strategically aligned, and are not being fully leveraged. Lacking a framework to evaluate and prioritize partners, the client has witnessed a decline in brand equity which has stressed Marketing capabilities.

The Task:To develop a framework that evaluates and prioritizes partnerships based on relevant criteria. This will score every partner on an index and can be used to prioritize existing partnerships, identify future partnerships, or review risky partnerships.

Analytical Framework:Developed a framework based on Multi Criteria Decision Analysis (MCDA) technique. Partnerships are evaluated on criteria like Brand equity, financial health, strategic alignment, consumer perception, etc and scored on an index of 1-10.

The Result:• After evaluating existing partnerships, 7 were identified as brand diluting and risky, and have been reviewed.

• The marketing team compared the scores for existing partners against the marketing funding received from partners for joint marketing activities. When viewed from a strategic alignment perspective, some partners had very high synergy but were not being fully leveraged in terms of marketing activities. This resulted in $20M increased partner funding towards marketing activities.

• This framework was further used to identify and prioritize new partnerships in Education, Healthcare and Technology segments.

Analytics in ActionEvaluate and Prioritize Business Partnerships

Client: Among the top PC manufacturers in the world

Decision scorecard for new partners in Education

Decision scorecard for existing partners

Define parameters

Gather data and score the

parameters

Assign weights based on user preferences

Score partnerships and derive insights

Identify all parameters that evaluate a partnership. Can vary by vertical,

geography, etc.

Qualitative and quantitative data is scored by ranking the outcomes in a hierarchy.

After a discussion with all the stakeholders, assign weights to parameters based on the

decision makers preference.

Score the partnerships on an index of 1-10 based on the weighted average of selected

parameters.

MCDA framework process flow

Business Situation:

The client, a US based database publisher, has 12-15 specific products designed for Enterprise Markets. Products vary from repository of

research documents, tools that aid research processes; research documentation; databases that identify new technologies, and technology

partners; engineering, Oil and Gas, pharmaceutical and other domain specific databases. Higher versions of a product package are also

available. Corporate clients that are research oriented subscribe to some products, and not to others due to lack of need and/or awareness.

The Task:

To build a cross-sell strategy that identifies customers with a propensity to buy a specific product in addition to their current portfolio. To build

an up-sell strategy that identifies customers with a propensity to upgrade current products to higher versions. The exercise consisted of

scoring both the customer and each product with respect to the customer’s profile and life-cycle.

Analytical Framework:

The analytical framework was built using Market Basket principles. A scoring model was then developed to evaluate each Customer, followed

by evaluation of each product with respect to the Customer.

The Result:

• Cross-sell and up-sell recommendations implemented by business in a targeted fashion

• Revenues from current customers increased by 18% in Q1 and Q2 2013 as compared to the same period last year

• The Marketing team now uses the Cross-sell framework as an enabler in setting new Account Expansion strategies

Analytics in ActionIncreasing B2B Customer Engagement via Targeted Cross-sell

Client: Leading Business Database Publishing House

Business Situation:

The client, a South-east Asia based oil and gas Retailer encountered a significant increase in customer churn at their gas filling stations despite

having a tried and tested loyalty program in place. This resulted in a 4.6 % drop in sales during Q2 2012. The business wanted to monitor and

control Customer churn at regular intervals.

The Task:

To develop and implement a program that monitors Customer engagement levels and attrition risk, measure business impact from Customer

churn, and develop actionable strategies to manage Customer Attrition.

Analytical Framework:

High value customers that left the business impacted sales significantly. Segments were developed to slot each high-value Customer on the

basis of recent purchase patterns. Movement of Customers across segments and over time was used to identify the level of ‘engagement’ the

Customer had with the Business. Segment-specific offers and campaigns were implemented to manage customer attrition. Results from the

campaigns were used to continuously refine targeting and messaging.

The Result:

• Based on the analysis, the Business was able to identify high value Customers at risk of attrition. Suitable Retention programs were

designed and implemented for these Customers.

• Business was able to more efficiently utilize its Retention budget as targeted customers consisted of only 15% of the overall customer base

• Sales in Q4 2012 were up by an average of 2.1 % as compared to the previous two quarters.

Analytics in ActionProactively Retaining your most Valuable Customers

Client: A Leading Petroleum Retailer

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MANAGEMENT TEAMGLOBAL EXPERIENCE.

PROVEN RESULTS.

Roy K. CherianCEORoy has over 20 years of rich experience in marketing, advertising and mediain organizations like Nestle India, United Breweries, FCB and FeedbackVentures. He holds an MBA from IIM Ahmedabad.

Anunay Gupta, PhDCOO & Head of AnalyticsAnunay has over 15 years of experience, with a significant portion focusedon Analytics in Consumer Finance. In his last assignment at Citigroup, he wasresponsible for all Decision Management functions for the US Cardsportfolio of Citigroup, covering approx $150B in assets. Anunay holds anMBA in Finance from NYU Stern School of Business.

Kakul PaulBusiness Head, CPG & RetailKakul has over 8 years of experience within the CPG industry. She waspreviously part of the Analytics practice as WNS, leading analytic initiativesfor top Fortune 50 clients globally. She has extensive experience in whatdrives Consumer purchase behavior, market mix modeling, pricing &promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.

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MARKETELLIGENT, INC.80 Broad Street, 5th Floor, New York, NY 10004

1.212.837.7827 (o) 1.208.439.5551 (fax) info@marketelligent.com

CONTACT www.marketelligent.com

Industry Business Focus Tools and Techniques

Consumer Finance Investment Optimization SAS, SPSS, R, VBA

Credit Cards Revenue Maximization Cluster analysis

Loans and Mortgages Cost and Process Efficiencies Factor analysis

Retail Banking & Insurance Forecasting Structural Equation Modeling

Wealth Management Predictive Modeling Conjoint analysis

Consumer Goods and Retail Risk Management Perceptual maps

CPG & Retail Pricing Optimization Neural Networks

Consumer Durables Customer Segmentation Chaid / CART

Manufacturing and Supply Chain Drivers Analysis Genetic Algorithms

High Tech OEM’s Supply Chain Management Support Vector Machines

Automotive Sentiment Analysis

Logistics & Distribution

YOUR PARTNER FOR

DATA ANALYTICS SERVICES

Greg FerdinandEVP, Business DevelopmentGreg has over 20 years of experience in global marketing, strategic planning,business development and analytics at Dell, Capital One and AT&T. He hassuccessfully developed and embedded analytic-driven programs into avariety of go-to-market, customer and operational functions. Greg holds anMBA from NYU Stern School of Business