Application of Decision Sciences to Solve Business Problems. Customer Relationship Management

Application of Decision Sciences

to Solve Business Problems

Acquisition

Usage & Loyalty

V

al

ue

Time

Retention

Activation

Make targeted decisions for effective relationship

management with the customer through every phase of

CRM

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 ensuring

high 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 are

then 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

0% 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10

Hot Leads Warm Leads

Random; 10.9% leads bought a new car Predictive Model % L ead s w h o p u rch as ed a car

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 and

buying patterns have been constantly evolving. For retaining the profitable and loyal customers, it is

therefore 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 customizing

product and service offerings and marketing campaigns to target them effectively. It is typically done using a

combination 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 > 50K Gold: Current investment > 5K; < 50K Silver: Current investment < 5K

Tenure<12mo All Customers 1,889 1,637 MM EAD 87k AED/Customer New Customers 4,568 (24%) 433 MM EAD (27%) 95k AED/Customer Existing Customers 11,573 (76%) 1,203 MM EAD (73%) 84k AED/Customer Savers 2,944 (16%) 39 MM EAD (2%) 13k AED/Customer Investors 7,316 (38%) 812 MM EAD (50%) 111k AED/Customer Redeemers 871 (5%) 60 MM EAD (4%) 69k AED/Customer Revolvers 3,190 (17%) 292 MM EAD (18%) 92k AED/Customer Platinum Gold Silver Platinum Gold Silver Platinum Gold Silver Platinum Gold Silver Platinum Gold Silver

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 loyal

customers. Having special schemes for these customers in form of offers and discounts, can help in realizing

the long term goals of increasing profits and expanding customer base.

Organizations use customer profitability and loyalty analysis to identify the most valuable customer segments

to prioritize marketing, sales and service investments. Transactional behaviour is analysed for creating a

Customer Value Score (C-score) for each customer, which explains their engagement levels. The C-Score can

be 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?

0% 10% 20% 30% 50% P rof it C ont ri but ion 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Watch Out: Customers accounting for losses. Focus: Customers who have growth potential Sustain: Highly profitable customers Invest & Sustain:

Profitable customers %Customers Invest : Highly profitable customers 40%

Cross Selling & Upselling Strategies

It is not just enough to retain the profitable and loyal customers, but it has become a necessity to increase

the revenue contribution from the existing customer base. Cross Sell involves the sale of additional items in

order to increase the wallet share from the customers.

Market basket analysis is the technique used to evaluate customers’ purchasing behaviour and to identify the

different items bought together in the same shopping session. It uses transactional data and employs

predictive modelling techniques to identify customers’ preferences based on the associations between the

products recently purchased. It helps to determine which products are to be offered and which are the

customer 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%

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 econometric

modeling techniques are used to help companies refine their promotion strategies, to understand the lift

generated 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 on

customer’s expected retention and spending rate. It can be defined as the present value of the total profit

expected from the customers during the entire period they do business with the company. CLV analysis uses

customers’ past transaction data and employs predictive modelling techniques to forecast how much each

customer would contribute to the company’s revenues and profits till they remain with the company and do

not attrite. The analysis can also be extended to estimate the lifetime values of new customers. CLV analysis

takes 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

Monthly Expenses Monthly Net Revenues Customer Tenure Net Margin Accumulated Margin Acquisition Costs Customer Lifetime Value

Predict monthly Revenues

Predict Customer Attrition

Predict Response Rates From existing P&L’s

Customer

Retention

Churn Management

To retain customers, it is very essential to keep tracking customers’ activity regularly — their frequency of

shopping, evolution of their shopping patterns, how often do they shop and so on. Customers attrite on a

definite path to inactivity which can be identified and therefore managed. Also, acquiring new customers has

become far more expensive than retaining existing ones and hence customer retention has become a major

corporate priority. By employing attrition analysis, customers whose engagement levels have lowered and

who 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 the

right people

•

Understand the variables that make high-value customers most likely to purchase and offer

incentives and personalized service

Attrition rate by customer tenure

A tt rit ion R at e % 0% 10% 20% 30% 40% 50% 60% 70% 1 2 3 4 5

0 - 0.5 year 0.5 - 1 year 1 - 3 years 3 - 5 years > 5 years

# C us tom e rs (M M )

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 Action

Increasing 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% sales from FMCG & Staples Early morning Weekend Early Morning Weekend Shoppers Large family High visits 60% sales from FMCG & Staples. Multi-category shopping Afternoon to Evening

High sales, large family shoppers Salaried staples shoppers 70% sales from Staples Shops in rice, oil, pulses and flour Morning to Afternoon 1st _{10 days} Salaried, Health conscious, staples shoppers Salaried Large family 50% sales-staples, 30%-FMCG. Multi-category shopping Morning to Afternoon Weekend 1st 10 days

Salaried, large family, weekend shoppers

Low visits Low sales and high margin 45% sales from Apparels Shops in Men’s casual and formal, ethnic wear Morning to Afternoon Weekend

Weekend, apparel buying shoppers Single family with kids Health conscious 70% sales-FMCG High proportion of baby care and health SKUs Morning to Afternoon single/small family shoppers Discount seekers 50% sales from Home needs. Shops in utensils, bed and luggage Afternoon to Evening Weekend 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 category

of 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 generally displayed 4 possible outcomes:

Resources were being wasted on pursuing unlikely Prospects classified in red boxes above. The insurance provider wanted to determine which members were more likely to complete at each stage , and then fast track the application through the approval process. By reducing the proportion 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 Action

Targeted Prospecting. Increasing Profits by 14%.

Client : An Insurance Provider in the US

Targeted

Prospects

Approved

Completed

Forms

Paid Premium

Didn’t Pay

Did not

Declined

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 sites across the internet. Individual leads came with limited information – name, address, email, time frame of purchase, vehicle of interest and trade-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; depending on 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 Lead and 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 dealership depending 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 leads converted 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 transformed into 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 the appropriate dealership for immediate follow-up by their best salesmen. ‘Warm’ leads had a purchase rate of 11% and were actioned upon in 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 Action

Identifying Hot Auto Leads. Increasing Sales by 12%

Client : An Automotive OEM in the US

0% 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10 % Le ad s w h o p u rc h as e d a C ar

Predictive Model Deciles; Each decile has 10% of Leads

Hot Leads Warm Leads Cold Leads

Random; 10.9% leads bought a new car Predictive

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 Action

Evaluate 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.

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 Action

Increasing B2B Customer Engagement via Targeted Cross-sell

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 Action

Proactively Retaining your most Valuable Customers

Client: A Leading Petroleum Retailer

10% 39% 21% 32% 39% 24% 30% 6%

# of customers Revenue contribution

0% 25% 50% 75% 100%

MANAGEMENT TEAM

GLOBAL EXPERIENCE.

PROVEN RESULTS.

Roy K. Cherian CEO

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

Anunay Gupta, PhD COO & Head of Analytics

Anunay has over 15 years of experience, with a significant portion focused on Analytics in Consumer Finance. In his last assignment at Citigroup, he was responsible for all Decision Management functions for the US Cards portfolio of Citigroup, covering approx $150B in assets. Anunay holds an MBA in Finance from NYU Stern School of Business.

Kakul Paul

Business Head, CPG & Retail

Kakul has over 8 years of experience within the CPG industry. She was previously part of the Analytics practice as WNS, leading analytic initiatives for top Fortune 50 clients globally. She has extensive experience in what drives Consumer purchase behavior, market mix modeling, pricing & promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.

ADVANCED ANALYTICAL SOLUTIONS

MARKETELLIGENT, INC.

80 Broad Street, 5th Floor, New York, NY 10004

1.212.837.7827 (o) 1.208.439.5551 (fax) [email protected]

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

Y O U R PA R T N E R F O R

D ATA A N A LY T I C S S E R V I C E S

Greg Ferdinand

EVP, Business Development

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