1

2

The Cogitaas Model

3

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

12

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

18

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

26

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

27

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

30

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

31

Brand Engine Strategic Analysis

Proposition

Equity

Economy

Brand Steering Execution Analysis

Pricing

Distribution

Media

Trade & Consumer Promotions

Competition

31

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

41

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!