The document discusses how retail energy providers can apply decision science techniques like forecasting, predictive modeling, segmentation, and optimization to improve operations in areas such as demand planning, customer acquisition, loyalty analytics, campaign management, product design, and driving profitability. It provides examples of how these techniques are used to more accurately predict demand, target high value customers, increase customer lifetime value, optimize marketing spend, and reduce costs like bad debt. The goal is to help energy providers better manage risks, increase revenues and margins, and make more informed business decisions.

1. Application of Decision Sciences
to Solve Business Problems
For Retail Energy Provider’s (REP’s)

2. Retail
Energy
Providers

3. Demand Planning
Forecasting
In the domain of energy, there is a need to respond to shifting production constraints and changing demands
on a regular basis. In order to determine how best to buy electricity in the market, any REP must accurately
predict and forecast future demands, so that they can plan supply accordingly. Energy trading & hedging is
one of the most crucial activities for ensuring reliable electricity supply and achieving economy. Forecasting is
a 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. These
models use the data to project demand in the near future for different geographical locations.
Development Validation Forecast
Energy
(MM
kWhr)
Predicted Development Predicted Validation Forecast
0
2
4
6
8
10
12
14
16
18
20
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

4. Customer
Acquisition
0%
5%
10%
15%
20%
25%
1 2 3 4 5 6 7 8 9 10
Hot Leads Warm Leads
Random; 10.9% leads
Predictive
Model
%
Leads
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 consisted
primarily of brand awareness and public relations efforts. However, these days, deregulation allows customers
to choose their suppliers. Also, pricing pressure, thin margins, energy efficiency, load management, renewable
energy, frequent M&A activity, etc add to the complexity and competition, and hence acquiring new
customers 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 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 spends be lowered while maintaining results?

5. Loyalty Analytics
Tenure<12mo
All Customers
1,889
1,637 MM USD
87k USD/Customer
New Customers
4,568 (24%)
433 MM USD (27%)
95k USD/Customer
Existing Customers
11,573 (76%)
1,203 MM USD (73%)
84k USD/Customer
Savers
2,944 (16%)
39 MM USD (2%)
13k USD/Customer
Heavy Users
7,316 (38%)
812 MM USD (50%)
111k USD/Customer
Switchers
871 (5%)
60 MM USD (4%)
69k USD/Customer
Seasonal
3,190 (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 shift
from 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 product
and service offerings and marketing campaigns to target them effectively. It is typically done using a
combination of transaction data, demographic data, psychographic information, location and premise
attributes. Besides increasing conversion rates, targeted strategy helps drive energy efficiency and peak load
reduction to optimize the economic return on smart grid & smart meter investment programs. It aids in
answering 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

6. Loyalty Analytics
0
0.5
1
1.5
2
2.5
<15 15-25 25-40 40-60 >60
100%
80%
60%
40%
20%
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%)
Increasing
CLV
Customer Life Time Value
Wherever markets have been deregulated, utilities are under pressure to maximize their revenues as well as
control operating expenses. Higher costs, unforeseen service disruptions and increased customer
expectations 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 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. 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. 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
0
100
200
300
400
500

7. Customers : 1,050
DNP : 8.2%
Customers :2,127
DNP : 2.6%
Customers : 685
DNP : 10.4%
Customers : 565
DNP : 4.5%
Customers : 616
DNP : 9.1%
Customers : 1,511
DNP : 1.3%
Customers : 546
DNP : 11.0%
Customers : 139
DNP : 2.6%
Customers : 393
DNP : 11.5%
Customers : 223
DNP : 1.6%
Credit Range
550-679 <549, >680, No credit score, Pre-approved
Dwelling type
TDSP
ONC AEPC,AEPN,CNP,TNMP APARTMENT HOUSE, MOBILE
Contract term
Agent, Internal Sales, Telesales
6,9,12 months 18,24 months
Sales channel
Online
Total Customers : 2,177
DNP : 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 the
rise for lower bills, better tariff plans or better customer service. To retain them, it is very essential to keep
tracking 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 be
identified and therefore managed. Also, acquiring new customers has become expensive and hence retention
has become a major priority. By employing attrition analysis, customers whose engagement levels have
lowered 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

8. Campaign
Management
-1000
-500
0
500
1000
1500
2000
$
Profits/Campaign
$ 2,000
$1,500
$1,000
$500
$0
-$500
-$1000
Profitability 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 or
small commercial customers or institutions. Competitive retail electricity firms often use direct sales (including
telephone, door-to-door canvassing, mails, online) to acquire customers. Predictive modeling techniques on
past promotion data can help refine the promotion strategy by understanding lift of various campaigns, their
ROI 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

9. Product Design
Product Design
Retail energy marketers use value-added services to improve customer service and generate incremental
revenue. Due to de-regulation and increasing competition, bundling of products & services has become a
point of differentiation for retail energy providers. Some REPs offer air conditioning maintenance, smart home
technologies like smart thermostats, solar panels, home security systems or customized information on their
energy consumption as part of service bundling. However, it is important to gauge consumer perception
regarding different services. It is essential for—a) Creating the right product plans based on usage patterns
b)Identifying the right value added services
Conjoint analysis techniques are employed on survey data to evaluate how much consumers weigh each
component of the tariff plan and the add on service component in their purchase decision process. It helps in
segmenting consumers as per their preferences. This then helps the energy provider in designing the right
plans and value added services and selling it to the right set of customers.
0%
5%
10%
15%
Superlock 12 Fixed 24 Rate Protect
12
Safe Rate 12 Certified
Fixed 12
Simplicity
Online 12
Standard
Month to
Month
Earth Saver
Online 12
Winter 11
Special
Guaranteed
Fixed 12
Power As
You Go Plus
Renew Clean
and Green
12
True Green
Savings 24
Revenue Contribution by Product Plans

10. Driving Profitability
0%
2%
9%
12%
6%
4%
11%
18%
22%
13% 13% 13%
7%
14%
9%
15%
14%
4%
11%
13%
13%
22%
8%
2%
0%
5%
10%
15%
20%
25%
0-299
400-499
530-549
580-599
610-619
630-639
650-659
680-699
720-739
760-779
800-849
900-949
Bad Debt DNP Profile
0%
2%
9%
12%
6%
4%
11%
18%
22%
13% 13% 13%
7%
14%
9%
15%
14%
4%
11%
13%
13%
22%
8%
2%
0%
5%
10%
15%
20%
25%
0-299
400-499
530-549
580-599
610-619
630-639
650-659
680-699
720-739
760-779
800-849
900-949
% 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 required
to make deposits with the Retail Energy Providers. It serves as a security in case the customer enters into
arrears and turns to be a bad debt for the company. However, these deposits might differ for different
customers based on their credit history and demographic profile. Defining deposit rules by customer profile is
very essential to curb bad debt and losses for energy providers. Deposit rules for different customers are
defined as a function of many elements like credit score, dwelling type, product plan, tariff plan, demographic
attributes (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 the
disconnect rate and bad debt are high are segregated from the others. This serves as the basis for defining the
deposit rules of the energy provider for different customers. Optimization algorithms are then built for
identifying the right deposit for each of these rules.

11. Driving Profitability
Pre-paid customer yielded a negative margin for
current year
Decline in revenue compounded by high cost of energy and high
bad debt result in this decline
Margin Analysis
Profit margins are expressed as a ratio, specifically “earnings” as a percentage of sales. Margin analysis helps
companies manage their costs and expenses better and generate higher profits. This involves regular tracking
of P&L statements for different customer groups to evaluate profitability movements by analyzing historic
disconnect rates, bad debt as a % of margin, costs and revenue contribution by tariff plans & demographic
profiles (like credit score, age etc.).
It helps in generating a detailed demographic profile of high margin customers vs. low margin customers and
answering 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.

12. 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 two
challenges they face—a) Collection efforts start only after a customer enters into arrears b) Mostly a standard
approach is employed for all customers regardless of their demographics. By using predictive analytics in their
customer strategy, utility companies can get the right message to the right customer at the right time. Loyal
customers who have consistently paid on time, will be treated different from chronic late payers. Predictive
analytics 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, including
payment reminders and customized messages.
Re-active: It helps to determine who to invest effort in and to prioritize collections activities. Utility companies
can then rank the customers who will most likely pay their debt. This ensures organizations spend time and
resources 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

13. Vendor
Management
2,487 sales
1.34 MM$
109$/mo./customer
1235 sales
0.93 MM$
112$/mo./customer
1099 sales
0.49 MM$
98$/mo./customer
911 sales
0.32 MM$
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
ARDC
Telesales
Marketing
Online
Amalgam
Door-to-Door
Telephone Relations
Telesales
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 the
range of services they provide: relatively low risk transportation to high risk line work, production and
transmission of power, deploying smart metering services, collecting utility payments and managing the credit
collection. Effectively managing vendor and supplier compliance with corporate, legislative and regulatory
requirements 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 to
suitably 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, Quality
Specifications and delivery support).

14. 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.
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.
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