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The Cogitaas Model
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Input Parameters Data: Moving
Window of Past 6 months data
Self Learning Algorithms to
update model parameters
MER prediction
Update performance
data every 2 weeks
Day
Station
Time
Cost of spot
Week # of prior airings
Feedback loop
Specifics of the Model Evaluation
4
Count of Spots Predicted
High Low
Observed/Reality High True Positive False Negative
Low False Positive True Negative
In terms of total spend:
Loss - Crucial to reduce
the number of false
positives
Savings potential (in
terms of spend)
Opportunity
loss (in terms
of spend)
Model should have:
High savings potential; Loss due to false positives should be low
Performance Prediction for February, March and April, 2016
5
Feb’16 Mar’16 Apr’16 Benchmark
Savings Potential % 79% 89% 87% 75%
Error Rate % 15% 9% 7% 15%
Over the last 3 months,
Savings Potential has been significantly high
Error Rate has been low and decreasing
Implementing Model Results…
6
1.3
1.9
Jun'15-Nov'15 Dec'15-May'16
Up by 50%
Average 6 months
MER Comparison
…Has resulted in a 50% increase in
MER over a span of 6 months
MARKETING ANALYTICS
But what exactly are we calling Marketing Analytics?
9
• What is the purpose?
• What value is it adding?
• What are the substrata of
Marketing Analytics as an
industry?
• What is the value chain of
different Marketing Analytical
platforms? Marketing Analytics
CPG
Me
dia
Today, your business manager faces information overload
10
But senior management still needs relevant decision support for growth,
pricing, competitive advantage, resource allocation
Insights
Reports
Data &
Analytics
It’s a VOCA world – difficult to navigate
11
Increasing
complexity needs to
be accounted for
VOCA world
needs ‘speedy
precision’
Need to know
quickly and
steering control
Economy
Marketing
Finance
Strategy
Competition
Need to move faster
Need higher levels of accuracy and precision
Learning cycles have to be shorter
Need continuous scenarios and simulations
Consumer
Marketing Analytics: Evolution
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Brand Equity
Market Basket Analysis
Customer Lifetime Value
Customer Relationship
Management
Social Media Analytics
Consumer Segmentation
Churn Analysis
Marketing ROI
Marketing Mix Model
WHAT GETS ANSWERED?
Decision-Making has become Decision-Science – Needs analytical support
15
Competitive
Strategy
Pricing &
Brand Value Brand Investment Growth
Competitor grew by
50% in less than 3 years.
Negative impact on
my top-line and
bottom line .
I need to raise prices
due to cost and margin
pressures, but
competitors are not
doing so.
My trade investment
has been growing at
over 20% every year and media spend at
25%. Has my brand
equity grown in line?
I have spent over 50
M USD over a new
launch. The launch
has not yet met
expectations.
How can I
measure my
strengths and
weakness and
theirs to
effectively fight
competition?
What is my
optimal price,
given competition
pressures? What if
I charge more or
less?
Is there a
scientific method
for measuring
how my
consumers’
perceive better
brand equity?
How much
should I
continue
investing in
building the
new
brand/variant?
These are issues where accurate planning and speedy execution are essential
TODAY’S ANALYTICS
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PRICE
ELASTICITY
MODELS
CONJOINT
ANALYSES
MARKETING
MIX
MODELLING
PARTIAL ANSWERS
CONSUMER
SEGMENTATION
COST OF
ACQUISITION
WHAT DOESN’T GET ANSWERED
21
FINDING THE ANSWERS
The Usual Suspects
24
Our products
are overpriced
Sales team is not
doing a good job
X
Our equity is collapsing
Our product quality
is deteriorating
X
We are
underinvesting
Economy is
slowing down
Data is wrong!
X
•What is the root cause of issue?
Diagnostic
•What should be done now?
Prescriptive
•What will be the likely impact of recommended actions?
Predictive
•Have the results been as predicted? What next?
Confirmatory
DPPC: Deep digging, Precise action, Building scenario, Checking
25
Getting the Job Done
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Analytics – Diagnostic, Predictive
Advanced analytics, combining industry-tools with the leading
research from academia has helped in finding solutions and set self-
learning mechanisms.
Consultancy – Strategic
When data converts to decisions, strategic calls have to be made,
consultancy based on data science is needed to build strategy.
Business-partnering- Simulation, Validation & Operational Processes
Partnering with businesses after project completion to continuously
monitor targets while assessing need for course-corrections to
capture market changes.
Combining Diagnostic, Prescriptive, Predictive & Confirmatory
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Having done some high end modeling, go beyond modeling.
Use a multi-disciplinary approach
Only accept what the data says
Use different tools for different purposes, there is no ‘one size fits
all’
Form hypotheses and test each one out diligently
Ask the right questions, do not accept conventional wisdom
Understand the business thoroughly
CASE STUDY – HAIR COLOR
HAIR COLOR COMPANY - BACKDROP
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Approached with the simple question
“If I drop my price, will it counter the growth of the competition brand?”
Hair color Brand A was the leader in the crème segment
Local Competition brand launched a new product in
the same category at almost half the price
Growth of competition brand rose fast across a short time period
Brand A was worried about competition growth, did
internal studies and concluded that price was the
defining factor.
But was worried about profitability
Cogitaas intialized a deep diagnostic of Brand A
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Brand Engine Strategic Analysis
Proposition
Equity
Economy
Brand Steering Execution Analysis
Pricing
Distribution
Media
Trade & Consumer Promotions
Competition
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Optimal
Roadmap Brand Optima
Cogitaas’ Demand Accelerator Framework
Cogitaas’ User Preference Model
32 32
• Cogitaas’ User Preference
model computes the role of
different attributes in driving
probabilities of consumer
behavior such as loyalty,
lapsing and differentiation
• This non-linear regression
model analyzed attribute
and purchase data from
Nielsen’s dipstick work
• It uses a set of measures like
cross-validation to test and
see whether these results are
‘robust’ – i.e. they accurately
represent market reality
OD
DS O
F U
SER
40%
90%
PRODUCT CHARACTERISTICS
Nourishes my hair
Available in
different shades
Does not contain
chemicals
Innovative brand
Attractive
packaging
`
`
80%
70%
60%
50%
Illustration
Brand Proposition Results
33 33
Competition has low equity, is preferred by the older consumers and riding on the heritage of powders and distribution strength (PUSH Led)
Brand A’s consumers are young and prefer it for it’s strengths in Modernity, Innovativeness and Confidence (PULL Led)
Brand A enjoys a high equity and is highly differentiated from Competition brand
Learnings from Brand Proposition Analysis
34 34
Strengthen modernity,
innovativeness and confidence
attributes
Different target consumer
Brand A growth strategy
Brand Equity & Pricing Results
35 35
BRAND EQUITY & BRAND PROPOSITION STRONG
GOOD PRICING POWER – CAN MAINTAIN PREMIUM PRICE
How does Brand A’s equity
compare with competition?
Brand A’s MCV is greater
than Competition’s MCV
Brand A Competition
3917
1603
1231
750
>
Brand A Competition
3917
1603
1231
750
> CSF = 2.4 CSF = 1.6
How does Brand A’s pricing power
compare with competition?
Brand A’s CSF is also greater
than Competition’s CSF
Media; Consumer & Trade Promotions Results
36 36
IF EQUITY, PROPOSITION, PRICE, MEDIA, CONSUMER PROMOS
& TRADE PROMOS ARE NOT MAJOR ISSUES, THEN WHAT?
Channelize more Consumer Promotions to
Brand A and variants to attract new users
Have good ROI, however spends should be
increased to drive up distribution relative to
Competition
TV is effective, has been found to play an
important supporting role in driving distribution;
focus on key Brand attributes and not play on
competition’s turf
Distribution Results
37 37
BRAND A NEEDS TO MAINTAIN RELATIVE DISTRIBUTION
Will increase in distribution drive significant gains for Brand A?
Brand A is led by branding and Competition is led by distribution – but
given category is distribution led, Brand A has to play full defense on
distribution, to maintain share. However, the main role of countering
Competition and capturing upgraders has to be played by Brand A and
Variants.
60% 46%
2013 2014
No. Distributers for Brand A
has fallen in 2014
Distribution not a key driver for Brand A,
but relative distribution w.r.t. crème
category should be maintained
Brand A new variant has to increase
distribution to fight Competition directly
to gain share of upgrading powder users
ADDITIONALLY…
38 38
…Cogitaas’ study revealed that the rapid growth of the
Competition brand would level off and Brand A’s introduction of a
variant to combat Competition’s new product would grow in spite
of premium pricing
0%
10%
20%
30%
40%
JUL1
2
SEP
12
NO
V1
2
JAN
13
MA
R1
3
MA
Y13
JUL1
3
SEP
13
NO
V1
3
JAN
14
MA
R1
4
MA
Y14
JUL1
4
SEP
14
NO
V1
4
JAN
15
MA
R1
5
MA
Y15
Competition market share
trend line
0%
2%
4%
6%
8%
10%
JUL1
2
SEP
12
NO
V1
2
JAN
13
MA
R1
3
MA
Y13
JUL1
3
SEP
13
NO
V1
3
JAN
14
MA
R1
4
MA
Y14
JUL1
4
SEP
14
NO
V1
4
JAN
15
MA
R1
5
MA
Y15
Brand A new variant market share
trend Line
Competition market share has
levelled off since September 2014
Brand A new variant market share is
increasing since its launch
INTRODUCING COGILABS
TELEVISION MEDIA COMPANY - BACKDROP
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Approached with these questions:
A TV Media Company has a fixed number of
media spot inventory (Xn)
Different spots are sold for different prices
(X1 to X10 = P1, X11 to X25 = P2)
The spots are sold on the basis of
Business Intelligence: GRP, RMS, SAP, OnAir analysis etc.
as well as Client volume and loyalty.
But they wanted a better system to minimize loss of
opportunity.
“Are all spots being sold at the optimum price, hence creating maximized profits? If
not, how can we ensure that?”
“Can we create an integrated system, within our system, to make available the same
intelligence and analysis across all sales representatives? ”
Ad-Sales Revenue
42
CogiLabs diagnosed their current system
National level
revenue target
35% - 45% exp from
Enterprise Clients
Rest from Corporate
and Strategic Clients
Direct client
participation is high
Agency participation
is high
Were they leaving too much on the
table by giving away about 40% of
the inventory to Enterprise Clients at
lower prices?
What is the optimal trade off
between low risk, annual contracts
and high risk, corporate clients?
Opportunity with Corporate and Strategic Accounts
43
RFP: Budget of Rs. 5 Cr to target a CPRP of Rs.
50K; Constraints on TG, P/NP etc.
Sales person works
out (manually) a
proposed list of
spots to meet
client requirement
Needed:
A model to
identify
possibilities
for higher
CPRP
realization
One Conversation Multiple simultaneous
conversations
Needed:
A model that
optimizes
opportunities across
multiple
proposals
The task was to ensure that each asset is priced optimally, allocated to
the appropriate deal and maximize the total realized Ad Sales revenue.
INNOVATION AND INVENTION
44
So CogiLabs invented a unique
Pricing Engine with a user friendly UI
to help integrate into their systems
and maximize their profitability.
NEW PRICE ENGINE
45
Conservative increase in earnings estimate: $ 35 Million
Pricing Engine ‘R’ Based
Pricing Algorithm
Social Media Connector
Elasticsearc
h
Connectors
(Data
Collection)
Web Services
Proposal Component
OnBoard
(CRM)
Proposal UI
RMS
Connector
OnAir
Connector
BW/BI
Connector
OnBoard
Connector
3
1
2
4
DB 10
5
8
6 7 9
Data from
OnBoard
system
Data
Streaming
(Kafka)
Business
Logic
NoSQL
Data Store Pricing
Algorithm
Proposal
Creation
DATA FOR DECISIONS
Marketing Analytics needs to ‘stretch & expand’
48
Has to become ‘need to know’ and not just ‘nice to know’
Brand
Strategy
Consumer
Centric Pricing
Competitive
Advantage
Consumer
Segmentation
Marketing Resource
Allocation
Profitability
Shoppers’ Analyses
& Distribution
Media Inventory
& Pricing
Marketing Analytics has to get involved in all aspects of business
49
Beyond Insights
Forecasts &
Simulations
Planning Tools
Operating
Procedures
User
Interfaces
Machine Learning
& Updates
Business Benefits
Our Innovative tools create high business impact
50
Opportunity
Realized
Growth Strategy
Competitive
Strategy
Resource
Allocation
Pricing Strategy
$1225 Mn
$200 Mn (realized to date)
$80 Mn
Opportunity
Identified
$50 Mn
$60 Mn
$10 Mn
$1020 Mn
$115 Mn
$60 Mn
$30 Mn
Total Impact
We are currently partnering with businesses to realize full potential
Expand across various functions - ‘Bring Together’
51
CEO CFO CMO CTO
Sales & Customer
Marketing Product
Development
Product
Planning
Not Without Analytics
52
Marketing Analytics is an imperative, not a choice!
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