Case study : Price pack architecture solution for a leading global
CPG company
Background
 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
Objective
 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
Impact
 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
© Absolutdata 2014 Proprietary and Confidential
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Case study : Absolutdata’s price pack architecture solution
Price Pack Architecture is a powerful solution which enables you to create a market advantage through redesigning your product portfolio to fit with market needs
Business Objective
Business Solution
Price Pack Architecture
Technique
Conjoint (choice based conjoint) +
Calibration through Market Mix Modeling
Built a pricing based
strategy to increase
revenues and
profitability of biscuit
portfolio
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
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-à-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
© Absolutdata 2014 Proprietary and Confidential
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Case study : Conjoint design - Translating design into a shelf display
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.
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
Respondents were able to fill in the number of
cookie packs they will buy for each brand
© Absolutdata 2014 Proprietary and Confidential
Hovering on a certain
product enlarges the image
and product details (price,
pack size etc)
None: Would reserve these purchases for
another time or at other store
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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
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Case study : Absolutdata resolution - Calibration of conjoint results
using MMM
 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
Calibrated model
Elasticity derived from conjoint data
Pc1
Pc2
Demand
Pc3
Ps1
Ps2
Ps3
Demand
Elasticity derived from
true sales data
Price
Price
© Absolutdata 2014 Proprietary and Confidential
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Case study: Sample key findings - Price impact on individual SKU
and biscuit portfolio
Base price
Base price
 Vanilla 6 cookie SKU has high price sensitivity as well
as the highest price elasticity in the portfolio
 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
An SKU level analysis was done in terms of looking at the
impact of price increase decrease on SKU volumes and
revenue.
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
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Case study: What changes needs to done to portfolio?
Current Portfolio
2 cookies
New Scenario
Rs 5
6 cookies Rs 12
12 cookies Rs 25
6 cookies Rs 15
4 cookies
Rs 10
A workshop was conducted
with the client team to try out
different scenarios and to
measure its impact on
portfolio.
12 cookies Rs 30

The portfolio mix with an addition of small pack (4 cookie) in Choco Crème leads to an
overall 6% and 4% growth in volume and revenue respectively of the biscuit 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
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Case study: Using simulator for price sensitivity measurement
analysis
Price Sensitivity
Price elasticity curve for Brand
60%
Demand
50%
40%
30%
20%
10%
0%
Base - 5
Base - 2
Base
Price
Base + 2
Banse + 5
 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
© Absolutdata 2014 Proprietary and Confidential
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Case study: Impact
Current Portfolio
v
2 cookies
a
n
6 cookies
i
l
l 12 cookies
a
6 cookies
Recommended Portfolio
2 cookies
5
25
0.15
10
10 cookies
0.1
20
15 cookies
27
15
6 cookies
12 cookies
Growth due to increased consumption
Growth drawn from competition
5
5 cookies
12
30
Growth over Current Portfolio
0.05
15
0
15 cookies
30
SKUs in the current portfolio
11%
3%
2%
3%
2%
Packs
Volume
Revenue
SKUs different from the current portfolio
 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
“client is doing quite well & did see a significant spike post new pricing, so you guys were quite right !”
– AVP Marketing
© Absolutdata 2014 Proprietary and Confidential
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