Application of Decision Sciences

to Solve Business Problems

CPG Industry

Analytics

for CPG

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?

Nee

ds

Satisfaction

Hig

h

Low

High Low

Hygiene needs

Unmet needs

Satisfied needs

Underdeveloped needs

Has enjoyable flavour

Cleans thoroughly

Provides fresh breath

Whitens teeth

Has anti-cavity action

Has anti-bacterial action

Soothes gum irritation, inflammation and bleeding Relieves

teeth sensitivity

Controls tartar

Strengthens enamel

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.

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?

Cu

mu

lati

ve R

eve

nu

e

85%

Top Selling

Cu

mu

lati

ve R

eve

nu

e

SKU in order of decreasing Revenue Contribution

100% 98%

80%

Top Mid Bottom

Recommended for Rationalization

80%

Mid Selling

Cu

mu

lati

ve R

eve

nu

e

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).

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:

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 improve process and knowledge sharing across teams

Actions Actions

Low

High

Ensure flexibility and penalty clauses are established for product delivery, establish alternate source of activity and divulge as little proprietary information as possible.

Actions

Establish control need based on three secondary factors, develop appropriate contracting relationship type and negotiate contract

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

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

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

Time Release 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.

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, 56.5 4.4, 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.

AP, 178.6

Dam, 8.2

Delhi, 269.3

Goa, 187.7

Har, 76.2

Kar, 83.4

Ker, 75.7

Mah, 41.9Mum, 76.5

Pondi, 24.8

Raj, 127.1

UP, 62.0

0%

5%

10%

15%

20%

25%

0% 5% 10% 15% 20% 25% 30%

Co

mp

etit

or

bra

nd

Mar

ket

shar

e YT

D 2

01

2

Industry salience YTD 2012

Co

mp

etit

or

Bra

nd

sh

are

: 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%

Co

mp

etit

or

bra

nd

Mar

ket

shar

e YT

D 2

01

1

Industry salience YTD 2011

Co

mp

etit

or

Bra

nd

sh

are

: 4

.4%

YTD 2011 YTD 2012 Change 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

0.0

1.0

2.0

3.0

4.0

5.0 Actual Sales Forecasted Sales Base Line Sales

Mill

ion

cas

es s

old

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.

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

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Wk-

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Wk-

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1)

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'11)

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Wk-

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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

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

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.

Market Performance

Jan

’09

Feb

’09

Mar

’09

Ap

r’0

9

May

’09

Jun

’09

Jul’0

9

Au

g’0

9

Sep

’09

Oct

’09

No

v’0

9

Dec

’09

Jan

’10

Feb

’10

Mar

’10

Ap

r’1

0

May

’10

Jun

’10

Jul’1

0

Au

g’1

0

Sep

’10

Oct

’10

No

v’1

0

Dec

’10

Vo

lum

e, ‘0

00

un

its

Med

ia spen

d, ‘0

00

USD

2%

4%

6%

8%

10%

12%

14%

Total Spends Magazine TV Daily

Evaluate “Efficiency/ROI” from each media vehicle

Effi

cien

cy

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

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 young In charge 15%

Sensuous & Sophisticated 14%

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 pathway Non-damaging pathway Experiential

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.

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

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

Imagery Performance

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.

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 preparation for 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

/200

9

6/14

/200

9

7/6/

2009

7/28

/200

9

8/19

/200

9

9/10

/200

9

10/2

/200

9

10/2

4/20

09

11/1

5/20

09

12/7

/200

9

12/2

9/20

09

1/20

/201

0

2/11

/201

0

3/5/

2010

3/27

/201

0

4/18

/201

0

5/10

/201

0

6/1/

2010

6/23

/201

0

7/15

/201

0

8/6/

2010

8/28

/201

0

9/19

/201

0

10/1

1/20

10

11/2

/201

0

11/2

4/20

10

12/1

6/20

10

1/7/

2011

1/29

/201

1

2/20

/201

1

3/14

/201

1

4/5/

2011

4/27

/201

1

5/19

/201

1

6/10

/201

1

2009

Daily

retu

rns

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

Daily

Ret

urns

Actuals

Forecast

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 hierarchy in 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 recommendations were 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 communication strategy to

capture this ₋ Even though health benefit is key, consumer’s eventually desire the beauty aspect – Redesign communication strategy 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 strategy on the core “Health” benefit & it’s eventual impact on making oneself attractive is 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 0.29 0.15

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

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-centric in 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.

• Recommendations used to optimize marketing spends across channels to maximize Sales.

Analytics in Action Increasing ROI by Optimizing Media Spends

Client: A Leading Beauty Products Manufacturer

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Baseline Sales Magazine Incr. Sales TV Incr. Sales Daily Incr. Sales

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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.

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.

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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