SVAMA

1

• SV-AMA
• Case Study Conjoint Analysis
• Kurt Wolf
• Director, Technical Marketing
• Non-Volatile Memory Group, AMD
• [phone]
• January 14, 1998

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Agenda

• Conjoint Analysis Overview
• Purpose and Logistics
• Interpreting Utility Values
• Relative Purchase Likelihood
• Positioning and Competitive Response
• Resource Allocation

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Conjoint Analysis

• Typical question and answer market research is
  predictable
• Higher performance is typically preferred over
  lower performance
• Lower price is typically preferred over higher
  price
• However, customers typically buy a product that
  has a combination of specific features/attributes
• A 20,000 car, status of BMW, performance of a
  Ferrari
• What is the optimal combination of attribute
  levels a particular market segment is most likely
  to purchase
• Conjoint analysis measures the trade-off process
  customers make when purchasing products
• This capability is computer driven

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Product Attributes

• Can include
• Product features (speed, reliability, etc.)
• Marketing/customer service (credit policy,
  quotation, billing, etc.)
• Image, compatibility, endorsements
• Price (absolute or relative)
• They can be defined on two levels
• What currently exists
• What might exist

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Purpose of Conjoint Analysis Project

• Define Next Generation Flash memory product
  attribute set (Flash memories are electrically
  reprogrammable and non-volatile)
• First generation Flash products in the industry
  did not adequately address customer needs
• Actively incorporate the customer (by market
  segment) into the definition process
• Industrial customer base

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Logistics of Conjoint Project

• 3rd party consultants implemented mechanics of
  conjoint analysis
• Product attribute levels and demographics at AMD
• Consultants integrated these project specifics
  into standard software programs
• AMD Field Applications Engineers (FAEs)
  administered the project with target customers
• Collaboratively developed market segment focus
  and identified appropriate contacts/candidates at
  specific customers
• FAEs explained conjoint project to participants
  with aid of explanatory documents
• Communicate AMDs purpose of project, value of
  customer participation, and overview of conjoint
  process

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The Commercial

• Existing product families are based on results of
  conjoint analysis
• AMD is continuing to gain market share
• 24 worldwide Nov-YTD based on WSTS
• Unit shipments growing 50 faster than market
• Greater than 50 of all new memory sockets are
  AMD compatible

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Attribute Levels and Utility Values

• Flash memory attribute examples
• Attribute Level
• Programming Voltage 5.0 Volt
• 12.0 Volt
• Sector Erase Chip erase
• 4K Byte sector erase
• 8K Byte sector erase
• Customers have different utility values for each
  product attribute level

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Customer Participation

• Conjoint project administered by FAEs
• Customers went through conjoint process on their
  own time
• Estimate a realistic time for completion of
  project, then double it
• Many parties involved all have their own time
  schedule

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Interpreting Utility Values

• Utility ?Utility
• Attribute Level Value Value
• Program 5.0V 35 33
• Voltage 12.0V 2
• Sector Chip Erase 10
• Erase 4K Byte Sector 22 12
• 8K Byte Sector 20 10
• Price 1.2 x EPROM 68
• 1.6 x EPROM 35 -33
• 1.8 x EPROM 12 -56

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Interpreting Utility Values

• Utility ?Utility
• Attribute Level Value Value
• Program 5.0V 35 33
• Voltage 12.0V 2
• Price 1.2 x EPROM 68
• 1.6 x EPROM 35 -33
• 1.8 x EPROM 12 -56
• As an individual feature 5.0V programming is more
  valuable than 12.0V programming
• Customers are indifferent when choosing between a
  5.0V device priced 1.6 x EPROM and a 12.0V device
  priced at 1.2 x EPROM
• The system level cost of providing 12.0V power
  supply is
• equivalent to 0.4 x EPROM according to customers

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Hypothetical Example of Relative Purchase
Likelihood

• Product A Product B
• Utility Utility
• Attribute Level Value Level Value
• Program
• Voltage 5.0V 35 12.0V 2
• Sector
• Erase Bulk 7 8K Byte Sector 65
• Total Utility Value 42 67
• Relative Purchase Likelihood 17 24
• A customers Relative Purchase Likelihood (RPL)
  is modeled as the normalized sum of utility
  values per product. Larger RPLs are better.

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Product Simulations

• Product simulations are the first step in
  defining next generation products
• Create a table of products by combining different
  attribute levels. Rank order these hypothetical
  products by RPLV
• The intent is to determine the product with the
  greatest RPL value that can realistically be
  produced

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Relative Purchase Likelihood Values

• RPLV Product Definition
• 5 BC _at_ 1.6 x EPROM
• 12 Base Case (BC)
• (12.0 programming, bulk erase,
• 1.2 x EPROM)
• 12 BC _at_ 5.0V, 1.6 x EPROM
• 19 BC _at_ 5.0V, 8K Byte sectors
• 27 BC _at_ 5.0V, 8K Byte sectors,
• 1.3 x EPROM

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Strategic Positioningand Competitive Response

• Customers value of competitive products can be
  modeled
• Include competitor specific attributes and levels
  in the implementation phase
• Where do you want to position your product?
• If a competitor changes their product feature
  set, this can be modeled also
• What degrees of freedom do your competitors have?
• What degrees of freedom do you have?
• What is your plan when your competitor moves?

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Price Interpretations

• By changing only the price attribute during a
  product simulation, the relative effect on RPLV
  is observed
• How elastic is demand to price
• Different application segments may absorb
  different price premiums for a specific attribute
  level
• Example Segment Characteristic
• 5.0 programming I One Flash device/system
• II Two Flash devices/system
• III Four Flash devices/system
• The utility value for 5.0V vs. 12.0V programming
  is best thought of as the incremental value of a
  5.0V system vs. a 12.0V system
• The price premium per device is the cost of
  implementing 12.0V programming amortized over the
  number of devices/system

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Price InterpretationsUsing Utility Values

• Customers receive equivalent value between
• 5.0V devices _at_ 1.6 x EPROM
• and
• 12.0V device _at_ 1.2 x EPROM
• Maximum price premiums per system for a 5.0V
  implementation is 0.4 x EPROM (? 30)
• Price premium per device by segment
• Segment Price Premium
• I 30
• II 15
• III 7.5

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Market Segments

• Determine if different market segments prefer
  devices with mutually exclusive product attribute
  levels
• 4K Byte and 8K Byte have equivalent utility
  values (22 and 20 respectively)
• Could indicate customers are indifferent to
  sector size, or that some segments prefer smaller
  while others prefer larger sectors
• If differences exist, these segments must be
  identified and researched further

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Attribute Level Definition

• Intermediate attribute levels can be interpolated
  via utility value algorithms
• Extend attribute levels beyond (above and
  below) anticipated boundaries
• After original conjoint project was completed,
  the implementation costs for the sector erase
  attribute were higher than estimated
• Re-issued conjoint analysis to a smaller select
  group with expanded sector size attribute levels

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Utility Value Categories

• Categories

Linear Plateau Increasing
then decreasing (vice versa)
Utility Value
Attribute Level (These can be misleading)
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Utility Value/Cost Ratios

• Distribute limited resources to implement the
  attributes that provide the greatest utility
  value/cost ratio
• Cost should be broadly defined to account for
  resources, incremental investment, as well as
  time to market. Use a metric for cost like
  relative die size
• Each attribute level with the highest value/cost
  ratio per attribute type should be considered in
  descending order
• Market and/or application issues may lead to the
  choice of a particular attribute level that does
  not have the highest ratio
• Look for attribute levels that satisfy the
  broadest or most attractive market segments

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3rd Party Support

• Sawtooth Software
• [phone]
• www.sawtoothsoftware.com
• Analytical Services Group
• Market Research Data Processing
• Mark Olsen, President
• [phone]

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Final Thoughts

• Conjoint analysis is a flexible and insightful
  technique to have and use in your Marketing Tool
  Kit
• It is cost effective and efficient to work with
  3rd parties to implement the specific design of
  your conjoint project
• Conjoint can be performed periodically to monitor
  customer shifts in preferences and/or to model
  value of different attribute levels