«Marketing Treatment Strategy» - veien til økt kundeverdi

Highlights from DMA and

creating communication

with left & right side brain Bo Sannung, Nordic director of Centre of Excellence - IMM

9 NOVEMBER 2012 2012 COPYRIGHT SAS INSTITUTE 1

http://www.youtube.com/watch?v=q82NlrF1ArY



Digital Dashboard

9 NOVEMBER 2012 2

2012 COPYRIGHT SAS INSTITUTE

Competitor Earned Bought Owned

You

Comp 1

Comp 2

Comp 3

Comp 4

9 NOVEMBER 2012 3

2012 COPYRIGHT SAS INSTITUTE

Delivering Cross-Touchpoint Customer Experiences Drives Need For New Capability

“The Emergence Of Customer Experience Management Solutions”

5

Channel / Media Trustworthiness

Epsilon Targeting

Step 3: Multichannel Marketing

Time to Get it Right

Treat customers the way we want to be treated…

…and generate double-digit increases in response and revenue

Overview

7

Message Overload On any given day, the

customer will be

exposed to nearly 3,000

media messages.

They will pay

attention to 52.

They will positively

remember 4.

The chance they will

remember your ad is

0.013%!

D. Mastervich, VP, Sales Strategy, U.S. Postal Service, VDP Conference Presentation


8

Let’s Define “Relevance” “Per the DMA, 93% of

marketers using multiple

channels have attempted to

integrate their messaging.

Only 27.4% of these

said their efforts

are „effective‟. . .”

DMA Report, “Rowing

as One: Integrated

Marketing Today,” 4/11

1. Right message.

2. Right time.

3. Right person.

4. Delivered per that individual’s media preferences.

Without this, all we have achieved is. . .

Integrated, multichannel

irritation!


2: Create processes for generating feedback from your social media channels and your sales and service reps. This will provide ongoing qualitative and quantitative VoC guidance.

1: Start with the Customer (VoC). 1

2

3

4

5: Customer Lifecycle Marketing: 1) Communications must be deployed at appropriate points in the buying cycle, and 2) Contacts should be driven by opt-in preferences.

4: Re-conceive Inbound as a high value customer interface. By definition, Inbound callers are more 1) Qualified, and 2) Likely to spend.

3: Synchronize your multichannel mix with precision and value.

5 Principles of Multichannel Marketing

5


10

Don’t

re-engineer

your

relationship

marketing

strategies from

the isolation

of your

conference

room. . .

This economy and social media have profoundly

changed buyer’s priorities and expectations.

If you have not recalibrated strategies

within the past 12 months, you are out of

sync with your customers.

VoC insights ensure you develop truly customer-

focused strategies to drive relevance and revenue.

Step 1: VoC Research

11

VoC Learnings

Question Answer

Which has more impact on retention and repeat

purchases; Customer Satisfaction or

Customer Engagement/Relationship?

Engagement/Relationship strength has 12 times more influence on retention and

repeat purchases than Satisfaction.

Satisfaction is a minimum expectation.


12

VoC Learnings

Question Answer

Which is a more significant driver of word of mouth

recommendations; Customer Satisfaction or

Engagement/Relationship?

Engagement/Relationship strength has 18 times more influence on word of mouth recommendations

than Satisfaction.

This has profound implications for re-allocating greater budget for

Retention/ Relationship building.


VoC Learnings

Per McKinsey research, as cited in the Wall Street Journal, people who

participate in an effective online community, return to a site:

times as often times as long This represents a 45 time increase in

loyalty!

4. The Importance of Community


Community-driven,

online marketer specializing in

T-shirts designed by members of the community.

Community is made up of 3 groups:

1. Purchasers 2. Designers 3. Reviewers

Results:

• Over 1 million users,

• Over $30 million dollars in annual sales,

• Approximately 30% margins.

According to the Sloan Management Review: 95% of those purchasing from Threadless.com have voted and posted comments…before making a purchase.


15

As a result, customers and

prospects view

personalization as

the next step in a company‟s

commitment to service

excellence.

• Personalization is viewed as a service and benefit, not just a sales tool.

• Online shoppers view personalization as a requirement for their preferred shopping venues, rather than as simply a perk.

• Many BtoB decision-makers use Amazon as their point of reference regarding expectations for BtoB personalization.

• BtoB and BtoC marketers have to at least match Amazon!

Step 2: Opt-In Engagement

Meaningful Personalization

Customers are also savvy regarding the

type of personalization they want.

They want it to be more than just transaction-

based.

“I expect more than just ‘we’ve looked at everything you’ve bought over the last X years

and this is what we think you’ll like’. With today’s technology, I expect much more than

that!”

Step 2: Opt-In Engagement

Customer Engagement


We at Academic PCS would like to see Flash in 64-bit version as soon as possible.

This is very important creating and taking advantage of current hardware technologies.

“…customers with highest feedback scores

also had the greatest lifetime values.

Differences in lifetime value between customers with

lowest and highest feedback scores ranged from: 43% among retail customers to 288% among key

business accounts."

Forrester Research, 12/8/11

Customer Engagement


1. Providing Value

VoC Learnings

“Self-serve makes it easy for you, not the

customer.” “Don’t just sell me the service. Provide ongoing

value at key times.”

“Email blasts do not equal ‘relationships’.”


“The quality of your service is key to how we

judge you.”

2. Relationships

VoC Learnings

“The fastest way to be forgotten is to buy

from you.” “We buy. You disappear

without a trace. Oh, except for the monthly

bills.”

“Relationship? You guys are about ‘buy

and die’!”


VoC Learnings

3. The Web

In Step 4, we’ll analyze the site BtoB magazine ranked #1, and see how it compares, per VoC Research findings.

“I don’t just want to transact. I want to connect with your

company, your brand and your community.”

“An easy navigation and commerce process is a

minimal competency. . . You better be at least as

good as Amazon.”

“When you tell me to go to the web for service, especially when I am

growing old waiting for a phone rep, what I hear is, ‘Go. . . help yourself.”


Global CMO Survey:

For 42% of CMOs:

“…representing the voice of the customer

is one of the most critical factors in ensuring

personal success as a marketer”.

“CMOs and their peers understand that the real challenge is

…to become the experts of the customers…They must understand what customers

represent for the whole organization to help shape the strategy for the overall

business.”

-- Luca Paderni, VP and Principal Analyst, Forrester.

Heidrick & Struggles and Forrester Research, 1/23/12


Using Voice of Customer

to Increase Engagement &

Drive Sales

Who We Are

• Launched in 2007

• Flash-sales category founder in

US and leader with over $500MM

revenue

• Curate broad range of daily sales

• Evolved beyond women’s fashion

to Men, Home, Kids, Travel, Food

& local offerings

25

• Measure VoC: Utilize various sources including purchase,

browsing, waitlist, email click, as well as an advisory panel to get

member feedback

• Share VoC Insights Internally: Weekly presentation by the

Customer Service team to share VoC insights to senior

management

• Disposition Reporting: To keep middle-management up to date

• Customer First Experience: mandatory experience for all Gilt

Employees focused on connecting employees with actual VoC

How We Use VoC

26

How We Use VoC • Personalization

• Merchandising

• Segmentation

• Policies

• Loyalty

• Social Engagement

• Customer Service

• Launch of New Businesses

27

How We Use VoC - Personalization

• We produce 2,500+ versions of personalized emails

28

• VoC drivers include: purchase,

browsing, email click, and

brand preference


• Favorite brands are appended to

one’s profile and help drive e-mail

personalization

29


“I don’t buy men’s goods on gilt.com because

they sizing information isn’t good enough, you

have only general size information, you need to

have brand specific size info.”

Clothing by A.P.C is cut on the slimmer

side of the sportswear spectrum,

making for a modern European fit …

30

How We Use VoC -

Merchandising

• Use Facebook’s Face Off Application to

empower members to curate sales

31

Which handbag do you like best? Vote for your favorite and

we‟ll feature it as our Facebook Fan Pick in the Kooba sale

starting Thurs. 3/22 at noon ET. Click below the play button

below to vote right from your newsfeed.

How We Use VoC - Merchandising

• Announce winning selection on Wall and drive

to sale featuring the “Facebook Fan Pick”

label

32

And the Kooba Face-Off winner is…the Maci, with 206

votes! Find the Maci in today‟s Kooba sale along with

other styles we love from the line: http://gi.lt/GKofqP

How We Use VoC -

Merchandising

KPI RESULTS

Engagement Likes & Comments

7X Higher than average post

Unique

Impressions Unique fans who have

seen the post


• Use Facebook’s Face Off Application to

empower members to curate sales

33


• Crowd source ideas involving fans to create new

products

Fans vote

on favorite

design

Fans vote

on favorite

color

Winner is shown 34

We‟re thrilled to announce that Rebecca Minkoff will be

producing a handbag exclusively for Gilt Members. Even

better – we want you to be a part of the process. Vote on

your favorite design by liking one of the two sketches, and

the sketch with the most votes will be produced. Be sure

to tell your friends to vote…

We‟re exited to reveal that the winning sketch

to be produced by Rebecca Minkoff exclusively

for Gilt is “Luscious Hobo with Spine Studs”!

Now‟s your chance to select the handbags

color. Vote on one of the swatches below by

liking the picture…

And the winning Rebecca Minkoff handbag combination is…”Luscious Hobo” with spine

studs in soft leather metallic rose gold. Keep your eyes peeled for this creation,

available only to Gilt members. Big thank you to everyone who voted.


• Crowd source ideas involving fans to create new products

KPI RESULTS

Engagement Likes & Comments


Unique

Impressions Unique fans who have

seen the post


35

How We Use VoC - Segmentation

Brand Seekers

“I am always shopping to

keep up with the latest

fashions. I own the hottest

brands”

Self-Expressionists

“My style is an expression of my

personality. I am always looking

for inspiration …”

36

How We Use VoC - Policies

• Online Panels, Customer Service Feedback and

Research told us that Shipping Fees were biggest

customer pain point

• Verified with quantitative research and testing, then

reduced fees

37

How We Use VoC - Policies

• Measure the impact of new shipping fee policies on Consumer Awareness

and Satisfaction:

“In general, the cost of shipping on Gilt is:”

Old Policy

New Policy

16%

52%

Much too high

Somewhat too high

Just right

Lower than you would expect

I don't know enough about the

current shipping policies to answer38

Just right

Just right

How We Use VoC – Loyalty

• Quarterly member dinners provide “multi-

channel” insights and ensure that strategies

and policies are on track

39

How We Use VoC – Loyalty

• Quarterly member dinners…

KPI RESULTS

Spending by Best

Customers

10-15X Higher than average

customer

Churn Rate 50%+ Lower than average

customer

40

How We Use VoC – Social

Engagement • Senior Officers engage with members

41

How We Use VoC – Social Engagement

• Senior Officers engage with members

42

How We Use VoC – New Businesses

• Gilt Taste idea originated

from Gilt Employee

• Business launched

within 5 months

• With that speed VoC

was crucial to getting it

right:

• Customer Surveys

• Advisory Board

• Usability

43

Bringing High Quality Customer Service Into The

Social Arena

• Authenticity: Team is encouraged to be

themselves

44

Bringing High Quality Customer Service Into

The Social Arena

• Follow Up: Social feeds are tagged for follow

up, even if it takes months

45

• Transparency: All postings are valid

Bringing High Quality Customer

Service Into The Social Arena

46

• Surprise & Delight:

• Per CSR Feedback, women often

volunteer that they are pregnant.

• Team is trained to actively

engage with members and

empowered to surprise and

delight.

Bringing High Quality Customer Service Into The

Social Arena

47

5 Tactics to Leverage VoC

48

1. Listen and Invite Feedback

2. Respond, always, and make responses personal

3. Drive Awareness of VoC in organization, make it core

to the Culture

4. Start somewhere, you don’t need a lot of resources to

begin listening to your Customers

5. Follow up, we are sometimes wrong and so are

customers


BONUS:

• Do Not Wait! to hear from your customers

• Recently launched an outreach program to

proactively re-activate lapsed (best) members

“Thank you for reaching out and I look forward

to working with you. What fun! "

49


KPI RESULTS

Reactivation +45% vs. control group

Incremental Sales From reactivated customers

+40% vs. control group

• Best Customer Outreach Call Program

50

51

Copyright © 2011, SAS Institute Inc. All rights reserved.

Max=(r2+k3)*(TIME-(n+g))

52


DRIVING VALUE FROM CUSTOMER RELATIONSHIPS IS INCREASINGLY COMPLEX

Products

Channels &

Business

Functions

Offers,

Services &

Pricing

Checking

Savings

Loans Credit Cards Mortgages

Customers &

Prospects

Web E-mail Mail Print Branch Phone Mobile Social Risk Advisor ATM Collections TV Radio Finance

Insurance

Investments Lines

$

Service

http://www.livingbiblemuseum.org/_images/FaceBook-Logo.png

53


ACCENTURE RESEARCH 2011

COMPANIES THAT INVEST IN ADVANCED

ANALYTICAL CAPABILITIES OUTPERFORM

THE S&P 500 ON AVERAGE BY 64%

65% OF HIGH-PERFORMING COMPANIES

HAVE SIGNIFICANT DECISION-SUPPORT

AND ANALYTICAL CAPABILITIES

77% OF HIGH-PERFORMING

COMPANIES HAVE ANALYTICAL

CAPABILITIES ABOVE AVERAGE

INADEQUATE INFORMATION ACCESS

REDUCES KNOWLEDGE WORKERS’

PRODUCTIVITY BY 54%

54


55


Entry Points to ITV Player

Multiple

entry

points

to ITV

Player

56


Next Best Product - Examples Case Study: Erste Bank Group

57


The NBO 4 main components

Customer Behavior Importance

Previous Contacts Restrictions

“The product was

already offer to this

customer”

“The probability of the

customer to aquire the

product”

“The profitability

generated if the

customer aquire the

product”

“The product can be

sold to the customer”

58


Personalized “Next Best Product” offer executed across…

nbp

Branch/Advisor

59


How to get started ?

Value

Phase 1

Phase 3

Time

Cross- and up-sale

Profitability

Phase 2

Churn & Credit Risk

60


Lønsomhedsopgørelse Omsætning:

Forbrugs DB 100

Abonnement DB 100

= Samlet DB1 200

Direkte kapacitetsomkostninger:

Salg 10

Marketing 20

Kampagne 30

= Samlet direkte kapacitetsomkostninger 60

= Samlet DB2 140

Indirekte kapacitetsomkostninger:

Kundecenter 15

Billing / Produktskifte 25

Debitorer 35

= Samlet direkte kapacitetsomkostninger 75

= Samlet omkostninger 135

= Samlet DB2,5 65

Øvrige omkostninger 50

= EBIT 15

61


Lønsomhed på kunde

62


Copyright © 2011 SAS Institute Inc. All rights reserved.

4 slides on analytics – that’s it !

64


0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10Decile

Cu

ml

Ga

ins

( C

ap

utr

e)

30%

48%

62%

72%

Targeting the top 10% of customer

base capture 30% churners

Why Predictive Modeling?

Benefits of Modeling vs. Random Targeting

•Increased response rate by contacting the right customers

•Reduced campaign cost by selecting the most-likely to act customers

•Conveying the right message by understanding target population

16

65


Predictive Modeling Techniques

Decision Tree

Attempts to split a population into subgroups that tend to be more

homogeneous than the original sample. Each of the subgroups continue to

be split into even smaller subgroups until the model cannot be improved.

Pros: Allows for non-linear relationships, very intuitive Cons: Clumping of

probabilities and less distribution

Clustering

Identify groups of individuals based on their proximity to each other.

The cluster procedure and discriminate* analysis utilizes an effective

method for finding initial clusters with a standard iterative algorithm for

minimizing the sum of squared distances from the cluster means .

Logistic Regression

A generalized linear model for predicting probabilities. Logistic Regression

calculates the probability of a particular record being a member of a target

group, based on the values of the predictor fields.

Yi = B0 + B1Xi1 + B2Xi2 + … + BkXik + E

Predicted Churn = B0 + B1(Cell Minutes) + B2(Customer value) + E

1

3

2

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

$0 $25 $50 $75 $100 $125 $150 $175 $200

ARPU

Pro

po

rtio

n C

hu

rn

Cluster 3

22.5% of the upgraders

5.90% churn

$36.67 avg. ARPU

Cluster 1


1.80% churn

$87.14 avg. ARPU

Cluster 2


3.17% churn

$173.40 avg. ARPU

66


Neural Networks

Data can be processed in parallel and complex relationships can be

found quickly. Nodes in Neural Networks sums information from other

nodes connected to it and passes information to the other nodes.

Pros: Allows for more complex, non-linear, relationships Cons:

Interpretation very difficult - Called a “black box”

Survival Model

Method of statistical analysis used for determining time-to-event for one-time

Events. Includes both the actual probability of event and effects of covariates. Enables to:

•Study survival trends by demographic area, channel, credit

class, rate plan, type of churn etc

•Estimate remaining lifetimes for present customers

Survival Curves by Credit Class 2004

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 365 730 1095 1460 1825 2190 2555 2920 3285 3650

Tenure (days)

Re

ma

inin

g (

%)

A_surv

B_surv

C_H_surv

D_surv

E_surv

N_surv

Other_surv

total_surv

Predictive Modeling Techniques

67


Develop Treatment Strategy -- Example

1

3

2

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

$0 $25 $50 $75 $100 $125 $150 $175 $200

ARPU

Pro

po

rtio

n C

hu

rn

Cluster 3


5.90% churn

$36.67 avg. ARPU

Cluster 1


1.80% churn

$87.14 avg. ARPU

Cluster 2


3.17% churn

$173.40 avg. ARPU

1. Model –Churn model to select at-risk customers

2. Segmentation – Multivariate segmentation to understand the profile and usage patterns of specific target populations 3 . Value – Derived from revenues, costs, and expected customer lifetime based on survival analysis to optimize the right offer to the right customer

Three Tier Approach: 1) Predictive Modeling 2) Segmentation 3)Value

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10Decile

Cu

ml

Ga

ins

( C

ap

utr

e)

30%

48%

62%

72%

Model can capture 62% of Churners by targeting 30% of the entire base

23

68


Ch

urn

&

Re

co

nc

ilia

tio

n

Ma

xim

ize

Re

ve

nu

e

Applying Predictive Models to Marketing Strategy

Question Modeling Approach Treatment Strategy

Why Will Customer Churn?

Who is Savable?

When Will Customer

Churn?

Propensity to Churn

Survival Model (Time until

churn)

Propensity to Stay

Who Will Buy? What?

Which product will

Customer Buy Next?

Propensity to Buy

Product Basket

When Will Customer

Buy? Survival Model (Time until

Purchase)

Value Segment

Decile H M L

1

2

3

4

5

Pro

pensity

Score

Marketing Objective


Customer insight in action at Tesco

70


Tesco Overview Formed in 1924

The UK’s largest food retailer

Operating stores in all formats – convenience, high street, super markets and hyper markets.

Operating in 13 countries around the world

The world’s leading internet grocery retailer

Substantial Finance and telecoms businesses

71


Tesco and SAS Currently use SAS across the business to help

Select locations

Plan investment in refurbishments

Margin and revenue reporting

Analysis of operational performance in Tesco.com

Through SAS with Dunnhumby

72


Tesco- a truly customer focussed business

Sir Terry Leahy, Chief Executive

“Our mission is to earn and

grow the lifetime loyalty of

our customers”

Tesco has a core aim “to

understand customers

better than anyone”

“Contiually increasing

value for customers

to earn their lifetime

loyalty.

Tesco PLC, Annual review and

summary financial statement

73


Data Miss virtanen

Translate data in to a clear picture of a customer

74


Miss virtanen

Shopping behaviour can explain a lot…

is a busy young lady looks after her health and loves fresh

produce drives to the supermarket on a

Saturday morning reads lifestyle magazines has a cat doesn’t pay attention to the price of

products does look out for promotions

Data

Tesco know 12 million customers as well as we now know Miss Virtanen

75


Step 1: develop a meaningful customer segmentation Segmentation Requirements

• Simple and intuitive

• Categorises appropriate share of the customer database

• Segments of significant sizes

• Sufficient differentiation

• Actionable

Satisfaction

Engagement

Value

Participation

Knowledge

Opportunity Segmentation

Research Data

• 2800 survey respondents

• Shopping behaviour

• Loyalty programme participation

• Satisfaction

Transactional Data

• Points accrual transactions

• Points redemption transactions

• Shopping behaviour

across 17 retail and

service brands

• Card usage vs

automatic points

collection

• Response to

promotions

76


The Nectar Marketing Communications Segmentation

Nectar Indifferents Routine Grocery

Shoppers

Swipeless Savers Contented X-

Shoppers

Savvy Supermarket

Shoppers

Bonus Seekers

Engaged Enthusiasts

77


Average Customer Profitability and Ability to Promote by Segment

Profitability Index

Pro

mo

tab

ilit

y I

nd

ex

Step 3: Overlaying financial data allows for improving the allocation of customer marketing investment

Low High

High

Low

Low profitability, little

opportunity to improve

via incentives

Low profitability, some

opportunity to improve

via incentives

Highly profitable

segments


Cross - upsale

79



Value

Phase 1

Phase 3

Time

Cross- and up-sale

Profitability

Phase 2

Churn & Credit Risk

80


Kunde scoring - Produkt

Kunde Prod A Prod B Prod C

1 90 20 90

2 80 8 4

3 60 9 65

4 55 3 21

5 75 16 50

6 75 65 60

7 75 15 5

8 65 14 33

9 80 47 36

81


Kunde scoring - respons

Kunde Kamp A Kamp B Kamp C

1 90 20 90

2 80 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 75 90 65

8 65 60 60

9 80 30 75

82


“WOULD-BUY-ANYWAY” CLIENTS

“NO-IMPACT” CLIENTS

Will buy anyway

A communication may

disturb their buying

process

Haven’t made up their

mind

Can be positively

influenced by

communication

Won’t accept offer

No impact of

communication

Not likely to accept offer

But likely to end relation

if communicated to

Net lift

“SWING” CLIENTS

“DON’T-POKE” CLIENTS

PR

ED

EIC

TIV

E M

OD

EL

LIN

G

NET LIFT


Aviva online experience

../Aviva Marketing.mp4


84



Value

Phase 1

Phase 3

Time

Cross- and up-sale

Profitability

Phase 2

Churn & Credit Risk

85


Churn - Score

Kunde Churn Q1 Churn Q2 Churn Q3

1 90 20 90

2 80 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 75 90 65

8 65 60 60

9 80 30 75

86


Churn - message

RDM Generated offer or

message

87


Opsalg ved booking

Retrieve customer and

flight information

Check miles balance to

see if free flight

available

If free flight not

available, make value

based loyalty offer

88


Analytics embedded in customer process

Internet Banking (New!)

Contact Center Personal

Bankers Tellers

U.S. Bank ATM

(New!)

Behavioral Insights Predictive Analytics Relationship Strategies

Mining up to 15 million

transactions each day to

identify out-of-pattern

behaviors that may signal

need needs

Evaluating customer value,

creditworthiness, purchase

propensity and future

potential for over 13 million

consumers

Converting insights into

decisions and guidance that

is passed to legacy systems

and customer facing

employees

Prioritized offers and

consistent treatment

for each customer

“Conversational Data”

about customers’

financial objectives &

existing relationships


Embedded analytics



90

Copyright © 2011, SAS Institute Inc. All rights reserved. 90

When did they buy?

Did they buy multiple products?

What prices where they quoted?

How much did they pay?

Where did they buy it?

What else did they consider?

How many did they buy?

Who is buying what?

What are they requesting?

What are my competitors doing?

A Vast Universe of Data

91


When did they buy?

Did they buy multiple products?

What prices where they quoted?

How much did they pay?

Where did they buy it?

What else did they consider?

How many did they buy?

Who is buying what?

What are they requesting?

What are my competitors doing?

Capture and Analyze a Small Portion

92


We Miss the Elegant Patterns

93


Customer experience across datasilos

The Guest

Visitation Survey Attendance

Patterns

Resort GSM

Marketing mix

Geo-Demogra

phic

Social Media

Prior Stay

Pass holder

Resort and Theme Park

Spending

MDV

VPK Request

Internet registrat

ion

Resort Shop

Model Scores

Segmentation

Scores

Communication History

Offer History -

RM

Dimensions of data

Operations Data

Behavioral Data

Inbound Guest Data

Research/Metric Data

Operations Data – created from WDPR

marketing efforts

Individual – What we know

about the guest from their

past behavior

Inbound Guest activity – Response to

marketing efforts

Research/Metric– Non-guest centric

data that helps understanding of Guest

Mindset

94


Customer Analytics Generic roadmap

TEXT ANALYTICS

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MARKETING MIX OPTIMIZATION

AN

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DIM

INIS

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ON

IN

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CUSTOMER LINK ANALYTICS

AN

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Pradigmeshift in Marketing processes

96


Campaign Management Process

Campaign Planning

Campaign Management

Creating Target Groups

Campaign Management

Campaign Execution

Campaign Management

Exclusion Criteria

Campaign Management

Response

Modelling etc

Analysis

97


Is S3 the Optimal Solution?

Setting the Max Offers constraint at 512K delivers profit of Kr 13.5M

Sensitivity analysis tells us that we could find more expected value, but the value is limited – Increasing the number of offers to 590K would only deliver additional expected value of Kr 0.09M – surely not worth the additional expenditure

Maybe the Max Offers value is too high – do we really want to make most of the offers with only a marginal expected value?

98


Results - 3. Switch Focus to Profitability

S1 - Solution

• 512k offers

• Expected Profit: Kr -4.93M

S7 – MO Maximize Profit

• Max offers 512k constraint

• Maximize profit objective

• Only makes 178k offers

• Expected Profit: Kr 7.986M

Effect of using SAS MO

-6.000.000

-4.000.000

-2.000.000

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

Offers Expected Profit

S1 Solution S7 Max Profit

Scenarios

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Campaign Planning

Campaign Management


Campaign Management

Campaign Execution

Campaign Management

Exclusion Criteria

Campaign Management

Response

Modelling etc

Analysis

100


choices and constraints:

• many customers, offers, channels

• necessary strategic actions (must push offer A)

• customer contact policies

• many operational constraints:

• budgets, resources, capacities

offer optimization - a complex problem

challenges:

• which offers maximise profit / ROI, but stay within constraint boundaries?

• how do you find the optimal investment strategy?

• how do you reconcile competing goals (product vs. customer)?

• how do you plan effectively for change?

101


and actually what happens is….

• subjective planning and decision making

• poor ROI from marketing campaigns

• low response rates

• ineffective use of channels

• unnecessarily high costs

• ineffective contact policy

• over-contacting customers

• lack of enforcement

• no ability to understand tradeoffs between key elements e.g. volume vs. profit

102


Why optimization outperforms other decisioning techniques

103


Client Camp A Camp B Camp C

1 100 120 90

2 80 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 75 90 65

8 65 60 60

9 80 140 75

Campaign A

Campaign B

Campaign C

• Model scores define response probability for each campaign

• Probability * Expected Revenue = Expected Value

• Expected Value drives campaign allocation

• Constraints: 1 customer 1 campaign & 1 campaign 3 customers

Simple Example – Prioritizing Campaigns

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1 100 120 90

2 80 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 75 90 65

8 65 60 60

9 80 140 75

675 Expected Return:

?

?

?

?

?

Level 1: Campaign Prioritization

• Constraints: 1 customer: 1 campaign / campaign - 3 customers

• Campaigns assigned priority

• Top customers selected for each campaign based on their expected value

105



1 100 120 90

2 80 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 75 90 65

8 65 60 60

9 80 140 75

705 (+30) Expected Return:

?

?

?

Level 2: Customer Offer Prioritization


• Priority assigned based on the customer

• Top campaign selected for each customer based on their expected value

106



1 100 120 90

2 80 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 75 90 65

8 65 60 60

9 80 140 75

780 (+75) Expected Return:

Level 3: Optimization Approach


• Optimisation evaluates ALL possible solutions to find the best

• While also respecting constraints

107



Campaign Planning

Campaign Management


Campaign Management

Campaign Execution

Campaign Management

Exclusion Criteria

Campaign Management

Response

Modelling etc

Analysis

108


Cultural Impact Post Optimization Minimal impact on Campaign Management Process

Campaign Planning

Campaign Management

Scenario 3

Scenario 2 Scenario 2

Scenario 1

Creating Eligible Groups

Campaign Management

Target Groups

Campaign

Execution

Campaign

Management

Campaign Optimization

Marketing Optimization

• Ideally marketers now create eligible groups, rather than target groups • Propensity models drive campaigns.. • ….along with what the business is trying to achieve (goals and constraints) • An extra step, yes, but it can smooth the process – campaigns become simpler

Exclusion Criteria

Campaign Management

Response

Modelling etc

Analysis

Campaign Execution

Campaign Management

109


Tips to get started

Get started

Know your customers motivation to engage with you and do a communication segmentation

Start to collect data on: Transactions, response behaviour, social behaviour, demographics

Do simple datamining

Explore your datamining segments in order to apply communication segment to each customer/prospect

Execute, Execute

110


Customer Analytics Generic roadmap

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