Application of Decision Sciences

to Solve Business Problems

For Retail Energy Provider’s (REP’s)

Retail

Energy

Providers

Demand Planning

Forecasting

In the domain of energy, there is a need to respond to shifting production constraints and changing demandson a regular basis. In order to determine how best to buy electricity in the market, any REP must accuratelypredict and forecast future demands, so that they can plan supply accordingly. Energy trading & hedging isone of the most crucial activities for ensuring reliable electricity supply and achieving economy. Forecasting isa pre-requisite for hedging.

Forecasts models are built by taking into account historical power consumption patterns, production costs,operational constraints and regulations, peak selling times, value of carbon credits, weather forecasts(forecasts of temperature, wind, rain & humidity), grid transmission capacity amongst other factors. Thesemodels use the data to project demand in the near future for different geographical locations.

Development Validation Forecast

Ener

gy (

MM

kW

hr)

Predicted Development Predicted Validation Forecast

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Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12

Customer Acquisition

0%

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1 2 3 4 5 6 7 8 9 10

Hot Leads Warm Leads

Random; 10.9% leads

Predictive Model

% L

ead

s

Predictive Model Deciles; Each decile has 10 % of Leads

Cold Leads

Response models to target the right prospects and optimize acquisition budgets

Prospect Targeting

Earlier, REP’s used to function in a regulated, non-competitive environment where marketing consistedprimarily of brand awareness and public relations efforts. However, these days, deregulation allows customersto choose their suppliers. Also, pricing pressure, thin margins, energy efficiency, load management, renewableenergy, frequent M&A activity, etc add to the complexity and competition, and hence acquiring newcustomers has become a challenge.

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 spends be lowered while maintaining results?

Loyalty Analytics

Tenure<12mo

All Customers1,889

1,637 MM USD87k USD/Customer

New Customers4,568 (24%)

433 MM USD (27%)95k USD/Customer

Existing Customers11,573 (76%)

1,203 MM USD (73%)84k USD/Customer

Savers2,944 (16%)

39 MM USD (2%)13k USD/Customer

Heavy Users7,316 (38%)

812 MM USD (50%)111k USD/Customer

Switchers871 (5%)

60 MM USD (4%)69k USD/Customer

Seasonal3,190 (17%)

292 MM USD (18%)92k USD/Customer

Customer Segmentation

Energy providers are transition from supply to demand, from production to marketing. To manage the shiftfrom being cost centers to revenue opportunities, it is important to understanding the customer base better.

Segmentation is the practice of identifying homogenous groups of customers based on their needs, attitudes,usage & consumption behavior. It enables identifying profitable customer segments and customizing productand service offerings and marketing campaigns to target them effectively. It is typically done using acombination of transaction data, demographic data, psychographic information, location and premiseattributes. Besides increasing conversion rates, targeted strategy helps drive energy efficiency and peak loadreduction to optimize the economic return on smart grid & smart meter investment programs. It aids inanswering critical business questions like: How can energy providers cut costs & focus resources & investments on secondary products? How can they connect the right offers to the right customer segments and respond to the needs in

electricity generation & transmission value chain? How do they comply with regulatory guidelines for energy efficiency based on different customer

segments’ energy consumption patterns during peak or off-peak hours? Which customer groups are most likely to enroll for different tariff programs like energy-efficiency and

what are their characteristics? How should contact channels be aligned to communicate with them?

Segmenting customers based on their revenue contribution

Loyalty Analytics

00.5

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22.5

<15 15-25 25-40 40-60 >60

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0%

Revenue Break-up by Age-Group Revenue Break-up by Cities

3,327(19%)$12000/Customer

$40MM(12%)

7,750(44%)$5,000/Customer

$39MM(11%)

4,421(25%)$25000/Customer

$111MM(33%)

2,118(12%)$70000/Customer

$148MM(44%)

Incr

easi

ng

CLV

Customer Life Time Value

Wherever markets have been deregulated, utilities are under pressure to maximize their revenues as well ascontrol operating expenses. Higher costs, unforeseen service disruptions and increased customerexpectations have made it essential for utility companies to give importance to high value customers.

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. CLV analysis takes into account estimated annual profits from customers, duration of businessrelation of the customer, and the discount rate to assess the net present value of the customers. It helps in: 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

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Customers : 1,050DNP : 8.2%

Customers :2,127DNP : 2.6%

Customers : 685DNP : 10.4%

Customers : 565DNP : 4.5%

Customers : 616DNP : 9.1%

Customers : 1,511DNP : 1.3%

Customers : 546DNP : 11.0%

Customers : 139DNP : 2.6%

Customers : 393DNP : 11.5%

Customers : 223DNP : 1.6%

Credit Range

550-679 <549, >680, No credit score, Pre-approved

Dwelling typeTDSP

ONC AEPC,AEPN,CNP,TNMP APARTMENT HOUSE, MOBILE

Contract term

Agent, Internal Sales, Telesales6,9,12 months 18,24 months

Sales channel

Online

Total Customers : 2,177DNP : 3.5%

Rules to identify customers having a higher likelihood of disconnecting for non-payment (DNP)

Churn Management

Due to de-regulation and increasing competition in the energy utilities market, customer attrition is on therise for lower bills, better tariff plans or better customer service. To retain them, it is very essential to keeptracking customers’ activity regularly — their frequency of consumption, evolution of their usage patterns,how often do they consume and so on. Customers attrite on a definite path to inactivity which can beidentified and therefore managed. Also, acquiring new customers has become expensive and hence retentionhas become a major priority. By employing attrition analysis, customers whose engagement levels havelowered and who are likely to attrite can be identified and usage patterns can be monitored separately.

Churn analysis helps answer key business questions like: Which are the customer segments, with a high likelihood of attrition, with a bad debt How do we identify the factors which are most likely to drive customers to stay Which are the most effective retention programs - constant tracking & monitoring of retention offers

helps gauge the efficacy of program

Loyalty Analytics

Campaign Management

-1000

-500

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rofi

ts/C

amp

aign $ 2,000

$1,500

$1,000

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$0

-$500

-$1000Profitability Segment

High Low

Customer Segments unprofitable and removed from telemarketing

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 special pricing, promotional contests,telemarketing campaigns, reward programs and so on. For utilities, campaigns can be directed at residential orsmall commercial customers or institutions. Competitive retail electricity firms often use direct sales (includingtelephone, door-to-door canvassing, mails, online) to acquire customers. Predictive modeling techniques onpast promotion data can help refine the promotion strategy by understanding lift of various campaigns, theirROI and targeting only the customers with the propensity to buy.

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 & maximizing ROI Measure the campaign effectiveness for continuous improvement Targeting only those customers who have a higher propensity to convert

Product Design

Product Design

Retail energy marketers use value-added services to improve customer service and generate incrementalrevenue. Due to de-regulation and increasing competition, bundling of products & services has become apoint of differentiation for retail energy providers. Some REPs offer air conditioning maintenance, smart hometechnologies like smart thermostats, solar panels, home security systems or customized information on theirenergy consumption as part of service bundling. However, it is important to gauge consumer perceptionregarding different services. It is essential for—a) Creating the right product plans based on usage patternsb)Identifying the right value added services

Conjoint analysis techniques are employed on survey data to evaluate how much consumers weigh eachcomponent of the tariff plan and the add on service component in their purchase decision process. It helps insegmenting consumers as per their preferences. This then helps the energy provider in designing the rightplans and value added services and selling it to the right set of customers.

0%

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15%

Superlock 12 Fixed 24 Rate Protect12

Safe Rate 12 CertifiedFixed 12

SimplicityOnline 12

StandardMonth to

Month

Earth SaverOnline 12

Winter 11Special

GuaranteedFixed 12

Power AsYou Go Plus

Renew Cleanand Green

12

True GreenSavings 24

Revenue Contribution by Product Plans

Driving Profitability

0%

2%

9%

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13% 13% 13%

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Bad Debt DNP Profile

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49

% of Disconneted

Credit score range 600-649 for Apartment owners accounts for a high disconnect rate & bad debt

Optimizing Deposit Rules

For availing of electricity supply and consumption, residential and institutional consumers are usually requiredto make deposits with the Retail Energy Providers. It serves as a security in case the customer enters intoarrears and turns to be a bad debt for the company. However, these deposits might differ for differentcustomers based on their credit history and demographic profile. Defining deposit rules by customer profile isvery essential to curb bad debt and losses for energy providers. Deposit rules for different customers aredefined as a function of many elements like credit score, dwelling type, product plan, tariff plan, demographicattributes (age, income, etc.).

Customer profiles are evaluated by analyzing historic disconnect rates, bad debt as a % of revenue/margin,revenue contribution, credit score, service plan, dwelling type and so on. Customer profiles where thedisconnect rate and bad debt are high are segregated from the others. This serves as the basis for defining thedeposit rules of the energy provider for different customers. Optimization algorithms are then built foridentifying the right deposit for each of these rules.

Driving Profitability

Pre-paid customer yielded a negative margin for current year

Decline in revenue compounded by high cost of energy and highbad debt result in this decline

Margin Analysis

Profit margins are expressed as a ratio, specifically “earnings” as a percentage of sales. Margin analysis helpscompanies manage their costs and expenses better and generate higher profits. This involves regular trackingof P&L statements for different customer groups to evaluate profitability movements by analyzing historicdisconnect rates, bad debt as a % of margin, costs and revenue contribution by tariff plans & demographicprofiles (like credit score, age etc.).

It helps in generating a detailed demographic profile of high margin customers vs. low margin customers andanswering business questions like: Do customers with a lower credit score generate greater margin than the bad debt they create? Do customers with extended split deposit option generate more margin than the bad debt they create as

compared to the full deposit customers? Do customers on different tariff plans behave differently vs. other customers in terms of margin?

Business can then accordingly impose the right business rules to reduce risk exposure from these customers.

Driving Profitability

1,560 $439K

Low(<50$) Medium-Low(50-300$) Medium-High(300-500$) High(>500$)

8% 0%

40%21%

37%

30%

15%49%

0%

25%

50%

75%

100%

Bad Debt Consumers Bad Dedt $

49% of the bad debt comes from 15% of the customers

Bad Debt management

Companies in energy domain typically write-off millions each year due to bad debts and there are mainly twochallenges they face—a) Collection efforts start only after a customer enters into arrears b) Mostly a standardapproach is employed for all customers regardless of their demographics. By using predictive analytics in theircustomer strategy, utility companies can get the right message to the right customer at the right time. Loyalcustomers who have consistently paid on time, will be treated different from chronic late payers. Predictiveanalytics can aid the 2 most commonly used approaches.

Pro-active: It helps identify the triggers and events that take place before a customer starts missing payments.Once these triggers are identified, proactive measures are taken to communicate with customers, includingpayment reminders and customized messages.Re-active: It helps to determine who to invest effort in and to prioritize collections activities. Utility companiescan then rank the customers who will most likely pay their debt. This ensures organizations spend time andresources only on the cases that are most likely to have successful outcomes.All of this aids companies in better bad debt management by: Formulating an optimally strategic plan that manages bad debt while maximizing revenues & profitability Segregating regular customers vs. bad debt customers and evaluating:

If there is a typical demographic profile of customers that generate most bad debt If there are any seasonal patterns or any changes in transaction before disconnecting

Vendor Management

2,487 sales1.34 MM$

109$/mo./customer

1235 sales0.93 MM$

112$/mo./customer

1099 sales0.49 MM$

98$/mo./customer

911 sales0.32 MM$

103$/mo./customer

- 23.5 MM kWhr- 1,012 kWhr/mo./cust- 11% clawback- 5% sales drop- 11% DNP- 69% post-paid- 33% pre-paid- 15% early termination*- 5% renewal*- 10% pre-approved- Tenure: 148 days- 7% bad debt- 12% deposit- ---- Avg. credit score: 627

- 4.6 MM kWhr- 1,106 kWhr/mo./cust- 6% clawback- 3% sales drop- 3% DNP- 94% post-paid- 6% pre-paid- 6% early termination*- 1% renewal*- 7% pre-approved- Tenure: 105 days- 6% bad debt- 7% deposit- ---- Avg. credit score: 787

- 8.7 MM kWhr- 1,079 kWhr/mo./cust- 14% clawback- 7% sales drop- 4% DNP- 100% post-paid- 0% pre-paid- 29% early termination*- 10% renewal*- 56% pre-approved- Tenure: 209 days- 6% bad debt- 2% deposit- $196K commission- Avg. credit score: 748

- 2.6 MM kWhr- 911 kWhr/mo./cust- 9% clawback- 4% sales drop- 13% DNP- 97% post-paid- 3% pre-paid- 33% early termination*- 3% renewal*- 0.4% pre-approved- Tenure: 120 days- 30% bad debt- 7% deposit- $98K commission- Avg. credit score: 624

ARDCTelesales

MarketingOnline

AmalgamDoor-to-Door

Telephone RelationsTelesales

Top 4 Vendors account for 71% of the sales, 75% of the revenue

Risk & Reward analysis

The number of vendors and suppliers involved in the generation and transmission of power is large. So is therange of services they provide: relatively low risk transportation to high risk line work, production andtransmission of power, deploying smart metering services, collecting utility payments and managing the creditcollection. Effectively managing vendor and supplier compliance with corporate, legislative and regulatoryrequirements is critical for the efficient and smooth functioning of any utility company.

Constant monitoring and detailed performance evaluation of all vendors is essential to control costs and tosuitably draft the risk & reward policy for each vendor. It includes vendor identification, recruitment,monitoring and quantifying the performance of vendors by evaluating on KPIs (like Pricing, QualitySpecifications and delivery support).

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.

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

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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