Application of Decision Sciences to Solve Business Problems in the Consumer Packaged Goods (CPG) Industry

1. Application of Decision Sciences
to Solve Business Problems
CPG Industry

2. Analytics
for CPG

3. New Product
Launches &
Innovation
Need Gap Analysis
It is an approach to identify the unmet needs of consumers, in which respondents are asked to envisage the
ideal brand or product, and then to rate various existing brands or products on key attributes. If there are no
existing brands measuring up to the ideal, there exists a need gap which could be a potential for a new
product.
It provides answers to critical business questions like:
What is the consumer’s perception of the brand/product?
What are the consumer needs yet to be catered to and are there competitors providing alternatives?
Identify new consumer segments and market potential for a new product.
What is the brand image in the consumer’s mind? If needed, how is it to be re-branded and re-positioned?
Needs
Satisfaction
HighLow
HighLow
Hygiene needs
Unmet needs
Satisfied needs
Underdevelopedneeds
Has enjoyable flavour
Cleans thoroughly
Provides fresh breath
Whitens teeth
Has anti-cavity action
Has anti-
bacterial action
Soothes gum
irritation,
inflammationand
bleedingRelieves
teeth
sensitivity
Controls tartar
Strengthens
enamel

4. Product & Concept Testing
PI Believability Uniqueness Value
Disclose technical
formula
DEL DEL MNB MB
Sensory ingredients IND DEL IND DEL
Natural ingredients IND DEL IND DEL
Easy to apply IND HYG
DEL = Delight
IND = Indifferent
TRNF = Turnoff
MNB = Must not be
MB = Must be
HYG = Hygiene
New Product
Launches &
Innovation
Product & Concept Testing
Estimate the market potential of an idea or a concept, before actually developing the product based on
consumer response on multiple metrics like: uniqueness, believability, feasibility, price, desirability,
advantages, disadvantages, etc.
Only successful concepts pass to the next phase, thereby minimizing R&D and marketing costs.
Apart from estimating the market potential, it also helps:
Identify critical success factors for a new product/service
Estimate price sensitivity and purchase likelihood
Bundle product/service features
Identify potential consumer segments and assess competition
Understand the purchase process and decision making
Optimize advertising messages and improve promotional offers
Statistical techniques (like Conjoint analysis, Discrete choice modeling, KANO analysis) are applied on the
consumer responses collected.

5. Supply Chain
SKU rationalization exercise is usually
supplemented with an impact study to
answer questions like:
 What is the revenue impact associated
and how can it be minimized?
 What is the inventory carrying impact
and overall savings?
 Will it result in consumer dissatisfaction?
 What is the consumer reactivation rate
on rationalized SKUs?
 Is the product seasonal? What is the time
frame to rationalize the category?
 What are the substitute products that
the consumer can be offered?
CumulativeRevenue
85%
Top Selling
CumulativeRevenue
SKU in order of
decreasing Revenue
Contribution
100%
98%
80%
Top Mid Bottom
Recommended for Rationalization
80%
Mid Selling
CumulativeRevenue
SKU Rationalization
The objective of SKU rationalization is to reduce the business complexities arising from a burgeoning product
portfolio, from managing too many items, product life cycles, consumer preferences, etc., while ensuring
consumer satisfaction. It is the process of re-looking at the product portfolio and optimizing it.
It starts with the parameters that form the basis—identifying and retaining high margin SKUs, high volume
SKUs, SKUs that have a higher shelf life and those which are in tune with consumer preferences.
After analyzing the cost drivers for each SKU, the portfolio can be assorted and rejected products can be re-
evaluated for further action (merge, sell, milk or kill).

6.  Pricing: Competitive pricing
(comparable to other vendors),
stability (low variance), accuracy,
advance notice of price changes.
 Quality: Compliance with purchase
order, conformity to specifications,
reliability (rate of product failures),
durability, support, warranty.
 Delivery: Time, quantity, lead time,
packaging, emergency delivery and
technical support.
Partner Strategic Fit Brand Equity Financial Health
Ability to
operationalize
Final Score Status
Vendor 1 9 8 10 7.4 8.75 Pass
Vendor 3 10 9 8 7.4 9.00 Pass
Vendor 3 10 7 6 7.4 7.50 Pass
Vendor 4 10 10 8 10.0 9.50 Underleveraged
Vendor 5 9 7 8 7.4 7.75 Pass
Vendor 6 2 7 6 8.2 5.50 Risky
Partner Filtration Methodology & Process Flow
Supply Chain
Vendor Management
It enables organizations to control costs, strive towards service excellence and mitigate risks to gain increased
value from their vendor by:
 Minimizing potential business disruption
 Avoiding deal and delivery failure
 Improving operational efficiencies, controlling costs and planning of workforce and labor
It includes vendor identification, recruitment, monitoring, tracking and evaluating vendors on certain KPIs:

7. INSOURCE
High
Demand Flexibility?
Low
High
OUTSOURCE
Low
Competitive advantage?
Capability of
supplier
Process maturity of
supplier
Strategic risk with
supplier
IMPLEMENT
OUTSOURCE
High High Low
Low
Establish norms for product
quality, process for transferring
knowledge& monitor quality
tracking measures
Establish process monitoring
measures, plans to
continuously improveprocess
and knowledgesharing across
teams
Actions Actions
Low
High
Ensure flexibility and penalty
clauses are established for
product delivery, establish
alternatesourceof activity and
divulgeas littleproprietary
information as possible.
Actions
Establish control need based on
three secondary factors,
develop appropriate
contracting relationship type
and negotiatecontract
Supply Chain
Sourcing Strategy & Production Planning
Strategic sourcing continuously improves and re-evaluates the purchasing activities of a company. Sourcing
optimization helps evaluate different procurement inputs by considering supply market, specific supply chain
conditions, individual supplier conditions and offers alternatives to address the buyer’s sourcing goals.
It helps in:
 Assessing the supply market, the company’s spending and identifying suitable suppliers
 Optimizing production related sourcing decisions, concerning where to produce or source products,
based on a total supply chain cost analysis
 Selecting a suitable manufacturing site, optimal capacity utilization of plants and product allocation
among the different plants and distribution centers
 Strategic planning for manufacturing and inventory optimization
 Increasing manufacturing and distribution asset utilization

8. Project Area Identified Savings (to date)
Transportation 16%
Warehouse 12%
Supply Chain 3%
Total 15%
Supply Chain
Network Optimization
Network optimization helps in designing the optimal supply chain network with the lowest total cost
structure, given operational constraints. It uses statistical modeling to describe the transport network to be
followed. It helps senior management in making the most efficient use of resources while identifying the
most economical routes.
It aids in:
 Reducing transportation overheads and ensuring that the right product reaches the right location on
time
 Improving transportation mode selection, load consolidation and resource utilization
 Quantifying operational, financial costs of alternative networks and identifying scopes of improvement
 Ensuring reduced freight costs and increased operating efficiency
 Streamlining warehouse activities, thereby reducing time to dispatch and optimizing productivity levels

9. Lead time : It is the time lag between
when the order is placed and the point at
which the stocks are available; A lead
time of 4 days implies that there should
always be stock for 4 days supply to avoid
stock-out scenario
Safety stock is the buffer quantity to
cover any unplanned excess requirement
taking into account delivery delays
Reorder point is the minimum level of
stock at which procurement should be
triggered and quantity of warehouse
stock should never go below this point
If the quantity of warehouse stock is less
than re-order point, there is shortfall
Stock
TimeRelease date
Safety
Stock
Reorder
point
Availability date
Lot size
Replenishment
lead time
Supply Chain
Inventory Management
Optimal inventory management is an indispensable function to ensure un-interrupted product supply to
meet the changing demand. Stock out analysis helps in:
 Optimizing inventory and service levels by streamlining ordering processes
 Minimizing stock out—stock out can lead to loss of sales
 Handling overstock—overstock leads to increased inventory costs and costs to liquidate excess inventory
 Maximizing warehouse space utilization
Lead time is the time lag between when the order is placed, and the point at which stocks are available. The
buffer quantity to cover any unplanned excess requirement, taking into account delivery delays, is referred to
as safety stock. Providing for safety stock on top of lead time demand, will give the re-order point, which is
the minimal level of stock at which procurement should be triggered. Warehouse stock should never go
below the re-order point. Re-order point will assist in deciding what would be the best optimal order
quantity and when to place an order.

10. States
States
States
States
Zones
YTD
MOM
Salience
Brand share
YOY
States
Zones
States
Zone
Increase in brand share
Decrease in brand share
No change in brand share
25.6,61.3
6.5,56.54.4,78.1
16.8,79.7
12.3,73.3
8.3,76.0
1.0,66.7
10.9,84.8
0.3,82.7
3.0,50.5
2.5,60.0
0.2,65.2
1.0,33.9
1.0,61.9
0.2,68.7
% Salience, %Brand Share
Sales & Channel
Planning
Sales Tracker
Constant monitoring and tracking provides the sales team with accurate information related to market
dynamics, so that they can have an action plan before the next sales cycle starts. Also, it serves as the base
for formulating sales strategies. It:
Identifies which products and SKUs are selling the most
Analyses market trends and geographic buying patterns
Evaluates growth potential for product portfolio (products, regions, markets)
Identifies the epicenter for market share loss – Root-cause analysis
Interactive visual dashboards on market performance across geographies provide further assistance vs.
analyzing large volumes of data.

11. AP, 178.6
Dam, 8.2
Delhi, 269.3
Goa, 187.7
Har, 76.2
Kar, 83.4
Ker, 75.7
Mah, 41.9
Mum, 76.5
Pondi, 24.8
Raj, 127.1
UP, 62.0
0%
5%
10%
15%
20%
25%
0% 5% 10% 15% 20% 25% 30%
CompetitorbrandMarketshareYTD2012
Industry salience YTD 2012
CompetitorBrandshare:4.0%
AP, 213.6
Bih, 11.7
Dam, 6.9
Delhi, 243.6
Goa, 238.3
Har, 77.0
Kar, 60.1
Ker, 164.1
Mah, 40.8
Mum, 66.7
Oriss, 10.8
Pondi, 9.6
Raj, 36.9
TN, 173.5
UP, 64.4
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
0% 5% 10% 15% 20% 25%
CompetitorbrandMarketshareYTD2011
Industry salience YTD 2011
CompetitorBrandshare:4.4%
YTD 2011 YTD 2012Change in competitor brand strategy
Sales & Channel
Planning
Competitor Analysis
Monitoring the performance of the brand versus key competitors on a continuous basis assists in:
 Detailed understanding of competitors’ portfolio, marketing and sales strategies
 Studying competitors’ response to any new strategy in place
 Evaluating the expansion and growth strategy of competitor brands across markets
Based on competitor assessment and their impact on brand’s share, the micro and macro level strategies are
outlined.
High industry salience,
Low competitor brand share
High industry salience,
High competitor brand share

12. 0.0
1.0
2.0
3.0
4.0
5.0 Actual Sales Forecasted Sales Base Line Sales
Millioncasessold
Sales & Channel
Planning
Sales Forecasting
A good demand forecast helps improve sales volume, cash flow and hence the profitability, by optimizing
inventory and by minimizing out-of-stock. Besides considering historical data, external factors like promotion,
seasonality, price changes, macro-economic conditions are also considered for more accurate forecasts. It
helps create better solutions for:
 Inventory Control: Optimizing inventory & service levels by streamlining ordering processes
 Minimizing Out of Stock: Out of stocks equal lost sales which can have a negative impact on sales
 Improving product freshness & warehouse efficiency: Too much inventory can result in excess “expired
inventory” that must be liquidated at or below cost, which is a cash flow drain
 Maximizing warehouse space utilization: As SKU proliferation continues, forecasting can help maximize
the use of warehouse space
 Capitalizing on peak sales weeks: Accurate forecasting ensures the right product mix to take full
advantage of operational capacity and peak market demands
Statistical techniques (like Moving Average, Holt Winters, Regression, ARIMA) are applied on historical data.

13. 53.2
44.0
30.4
20.4 19.1 18.3
0
10
20
30
40
50
60
70
0
5
10
15
20
25
30
35
$0.90
to
$0.98
$0.99 $1.00
to
$1.08
$1.09 $1.10
to
$1.18
$1.19 $1.20
to
$1.28
$1.29 $1.30
to
$1.38
$1.39 $1.40
to
$1.48
$1.49
% ACV Brand A sales rate
Identify price threshold
0
10
20
30
40
50
60
70
80
90
100
110
120
Wk-1('09)
Wk-4('09)
Wk-7('09)
Wk-10('09)
Wk-13('09)
Wk-16('09)
Wk-19('09)
Wk-22('09)
Wk-25('09)
Wk-28('09)
Wk-31('09)
Wk-34('09)
Wk-37('09)
Wk-40('09)
Wk-43('09)
Wk-46('09)
Wk-49('09)
Wk-52('09)
Wk-3('10)
Wk-6('10)
Wk-9('10)
Wk-12('10)
Wk-15('10)
Wk-18('10)
Wk-21('10)
Wk-24('10)
Wk-27('10)
Wk-30('10)
Wk-33('10)
Wk-36('10)
Wk-39('10)
Wk-42('10)
Wk-45('10)
Wk-48('10)
Wk-51('10)
Wk-2('11)
Wk-5('11)
Wk-8('11)
Wk-11('11)
Wk-14('11)
Wk-17('11)
Wk-20('11)
Wk-23('11)
Wk-26('11)
Wk-29('11)
Wk-32('11)
Wk-35('11)
Wk-38('11)
Wk-41('11)
Wk-44('11)
Wk-47('11)
Wk-50('11)
Price index vs. competition Volume share
Optimum price corridor
Identify optimum price corridor
Sales & Channel
Planning
Pricing Analysis
Pricing strategies are crafted to meet two key objectives: profit and revenue maximization. It helps in
identifying the best pricing strategy in a dynamic market, in response to the competitive scenario, by:
 Evaluating the brand’s own price elasticity and competitor brands’ cross price elasticity
 Identifying price gaps/thresholds which can result in significant share changes for the brand
 Identifying the right price gap/threshold with respect to the key competitors

14. Simulator for effective allocation of trade spends
Sales & Channel
Planning
Promotional Effectiveness
Promotions provide great value for brand through both incremental sales and increased brand awareness. It
is a technique of evaluating the extent of success of an activity using past data, by correlating the sales data
and marketing efforts. Main objective is to assess the impact and effectiveness of promotions.
Trade promotion optimization (TPO) utilizes advanced econometric modeling techniques to help brands
refine their promotion strategies, identify the right price and discount point that maximized sales lift and ROI,
and eventually help manufacturers enlarge their consumer basket and have a sustained impact on baseline
sales.
TPO helps companies:
 Allocate more for promotion sensitive brands and SKUs
 Collaborate with retailers and restructure their trade programs
 Design unique programs specific to a retailer/channel instead of following a “one-size fits all” approach

15. Streaming Sales Data
fed weekly or
monthly as is
available
Promotion Calendar fed
into the system
periodically
Marketelligent
PRISM
µ Display
µ Feature
µ Consumer
µ TPR
Decomposed Lift (µ)
Sales & Channel
Planning
Real-time evaluation of promotions
Marketelligent has developed an in-house proprietary tool called PRISM, for continuous monitoring and
evaluation of trade and marketing promotions on a real time basis, using the test-control approach.
Identifying the control samples for each of the test group takes most of the time/effort. PRISM minimizes
the time required for the same and identifies the control samples on a real time basis, based on historical
sales trends and outlet demographics.
PRISM uses sales in test and control outlets, to calculate the lift factor for each or combinations of trade
marketing programs. Based on the lift factor, incremental sales and ROI are calculated for each activity. The
effectiveness of promotions can be compared at different levels – channels, categories, brands and markets.

16. Market
Performance
Jan’09
Feb’09
Mar’09
Apr’09
May’09
Jun’09
Jul’09
Aug’09
Sep’09
Oct’09
Nov’09
Dec’09
Jan’10
Feb’10
Mar’10
Apr’10
May’10
Jun’10
Jul’10
Aug’10
Sep’10
Oct’10
Nov’10
Dec’10
Volume,‘000units
Mediaspend,‘000USD
2%
4%
6%
8%
10%
12%
14%
Total Spends Magazine TV Daily
Evaluate “Efficiency/ROI” from each media vehicle
Efficiency
0
100
200
300
400
500
600
700
800
900
0
2
4
6
8
10
12
14
16
18
20
Baseline sales Online incr. sales TV incr. sales Daily incr. sales
Online spend TV spend Dailies spend
Decomposed sales into base line and incremental
Market Mix Modeling
Marketing budgets as a percentage of sales typically vary between 4-10% for a CPG company. Given the high
investment, marketers would like to evaluate the returns from each media vehicle and optimize their
investments.
Market Mix Modeling (MMM) helps brand managers identify the right mix of advertising media, manage
channels and allocate marketing spend in a manner that not only provides the required sales lift but also
maximizes the returns on investment by media vehicles.
The model captures the following:
 Cannibalization, if any, amongst the portfolio of brands
 Impact of competition media activity
 Saturation spends for each media vehicle based on diminishing returns
 Decay impact, if any for each of the media vehicles - also called ad-stock

17. Knowledgeable
2%
Quality Conscious
4%
Soft Shiny Hair
4%
Better Color
Experience 1%
Natural
Ingredients 2%
Pleasant
Fragrance 1%
Gray coverage
1%
Value for Money
0.4%
Feel youngIn
charge 15%
Sensuous &
Sophisticated14%
Perfect color
13%
Recommended
brand 11%
Brand that keeps
its promises 9%
Range of Shades
8%
Makes me feel
confident 9%
Intense, long
lasting colors 5%
Purchase Intent
Colour pathwayNon-damaging pathwayExperiential
Emotional response
Rational response
Brand image
Brand attributes
Market
Performance
Driver Analysis
Every organization needs to understand which product/service attributes have the greatest influence on the
consumer’s purchase decision. For instance, consumers might rate a personal care product based on its color,
scent, functionality, price, discount offer and so on. Driver analysis is a technique widely used to identify the
key consumer needs which translates to purchase behavior. It provides answers to critical questions like:
 What accounts for consumers’ proclivity to purchase the product?
 What causes consumers to switch to competitor brands?
 What is the core consumer segment that should be focused on?
Statistical techniques (Correlation, Multivariate Regression, and Structural Equation Modeling) are utilized to
identify the critical success factors of a brand which drives sales or revenue.

18.  Identify growth opportunities for niche
consumer segments
 Define the portfolio strategy for their
category by ensuring minimal consumer
segment overlap across brands
Based on the above, the marketing team
modifies their product/service offering and
deploys the desired positioning and
marketing communication to reach their
consumer base.
Healthy hair
Seekers
Natural
enhancers
Expressive
Age defiers
Young subtle
expressers
Young strong
expressers
Original color
of hair
without hair
colorant
Color of hair
with hair
colorant
(Aspired
Color)
Dark Brown
Medium Brown
Light Brown
Medium Brown
Medium Blonde
Dark Blonde
Light Brown
Dark Brown
Medium Brown
Medium Brown
Dark Blonde
Medium Blonde
Light Brown
Medium Brown
Medium Blonde
Dark Brown
Chestnut
Medium Blonde
Auburn
Dark Brown
Auburn
Auburn
Chestnut
Auburn
Chestnut
Market
Performance
Consumer Segmentation
Segmentation identifies homogenous consumer groups based on their needs, preferences, attitudes,
demographics, lifestyle measures (activities, interests, opinions and values) and behavior.
A mass marketing approach treats the market as a whole, while segmentation enables the business to target
different consumer groups by adapting its product and marketing mix to suit each targeted segment.
Segmentation results are leveraged to:
 Understand how the market is evolving in terms of changing consumer needs/preferences
 Identify the benefits sought by each consumer segment
 Improve the competitive position by focusing on the most profitable and sizeable segment

19. Assessing brand value helps in:
 Identifying optimal measures to build
strong brand equity
 Demonstrating the effect of strong
brand equity – in terms of market share,
consumer acquisition, brand loyalty and
other desirable outcomes
 Mapping the brand's equity against that
of key competitors Judgments
Resonance
Feelings
ImageryPerformance
Salience
Stages of brand development
4. Relationships =
What about you and me?
3. Response=
What about you?
2. Meaning=
What are you?
1. Identity=
Who are you?
Branding objective at each
stage
Intense, Active
loyalty
Positive, Accessible
reactions
Points-of-parity
& Difference
Deep, Broad
brand awareness
Keller’s Brand Resonance Pyramid
Market
Performance
Brand Equity Tracker
Brand equity tracker provides a framework for measuring the brand’s performance/health. This can be
assessed through consumer perception, which includes both rational and emotional aspects. Main criteria
for assessment — brand differentiation, brand relevance, the consumer’s knowledge of the brand and brand
image in the consumer’s mind.
Brand equity tracker defines the gap between what a brand wants to be and how a brand is actually
perceived by consumers, thereby giving a direction for branding strategy. Different components of brand
equity are depicted in the image.

20. Business Situation:
Client’s 70% to 80% of daily beverage production depends on empty bottle returns from previous day. As such, a highly accurate forecast
for daily bottle returns across all SKU’s was required for optimal production planning.
The Task:
Design, develop and implement a predictive model that will help in forecasting daily empty bottle returns for 10 different SKUs.
Analytical Framework:
Data preparationfor model building. Past sales data or production data didn't have much effect on the returns data and thus past 2 years
return data was used for the model building. Different models like ARIMA, Holt Winters, Year on Year growth model were built to forecast the
returns.
The Result:
• For all the SKUs considered, an accuracy of 75% was achieved for May-June 2011. This was significantly better than existing forecasts.
• For the SKU which contributed to 47% of the total returns, an accuracy of 92% was achieved for May-June 2011. The monthly accuracy for
May2011 being 85% and June 2011 being 97%.
Analytics in Action
Towards Better Production Planning by Accurate Forecasting
Client: A Leading Carbonated Beverage Manufacturer
Defining
Modelling
universe
Model
development
Validation
Past 2 years empty bottle returns data was
considered. The data was too volatile to fit
into the model and thus Centralized
Moving Averages was calculated to
smoothen the data and to get a better
model fit.
ARIMA model was built on Centralised
moving averages , Holt winters and Year on
Year growth rate models was built on
empty bottle returns. Bootstrapping
method was applied to choose best
forecast value.
May and June forecasts were compared
against actuals. Accuracy was calculated at
a daily-level for all SKU’s
-
5,000
10,000
15,000
20,000
25,000
30,000
5/1/2009
5/23/2009
6/14/2009
7/6/2009
7/28/2009
8/19/2009
9/10/2009
10/2/2009
10/24/2009
11/15/2009
12/7/2009
12/29/2009
1/20/2010
2/11/2010
3/5/2010
3/27/2010
4/18/2010
5/10/2010
6/1/2010
6/23/2010
7/15/2010
8/6/2010
8/28/2010
9/19/2010
10/11/2010
11/2/2010
11/24/2010
12/16/2010
1/7/2011
1/29/2011
2/20/2011
3/14/2011
4/5/2011
4/27/2011
5/19/2011
6/10/2011
2009
Dailyreturns
Actuals
Forecasts
Model Build on 2009 and 2010 data Model Application for May-June11
2009 2010 2011
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
1-May-11
3-May-11
5-May-11
7-May-11
9-May-11
11-May-11
13-May-11
15-May-11
17-May-11
19-May-11
21-May-11
23-May-11
25-May-11
27-May-11
29-May-11
31-May-11
2-Jun-11
4-Jun-11
6-Jun-11
8-Jun-11
10-Jun-11
12-Jun-11
14-Jun-11
16-Jun-11
18-Jun-11
20-Jun-11
22-Jun-11
24-Jun-11
26-Jun-11
28-Jun-11
30-Jun-11
DailyReturns
Actuals
Forecast

21. Business Situation:
The client, a leading hair care manufacturer wanted to identify drivers of brand preference for the category which would aid them in designing
the right marketing strategy & related collateral for strengthening market share for their existing brand.
Analytical Framework:
Design a Structural Equation Model (SEM) to identify key equity themes & their hierarchyin terms of importance in driving purchase for the
category, and identify the best pathway to improve the brand’s equity in consumer’s mind.
The Result:
The following recommendationswere made and implemented by the business:
₋ Leverage Brand’s strength on the “health” dimension – this goes in line with brand’s equity pyramid
₋ “Ingredient” is one of the key category drivers on which the brand is performing very well – strengthen communicationstrategy to
capture this
₋ Even though health benefit is key, consumer’s eventually desire the beauty aspect – Redesign communicationstrategy to convey this
as the end benefit
₋ Currently “beauty” dimension is weak – build credibility on that with consistent communication
Analytics in Action
Re-design Product Communication Strategies in line with Consumer
Preferences
Client: Leading Hair Care Manufacturer
Marketing strategyon the core “Health” benefit & it’s eventual impact on making oneself attractiveis the “Key”
Overall Brand Equity
Feel Confident
& Energised
15%
Trust
10%
Effective
8%
Expert Brand
3%
Leaves hair
soft ,smooth
and shiny
11%
Hair Health
10%
Color
Protection
1%
Conditioning
3%
Ingredients
11%
Brand for me
6%
Attractiveness
10%
Beautiful and
Empowered
1%
Leading Brand
2%
Fragrance
5%
Experience
1%
For men and
women
0.3%
Dandruff and
Scalp issues
4%
0.60
0.37
0.83
0.21
0.90
0.63
0.30
0.09 0.290.15
0.80
Strong relationship
Moderately strong relationship
Weak relationship
0.78 0.19 0.90 0.90 0.90
0.90
0.90
0.16

22. Business Situation:
Leading manufacturer in anti-aging cream category. Brand X occupies a dominant position in market; recently also introduced Brand Y.
• Brand X Volumes are down 6.5% vs. last year.
• Spending across Media has shifted from being TV-centric in 2007 to Dailies-centricin 2008.
• Manufacturer would like to understand the effectiveness and efficiency of his Media Spend; and to find optimal ways to reallocate media
spend across channels so as to maintain Sales
The Task:
Need to develop an optimal media investment strategy based on Media Mix Modeling to improve the brand equity of the client :
• Establish key relationships between Sales and Marketing driver inputs.
• Quantify impact of each marketing driver on sales.
• Optimize allocation spends across various drivers to maximize sales.
The Result:
The model gave clear directions for allocating budgets across various media :
• For every $ spend, Magazine gives 6 times the return of TV and dailies.
• Magazines seems to be operating above threshold and below saturation levels.
• Lower returns on TV could be due to operating levels below threshold in certain bursts, and low SOVs compared to Brand Z.
• TV has a bigger role of driving the brand health.
• Recommendationsused to optimize marketing spends across channels to maximize Sales.
Analytics in Action
Increasing ROI by Optimizing Media Spends
Client: A Leading Beauty Products Manufacturer
0
100
200
300
400
500
600
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800
900
0
2
4
6
8
10
12
14
16
18
20
JAN07
FEB07
MAR07
APR07
MAY07
JUN07
JUL07
AUG07
SEP07
OCT07
NOV07
DEC07
JAN08
FEB08
MAR08
APR08
MAY08
JUN08
JUL08
AUG08
SEP08
OCT08
NOV08
DEC08
Baseline Sales Magazine Incr. Sales TV Incr. Sales Daily Incr. Sales
test Magazine Spend TV Spend Dailies Spend
Volume,‘000units
MediaSpend,‘000SGD
-
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Total Spends Magazine TV Daily
Efficiency
Incremental Sales per ‘000 SGD media spend

23. Business Situation :
Over-the-Counter (OTC) market is a growing industry; consumers today are much more inclined to self-diagnosis and self-medication as they
prefer to have a greater role in their health affairs. And brand shares are impacted because of multiple influencers like FDA regulations,
mergers and acquisitions, patent expiry, Rx to OTC switch, entry of generics, etc.
The Task :
In this dynamically changing OTC market; there is a need to track OTC industry movement by category and evaluate market share changes
across key geographies /countries . This will enable a business to focus its marketing efforts on areas with the greatest return on investment.
Analysis :
• Collated historic (2005-2010) as well as forecasted sales (2011-2015) information.
• Accounted for all industry mergers & acquisitions – at company X brand X country level
• Evaluated category and brand performance at each of the levels defined below:
The Result :
• Insights from this analysis helped identify the focus markets and brands by each category. Based on this the client drafted their annual
strategic plan
Analytics in Action
Tracking Market Development in the OTC industry
Client : A Leading Global Manufacturer of Over-the-counter Drugs
Category
1. Cough & Cold
2. Analgesics
3. Vitamins & Minerals
4. Digestive Health…etc.
Geography
1. APAC
2. LA
3. NA
4. WE & EE…etc.
Country
1. USA
2. Canada
3. Brazil
4. China…etc.
Company
1. J&J
2. GSK
3. Merck
4. Reckitt Benckiser…etc.
Brands
(as an example brands
within Analgesics)
1. Tylenol
2. Aspirin
3. Advil…etc.

24. MANAGEMENT TEAM
GLOBAL EXPERIENCE.
PROVEN RESULTS.
Roy K. Cherian
CEO
Roy has over 20 years of rich experience in marketing, advertising and media
in organizations like Nestle India, United Breweries, FCB and Feedback
Ventures. He holds an MBA from IIM Ahmedabad.
Anunay Gupta, PhD
COO & Head of Analytics
Anunay has over 15 years of experience, with a significant portion focused
on Analytics in Consumer Finance. In his last assignment at Citigroup, he was
responsible for all Decision Management functions for the US Cards
portfolio of Citigroup, covering approx $150B in assets. Anunay holds an
MBA in Finance from NYU Stern School of Business.
Kakul Paul
Business Head, CPG & Retail
Kakul has over 8 years of experience within the CPG industry. She was
previously part of the Analytics practice as WNS, leading analytic initiatives
for top Fortune 50 clients globally. She has extensive experience in what
drives Consumer purchase behavior, market mix modeling, pricing &
promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.
ADVANCED ANALYTICAL SOLUTIONS
MARKETELLIGENT, INC.
80 Broad Street, 5th Floor, New York, NY 10004
1.212.837.7827 (o) 1.208.439.5551 (fax) info@marketelligent.com
CONTACT www.marketelligent.com
Industry Business Focus Tools and Techniques
Consumer Finance Investment Optimization SAS, SPSS, R, VBA
Credit Cards Revenue Maximization Cluster analysis
Loans and Mortgages Cost and Process Efficiencies Factor analysis
Retail Banking & Insurance Forecasting Structural Equation Modeling
Wealth Management Predictive Modeling Conjoint analysis
Consumer Goods and Retail Risk Management Perceptual maps
CPG & Retail Pricing Optimization Neural Networks
Consumer Durables Customer Segmentation Chaid / CART
Manufacturing and Supply Chain Drivers Analysis Genetic Algorithms
High Tech OEM’s Supply Chain Management Support Vector Machines
Automotive Sentiment Analysis
Logistics & Distribution
YOUR PARTNER FOR
DATA ANALYTICS SERVICES
Greg Ferdinand
EVP, Business Development
Greg has over 20 years of experience in global marketing, strategic planning,
business development and analytics at Dell, Capital One and AT&T. He has
successfully developed and embedded analytic-driven programs into a
variety of go-to-market, customer and operational functions. Greg holds an
MBA from NYU Stern School of Business