Absolutdata 2014 Proprietary and Confidential 1

Case study : Price pack architecture solution for a leading global CPG company

Client witnessed an exceptional growth in the first year (March 11). 2012 saw a slowdown with the actual growth of 85% vs. target of 120%. The slowdown was primarily attributed to

Price increase through absolute price increase or pack downsizing

Cannibalization by the launch of more expensive in one of the variant variant

Client was thus planning to develop a new pricing strategy to manage consumer value as well as profitability

Background

Absolutdata recommended based pack price architecture solution with following objectives to address the business problem Price Change Impact: Price sensitivity analysis

Optimal price points for biscuit packs

Estimate gain/loss to biscuit portfolio due to price change

Pack downsizing impact: Pack size sensitivity analysis

Impact of de-grammage on volume, value and transactions

New pack introduction impact Estimate the gain/loss to portfolio with new pack introduction

Optimal new pack dynamics (price, pack size, flavor) for introduction

Portfolio Optimization Build an optimal portfolio

Objective

Devised a new portfolio for client by introducing new packs and modifying the existing ones (in terms of number of cookies and price) The new portfolio helped to gain market share as well as increase consumption of existing biscuit consumers Optimal pricing and offering price incentive in larger packs helped boost volume sales

Impact

Absolutdata 2014 Proprietary and Confidential 2

Case study : Absolutdatas price pack architecture solution

Price Pack Architecture is a powerful solution which enables you to create a market advantage through re-designing your product portfolio to fit with market needs

Business Objective

Built a pricing based strategy to increase

revenues and profitability of biscuit

portfolio

Business Solution

1. Increase price of the SKUs

2. Reduce the pack size for the SKUs with price constant

3. Introduce a new SKU in the portfolio

Price Pack Architecture

1. New SKU Introduction Analysis Measures the impact of new SKU at various price points on portfolio

2. Price/Grammage sensitivity Analysis Assess any leverage points in terms of price and grammage vis--vis competition brands

3. Competitive Strategy Impact Analysis Assess the impact of changes made my competition on the portfolio

4. What-if Analysis Conduct what-if analysis leading to the optimal portfolio such as the impact of price change vs. pack size and new SKU introduction

Technique

Conjoint (choice based conjoint) +

Calibration through Market Mix Modeling

Absolutdata 2014 Proprietary and Confidential 3

Case study : Conjoint design - Translating design into a shelf display

Respondents were able to fill in the number of cookie packs they will buy for each brand

Out of the 10 packs that you plan to purchase in the next grocery shopping trips, please distribute them amongst the following cream biscuits or cookies product options. You can buy more or less than 10 packs if you feel that is what you would want to do in a given scenario, if the prices and products available seem attractive to you.

None: Would reserve these purchases for another time or at other store

Hovering on a certain product enlarges the image and product details (price,

pack size etc)

Brand 1 Brand 2 Brand 2 Brand 3 Brand 4

Brand 5 Brand 6 Brand 7 Brand 8 Brand 9

Brand 10 Brand 11 Brand 12 Brand 13 Brand 14

Absolutdata 2014 Proprietary and Confidential 4

Case study : Sample key findings - Hierarchy of decision making by the consumers

The hierarchy of decision making for the cream biscuits and cookie category shows that price is not the most powerful lever to influence the consumers purchase decision

Order of attributes that buyers look at while purchasing a cream/cookie biscuits Brand & Flavor Pack Size Price

Absolutdata 2014 Proprietary and Confidential 5

Case study : Absolutdata resolution - Calibration of conjoint results using MMM

DemandDemand

Price

Elasticity derived from conjoint data

Elasticity derived from true sales data

Calibrated model

Pc1

Ps2

Pc3

Ps1

Pc2

Ps3

Calibration of results was done using actual MMM data. MMM data along with other control variables was used to build a model and its price elasticity value was used as a reference point

Conjoint analysis, which gave us the flexibility to estimate demand at prices which are currently not offered in the market were then calibrated

Calibration was done using common price points and getting a calibration factor

Price

Absolutdata 2014 Proprietary and Confidential 6

Case study: Sample key findings - Price impact on individual SKU and biscuit portfolio

Base price

An SKU level analysis was done in terms of looking at the impact of price increase decrease on SKU volumes and revenue.

Vanilla 6 cookie SKU has high price sensitivity as well as the highest price elasticity in the portfolio

Base price

For Vanilla 6 cookie SKU, a decrease in price leads to gain in transactions and volume share in the portfolio but does not lead to any gain in revenue share

A portfolio level analysis was done in terms of looking at the impact on revenue, transaction and volumes of portfolio due to price change of an SKU

Similarly, grammage elasticity were also analyzed to understand their impact on volume, packs and value. This enabled us to decide the product attributes that could be altered to get increased volumes/revenue

Absolutdata 2014 Proprietary and Confidential 7

Case study: What changes needs to done to portfolio?

2 cookies Rs 5

6 cookies Rs 12

12 cookies Rs 25

6 cookies Rs 15

12 cookies Rs 30

4 cookies Rs 10

Current Portfolio New Scenario

The portfolio mix with an addition of small pack (4 cookie) in Choco Crme leads to an overall 6% and 4% growth in volume and revenue respectively of the biscuit portfolio

A workshop was conducted with the client team to try out different scenarios and to measure its impact on portfolio.

These scenarios were created using the excel based simulator and a recommendation was made based on feasibility of the portfolio existence and its impact on the portfolio

Absolutdata 2014 Proprietary and Confidential 8

0%

10%

20%

30%

40%

50%

60%

Base - 5 Base - 2 Base Base + 2 Banse + 5

Dem

and

Price

Price elasticity curve for Brand

Price Sensitivity

Demand curves helped measure the price sensitivity of different packages

In this case, we could calculate the impact of per unit increase/decrease in the package cost on the preference shares

This helped client understand the optimum pricing curve/accepted price range

Case study: Using simulator for price sensitivity measurement analysis

Absolutdata 2014 Proprietary and Confidential 9

Case study: Impact

Current Portfolio

30

15

25

12

5

12 cookies

6 cookies

12 cookies

6 cookies

2 cookies

SKUs different from the current portfolioSKUs in the current portfolio

30

15

27

20

10

5

15 cookies

6 cookies

15 cookies

10 cookies

5 cookies

2 cookies

The recommended scenario shows a 14% volume growth and 5% revenue growth over the current portfolio

Relatively low cannibalization as a significant proportion of volume growth is driven by increased consumption

Recommended Portfolio

3% 2%2%

11%

3%

0%

5%

10%

15%

Packs Volume Revenue

Growth over Current Portfolio

Growth due to increased consumptionGrowth drawn from competition

vanilla

client is doing quite well & did see a significant spike post new pricing, so you guys were quite right !

AVP Marketing