The Predictive Enterprise by Customer 360

white paper | 2007

The Customer-Driven Innovation Series: Part 1

The Predictive Enterprise

a division of Carlson Marketing Worldwide

The Customer Innovation Series: Part I:

The Predictive Enterprise Executive Overview

magine you are a high-end retailer with

tomer experience ahead of time so that at

over 300 stores. What if you could predict

every location and at every customer interac-

how many customers would walk through

tion, impeccable service turns into long-term

the door on a given day? Based on that insight,

relationships.”

>contents Executive Overview ____2

what if you were able to staff your retail floor

More and more companies like Coach are

with the right number of sales associates, and

realizing the benefits and return of becoming

even assign associates to individual, high-

“customer innovators.” They’re tapping into a

value customers so the best customer experi-

wider range of different data sources to uncov-

ence was delivered at the right time? And as a

er predictive insights about customers, using

result of those insight-based actions, you could

the insights to identify the right customer

Attributes of the Predictive Enterprise__5-8

predict how many transactions you would gen-

experience ahead of time, then delivering that

Case Study: Coach _____9

erate and how much sales you would capture.

experience at the right moment. In the follow-

If you had all this in place, would your compa-

ing white paper, we explore this topic in depth.

React or Predict: It’s Up to You _________10

ny be an innovator?

We unravel the traditional definition of inno-

You bet it would. But it isn’t just a pipe

vation and explain why customer interactions

dream. At this moment, Coach, the global

represent innovation’s new battleground. To

retailer of fine gifts and accessories, is

help you gauge your company’s “customer

putting the pieces together. “To get sales

innovation readiness,” we detail four attribut-

right, you need the right people in the right

es of the predictive enterprise and how data

place at the right time,” says Patraic Reagan,

mining and analytics can be used to enable it.

Director of Business Analysis at Coach. “But

Also included are the customer innovation sto-

to do that, you must be predictive rather than

ries of two esteemed international brands,

reactive. You have to design the best cus-

Coach and Debenhams.

Customer Interactions: Innovation’s New Battle ground ________________3 Case Study: Debenhams____________4

White Paper in Brief Who:

How:

Senior marketers and decision makers across the enterprise

• Expands the definition of innovation beyond products to include customer interactions

What:

• Documents why companies must become predictive enterprises

Strategies and tactics for enabling a more innovative, predictive enterprise that maximizes the value of every customer interaction to “get, keep and grow” customers

• Outlines four core attributes of the predictive enterprise, and how you can attain them • Shows how Coach and Debenhams use data mining and predictive analytics to achieve customer innovation, strengthen brand and lift sales

The Customer Innovation Series Part 1: The Predictive Enterprise

Customer Interactions: Innovation’s New Battleground Geoffrey Moore, marketing expert and author,

which customer will be on the telephone or the

once wrote: “Failure to innovate equals failure to

retail floor and which experience is the best one

differentiate equals failure to garner the profits and

for that customer at that moment. The predictive

revenues needed to attract capital investment.”i

enterprise knows which experience is right for a

Most executives would agree, and they’ve long

specific customer before the interaction actually

placed innovation near the top of their to-do lists.

occurs. It seeks to innovate by anticipating and

In a 2006 BusinessWeek survey, 70% of respon-

enhancing different customer interactions priori-

dents cited innovation as one of their company’s

tized by their impact on building and maintaining

top three strategic priorities.

profitable, long-term relationships. It might be the decision to route a high-growth customer to the

Expanding understanding

most knowledgeable service rep, to not approach

For decades, innovation has been most closely

a high-value customer the second she hits the

associated with the ability to launch new prod-

retail floor, or to give a disgruntled customer a dis-

ucts. But, capturing competitive advantage

count on his next purchase.

through product innovation alone—much less

What’s the payoff? An IDC report found that the

sustaining it—is getting tougher. No matter what

median ROI for projects that incorporate predictive

product you sell today, in a few years it will likely

capabilities and technologies was 145%.iii The

be a commodity. Consider that, according to

benefits that help to drive this ROI can come in a

some reports, as much as 80% of the 36,000 new

range of different forms, says Nucleus Research.

products brought to market in 2005 are destined

They include direct savings due to a reduction in

to fail.

marketing campaign costs, lower acquisition and

Companies like Coach are expanding upon the

retention costs, or even lower equipment and

traditional definition of innovation. They recog-

staffing costs. The ROI can also come from using

nize that innovation extends beyond products and

predictive analytics to increase productivity,

back-end processes to include how, when and

improved service levels or higher profits as a

where they interact with customers. “The concept

direct result of more targeted cross-selling and up-

of ‘product’ itself has been expanded beyond the

selling. A final ROI source: greater visibility across

tangible item itself to include the customer inter-

the enterprise that enables more efficient process-

actions that happen before, during and after its

es and operations.iv

purchase,” says Colin Shearer, Senior Vice

How can you become a more predictive enter-

President of Market Strategy, SPSS. This is the

prise capable of real customer innovation? It starts

new battleground for innovation as channels

with data. Lots of companies have plenty of data,

continue to proliferate and as the quantity of

but most are not taking advantage of the insights

customer interactions explode. This view is

and predictions it can offer. For example, accord-

shared by innovators such as Credit Suisse, Puma

ing to a 2006 survey by SPSS and Yankee Group,

North America, GE aircraft and British Telecom.

only 50% of organizations are applying advanced

They know that the customer experience is about

analytics to their data to drive predictive insight.v

more than products.

Even fewer take the next, innovative step of using

Lots of companies have plenty of data, but most are not taking advantage of the insights and predictions it can offer.

the insight to maximize the value of each cus-

It means being predictive

tomer interaction by delivering a great customer

Achieving customer innovation depends on

experience. But this is the new frontier of innova-

becoming a more predictive enterprise. Your cus-

tion—where leaders will distinguish themselves

tomer-facing personnel must know ahead of time

from laggards.

C AS E ST U DY

Customer Analytics are in Style at Debenhams “Style is primarily a matter of instinct." So said famed fashion designer Bill Blass. As one of the most influential fashion retailers in the UK Debenhams has competed on the basis that its style is about the customer experience and in trying to predict that instinctive process of stocking what its best customers will want when they want to buy it. “Things change quickly in this business,” says Debenhams commercial insight manager Matt Fryer. “Sometimes they change month to month. Analysis is crucial. It’s all about using information about our customers to come up with a competitive advantage. Retailing is a simple business. You want to create a market for your products. We use a lot of tools to find and keep the customers that create our market.” Debenhams is synonymous with style and high fashion, with 139 stores, 10.2 million square feet of trading space and 24,000 employees across the UK and Ireland. Understanding customer shopping habits is hard to predict but as Fryer says, critical for the company’s success. It employs a team of eleven analysts responsible for researching and evaluating all aspects of the customer relationship, including buying habits, as well as marketing campaigns and activity around new store openings. For example, when Debenhams is considering opening a new store, which is a key part of its strategy, its analytics team will assess the store’s potential based on a number of factors, such as market size and location. But that hasn’t always been the case. As recently as 2000, the Debenhams analysts were struggling to extract increasingly complex data, such as customer numbers and transactional information, in a timely manner. “We found we needed a new tool to manage the high levels of complicated data we were handling,” says Manjiry Tamhane, operations research and customer management controller. “We also needed several people to access the same data warehouse at the same time. And, on top of all of that, as always, we had to keep one eye on costs.” All of the company’s marketing campaigns were executed by a third party, complicating the data picture. After a comprehensive review, the SPSS predictive analytics tool was chosen to support planning and decision-making within buying and

merchandising, marketing and CRM activity and analyzing potential new stores. ‘Basket analysis’– looking at the contents of customers’ shopping baskets—plays a key role in successful merchandising in-store. As the amount of transactional customer data has grown, the SPSS solution has become the key to the success of this technique. Using the software, the analytics team can look at which special offers are generating the most sales, as well as identifying ‘cross-sell’ opportunities. The team also applies analytics to understand why some lines are selling better than others. Using clustered algorithms and SPSS technology, the team can analyze the pricing and optimize it to support Debenhams’ business objectives. Debenhams’ marketing and CRM activity is another important area of the analytics team’s work. Rolling out SPSS technology means that marketing campaigns can now be controlled in-house, and the success of campaigns is evaluated. This intelligence is then used to inform future campaigns and enable Debenhams to predict customer behavior. This has helped to generate significantly improved marketing ROI. The SPSS tool has also played a critical role in the lifecycle of new store openings – from strategy to operations – including preparing the initial business case, evaluating the optimum mix of product lines to carry, through to evaluating marketing campaigns for the new store. According to Tamhane, the results speak for themselves. “By moving our campaigns in-house, and concentrating on highly-targeted marketing and CRM activity, we’ve generated significant extra revenue for the company,” she said. “Not only that, but the time it takes us to respond to insight requests, both internal and external, has been cut significantly.” Debenhams has recently used the SPSS solution for further in-store analysis. For example, the software is currently being applied to identify potential incidents of fraud, such as investigating high numbers of unusual transactions. The tool analyzes transactional data at the checkout, and immediately registers any unusual activity, which is then passed to Debenhams’ in-house fraud team to investigate.

“We found we needed a tool to manage the high levels of complicated data we were handling” —Manjiry Tamhane, operations research and customer management controller

The Customer Innovation Series Part 1: The Predictive Enterprise

Attributes of the Predictive Enterprise Data mining and analytics enable an enterprise to

consistent and best-in-class customer experience.

become more predictive and customer innovative.

For other companies, the key problem to be

But, advises Shearer, “If you’re going to turn all

solved may be different. For example, it’s no

the data you have into real insight, then use the

secret that customers are becoming more intoler-

insight to make the most of each customer inter-

ant of irrelevant offers constantly being pushed

action, you need a focused plan of action as well

on them. It may be that the marketing department

as the technology.” How do you know if you have

of a retailer does a good job of protecting cus-

what it takes? Below are four attributes of the pre-

tomers from irrelevant direct mail but it uses out-

dictive enterprise you can use to start putting cus-

dated “batch and blast” techniques for email

tomer innovation to work at your organization.

communications, which annoys customers and cuts into open rates, click-throughs and conver-

1. Get to the Root of the Problem

sions. Or a telecom, for example, may be strug-

Businesses succeed by solving problems to

gling with churn among high-value customers,

achieve goals. The same logic is true for the

and its “win-back” programs aren’t preventing

predictive enterprise that seeks to innovate

defection. In this case, the problem is uncovering

around customers. But many companies don’t

what is causing these customers to reach the win-

take the time to define clearly what their spe-

back phase in the first place. Is it pricing? A poor

cific problems are, or how data mining and pre-

service policy that’s causing customers to jump

dictive analytics should be used to solve them.

through hoops? Uninformed call center reps?

“If you’re going to turn all the data you have into real insight, then use the insight to make the most of each customer interaction, you need a focused plan of action as well as the technology.”

When organizations do not “get to the root of

The scenarios are limitless. The point is, by

the problem” up front, they end up with a lot of

identifying up front the specific problem or prob-

—Colin Shearer

data and a lot of tools, but not a lot of predictive

lems to be solved, a company will get the most

insight or solid results. “It can’t be just a case of

out of its data and use its analytics to become a

Senior Vice President of Market Strategy, SPSS

‘let’s get some data and let’s use some analytics

more predictive enterprise. “Never underesti-

and see what comes out’, but this is still the pre-

mate the advantage of solving just a few of the

vailing approach at many organizations,” says

most important, root problems,” says Shearer.

Shearer. “It has to be driven by a business goal

“You don’t need to boil the ocean nor do you

[such as reducing churn by improving the cus-

need to predict with perfect accuracy to demon-

tomer service experience on the Web], which then

strate significant business benefit.” For example,

translates into an analysis goal. Once the analyti-

the Dutch insurance firm FBTO Verzekeringen set

cal piece is complete, it then has to be mapped

out to use predictive analytics to solve a specific

back to the original business goal and the results

problem: to reduce its overall mailing volumes in

deployed to improve business outcomes.”

marketing while better targeting customers. By centralizing its data and taking a closer look at

In practice

customer behavior, FBTO refined its cross-prod-

At Coach, for example, the root of the problem

uct campaigns and captured an ROI of 448% in

was identified as the in-store interaction where the

under three months.

brand promise becomes the brand experience (see the case study, “How Coach Lives Up to Its Brand”). With a strong commitment to customer

2.Consider all of Your Data Resources

service, Coach recognized the opportunity to pre-

Just about every company collects customer

dict customer traffic patterns at each retail location

data. Most often, it is descriptive data such as

and to use that knowledge to staff the store with

name and address along with some basic demo-

the right personnel at the right time to deliver a

graphics like gender, marital status or estimated

The Customer Innovation Series Part 1: The Predictive Enterprise

household income. Some organizations take it to

Companies can finally get their arms around loose

the next level by connecting their descriptive

data to gain predictive insight into customer atti-

data with behavioral (i.e., transactional) data,

tudes and interactions, and then use the insight to

such as purchase history. But only a few innova-

drive innovation at the customer interaction level.

tors add in the rich layer of attitudinal data that

For example, the ability to detect the presence of

captures customer preferences and perceptions,

“delight” in a customer’s inbound service email

or tie in interaction data that shows when, where

might help to predict the success of the next cross-

and with whom an interaction took place and

sell offer, while the presence of “dismay” would

what was communicated. In most cases, these

trigger the immediate instigation of a service

last two data resources (attitudinal and interac-

recovery tactic to stave off defection.

tion) are left out, which keeps enterprises from becoming fully predictive.

Getting going

Think about it. If all you know about “customer

“Many marketers believe that they need to first

A” and “customer B” is that each has a similar

‘get all the data’ before using analytics to dig out

descriptive profile and a similar purchase history

what’s predictive, but that just isn’t so,” says

with you, there is no reason to treat one differently

Shearer. Analytics can help you sort through all

than the other. You also don’t know why customer

the data points, decide which data pieces for

A behaves differently than customer B. If, however,

which customer types are most important to

you are able to distinguish them on the basis of

address a specific business problem, and prioritize

their attitudes (“self-reliant” vs. “traditional investor”

what new data has to be gathered, maintained and

in financial services, for example) or how they

refreshed. To reduce churn, for example, a retail

typically interact with you (over the Web versus

bank might be forced to examine many hundreds,

in-person, for example), you can more accurately

if not thousands of variables, just to discover the

predict their behavior and design just the right

critical 10 elements that provide a good

customer experience before an interaction occurs.

solution,” explains Hren. “But data mining

That’s why using all of your data resources is the

algorithms efficiently uncover and extract those

second key attribute of the predictive enterprise.

key components, easily identifying those data

“Never before in history has innovation offered promise of so much to so many in so short a time.” —Bill Gates Microsoft Founder

elements that provide the greatest insight. And

Not so unstructured anymore

as organizations acquire more knowledge about

Data and analytics professionals have always

the wealth of data at their disposal, the more

known that there’s a wealth of customer insight

predictive they become.”

hidden away in attitudinal and interaction data. But its common for survey data to be scattered or just sit on the shelf. In other cases, companies just couldn’t gain access to data because its “unstructured” data that doesn’t really “fit” on spreadsheets, such as email comments and open-ended inquiries typed into a Web site. “Historically, these free-form data have been nearly impossible to use effectively,” notes Richard Hren, Director, Product Marketing,

Rules for the Predictive Enterprise Know what you’re trying to achieve: Planning is the key to successful predictive initiatives. Focus on specific challenges and opportunities for business improvement. Get the right people involved: Using predictive insight to innovate around customers is a collective effort. Involve business sponsors and users throughout the process, and always secure IT buy in.

SPSS, “but new technologies have changed all that, allowing predictive enterprises to now be able to take full advantage of all of the data at their disposal.”

Line up the right data: Look for value from all four sources of data, especially the attitudinal data and survey data that often goes unleveraged.

Enterprise Feedback Management (EFM) systems,

Be strategic about technology: Ensure you have the breadth and depth

advanced text mining algorithms and improve-

of capabilities you need without taking on too much technology and too much cost. As a first step, determine current level of integration with IT infrastructure already in place (databases, operational systems, etc.).

ments in natural language processing make it possible to extract concepts and sentiments from customer survey data and unstructured data.

The Customer Innovation Series Part 1: The Predictive Enterprise

Mapping Customer Innovation

Predictive Enterprise Data Types Descriptive

In-Person

Democratize Insight

Customer Service

Behavioral Interaction

Use All Data

Customer Experience

Innovate Everywhere

Product Billing

Attitudinal Get to the Root of the Problem

Operations

The predictive enterprise distills insight from descriptive, behavioral, interaction and attitudinal data to get to the root of the key problems. It also democratizes new insight, and innovates at touchpoints across the customer lifecycle to enhance the customer experience.

3. Democratize Your Insight

Insight is bi-directional

It’s common to find analysts locked away in the

Achieving these cultural changes requires

far recesses of the company, receiving data

business decision makers at all levels to have

from the IT team then forwarding obscure

access to insight—not only to a “single version

reports to the marketing, sales or service

of the truth” represented in reports, but to the

departments. “Companies that still separate

very tools and datasets from which those

off analysis and analytics in a different area of

reports arise. In the predictive enterprise, busi-

the business are creating their own obstacles,”

ness decision makers are themselves able to

says Tom Khabaza, Director of Product

execute customized queries, conduct “what if”

Marketing for Data Mining, SPSS. “The ana-

analyses, and add their unique perspectives

lysts are essentially programmers, and any

into segment definitions or models. This keeps

insight they come up with doesn’t get out to

the insight free-flowing and bi-directional so

the people who need it.”

the enterprise can work together to solve its

The predictive enterprise doesn’t work that

key challenges. If there’s a customer churn

way. The ownership of and access to insight is

problem, for example, chances are that it

democratized, decentralized and distributed so

involves more than product development,

customer-facing personnel can make sense of

more than strategic pricing, more than channel

it and put it to good use. It starts with leader-

management, more than sales, and more than

ship. In the predictive enterprise, a senior-level

marketing. It involves and requires the view-

executive—often the CMO—shows a steadfast

points and the action of all. “Just as the con-

dedication to making fact-based decisions that

cept of quality in business has moved from

lead to superior customer experiences. This

being a department to being the responsibility

dedication can then become part of the culture.

of everyone, so too has insight transitioned

For example, knowing rather than guessing is

from a separated departmental and analytical

the rule when it comes to deciding how to ana-

function to a ubiquitous characteristic of the

lyze and use data resources, develop new cus-

company itself,” notes Shearer.

tomer experiences and treatment strategies and evaluate past decisions.

“Companies that still separate off analysis and analytics in a different area of the business are creating their own obstacles,” —Tom Khabaza

Director of Product Marketing for Data Mining, SPSS

Democratizing insight doesn’t mean that trained statisticians are marginalized. Quite the

The Customer Innovation Series Part 1: The Predictive Enterprise

contrary. Analysts in the predictive enterprise

factors contribute to the defection of high-value

become more valued and more highly utilized

customers allows companies to act long before a

as expert resources, and achieve greater orga-

win-back offer is needed. During the “grow cus-

nizational visibility. The “soft skills” of working

tomers stage,” the ability to identify customers

with business users become as important as

who have the most unrealized potential value

their technical sophistication, and the most

and understand their needs allows the predictive

successful are those who seek to share their

enterprise to craft more targeted, more relevant

new-found insight. That way, the entire enter-

communications.

prise can become predictive, not just a few analysts in a back room.

Bringing it all together Predictive enterprises become customer innovators when they bring together all four of the

4. Look to Innovate Everywhere

attributes above, from getting to the root of the

Different customers interact with your company

problem to innovating everywhere. To show the

at different times and across different channels.

point, consider the case of one large, European

And these customers do not view interactions as

cable network operator that provides a broad

isolated events. In their eyes, a phone call to ser-

array of interactive communications to both

vice and shopping on the Web are part of a single,

business and consumer markets. It had used

ongoing customer experience. In this environ-

data mining for some time, but the company

ment, every interaction counts, which puts signif-

wanted to do more in an effort to reduce cus-

icant pressure on every aspect of an enterprise,

tomer churn within one of its product offerings.

from customer-facing personnel to back-end

The company recognized that mid-way along the

processes and operational systems. “Essentially,

customer lifecycle, valuable customers began

anywhere a decision needs to be made—whether

considering defection. But why? And what

it’s by a person or by an automated system—you

should the company do to prevent it?

need the ability to be predictive,” says Shearer. As

The company believed that by making better

a result, the predictive enterprise actively looks to

use of its customers’ attitudinal data, it could pre-

innovate everywhere: across operational sys-

dict when customers were thinking about defec-

tems, across the customer lifecycle, across chan-

tion and which offers would work best in lower-

nels and to different customers.

ing churn. It worked with SPSS to create open-

“Whether it’s through marketing, sales or ser-

ended questionnaires, and sent them to cus-

vice, you are constantly looking to provide a sat-

tomers about to hit a seven-month milestone in

isfying customer experience in order to get, keep

the relationship (which was identified as the like-

and grow customers,” says Shearer. From how a

ly “weak point” at which customers began to

customer first becomes aware of a need, to the

think about defecting). The company translated

exploration of brands and solutions, to the pur-

the attitudinal feedback into a single satisfaction

chase decision itself, to the initial experience with

index based on the responses that they received.

the product, to service encounters, to buying

It then combined the insight with existing cus-

upgrades, to making a recommendation. All of

tomer data, including behavioral and descriptive

these are parts of the lifecycle that may be

data. A predictive satisfaction score and model

enhanced by anticipating when, where and with

were built, and the model was applied to the

whom in the organization customer interactions

entire customer base. Using its predictive

take place. During the “get customers stage,” for

insight, the company built more targeted reten-

example, the ability to predict a customer’s

tion strategies, tactics and offers and used them

immediate receptivity to an offer as well as the

to drive relevant interactions with customers at

downstream profitability yields an improved

the weak point in the lifecycle. The results:

quality of the customer base. During the “keep

among the treated customer group, churn plum-

customers stage,” the ability to identify which

meted from 18% to 2%.

“Innovation is the use of a new product, service, or method in business practice immediately subsequent to its discovery.” Barron’s Dictionary of Business Terms (3rd Edition)

C AS E ST U DY

How Coach Lives Up to Its Brand Remember the last time you

have an army of sales reps standing around,

shopped at a high-end

it’s also expensive. Being able to know ahead

retailer? Odds are, the sec-

of time what staff levels are needed keeps

ond you walked in the door, your expectation lev-

costs down,” says Reagan.

els for a premium experience went sky high. At Coach, the global designer and retailer of fine

Data in droves

accessories and gifts, delivering a premium cus-

Reagan says putting all the pieces together isn’t

tomer experience is a central part of living up to

easy. His team is sifting through 9 years of store-

its brand. “The Coach brand is well known for its

level data—from traffic patterns and purchase

quality products, but it doesn’t end there, says

histories to weather trends—all to get a clear

Patraic Reagan, Director of Business Analysis,

understanding of the ebb and flow of customer

Coach Retail Group. Every time customers interact

behavior. Coach actively gathers customer data,

with our brand, we have to make sure that their

too. In addition to the usual tactics of collecting

impression of our service is as strong as their

emails at point of sale and using in-store traffic

impression of our products. Our objective is to

counters, it sends thank you cards to each cus-

provide each customer with a unique experience,

tomer who makes a purchase of $250 or more to

something relevant to remember us by.”

keep the dialogue going. The challenge is turn-

>insight Coach uses in-store data such as 1. Traffic patterns 2. Purchase history 3. Weather trends

ing all the bits of data into predictive insight, and

Value on the retail floor

that’s where the SPSS Clementine tool comes in.

It all starts on the retail floor. “The in-store expe-

“We rely on SPSS to isolate the data holes, make

rience is the most important touchpoint for con-

educated assumptions and identify when cus-

veying our value propositions and building our

tomers will come into our stores each day. It

brand,” says Reagan. This puts a lot of pressure

helps us to stay innovative and one step ahead

on Coach to be predictive: to know when valu-

of our competition,” says Reagan.

able customers will come in to the store, which sales associates to have ready, and which expe-

No pressure...

riences are most relevant. “We can’t put a name

According to its mission statement, Coach seeks

and email address to everyone that walks in, but

to build “long-term relationships based on trust

we can use predictive analytics to get close,”

and satisfaction” by “treating customers as

says Reagan. “We can predict how many people

guests in our own home.” That’s a lot to live up

will walk in that day, how many transactions

to, but the retailer is backing up its brand

we’ll generate and how much sales we’ll proba-

promise by using predictive analytics to deliver

bly get. We also know who the top 1%-2% of our

value at every customer interaction at over 300

customers are.”

stores, from Austin, Texas to Madison Avenue.

Store managers use this insight to staff the

However, says Reagan, there’s always more

floor and assign sales associates to specific

work to do. In the coming months, Coach will

customers so the right experience is delivered

start inputting product-line and merchandising

at the right time. “This is key to sustaining

data into the customer behavior equation, too.

repeat purchases,” says Reagan. “Our best

“For example, we want to know why a particular

customers will buy 10-12+ bags a year, and that

bag flies off the shelves in Manhattan but not in

unique relationship with that specific sales

the Midwest,” he says. “Each piece helps us to

associate is a big reason why.””There are oper-

complete the data puzzle and make each cus-

ational payoffs, too. It not only looks bad to

tomer experience the best it can be.”

“Every time customers interact with our brand, we have to make sure that their impression of our service is as strong as their impression of our products.” —Patraic Reagan Director of Business Analysis, Coach Retail Group

The Customer Innovation Series Part 1: The Predictive Enterprise

React or Predict: It’s Up to You Innovation has always been about prediction—

these are symptomatic of a reactive enterprise.

that uncanny ability to “read the business tea

It’s also why companies that are able to incorpo-

leaves” and discern the future. The same is true

rate predictive capabilities and technologies into

for customer innovation. However, when it

their business see an average ROI of 145%.iii

organizations are more reactive than predictive.

Debenhams are turning the tables to become

They fail to capitalize on that moment of truth

customer innovators. They are solving specific

when a customer is deciding to make a pur-

business challenges by capitalizing on their data

chase, considering whether or not to defect, or

resources, putting their predictive insight into the

“145% is the median ROI of projects that incorporate predictive capabilities and technologies.”

just browsing the retail floor. To see this in

hands of customer-facing personnel across the

IDC, 2003

action, think about all of the marketing offers

enterprise and improving the customer experi-

and ads you see. By some estimates, 99% of all

ence across the customer lifecycle. The strate-

marketing messages are either ignored by

gies, the tools and the tactics are available. So it’s

consumers or simply go unnoticed. Results like

up to you. Will you react or will you predict? ■

comes to customer interactions, the majority of

Leading

organizations

Coach

and

Footnotes i Geoffrey A. Moore, “Darwin and the Demon: Innovating Within Established Enterprises,” Harvard Business Review, July-August 2004. ii BusinessWeekOnline, “Online Extra: The Top 100 Most Innovative Companies Ranking,” 24 April 2006. iii IDC, Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study, 2003. iv This information was taken from a presentation by Rebecca Wetteman, Evaluating the ROI of Data

Mining, Nucleus Research. October 2006. v Yankee Group and SPSS, “Yankee Group / SPSS Online Seminar Survey,” August 2006.

SPSS SPSS Inc. is a leading worldwide provider of predictive analytics software and solutions. Founded in 1968, today SPSS has more than 250,000 customers worldwide, served by more than 1,200 employees in 60 countries. You will find SPSS customers in virtually every industry, including telecommunications, banking, finance, insurance, healthcare, manufacturing, retail, consumer packaged goods, higher education, government, and market research. Our software helps organizations optimize interactions with their customers and ensure that the actions they are taking today will positively affect their ability to reach tomorrow's goals. www.SPSS.com

Peppers & Rogers Group Peppers & Rogers Group is a management consulting firm, recognized as the world’s leading authority on customer-based business strategy. Founded in 1993 by Don Peppers and Martha Rogers Ph.D., the firm is dedicated to helping companies grow the value of their business by growing the value of their customer base. Our goal is to develop and execute strategies that create immediate return on investment and longterm customer value. Peppers & Rogers Group maintains a significant voice in the marketplace with its 1to1 Media properties. Led by 1to1 Magazine, these print, electronic and custom publications reach more than 250,000 decision-makers. Peppers & Rogers Group is a division of Carlson Marketing Worldwide, and is headquartered in Norwalk, Conn. More information is available at: www.1to1.com

white paper | 2007

The Customer-Driven Innovation Series: Part 2

Let Customer Feedback Do The Driving

a division of Carlson Marketing Worldwide

The Customer Innovation Series: Part 2

Let Customer Feedback Do The Driving Executive Overview

ight now—at this very moment—your

hear, see and learn and turn it into the insight

customers are providing feedback. They

that answers the ultimate question of why

are speaking to your organization at

customers behave the way they do. This is the

multiple touchpoints, though multiple channels,

realm of latent needs where your next innovation

Executive Overview ____2

and through their words as well as their actions.

resides, waiting to be discovered.

Customers: The New Source of Innovation ___3

>contents

By listening well, an organization has a

As most senior executives readily acknowl-

breakthrough opportunity to let the customer

edge, gaining that level of competitive advantage

feedback do the driving on its road toward

by listening well to the voice of the customer is

Defining and Distinguishing EFM ____3

becoming more innovative and predictive. Jack

much easier said than done. Whether it is an

Case Study: Cablecom __5

Welch, the legendary CEO of GE said it best:

inability to systematically tap into the richness of

“We have only two sources of competitive

customer opinions, preferences and attitudes;

Customer Feedback in Practice _____________6

advantage—the ability to learn more about our

or a struggle with integrating data across the

Case Study: Yamaha ____8

customers faster than the competition and the

enterprise; or the challenge of institutionalizing

Five Rules of EFM ______9

ability to turn that learning into action faster

and operationalizing the process of collecting and

Can You Hear It?_______10

than the competition.”

distilling feedback, companies are too often

Innovators have historically been praised as doers, as visionaries and as entities of action and

stalled in their journey toward becoming true customer innovators. But not anymore.

leadership that propel the company years ahead

As the second installment in the series,

of the competition. 3M, Intel, GE and Toyota (to

this paper describes why and how Enterprise

name a few) are all companies known to act

Feedback Management (EFM) is a critical com-

fast to capitalize on the needs of the marketplace

ponent in solving the problem of enhancing

and launch new products and services that drive

customer-driven innovation and improving the

outstanding returns and the laudations of admir-

predictive capabilities of the organization. It also

ersi. But what about listening? A core quality of

illustrates EFM principles by examining the

innovators that often gets overlooked is their

practices of two companies—Yamaha and

ability to listen to customers well—keeping their

Cablecom—now using feedback to become

antennae up at all times, analyzing what they

customer experience innovators.

White Paper in Brief Who:

How:

Senior marketing professionals

• Explains why feedback could be the missing link to innovation within your organization • Shows how Enterprise Feedback Management (EFM) provides more timely, thorough and actionable insight into customer behavior • Discusses the linkages among EFM, analytics and data mining to create a single predictive enterprise • Describes three proven best practices to make EFM a reality

What: A roadmap to improving, using and taking action on the velocity and volume of customer-driven innovation within the enterprise

The Customer Innovation Series: Part 2-Let Customer Feedback Do The Driving

Customers: The New Source of Innovation Something has to change. It has become just too

Analytics, SPSS Inc. “It requires true and deep

risky (and too damn hard) to innovate the old way.

listening to the voice of the customer, analyzing

Consider the facts. Between 40 and 90 percent of all

it and pumping fresh insight into products or

new products fail, 70 to 90 percent of the 30,000

services.” In short, it requires customer-driven

packaged goods products in the United States have

innovation: new changes to facets of the

a marketplace lifespan of less than one year, and 47

complete customer experience (whether radical

percent of pioneering first-mover new product

incremental)

that

embody

customers’

introductions do not succeed . As a consequence, it

perspectives arising from systematic listening,

is perhaps no surprise that a study of 6,800 compa-

yielding improved levels of getting, keeping and

nies in 40 industries spanning 25 years showed that

growing customers.

periods of persistent and superior economic perforiii

mance are declining in duration .

More and more companies are discovering that Enterprise Feedback Management (EFM) can

“Increasing the odds in your favor can be

help to get it done. EFM enables companies to

done,” says Nancy Dobrozdravic, Vice President,

listen closely to customers, uncover their needs,

Product Marketing, Customer Feedback and

and utilize that new insight to fuel innovation.

Defining and Distinguishing EFM What is EFM? It is a systematic approach to planning,

with customers; customer satisfaction research,

gathering, analyzing and applying customer feed-

the goal of which is to monitor quantitative

back. Stated more precisely, EFM is…

changes in predefined measures at an aggregate

• A centralized system that allows organiza-

level; and traditional marketing research method-

tions to fully engage with their current or

ologies, in which the respondent is anonymous.

prospective customers…

Feedback management—in contrast to routine

• Through targeted feedback programs or by asking questions during naturally occurring

“Where do you get innovation? You get it as inspiration from your customers. Enterprise Feedback Management is all about really letting customers’ voices be heard.” —Colin Shearer, Senior VP, Market Strategy, SPSS Inc.

data collection—has three essential and distinguishing characteristics.

interactions in order to… • Utilize that information throughout the organization to drive customer-centricity and iv

business improvement .

1. Centralized system For EFM to be successful, it must be centrally controlled and directed, because its domain is that of the customer—an entity that ‘touches’

Certainly, it is a rare organization today that

the company through numerous channels,

doesn’t collect customer data to a greater or lesser

using a variety of technologies, and across

extent. EFM, however, supplements (rather than

a number of organizational areas (e.g., marketing,

supplants) such activities, including CRM, which

sales and service). Stated differently, EFM must

has in practice a strong “push” orientation rather

have the rigor to accommodate the flexibility

than engendering an open and free-form dialog

that is inherent in working with customers.

The Customer Innovation Series: Part 2-Let Customer Feedback Do The Driving

From an organizational perspective, this means

Another key distinction of customer feedback pro-

a company-wide commitment to customer

grams is the purposeful omission of respondent

feedback must be present. From a technology

anonymity, a feature universally found in traditional

perspective, this also implies an ability to

marketing research methodologies to ensure that

integrate into other existing systems, including

the information collected is “pure” and “unbiased.”

those that support call center, website and

In the realm of customer feedback, it is important to

point-of-purchase interactions.

be able to associate specific feedback with individual

“It is not unusual,” observes Dobrozdravic, “for

customers for three reasons. First, it allows

one organization to have 10 to 15 different cus-

the feedback to be integrated into an existing cus-

tomer feedback products in place, used by multiple

tomer database, and thus leveraged more exten-

departments for different purposes.” The lack of

sively. Second, it allows the company to immediate-

centralization incurs a cost. Without coordination at

ly act upon the feedback, by contacting the customer

the corporate level, there is no standardization of

to resolve an issue or answer an inquiry. Third, it

the information being solicited, reducing the ability

likewise permits the company to communicate back

to compare results across time or between

to customers (individually or in whole) the changes

functional areas; there is no oversight to ensure

it has implemented as a result of the feedback, and

compliance with corporate guidelines (e.g., privacy

thereby close the loop and tangibly demonstrate

or data retention policies); and there is no data qual-

the value placed upon customer feedback.

ity assurance, reducing confidence in the reliability and validity of the information. “In a purely financial

3. Utilize the Information

sense, some companies have had as much as a 50

Applying insights to affect customer focus and

percent reduction in their data collection and report-

drive business improvement throughout the

ing costs by centralizing customer feedback man-

company is a third key aspect of customer

agement,” notes Dobrozdravic, “while at the same

feedback. Traditional research may assist in the

time increasing the timely and widespread use of

achievement of the same aims, but because it is

results across the enterprise.”

mostly sponsored or conducted by a single department, it is naturally focused on questions

2. Feedback programs

that are specific to that organizational entity. As a

Collecting customer feedback bears a degree of

consequence, its customer and business impact

superficial similarity to what is often done by

is thereby limited in applicability. For example, if

many organizations with structured surveys,

you are asking questions about sales practices,

but it is in fact uniquely different in that it relies

you are unlikely to learn a lot about post-sale

more substantially upon open-ended questions

service levels—perhaps the area in which the

that are both more natural and conversational

root cause of the problem resides.

in tone. Historically, working with text responses

The scope of EFM, in contrast, is the

has been a tedious and laborious process of

enterprise. This is important not only as it

reading, coding and tabulating responses, but

applies to the planning of data collection, but

with the advent of predictive text mining utiliz-

also in the way that the information is acted

ing linguistic technologies, it is now quite

upon. For example, through customer feedback

feasible to automatically explore unstructured

you may learn that a customer is dissatisfied.

comments and to identify concepts. In this way,

Rarely, however, is the source of the dissatisfac-

text analysis allows the company to discover

tion confined to one department, but is

new insights rather than only uncover changes

more typically found in processes that cross

in known trends.

departmental boundaries.

C AS E ST U DY

Cablecom Innovates The Discipline of Customer Retention Innovation is nothing new at

by Cablecom is that unsatisfactory events in the

Cablecom GmbH, a communi-

early to midterm portions of the lifecycle have

cations company in Switzerland

profound implications for churn many months

that broadcasts television to 1.6

later in the relationship. By identifying and

million households. It was the first to enter the

addressing those ‘points of pain’ proactively,

broadband Internet business and the first to offer

churn could be lessened dramatically.

quadruple play services (fix line, mobile, broad-

For example, in the broadband area, Cablecom

band Internet and TV) in its market. It may now also

discovered that there was huge peak in churn

be the first to innovate the planning, collection and

behavior between the 12 and the 14 month period

use of customer feedback to achieve dramatic

of the lifecycle—but, the decision to churn was

reductions in churn in its industry.

actually made much earlier, around the ninth

The principle of using customer feedback to

month. Cablecom then put in place a feedback

attack a business problem and improve the quality

program executed at the seventh month to gather

of service is at the very core of the company,

information using a well-refined five question

whose values include this precept: “We see the

survey to understand the drivers of the customers’

world through our customers’ eyes. Our customers’

decision and the satisfaction level, including an

needs are paramount and we have to know what

open-ended question designed to probe their

these needs are.”

attitudes toward the company. “If you want to

Seeing the world through its customers’ eyes is

understand the feeling of a person, just ask him,”

exactly the approach that Cablecom used to

says Cesconi. “Simply ask him, ‘What’s your

enhance customer retention. “Win-back programs

feeling toward Cablecom?’ The customer loves

and other typical ways of attacking churn are

to share such kind of information with us.”

far too late in the lifecycle and definitely far too

Using that information, Cablecom scores each

late in the mind of the customer,” explains

customer on her or his likelihood to churn with a

Federico Cesconi, Head of Customer Insights and

predictive statistical model. Those in the upper

Retention at Cablecom. “A lot of win-back treat-

four deciles are directed to a retention team that

ments and offers are made when it’s too late,

proactively contacts each customer. Especially

because the customer has already made a

useful in the outbound contact is the information

decision that is both difficult and expensive to

from the open-ended question, which may contain

reverse.” The solution was as obvious to

key insight into why a customer is dissatisfied.

conceive as it was difficult to implement: contact

From the customer’s perspective, the call they

the customer before she or he has made the

receive is an unexpected surprise—and, often a

decision to churn, to ameliorate dissatisfaction

delight, too, because the agent is able to offer

and restore the relationship.

a solution that addresses the reason for the

It requires a lifecycle-based approach to

dissatisfaction. In many cases, that solution may

customer feedback. In the pre-sales stage, for

be something as straightforward as explaining to

example, feedback is collected through sales

the customer how to use a feature of the service

channels to understand if a subscription exists

that already exists.

with a competitor and when it will terminate.

Through a comparison with a control group,

Immediately after a sale is made, feedback is

Cablecom knows that its customer feedback

solicited to understand the customer’s satisfac-

practices are working—and working exceedingly

tion with the purchase, installation and initial

well, with churn reduced from about 19 percent to

usage of the service. The key insight discovered

2 percent.

“A lot of win-back treatments and offers are made when it’s too late, because the customer has already made a decision that is both difficult and expensive to reverse.” —Federico Cesconi, Head of Customer Insights and Retention, Cablecom

The Customer Innovation Series: Part 2-Let Customer Feedback Do The Driving

The ABC’s of EFM

Affect Operational Processes Organizations can directly affect how they conduct business

Better Interactions Across Multiple Channels Feedback can be acquired using many different touchpoints

Centrally Managed The entire organization uses the same technology for Feedback Management

For feedback to be valuable, it must Affect operational processes to ultimately impact the customer experience. The feedback must also be acquired through Better interactions using options (and opportunities) at multiple channels in a cost-effective manner. Finally, the feedback must be Centrally managed to ensure quality and enable its influence across the enterprise.

Marketing Sales Service Online Telephone Mail Security Compliance (privacy) Integration

SOURCE: SPSS

Customer Feedback in Practice vi

In a recent research study CEOs from different

The authors continued with this ominous warning:

industries across the globe were asked the

Strategies based on imitation are transparent

weighty question, “Where do your innovative

to competitors who have already mastered

ideas come from?” As might be expected, some

them. Moreover, successful competitors rarely

executives (14 percent) gave the predictable

stand still. So it is not surprising that many

answer of “internal R&D.” Yet, more surprisingly,

executives feel trapped in a seemingly endless

over twice that number (36 percent) of CEOs

game of catch-up, regularly surprised by the

declared that “customers” were the source of

new accomplishments of their rivals.

their innovative ideas.

Avoiding the “endless game of catch-up”

EFM is the enabler of listening—and therefore

requires putting customer feedback into practice.

of customer-driven innovation. In the absence of

Thus, it is not surprising to find Gartner predicting

effectively listening to the voice of the customer, a

that “Through 2008, 40 percent of total feedback tool

company’s innovation potential necessarily

deployments will be done through EFM systems.”

becomes limited, and it may be forced to become

The next section of this white paper presents three

an imitator. Unfortunately, when it comes to beat-

proven (but not obvious) best practices that will

ing the competition, “Imitation may be the sincer-

jumpstart an organization on implementing EFM.

“Constant reinvention is the central necessity at GE.…We’re all just a moment away from viii commodity hell.” —Jeffrey Immelt, Chairman and CEO, GE

est form of flattery, but it will not lead to competitive revitalization,” as Gary Hamel and C. K.

Best Practice 1: Take Plenty of Attitude

Prahalad explained in their groundbreaking

On the road toward improving innovation

vii

Harvard Business Review article Strategic Intent .

through EFM, it is important to take plenty of

The Customer Innovation Series: Part 2-Let Customer Feedback Do The Driving

attitude along for the ride. Why? First of all,

attitude because the benefit of completing

because attitudinal data completes the customer

the customer picture is far from being just

picture (see figure “The In Complete Customer

theoretical. “Incorporating attitudinal data into

View”). Organizations may have descriptive

statistical predictive models or segmentation

data about their customers, and know basic

solutions,” says Dobrozdravic, “is known to

information about who they are (e.g., age,

improve the result by 10 to 30 percent.” It allows

martial status); or, they may have behavioral

better business decisions to be made and allows

data, and know what they have purchased

marketing to become more effective, because

(e.g., transactions); or, they may have interaction

communications can be targeted more selectively

data, and know how they have responded (e.g.,

and the messages therein can be tailored more

offers, click streams). Unless an organization

appropriately to address the underlying need

has attitudinal data (e.g., opinions, preferences),

of each individual customer.

however, they will not really know why a customer

Third, an understanding of a customer’s

grows, remains stable, or declines in worth

opinions and preferences opens the door for

to the company over time. In the absence of

increased opportunity by expanding the needs

knowing “the why,” a company is forced to

set . Through insight into “the why” behind

take its best guess and apply that speculation

customers’ actions, a company has the chance

uniformly to all customers. Clearly, such a situation

to offer complementary products or services

is suboptimal for driving innovation.

that may grow both the breadth and depth

Second, it is important to have plenty of

In the absence of knowing “the why,” a company is forced to take its best guess and apply that speculation uniformly to all customers.

of the relationships.

The In Complete Customer View Enterprise Data Sources

ATTITUDINAL DATA

INTERACTION DATA

DESCRIPTIVE DATA

Opinions Preferences Needs Desires

Mining Text

back Feed

Offers Results Context Click Streams Notes

Web M ining

Marketing Attitudinal Interaction Web Call-Center Operational

The “Missing Link”

Cu w sto mer Vie

Attributes Characteristics Self-declared info (Geo) demographics

Customer Contact Channels Website Email Phone Mail Branch ATM Agent Mobile

BEHAVIORAL DATA Orders Transactions Payment history Usage history

Even if you know quite a bit about a customer based upon her or his interactions with your company, based upon the person’s descriptive demographic characteristics and based upon operational transactions, the view is still incomplete. It is the “why” behind the customer’s actions that allows a holistic perspective to be achieved, by understanding the opinions, preferences, attitudes and needs of the individual. In the absence of a complete customer view, opportunities for innovation may remain obscured.

SOURCE: SPSS

C AS E ST U DY

Yahama Accelerates Innovation With EFM The wheels on its motorcycles aren’t the only things turning fast at Yamaha Motor Europe N.V. (YME), the European subsidiary of Yamaha Motor Corporation. Headquartered in the Netherlands, the company has the remit to manufacture, market and sell motorcycles across all of Europe. “The motorcycle market is extremely competitive, and technical development times are crucial,” says Hennes Fischer, Product Planning Consultant at YME. Under these conditions, the importance of customer feedback becomes heightened, to ensure that the right products are designed and manufactured to exceed the ever evolving requirements of customers. It’s not an easy task to accomplish. Previously, the product development teams at YME relied primarily on the use of focus groups to gather insight from prospects and customers in cities throughout Europe—a helpful exercise, but one that had high expense and long cycle times. Using telephone interviews to gather responses to structured questions from three- to four-thousand individuals also wasn’t a viable option, due to its high repetitive cost. Beyond the monetary and timing issues, there were also indications that the quality and applicability of the information being gathered though such avenues wasn’t satisfactorily fulfilling the company’s needs. These considerations forced YME to think differently about the problem of gathering customer feedback, and ultimately resulted in the launch of xi the Yamaha Design Café in 2000, a virtual meeting place on the Web for motorcycle enthusiasts of all brands to chat with one another to “increase the fun factor on two wheels” and to dialog directly with YME’s product developers, too. Getting the right prospects and customers engaged in dialog with YME has proven an effective innovation technique. The circumstances of introducing an electric scooter originally designed for the Japanese market into Europe—where customers are not known for buying this type of bike—are a case in point. In only two weeks, YME was able to leverage its Design Café to assess the potential for the scooter in Europe and identify the technical modifications

needed to make it a successful import. Through many such rapid customer feedback cycles, YME has learned several key lessons. “Easier is not necessarily better” says Fischer, when it comes to collecting customer feedback, “because it is essential to focus on the quality (rather than quantity) of information.” For this reason, YME purposely makes it a bit more difficult for people to answer its questionnaires, to increase the likelihood that only thoughtful responses are gathered. It will use, for example, questions that require a level of motorcycle knowledge and sophistication, to make certain that it is actually listening to its core audience of enthusiasts. A second lesson that Fischer has learned is to tap into the emotional passions—and not the economic pecuniary interests—of the Design Café members when soliciting customer feedback. “No prizes or incentives are used as part of our surveys anymore,” explains Fischer. “People do it because they like the topic. And we can give them the feeling that their opinion will influence our planning and our new products in the future. So, we have far fewer answers, but we have much better answers now.” Those answers are to open-ended questions as simple as, “Do you have any comments on how to improve the bike?” to more psychologically probing questions designed to capture the attitudes and motivations of their enthusiasts, such as whether it is “pure adrenaline,” “adventure,” or “makes me feel cool” as reasons for appreciating motorcycling. Putting these lessons into practice has delivered business benefit. For example, when YME was innovating its styling for one of its motorcycles, it first secured quality customer feedback through its Design Café on the prototype. By listening to its customers, the company unexpectedly learned that some aspects of the restyled bike needed modification, such as the fuel tank capacity and horsepower. As a consequence, YME was able to change those elements on the prototype before the motorcycle went into production. “So rather than get the feedback after the launch and incorporate it into the next development phase as was traditionally done, we could do it sooner—making or breaking sales and the success of the product,” explains Fischer.

“Easier is not necessarily better,” when it comes to collecting customer feedback,” says Fischer, “because it is essential to focus on the quality (rather than quantity) of information.” —Hennes Fischer, Product Planning Consultant, Yamaha

The Customer Innovation Series: Part 2-Let Customer Feedback Do The Driving

Best Practice 2: Listen to the Community

In the old model of closed innovation, compa-

EFM is not only about capturing and recording

nies looked inward to their Research &

feedback from individual customers, but it

Development departments for innovation. In the

is also about setting the conditions for feedback

new age of open innovation , the horizon has

to ferment, bubble, expand and overflow

expanded beyond the borders of the company to

through interactions among multiple customers

encompass not only uncovering unmet cus-

to drive innovation. Customers are social

tomer requirements, but also to find and see

beings, and as such communicate with and

how the vanguard of “lead users”

influence one another, as evidenced by the

may be addressing those needs. One study,

importance of word-of-mouth recommenda-

for example, examined the behavior of four com-

tions in a purchase decision. Constructing

munities of sport enthusiasts and found

a community—and using it as a listening

that nearly one-third (32 percent) had developed

post—may therefore be productively used

or modified equipment, and nearly one-fourth

to enhance innovation. Witness the success that

(23 percent) of the innovations would be brought

Yamaha Motor Europe, for example, has

to market by a manufacturer . In one case, for

had with its Design Café, a community

example, users fond of the sailplaning sport (fly-

motorcycle

enthusiasts

(see

xii

xiii

themselves

xiv

sidebar

ing in an engineless glider) created innovations

“Yamaha Accelerates Innovation With EFM”

as exotic as a “new emergency system where

—previous page).

pilot gets out of the cockpit with a rocket”!

Five Rules of EFM 1. Keep it simple. What is especially important in the early stages of introducing EFM to the organization is simplicity. As Cablecom discovered, for example, the process of learning why a customer is satisfied or dissatisfied with the company is as straightforward as simply asking her or him that question using an open-ended format.

format. Let your customers “sound off.” Using today’s advanced natural language processing tools, it’s quite feasible to easily extract the central themes from those comments, combine it with other customer data, and leverage it more fully. What used to be avoided because of its practical difficulty can now become a driver of innovation.

2. Less is more. The goal is always to gain the best customer insight possible in order to be as innovative as possible, not to collect as many responses to a survey as possible. Depending upon the details of the situation, “less” may mean less questions on a single survey, or less surveys in total, or less (but more qualified) respondents to a specific targeted survey, or less frequency of customer contact by integrating the data collection into interactions that are already naturally occurring.

4. Start where you are weakest. Yes, EFM and predictive analytics do work together, but there is no reason to necessarily focus on one before the other. Start where you have the greatest opportunity to impact the business.

3. Free form is good. Customer feedback benefits from strong conversational questions using an open-ended

5. Use passion to your advantage. Do you have brand advocates or category enthusiasts? Then take advantage of their passion to garner feedback and put it to good use. Give them the opportunity to interact with one another and to reinforce their excitement with the product category generally and your company’s products and services specifically.

The Customer Innovation Series: Part 2-Let Customer Feedback Do The Driving

Best Practice 3: Put All of Your Tools in One Belt

predict the likelihood of customer churn, and

Collecting non-directed and general customer

customized treatments are devised and deployed

feedback to ignite inspiration is as essential as

that speak to the underlying reason (i.e., “the

collecting directed and specific feedback to

why”) that may be motivating a customer’s

address a known business problem. Both deliver

decision to defect. In this way, the tools of CRM,

a noticeable business benefit through innovation,

of structured and unstructured data, and of BI

but the latter has immediacy and a focus upon a

come together to enable the predictive enterprise

tangible outcome. It is the realm where “putting

and solve the problem in a more effective

all your tools in one belt”—i.e., integrating CRM,

way than is otherwise possible. (For an actual

databases and BI—can be especially productive.

example of these principles in action, see the

For example, a company may identify the problem of customer retention as a key challenge

especially for the most valuable customers,

“EFM increases the opportunities for customers to give you feedback, and to communicate their ideas to you.”

sidebar “Cablecom Innovates the Discipline of Customer Retention.”)

to be addressed. It starts by designing a non-

Introducing EFM into an organization by focus-

anonymous questionnaire that works across

ing upon specific business problems also allows a

multiple channels (e.g., Web, telephone), is

phased approach to be utilized. “A complete inte-

integrated into the existing customer database so

gration across systems and databases has merit,”

as not to ask for information that is already

says Dobrozdravic, “but business benefit can

known, and links together the responses from the

be realized at each step along the path leading to

questionnaire to operational data to allow an

that end goal. Fortunately, you don’t have to do

assessment of customer value. Analytics are

everything at once to achieve the benefits of

applied to the resulting dataset to extrapolate and

customer-driven innovation.”

—Colin Shearer, Senior VP, Market Strategy, SPSS Inc.

Can You Hear It? Can you hear your customers providing feed-

customer interactions, and to thereby successfully

back? Even more importantly, can you systemat-

attack the problem of customer churn in the

ically ask them the most probing questions, and

extremely competitive communications industry.

then collect, distill and leverage what they are

Are you doing it, too?

telling you? Those are the outcomes that EFM

“EFM can provide a much needed ‘edge’ for

enables, and it can make all the difference in

companies today,” says Dobrozdravic. “If you’re

mastering customer-driven innovation and in

collecting the same types of data in the same

adroitly anticipating and addressing customer

way as your competitors, the results are likely to

actions. Yamaha is doing it through its Design

the same. EFM provides an opportunity to a

Café to accelerate the pace of feedback cycles,

company to forge ahead of its competition in a

and to thereby rapidly improve its products.

way that is sustainable, and to truly make

Cablecom is doing it to increase the quality of

customer feedback an organizational asset.” I

The Customer Innovation Series: Part 2-Let Customer Feedback Do The Driving

Footnotes i (2006, April 24) The Top 100 Most Innovative Companies Ranking. BusinessWeek, retrieved February 21, 2007 from http://www.businessweek.com/magazine/content/06_17/b3981413.htm. ii Gourville, J. T. (2006, June) Eager Sellers, Stony Buyers. Harvard Business Review, 84(6), 99-106. iii Wiggins, R. R. and Ruefli, T. W. (2002, January-February) Sustained Competitive Advantage: Temporal Dynamics and the Incidence and Persistence of Superior Economic Performance. Organizational Science, 13(1), 82-105. iv Jethwa, H. (2006, September 13) From “Good to Great:” How Top Companies Operationalize Around Customer Insight. Retrieved September 13, 2006 from http://spss.com/events/event.cfm?E_ID=1853&Country=US. v Cablecom's values may be found at http://www.cablecom.ch/en/vision-ir/values-ch.htm. vi IBM Business Consulting Services (2006) Expanding the Innovation Horizon. Retrieved February 21, 2007 from http://www-935.ibm.com/services/uk/bcs/pdf/g510-6259-01-the-global-ceo-study-2006.pdf. vii Hamel, G. and Prahalad, C. K. (1989) Strategic Intent. Harvard Business Review, 67(3), 63-78. viii Schonfeld, E. (2004, July) GE Sees the Light by Learning to Manage Innovation. Business 2.0, 5(6), 80. ix Kolsky, E. (2005, May 6) Make the Transition from Surveys to Enterprise Feedback Management. Retrieved February 21, 2007 from http://123interview.com/fr/From_Surveys_to_EFM.pdf <http://123interview.com/fr/From_Surveys_to_EFM.pdf> . x Peppers, D. and Rogers, M. (2004) Managing Customer Relationships: A Strategic Framework. Hoboken, NJ: John Wiley & Sons. xi To experience the Yamaha Design Café, visit http://www.yamaha-motor-europe.com/designcafe. xii Chesbrough, H. W. (2003, Spring) The Era of Open Innovation. MIT Sloan Management Review, 44(3), 35-41. xiii Hippel, E. (2006) Democratizing Innovation. Cambridge, MA: The MIT Press. xiv Franke, N. and Shah, S. (2003) How Communities Support Innovative Activities. Research Policy, 32(1), 157-178.