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The Utility Customer Experience Investment

WHITE PAPER

Unlocking the Full Potential of Personalization

THE UTILITY CUSTOMER EXPERIENCE INVESTMENT

2Copyright © 2019 Uplight, Inc. All rights reserved. uplight.com

Unlocking the Full Potential of Personalization

81% of consumers want

brands to understand

them better and know

when and when not to

approach them

Look around and it’s not hard to see: no matter which industry, we’re in the

midst of a revolution— in customer personalization. Thanks to innovations in

customer analytics pioneered by Amazon, Netflix, Pandora and others, not

only do companies have the ability to offer greater levels of personalization

and sophistication in the consumer experience—but customers are now

coming to expect this.

In a summary of how the customer experience landscape is changing, with

increasing connectivity across a multiplicity of devices, MarTech Today’s

Andy Betts has gathered a compelling list of trends1:

81% 50%>

More than 50% of

companies are

redirecting investments

towards customer

experience innovations

1 “A new era of personalization: The hyperconnected customer experience,” Andy Betts, January 23, 2018, MarTech, https:// martechtoday.com/new-era-personalization-hyper-connected-customer-experience-209529

60%

60% of marketers

struggle to personalize

content in real time, yet

77% believe real-time

personalization is crucial 94%

94% of marketers are

focusing on their data

and analytics capabilities,

personalization

technologies and

customer profile data

management capabilities

to deliver personalized

customer experiences

(Source: Accenture) (Source: Gartner)

(Source: Adobe)

(Source: Forrester/Janrain)



Indeed, today’s marketers, customer experience

managers, and journey architects have the

right idea about where to concentrate their

personalization technology investments.

Modern customer personalization for any

organization requires leveraging customer data,

predictive analytics, and consistent delivery

of recommendations across any channel the

customer may reasonably expect (including

mobile, call center, IVR, portals, email, SMS

text, direct mail, even smart speakers).

But while utilities are starting to think about

the importance of this level of personalization,

many have not yet begun to develop the

initiatives required, to provide comprehensive

recommendations across communication

channels, or to manage and prioritize those

recommendations on an ongoing basis.

What’s more, getting to sophisticated and

accurate analytics-based personalization is

challenging enough for a consumer products

e-commerce company, where consideration must

be given to past purchase history, browsing

behavior, marketing campaign interactions and

available demographics. But for gas and electric

utilities, delivering the kind of personalized

experience that today’s customers have come

to expect is exponentially more difficult.

In addition to all of the above, utilities have to

factor in analysis of customers’ energy usage

(including average kWh load, peak periods, derived

cooling/heating degree days, etc.), rate plans,

income eligibility, program history, premise details

(heating/cooking fuels, age, square footage,

AC type, owner residency, single/multi-family,

etc.), creditworthiness, the weather, meter type,

geography, neighbor comparisons, outage history,

and much, much more!

Companies such as Amazon and Netflix, which are

heralded as the gold standard for personalization,

have the advantage of being able to focus their

energy primarily on websites and apps with the

singular goal of driving revenue. Utilities don’t

have this luxury. They are mandated to serve all

customers equally and must deliver on multiple

objectives, from driving revenue and reducing

costs to serving low-income communities,

achieving energy efficiency goals, improving

customer satisfaction, meeting regulatory

requirements, and improving operational

efficiency. The combination of these highly

disparate challenges can make embarking on

the journey of personalization seem daunting.



The good news is, even given these extra

challenges, utilities can harness the abundant

customer data available to take advantage of

modern machine learning and analytics tools that

can deliver the personalization their customers

expect. The catch is—utilities unfortunately

can’t rely on the exact same personalization and

analytics strategies that retail-focused companies

use (and this even applies to utilities that are

trying to increase their own e-commerce revenue).



It All Starts with the Customer JourneyWhen talking about personalization, what we’re

really describing is the ability to understand the

customer’s world at a critical moment in their

journeys, and then putting the most useful and

attractive offer in front of them. If that offer

is grounded in a deep understanding of the

customer’s needs right there, then the likelihood

of that personalized offer being adopted

increases dramatically.

Retail companies typically make personalized

offers for a specific product or service they would

like to sell and is likely to be adopted. But for

an electric or gas utility, true personalization

must include a wider selection of messages that

support the utility’s diverse goals. These range

from energy products to new rates, EE programs,

savings tips, and safety messaging, as well as

expanded e-commerce offerings (advanced

thermostats, power strips, light bulbs, etc.); not

to mention, for some utilities, an ever-widening

array of value-added services, from installation to

appliance repair, to tree service, to energy storage,

to solar, to vehicle financing! In other words,

modern utilities have a much wider pool of

actions to recommend.

So for a utility to determine the optimum mix

of data, analytics and delivery, they first have

to identify the particular customer journeys

they would like to impact, which will then lead

to the pool of actions they want to recommend.

For example, a program focusing on low and

moderate income (or income- qualified) customers

would have actions offering discounted energy

rates, fuel assistance, home weatherization or bill

payment plans. By first establishing the customer

journeys and the related action pool, a utility

then opens the door to figuring out the required

collection of data and energy analytics necessary

to make their personalization story sing.



Putting Energy Analytics into ActionThe power of using state-of-the-art, yet proven,

analytics is that all applicable utility customers

can be evaluated to determine which action(s)

within the pool are most relevant to them at a

given time.

And the good news is, there are more options

out there than ever for businesses seeking to

better use analytics to create more personalized

experiences. For utilities, however, there’s a danger

of over-reliance on analytical approaches that

are primarily—or only—driven by e-commerce or

marketing campaign data. Although extremely

useful for consumer products companies,

data points that primarily rely on Web views,

MyAccount logins, shopping cart abandons,

click-through rates, etc.—though still valuable—

aren’t sufficient for powering the analytics

required to generate insights on a full portfolio

of utility actions.

In addition to e-commerce products like smart

thermostats, power strips and light bulbs,

utility actions can also include things like safety

messages, new billing plans, EE program

recommendations, and much more. Each of

these actions requires a specific mix of data and

personalization analytics to figure out the very

best action (and the second best, and the third,

etc.) to recommend to any customer based on

their unique situation and energy usage at a

particular moment.

So what kind of analytics are required for the

specialty needs of utilities? Generally, there are five

key categories of analytics that will be required

for utility personalization:

Each of these, of course, is a rich area of data

science, and applicable across lots of industries.

Where things get specific for utilities is the unique

mix of energy-related algorithms, regressions, tools,

models, and data sources, behind them.

Because “analytics” is a broad term, it can be useful

in the world of energy personalization to cluster

the various combinations of analytical methods

required for each personalized Next Best Action

(NBA) recommendation into conceptual groupings

that one might think of as “gears” (since, when put

together, they form the engine that powers the

utility personalization experience). Some of these

gears require utility-specific data; for example,

Average/Peak Load requires AMI data for a specific

customer. Similarly, others gears, such as Weather

Normalization or Cooling/Heating Degree Days,

combine utility data with third-party data sources,

such as local weather.

In this way, each desired action is driven by a

mix of gears that uncover the required energy

and customer insights to power utility

customer personalization.

1. Core Energy Analytics

2. Forecasting

3. Peer Analysis

4. Propensity

5. Ranking



Solving the Data ProblemBelieve it or not, when it comes to utility analytics,

more data isn’t always better—it has to be the

right data. For example, through highly relevant

for retail sites, utility personalization can’t be

reliant solely on marketing interaction or web

traffic data.

Two more reasons for this are what’s referred to

as the Data Sparsity and the Cold Start problems.

Data Sparsity refers to the fact that most

e-commerce-optimized analytics assume quite

large sets of marketing interaction type data

to power recommendations. For large retailers,

this is the case. But most utility websites and/

or marketplaces have much, much lower volumes

of web visitors, and typical retail recommender

algorithms simply aren’t optimized for these

low interaction volumes to make accurate

NBA recommendations.

Similarly, the Cold Start problem refers to the

fact that most retail- focused personalization

analytics require a minimum monitoring period

before the system has gathered enough data

about customer interactions to start making NBA

recommendations (and even then, sufficiently

high volume of website traffic are necessary,

as noted above). For example, a common best

practice for e-commerce analytics is to let the

software observe website behavior for at least

30 days before launching—with the potential to

substantially delay and complicate the process

of launching new utility customer NBAs.

There’s some irony here, of course, because

utilities are overflowing with data! But many

horizontal solutions haven’t optimized their

analytics or data object models for the unique

needs of utilities. And adapting these can lead

to unnecessary implementation and operational

expenses, and limits the ability to quickly scale

up products and services.

Instead, utilities are better advised first to identify

the internal data needed to enable the gears

required, then to precision-source the necessary

third-party data from the many available

vendors, such as Acxiom, CoreLogic, Experian

and more. When done right, such a system can

be automated and scaled up as the number of

Actions, analytics—and needed data types—

increases over time.



Deliver, Optimize, Learn...Precision energy analytics and unified data are

critical, but they don’t do the utility any good

if those recommendations don’t get in front of

the customer wherever they are at the moment

of need. Real, end-to-end personalization,

then, means the ability to integrate consistent

messaging across any and all channels the

customer is present in—from call center to chat

to IVR to web to mobile to text to social media

to digital assistants.

Of course, each of these channels has its own

integration formats, standards and challenges.

Being able to support integration across any

channel requires putting the work in upfront

to ensure that data is unified and properly

structured, and is able to leverage flexible

APIs (application programming interfaces)

for integration. Integrating personalized

customer NBAs into the plethora of channels

available to utility customers can seem

daunting. It is important to develop a channel

strategy to capture the greatest business

value through planning, implementation,

training, metrics development and reporting.

By staggering delivery channel deployments,

lessons learned can be incorporated into each

successive implementation.



However, even if personalized NBAs are

successfully deployed into every possible

customer delivery channel, there’s one more

mission-critical step before personalization can

be sustainably achieved—ongoing optimization.

This is because the various customer data inputs

that feed machine- learning based insights and

NBA recommendations are always changing. The

customer has a child, is laid off from work, buys a

more energy- efficient refrigerator or an electric

car, and suddenly their energy use profile changes:

recommendations should change accordingly.

In addition, every time a customer interacts

with their utility or adopts one of the products,

rates or programs recommended, fresh data

is generated, which can be fed back into the

analytics engine to improve future NBAs

delivered to that customer.

By combining continuous energy use analytics,

customer interactions, and what is already

known about available products and customer

profile history, this ongoing feedback optimization

can even proactively indicate the applicability

of a product the customer did not previously

qualify for. The result is a level of dynamic

engagement that delivers a game-changing

customer experience.



Getting Started in the Real World

Personalization sounds like a great idea. But

how and where can a utility realistically get

started when facing a seemingly overwhelming

number of potential customer journeys, offers,

programs, products, messages and channels?

What’s more, any such new initiative could

require cross-departmental buy-in, changes

to well-established processes, and the need to

integrate into aging IT systems—all of which can

seem like a big enough challenge to make the

most hardened utility leader run for the hills.

Now, this is the part of the paper where we’re

supposed to describe the formula for easy

personalization for any utility. Rather, as impactful

to customers as personalization initiatives

are—from a new IVR to a re- architected Start/

Stop process—they can’t achieve their full

potential until data-driven, prioritized actions

are being successfully routed to every relevant

customer. What’s more, without data-driven

personalization, utilities will be unable to

fully gain the organizational benefits of these

recommendations, including OPEX savings, c-sat

improvement, additional revenue, and increased

program enrollment.

Fortunately, there is a manageable way to get

started. Like many projects, even enterprise-level

personalization is often best tackled by starting

small. One best-practice is to utilize a 1/10/2

approach. This means selecting one key customer

journey, ten related customer actions to serve up,

and two relevant channels, each of which should

be strongly aligned with both critical customer

needs and the utility’s current business objectives

(such as reducing repeat calls, increasing

customer satisfaction, and so on). By using this

approach, utilities can get started on the path

to customer personalization with a limited,

manageable scope that allows them to prove the

value and then expand with confidence over time.



For years, utility customers were content with

stable rates and limited exposure to outages,

but now they are expecting more across all utility

touch points. Following the lead of personalization

pioneers like Amazon and Netflix, personalization

techniques such as the ones we’ve described today

have proven effective in increasing value and

customer satisfaction across industries ranging

from retail to media to telecom to banking.

Utilities can leverage the success of these

personalization front-runners to achieve

material results for their organization and, more

importantly, for their customers. By starting

with single impactful customer journey, utilities

can apply proven technology to create a unified

view of customers across data sources, leverage

data science to match the right actions to each

relevant customer, and deliver these best-fit

actions to customers as appropriate.

This is modern personalization. With this

foundation utilities will be equipped not only to

better scale their growth to support an increasing

number of customer journeys and actions over

time, but ultimately, to deliver a better customer

experience for everyone.

However, even if personalized NBAs are

successfully deployed into every possible

customer delivery channel, there’s one more

mission-critical step before personalization can

be sustainably achieved—ongoing optimization.

This is because the various customer data inputs

that feed machine- learning based insights and

NBA recommendations are always changing. The

customer has a child, is laid off from work, buys a

more energy- efficient refrigerator or an electric

car, and suddenly their energy use profile changes:

recommendations should change accordingly.

In addition, every time a customer interacts

with their utility or adopts one of the products,

rates or programs recommended, fresh data

is generated, which can be fed back into the

analytics engine to improve future NBAs

delivered to that customer.

By combining continuous energy use analytics,

customer interactions, and what is already

known about available products and customer

profile history, this ongoing feedback optimization

can even proactively indicate the applicability

of a product the customer did not previously

qualify for. The result is a level of dynamic

engagement that delivers a game-changing

customer experience.

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Interested in learning more about how Uplight can help you accelerate the clean

energy ecosystem? Send us a note [email protected] or visit www.uplight.com

Born from the merger of Tendril and Simple Energy, and the acquisitions

of FirstFuel, EEme, EnergySavvy and Ecotagious, Uplight is the leading

provider of end-to-end customer-centric technology solutions dedicated

solely to serving the energy ecosystem. Uplight provides software and

services to more than 75 of the world’s leading electric and gas utilities,

with the mission of motivating and enabling energy users and providers

to accelerate the clean energy ecosystem.

Uplight is the leader in Demand Side Management, Energy Analytics,

Utility Marketplaces, Utility Personalization, and Home Energy

Management. Together, these solutions form a unified, end-to-end

customer energy experience system that’s proven at enterprise scale, yet

nimble enough to deliver innovative solutions quickly. Utility leaders at all

levels rely on Uplight and its customer-focused digital energy experiences

to improve customer satisfaction, deliver energy and capacity outcomes,

reduce service costs, increase revenue, and reduce carbon emissions.

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