Application of Decision Sciences to Solve Business Problems. Customer Relationship Management
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- Lynette Heath
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1 Application of Decision Sciences to Solve Business Problems Customer Relationship Management
2 Make targeted decisions for effective relationship management with the customer through every phase of their lifecycle Retention Value Activation Acquisition Usage & Loyalty Time
3 CRM Analytics
4 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 % Leads who purchased a car 25% 20% 15% 10% 5% Predictive Model Hot Leads Warm Leads Random; 10.9% leads bought a new car Cold Leads 0% Predictive Model Deciles; Each decile has 10% of Leads
5 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? Segmenting customers based on their revenue contribution All Customers 1,889 1,637 MM EAD 87k AED/Customer Platinum: Current investment > 50K Gold: Current investment > 5K; < 50K Silver: Current investment < 5K Tenure<12mo 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 Platinum Gold Silver 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
6 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? Profit Contribution 50% 40% 30% 20% 10% 0% Watch Out: Customers accounting for losses. Focus: Customers who have growth potential Invest & Sustain: Profitable customers Sustain: Highly profitable customers 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% %Customers Invest : Highly profitable customers
7 Deepening Engagement 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 Increasing sales by creating cross-selling opportunities using MBA Probability that Product 8 is purchased given that Product 1 is bought is 31% 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 1 is purchased given that Product 8 is bought is 57%
8 Campaign Management 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 Promotion dashboard to track the effectiveness of different campaigns
9 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 From existing P&L s Computing CLV for a Cards Portfolio Predict Response Rates Monthly Expenses Net Margin Acquisition Costs Customer Lifetime Value Monthly Net Revenues Predict monthly Revenues Customer Tenure Accumulated Margin Predict Customer Attrition
10 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 # Customers (MM) Attrition rate by customer tenure year year 1-3 years 3-5 years > 5 years 70% 60% 50% 40% 30% 20% 10% 0% Attrition Rate %
11 Analytics in Action Increasing Revenues by better Understanding Customers Client: A Leading Retail Chain 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: Identify an appropriate Customer Base 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 Customer Personas: Early Morning Weekend Shoppers single/small family shoppers Salaried, Health conscious, staples shoppers Salaried, large family, weekend shoppers Discount seekers High sales, large family shoppers Evening shoppers Weekend, apparel buying shoppers Who? Small to Medium size families Single family with kids Health conscious Salaried staples shoppers Salaried Large family Discount seekers Large family High visits Large families shops mostly in FMCG and Staples Low visits Low sales and high margin What? 70% sales from FMCG & Staples 70% sales- FMCG High proportion of baby care and health SKUs 70% sales from Staples Shops in rice, oil, pulses and flour 50% salesstaples, 30%- FMCG. Multicategory shopping 50% sales from Home needs. Shops in utensils, bed and luggage 60% sales from FMCG & Staples. Multicategory shopping 70% sales from Staples & FMCG 45% sales from Apparels Shops in Men s casual and formal, ethnic wear When? Early morning Weekend Morning to Afternoon Morning to Afternoon 1 st 10 days Morning to Afternoon Weekend 1 st 10 days Afternoon to Evening Weekend Afternoon to Evening Evening Morning to Afternoon Weekend 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
12 Analytics in Action Targeted Prospecting. Increasing Profits by 14%. Client : An Insurance Provider in the US 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: Targeted Prospects Approved Completed Forms Paid Premium Declined Did not Didn t Pay 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 Target : 100 Target : 75 Approved : 85 Completed: 68 Paid : 58 Predictive Models Approved : 70 Completed: 62 Paid : 55 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.
13 Analytics in Action Identifying Hot Auto Leads. Increasing Sales by 12% Client : An Automotive OEM in the US 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, , 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, , 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. % Leads who purchased a Car 25% 20% 15% 10% 5% 0% Predictive Model Random; 10.9% leads bought a new car Hot Leads Warm Leads Cold Leads Predictive Model Deciles; Each decile has 10% of Leads 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.
14 Analytics in Action Evaluate and Prioritize Business Partnerships Client: Among the top PC manufacturers in the world 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 Define parameters MCDA framework process flow Identify all parameters that evaluate a partnership. Can vary by vertical, geography, etc. Decision scorecard for new partners in Education Gather data and score the parameters Qualitative and quantitative data is scored by ranking the outcomes in a hierarchy. Assign weights based on user preferences After a discussion with all the stakeholders, assign weights to parameters based on the decision makers preference. Decision scorecard for existing partners Score partnerships and derive insights Score the partnerships on an index of 1-10 based on the weighted average of selected parameters. 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.
15 Analytics in Action Increasing B2B Customer Engagement via Targeted Cross-sell Client: Leading Business Database Publishing House Business Situation: The client, a US based database publisher, has 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 Q 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
16 Analytics in Action Proactively Retaining your most Valuable Customers Client: A Leading Petroleum Retailer 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 Q 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. 100% 75% 30% 6% 24% 50% 39% 32% 25% 21% 39% 0% 10% # of customers Revenue contribution High Medium-High Medium-Low Low 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 Q were up by an average of 2.1 % as compared to the previous two quarters.
17 YOUR PARTNER FOR DATA ANALYTICS SERVICES MANAGEMENT TEAM ADVANCED ANALYTICAL SOLUTIONS 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 Logistics & Distribution 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. Sentiment Analysis GLOBAL EXPERIENCE. PROVEN RESULTS. 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. 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 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. CONTACT MARKETELLIGENT, INC. 80 Broad Street, 5th Floor, New York, NY (o) (fax) info@marketelligent.com
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