«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

Digital Dashboard
Competitor Earned Bought Owned

You

Comp 1

Comp 2

Comp 3

Comp 4

2

3

“The Emergence Of Customer Experience Management Solutions”
Delivering Cross-Touchpoint Customer Experiences Drives Need For New Capability

Channel / Media Trustworthiness

Epsilon Targeting

5 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

On any given day, the Message Overload
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


“Per the DMA, 93% of
Let’s Define “Relevance”
marketers using multiple
channels have attempted to 1. Right message.
integrate their messaging.
Only 27.4% of these
said their efforts 2. Right time.
are „effective‟. . .”
DMA Report, “Rowing
3. Right person.
as One: Integrated
Marketing Today,” 4/11 4. Delivered per that individual’s media preferences.

Integrated,
multichannel
irritation!

Without this, all we have
achieved is. . .


5 Principles of Multichannel Marketing

5
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
4: Re-conceive Inbound as a high value customer interface. By
definition, Inbound callers are more 1) Qualified, and
2) Likely to spend.

3 3: Synchronize your multichannel mix with
and value.
precision

2: Create processes for generating feedback from your social media

2 channels and your sales and service reps. This will provide ongoing
qualitative and quantitative VoC guidance.

1 1: Start with the Customer (VoC).

Step 3: Multichannel Marketing

Don’t
re-engineer
This economy and social
media have profoundly
your changed buyer’s priorities
relationship and expectations.

marketing If you have not
strategies from recalibrated strategies
within the past 12
the isolation months, you are out of
of your sync with your customers.
conference VoC insights ensure you
room. . . develop truly customer-
focused strategies to drive
relevance and revenue.

10 Step 1: VoC Research

VoC Learnings
Question Answer

Which has more impact on Engagement/Relationship
retention and repeat strength has 12 times more
purchases; influence on retention and
Customer Satisfaction or repeat purchases than
Customer Satisfaction.
Engagement/Relationship?
Satisfaction is a minimum
expectation.


VoC Learnings
Question Answer

Which is a more significant Engagement/Relationship strength
has 18 times more influence on
driver of word of mouth
word of mouth recommendations
recommendations; than Satisfaction.
Customer Satisfaction or
This has profound implications for
Engagement/Relationship? re-allocating greater budget for
Retention/ Relationship building.


VoC Learnings

4. The Importance
of Community
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!

Step 1: VoC Research

 Community-driven,
online marketer
specializing in
T-shirts designed
by members of According to the Sloan Management Review:
the community. 95% of those purchasing from Threadless.com have
 Community is made up voted and posted comments…before making a
of 3 groups: purchase.
1. Purchasers
2. Designers
3. Reviewers Results:
• Over 1 million users,
• Over $30 million dollars in annual sales,
• Approximately 30% margins.


As a result, customers and
prospects view
personalization as
the next step in a company‟s • Personalization is viewed as a service and benefit, not
commitment to service just a sales tool.
excellence.
• 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!

15 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.


Customer Engagement

“…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


VoC Learnings

1. Providing Value
“Self-serve makes it
easy for you, not the
“Don’t just sell me the customer.”
service. Provide ongoing
value at key times.”

“Email blasts do not
“The quality of your equal ‘relationships’.”
service is key to how we
judge you.”


VoC Learnings

2. Relationships

“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

“When you tell me to go
“I don’t just want to “An easy navigation and
to the web for service,
transact. I want to commerce process is a
especially when I am
connect with your minimal competency. . .
growing old waiting for
company, your brand You better be at least as
a phone rep, what I hear
and your community.” good as Amazon.”
is, ‘Go. . . help yourself.”

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


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

How We Use VoC
• 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

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
• VoC drivers include: purchase,
browsing, email click, and
brand preference

28

• 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

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.
31

How We Use VoC - Merchandising

• Announce winning selection on Wall and drive
to sale featuring the “Facebook Fan Pick”
label

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 32

How We Use VoC -
Merchandising
• Use Facebook’s Face Off Application to
empower members to curate sales

KPI RESULTS
Engagement 7X Higher than average post
Likes & Comments

Unique 5X Higher than average post
Impressions
Unique fans who have
seen the post

33

• Crowd source ideas involving fans to create new
products

Fans vote Fans vote
on favorite on favorite
design color

We‟re thrilled to announce that Rebecca Minkoff will be We‟re exited to reveal that the winning sketch
producing a handbag exclusively for Gilt Members. Even be produced by Rebecca Minkoff exclusively
to
better – we want you to be a part of the process. Vote on for Gilt is “Luscious Hobo with Spine Studs”!
your favorite design by liking one of the two sketches, and Now‟s your chance to select the handbags
the sketch with the most votes will be produced. Be sure color. Vote on one of the swatches below by
to tell your friends to vote… 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.
Winner is shown 34

• Crowd source ideas involving fans to create new products

KPI RESULTS
Engagement 27X Higher than average post
Likes & Comments

Unique 4X Higher than average post
Impressions
Unique fans who have
seen the post

35

How We Use VoC - Segmentation
Brand Seekers Self-Expressionists

“I am always shopping to “My style is an expression of my
keep up with the latest personality. I am always looking
fashions. I own the hottest for inspiration …”
brands”

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:”

Just right

Old Policy 16%

52%
New Policy
Just right

Much too high
Somewhat too high
Just right
Lower than you would expect
I don't know enough about the 38

current shipping policies to answer

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 10-15X Higher than average
Customers 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

Bringing High Quality Customer
Service Into The Social Arena
• Transparency: All postings are valid

46

Bringing High Quality Customer Service Into The
Social Arena
• 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.

47

5 Tactics to Leverage VoC

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

48


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


• Best Customer Outreach Call Program

KPI RESULTS
Reactivation +45% vs. control group
Incremental Sales +40% vs. control group
From reactivated customers

50

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

51
Copyright © 2011, SAS Institute Inc. All rights reserved.

DRIVING VALUE FROM CUSTOMER RELATIONSHIPS IS
INCREASINGLY COMPLEX

Customers &
Prospects

Offers,
Services &
Pricing

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

Functions
$

Checking Credit Cards Loans Mortgages Insurance
Products
Savings Lines Investments

52


ACCENTURE RESEARCH 2011

COMPANIES THAT INVEST IN ADVANCED
77% OF HIGH-PERFORMING
ANALYTICAL CAPABILITIES OUTPERFORM
COMPANIES HAVE ANALYTICAL
THE S&P 500 ON AVERAGE BY 64%
CAPABILITIES ABOVE AVERAGE

INADEQUATE INFORMATION ACCESS
REDUCES KNOWLEDGE WORKERS’
PRODUCTIVITY BY 54%

65% OF HIGH-PERFORMING COMPANIES
HAVE SIGNIFICANT DECISION-SUPPORT
AND ANALYTICAL CAPABILITIES

53


54


Entry Points to ITV Player

Multiple
entry
points
to ITV
Player

55


Next Best Product - Examples
Case Study: Erste Bank Group

56


The NBO 4 main components

Customer Behavior Importance

“The probability of the “The profitability
customer to aquire the generated if the
product” customer aquire the
product”

Restrictions Previous Contacts

“The product can be “The product was
sold to the customer” already offer to this
customer”

57


Personalized “Next Best Product” offer
executed across…

Branch/Advisor

nbp

58


How to get started ?

Value

Phase 3

Cross- and up-sale
Phase 2

Phase 1 Churn & Credit Risk

Profitability

59

Time

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
60


Lønsomhed på kunde

61


62


4 slides on analytics – that’s it !

Copyright © 2011 SAS Institute Inc. All rights reserved.

Why Predictive Modeling?
100
90
80
Cuml Gains( Caputre)

72%
70
62%
60
48%
50
40
30%
30
20 Targeting the top 10% of customer
base capture 30% churners
10
0
0 1 2 3 4 5 6 7 8 9 10
Decile

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

64

16

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
8.00%
Clustering
7.00% Cluster 3
Identify groups of individuals based on their proximity to each other. 6.00% 3
22.5% of the upgraders
5.90% churn

Proportion Churn
The cluster procedure and discriminate* analysis utilizes an effective 5.00%
$36.67 avg. ARPU Cluster 2
3.17% churn

method for finding initial clusters with a standard iterative algorithm for 4.00% $173.40 avg. ARPU

3.00% 2

minimizing the sum of squared distances from the cluster means .
Cluster 1
2.00% 1 1.80% churn
$87.14 avg. ARPU
1.00%
Logistic Regression 0.00%
$0 $25 $50 $75 $100 $125 $150 $175 $200

A generalized linear model for predicting probabilities. Logistic Regression ARPU

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

65


Predictive Modeling Techniques
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 Survival Curves by Credit Class 2004
100%
Events. Includes both the actual probability of event and effects of A_surv
covariates. Enables to: 90% B_surv
C_H_surv
•Study survival trends by demographic area, channel, credit 80%
D_surv
class, rate plan, type of churn etc 70% E_surv

Remaining (%)
•Estimate remaining lifetimes for present customers N_surv
60%
Other_surv
50% total_surv
40%

30%

20%

10%
0 365 730 1095 1460 1825 2190 2555 2920 3285 3650
Tenure (days)

66


Develop Treatment Strategy -- Example

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

100 8.00%
Model can capture 62% of
90
Churners by targeting 30% of
7.00% Cluster 3
the entire base
80 22.5% of the upgraders
Cuml Gains( Caputre)

72% 6.00% 5.90% churn
3

Proportion Churn
70 $36.67 avg. ARPU Cluster 2
62% 5.00% 4.19% of the upgraders
60 3.17% churn
$173.40 avg. ARPU
48% 4.00%
50
3.00% 2
40 Cluster 1
30% 73.4% of the upgraders
30 2.00% 1 1.80% churn
$87.14 avg. ARPU
20 1.00%

10 0.00%
0 $0 $25 $50 $75 $100 $125 $150 $175 $200
ARPU
0 1 2 3 4 5 6 7 8 9 10
Decile

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

67

23

Applying Predictive Models to Marketing Strategy
Marketing Objective Question Modeling Approach Treatment Strategy

Why Will Customer Churn? Propensity to Churn
Reconciliation

When Will Customer Survival Model (Time until
Churn &

churn)
Churn? Value Segment

Who is Savable? Propensity to Stay
Decile H M L

1

Propensity Score
2

3

Who Will Buy? What? 4
Propensity to Buy
5
Maximize Revenue

Which product will Product Basket
Customer Buy Next?

When Will Customer Survival Model (Time until
Buy? Purchase)

68


Customer insight
in action at Tesco


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

70


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

71


Tesco- a truly customer focussed business

“Our mission is to earn and
grow the lifetime loyalty of
our customers”
“Contiually increasing
Tesco has a core aim “to
value for customers
understand customers
to earn their lifetime
better than anyone”
loyalty.

Tesco PLC, Annual review and Sir Terry Leahy, Chief Executive
summary financial statement
72


Translate data in to a clear picture of a
customer

Data Miss virtanen

73


Shopping behaviour can explain a lot…

Data Miss virtanen
 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

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

74


Step 1: develop a meaningful customer
segmentation Research Data
• 2800 survey respondents
Segmentation Requirements • Shopping behaviour
• Simple and intuitive • Loyalty programme participation
• Categorises appropriate share of the customer database • Satisfaction
• Segments of significant sizes
• Sufficient differentiation
• Actionable

Knowledge

Transactional Data
• Points accrual transactions
Participation Engagement
• Points redemption transactions
• Shopping behaviour
across 17 retail and
service brands Value Satisfaction
• Card usage vs
automatic points
collection Opportunity
Segmentation
• Response to
promotions

75


The Nectar Marketing Communications
Segmentation
Engaged Enthusiasts

Bonus Seekers Savvy Supermarket
Shoppers

Contented X- Swipeless Savers Routine Grocery Nectar Indifferents
Shoppers Shoppers

76


Step 3: Overlaying financial data allows
for improving the allocation of customer
marketing investment
Average Customer Profitability and Ability to Promote by Segment

High

Low profitability, some
opportunity to improve
Promotability Index

via incentives

Low profitability, little Highly profitable
opportunity to improve segments
Low
via incentives

Low Profitability Index High

77


Cross - upsale



Value

Phase 3

Cross- and up-sale
Phase 2


Profitability

79

Time

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

80


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

81


Net lift
NET LIFT

“WOULD-BUY-ANYWAY” CLIENTS “SWING” CLIENTS

 Will buy anyway  Haven’t made up their
 A communication may mind
disturb their buying  Can be positively
process influenced by
communication
PREDEICTIVE MODELLING

“NO-IMPACT” CLIENTS “DON’T-POKE” CLIENTS

 Won’t accept offer  Not likely to accept offer
 No impact of  But likely to end relation
communication if communicated to

82


Aviva online experience



Value

Phase 3

Cross- and up-sale
Phase 2


Profitability

84

Time

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

85


Churn - message

RDM Generated offer or
message

86


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

87


Analytics embedded in customer process
“Conversational Data”
Prioritized offers and
about customers’
consistent treatment
financial objectives &
for each customer
existing relationships

Internet Banking Contact Center Personal Tellers U.S. Bank ATM
(New!) Bankers (New!)

Behavioral Insights Predictive Analytics Relationship Strategies
Mining up to 15 million Evaluating customer value, Converting insights into
transactions each day to creditworthiness, purchase decisions and guidance that
identify out-of-pattern propensity and future is passed to legacy systems
behaviors that may signal potential for over 13 million and customer facing
need needs consumers employees

88


Embedded analytics


A Vast Universe of Data

What are they requesting? How many did they buy?

What are my competitors doing?

Who is buying what?

What else did they consider?

Where did they buy it?

When did they buy? What prices where they quoted?

How much did they pay?
Did they buy multiple products?

90

90

Capture and Analyze a Small
Portion
What are they requesting? How many did they buy?

What are my competitors doing?

Who is buying what?

What else did they consider?

Where did they buy it?

When did they buy? What prices where they quoted?

How much did they pay?
Did they buy multiple products?

91

91

We Miss the Elegant Patterns

92

92

Customer experience across datasilos
Research/Metric– Non-guest centric
Visitation data that helps understanding of Guest
Dimensions of data Offer
History -
Survey Attendance Mindset
Patterns
RM
Commun
Resort
ication
GSM
History

Segmen
Marketing
tation
mix
Scores
Operations Data – created from WDPR
marketing efforts

Geo-
Model
Demogra
Scores
The phic
Guest

Resort Social Individual – What we know
Shop Media about the guest from their
past behavior

Internet
Prior
Inbound Guest activity – Response to registrat
Stay
marketing efforts ion

VPK Pass
Request holder
Behavioral Data Resort and
MDV Theme
Operations Data Park
Spending
Inbound Guest Data
Research/Metric Data 93


AD-HOC ANALYTICAL
SEGMENTATION

AD-HOC CHURN
PREDICTION

ANALYTICS
AD-HOC PRODUCT
ASSOCIATION ANALYSIS

AUTOMATED CHURN
PREDICTION

CUSTOMER AND PRODUCT
PROFITABLITY ANALYSIS

AUTOMATED CROSS-/UP SELL CHURN
PREDICTION
Generic roadmap

COMMUNICATION ANALYTICS (TIMING
AND CHANNEL)

PREDICTIVE ANALYTICS
CLV ANALYTICS

BEHAVIORAL PROFILING

CAMPAIGN LIFT ANALYTICS

PRICE AND PROMOTION ANALYTICS
Customer Analytics

DESIGN OF EXPERIMENTS

ANALYZING RELATIONS BETWEEN
CUSTOMERS

CROSS-CHANNEL DIRECT
MARKETING OPTIMZATION

ANALYZING CUSTOMER DIALOG
CUSTOMER LINK ANALYTICS

ANALYZING CHANNEL
SPILLOVER EFFECTS
TEXT ANALYTICS

ANALYZING DIMINISHING
RETURN ON INVESTMENTS
DIRECT MARKETING OPTIMIZATION
MARKETING MIX OPTIMIZATION

94

Pradigmeshift in
Marketing processes


Campaign Management Process

Campaign
Management

Exclusion
Criteria

Campaign Campaign Campaign
Management Management Management
Creating
Campaign Campaign
Target
Planning Execution
Groups

Analysis

Response
Modelling etc

96


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
Maybe the Max Offers value is too is limited – Increasing the
high – do we really want to make number of offers to 590K
most of the offers with only a would only deliver additional
marginal expected value? expected value of Kr 0.09M –
surely not worth the
additional expenditure

97


Results - 3. Switch Focus to Profitability
Effect of using SAS MO Scenarios
10.000.000 S1 - Solution
• 512k offers
8.000.000 • Expected Profit: Kr -4.93M

6.000.000
S7 – MO Maximize Profit
• Max offers 512k constraint
4.000.000 • Maximize profit objective
• Only makes 178k offers
2.000.000 • Expected Profit: Kr 7.986M

0
Offers Expected Profit

-2.000.000

-4.000.000

-6.000.000

S1 Solution S7 Max Profit

98



Campaign
Management

Exclusion
Criteria

Creating
Campaign Campaign
Target
Planning Execution
Groups

Analysis

Response
Modelling etc

99


offer optimization - a complex problem
choices and constraints:
• many customers, offers, channels
• necessary strategic actions (must push offer A)
• customer contact policies
• many operational constraints:
• budgets, resources, capacities

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?
100


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

101


Why optimization outperforms
other decisioning techniques

102


Simple Example – Prioritizing Campaigns
• 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

Client Camp A Camp B Camp C
1 100 120 90
2 80 70 75 Campaign
C
3 60 75 65
4 55 80 75
5 75 60 50
6 75 65 60 Campaign Campaign
7 75 90 65 B A
8 65 60 60
9 80 140 75

103


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

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 ?

Expected Return: 675
104


Level 2: Customer Offer Prioritization
• Priority assigned based on the customer
• Top campaign selected for each customer based on their expected value

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 ?

Expected Return: 705 (+30)
105


Level 3: Optimization Approach
• Optimisation evaluates ALL possible solutions to find the best
• While also respecting constraints

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

Expected Return: 780 (+75)
106



Campaign
Management

Exclusion
Criteria

Creating
Campaign Campaign
Target
Planning Execution
Groups

Analysis

Response
Modelling etc

107


Cultural Impact Post Optimization
Minimal impact on Campaign Management Process

Campaign
Analysis
Management

Exclusion Response
Criteria Modelling etc

Marketing Campaign
Optimization Management

Campaign
Creating Target Groups
Campaign Campaign
Planning
Eligible Campaign
Optimization Execution
Groups Execution

Scenario 1
Scenario 2
Scenario 2
Scenario 3

• 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
108


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

109


AD-HOC ANALYTICAL
SEGMENTATION

AD-HOC CHURN
PREDICTION

ANALYTICS
AD-HOC PRODUCT
ASSOCIATION ANALYSIS

AUTOMATED CHURN
PREDICTION

CUSTOMER AND PRODUCT
PROFITABLITY ANALYSIS

AUTOMATED CROSS-/UP SELL CHURN
PREDICTION
Generic roadmap

COMMUNICATION ANALYTICS (TIMING
AND CHANNEL)

PREDICTIVE ANALYTICS
CLV ANALYTICS

BEHAVIORAL PROFILING

CAMPAIGN LIFT ANALYTICS

PRICE AND PROMOTION ANALYTICS
Customer Analytics

DESIGN OF EXPERIMENTS

ANALYZING RELATIONS BETWEEN
CUSTOMERS

CROSS-CHANNEL DIRECT
MARKETING OPTIMZATION

ANALYZING CUSTOMER DIALOG
CUSTOMER LINK ANALYTICS

ANALYZING CHANNEL
SPILLOVER EFFECTS
TEXT ANALYTICS

ANALYZING DIMINISHING
RETURN ON INVESTMENTS
DIRECT MARKETING OPTIMIZATION
MARKETING MIX OPTIMIZATION

110

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