Product Design using Conjoint

Analysis

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 FLOW OF CONTENT
PO1, PO2, PO3,
Product Analytics &
CO2, CO3 K2, K3 PO4, PO5, PO6,
Price Analytics
PO7, PO8 Product Design (Conjoint Analysis)

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 Learning Objective
• Student should able to understand product design (conjoint
analysis)

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 Understanding Product Design
• Product design is the process of creating
products that align with customer
preferences, needs, and market trends.
• Emphasize the significance of well-designed
products in attracting customers and gaining a
competitive edge.

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 Role of Conjoint Analysis
• Conjoint Analysis is a powerful technique for
optimizing product design.
• Conjoint Analysis helps in understanding how
customers make choices by analyzing their
preferences for different product attributes.

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 Elements of Conjoint Analysis
– Attributes: Features or characteristics of a product
(e.g., color, size, price).
– Levels: Different variations of each attribute (e.g.,
red, blue, green for color).
– Profiles: Different combinations of attribute levels
that create unique product scenarios.

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 Steps in Conjoint Analysis
• A step-by-step breakdown of Conjoint Analysis:
– Attribute Selection: Choose the attributes that are
relevant to the product.
– Level Determination: Decide on the different levels for
each attribute.
– Profile Creation: Create various product profiles by
combining different attribute levels.
– Preference Data Collection: Gather data from
customers about their preferences for different
profiles.

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 Utility and Preference Scores
• Utility scores as the values assigned to
attribute levels based on customer
preferences.
• Preference scores are calculated using utility
scores to determine customers' overall
preferences for different product profiles.

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 Example Scenario: Smartphone
Design
• Present a hypothetical scenario of designing a
smartphone using Conjoint Analysis:
– Attributes: Screen size, camera quality, battery life,
and price.
– Levels: Different options for each attribute.
– Profiles: Create sample product profiles based on
attribute combinations.

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 Data Collection Methods
• Discuss various methods for collecting
preference data from customers:
– Surveys: Administering questionnaires to gather
customer preferences.
– Choice Experiments: Presenting customers with
different product scenarios and asking them to
make choices.

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 Analysis and Interpretation
• The analysis process:
– Calculate preference scores for each profile based
on utility values.
– Rank profiles based on preference scores.
– Interpret the results to identify which attributes
have the most significant impact on customer
choices.

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 Benefits of Conjoint Analysis
• Following are the advantages of using
Conjoint Analysis in product design:
– Informed Decision-Making: Data-driven insights
for designing products that resonate with
customers.
– Market Segmentation: Tailoring products to
different customer segments.
– Price Sensitivity: Understanding how changes in
attributes affect pricing strategies.

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 Real-World Applications
Example 1: Apple and iPhone Product Line
• Apple extensively used Conjoint Analysis to optimize
the design of its iPhone product line.
• Before Conjoint Analysis: Apple relied on intuition and
limited customer feedback for design decisions.
• After Conjoint Analysis: Apple conducted extensive
Conjoint Analysis surveys to understand customer
preferences for attributes like screen size, camera
quality, and battery life. The insights guided the
development of subsequent iPhone models, leading to
enhanced customer satisfaction and a strong market
presence.

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 Real-World Applications
Example 2: Starbucks and Menu Optimization
• Starbucks used Conjoint Analysis to redesign its menu and
offerings.
• Before Conjoint Analysis: Starbucks struggled to determine
the right mix of beverages and snacks to offer.
• After Conjoint Analysis: By analyzing customer preferences
through Conjoint Analysis, Starbucks identified which drink
combinations and food items customers valued the most.
This led to a streamlined menu that better resonated with
customers' preferences, improved operational efficiency,
and increased customer loyalty.

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 Real-World Applications
Example 3: Automobile Industry - Toyota
• Toyota applied Conjoint Analysis to create tailored car
models.
• Before Conjoint Analysis: Toyota had difficulty
understanding the specific attributes customers desired in
their vehicles.
• After Conjoint Analysis: Toyota conducted Conjoint Analysis
surveys to gather insights on customer preferences for
attributes like engine power, fuel efficiency, and interior
features. These insights guided the development of car
models that aligned with varying customer preferences,
resulting in increased sales and customer satisfaction.

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 Real-World Applications
Example 4: Fast Food Chains - Burger King
• Burger King utilized Conjoint Analysis to enhance its value
meal offerings.
• Before Conjoint Analysis: Burger King faced challenges in
determining the optimal combination of items for its value
meals.
• After Conjoint Analysis: Through Conjoint Analysis, Burger
King identified the most appealing combinations of burger,
fries, and beverage for its value meals. This data-driven
approach not only improved customer satisfaction but also
boosted profitability by promoting the most preferred meal
options.

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 Real-World Applications
Example 5: Consumer Electronics - Samsung
• Samsung employed Conjoint Analysis to refine its television
lineup.
• Before Conjoint Analysis: Samsung lacked precise insights
into the attributes customers valued most in televisions.
• After Conjoint Analysis: By conducting Conjoint Analysis
surveys, Samsung identified the attribute preferences of
various customer segments, such as screen size, resolution,
and smart features. This enabled Samsung to offer
television models that catered to diverse customer needs,
resulting in increased market share and customer loyalty.

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 Conclusion
• The key takeaways from the presentation:
– Conjoint Analysis is a powerful tool for optimizing
product design based on customer preferences.
– It helps businesses create products that align with
market demands, enhancing customer satisfaction
and competitiveness.

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 Learning Outcome
• Student are able to understand product design (conjoint analysis)

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 REFERENCES
• Hartman, Kevin, Digital Marketing Analytics in
Theory and in Practice, Ostmen Bennetsbridge
Publishing Services.
• Malhotra, Naresh, Marketing Research: An
Applied Orientation, Pearson Prentice Hall.
• Gupta S., Jathar A., Marketing Analytics, Wiley
Analytics Series for Management, Wiley.

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