Data Insights for Marketing (MiM810)

UNO Bread Marketing Research Project

# 0) Packages 
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(broom)
library(stringr)
library(forcats)

# clustering helpers
library(factoextra)   # visualization + elbow/silhouette
library(cluster)      # silhouette

# 1) File path
excel_path <- "UNO Survey Responses v4.xlsx"

# --------------------------------------------------
# UNO theme colors (brown / mustard / cream)
# --------------------------------------------------

# Primary bar color for simple count charts
primary_fill <- "#A8743A"   # warm brown
primary_fill_light <- "#C09A6B"
neutral_cream <- "#FBF6EC"
text_brown <- "#5B3A29"

# Likert 1–5 (UNO Q1)
likert_colors <- c(
  "1" = "#F2ECE4",
  "2" = "#E3D5C3",
  "3" = "#C9A77D",
  "4" = "#A8743A",
  "5" = "#7A4A1E"
)

# Likert 1–7 (UNO Q2)
likert7_colors <- c(
  "1" = "#F5EFE8",
  "2" = "#E9DDCF",
  "3" = "#D8C1A3",
  "4" = "#C09A6B",
  "5" = "#A8743A",
  "6" = "#8B5A2B",
  "7" = "#6A3E1E"
)

# Comparative (UNO Q3)
eval_colors <- c(
  "Very Bad"  = "#EFE6DC",
  "Bad"       = "#E0CDB8",
  "Same"      = "#C7B299",
  "Good"      = "#A8743A",
  "Very Good" = "#7A4A1E"
)

---

1 . Project Background
(MDP / MRP / Research Questions)

1.1 Management Decision Problem (MDP)

UNO aims to increase its market share in Türkiye and must decide between two growth paths:

1. Market penetration: Strengthening traditional packaged breads.
2. Product development: Launching an international/premium bread line (e.g., brioche).

1.2 Marketing Research Problem (MRP)

This research aims to identify which strategic direction—market penetration or product development—better aligns with consumer behavior, brand perception, and current market dynamics in Türkiye.

1.3 Research Questions & Hypotheses

We focus on consumer behavior, unmet needs, trial intent, segment differences, and UNO brand equity fit.

Key hypotheses include:

• H1 (Penetration): Purchase frequency can increase if UNO improves freshness, pricing, and availability.
• H2 (Product Development): Consumers perceive packaged breads as similar; differentiated products (brioche) may satisfy novelty needs.
• H3 (Product Development): Younger/urban consumers have higher adoption potential for international-style breads.
• H4 (Brand Equity): UNO is strongly linked to traditional breads, but can extend to premium through branding and packaging.

2 . Research Design & Data Sources

We use a mixed-method design: exploratory (focus group + expert interview) followed by a descriptive online survey and a conjoint-style rating experiment.

2.1 Qualitative Insights

Focus group insights highlight:

• Concerns about additives/shelf life,
• Packaging smell/material issues,
• Emotional connection to traditional white bread,
• Brioche as taste/experience (not daily health choice),
• Trial triggers: tasting, novelty, social media virality, “not found in bakeries”.

Expert (dietitian) interview highlights:

• Consumers struggle to find “clean-label + fresh” bread,
• Brioche is an indulgence means pleasure driven consumption (weekend breakfasts, guests/snacking),
• Core trial triggers: ingredient quality and taste,
• UNO is perceived as a reliable everyday brand (more traditional).

3 . Data Import (Survey Summaries + Conjoint)

Survey data were collected via Google Forms and exported to Excel. The dataset was then reorganized into multiple sheets representing demographic summaries, behavioral questions, and UNO brand evaluation questions. In addition, a separate sheet contains the conjoint experiment data used for regression analysis.

For clarity, the data import and preprocessing code is hidden in this report, while all analytical results and visualizations are shown and discussed.

read_summary_sheet <- function(path, sheet_name) {
  raw <- read_excel(path, sheet = sheet_name, col_names = FALSE)

  header_row <- which(apply(raw, 1, function(x) any(str_detect(as.character(x), "^Count$"))))[1]

 if (is.na(header_row)) header_row <- 1

  df <- raw[(header_row):nrow(raw), ]
  colnames(df) <- as.character(df[1, ])
  df <- df[-1, ] %>% as.data.frame()

  if ("Count" %in% names(df)) {
    df$Count <- as.numeric(df$Count)
  }
  df
}

# Demographics (Option/Count)
# Category behavior (Option/Count)
# UNO questions (grid counts)
# Conjoint (individual-level)
ageData       <- read_summary_sheet(excel_path, "Age")
incomeData    <- read_summary_sheet(excel_path, "Income")
genderData    <- read_summary_sheet(excel_path, "Gender")
educationData <- read_summary_sheet(excel_path, "Education")

usageOccasion <- read_summary_sheet(excel_path, "Bread Usage Occasion")
trialMotives  <- read_summary_sheet(excel_path, "Premium_Bread_Trial_Motivation")
purchaseSev   <- read_summary_sheet(excel_path, "Purchase Severity")
briochePurch  <- read_summary_sheet(excel_path, "Brioche Purchasing")


uno_q1 <- read_excel(excel_path, sheet = "UNO Question1")
uno_q2 <- read_excel(excel_path, sheet = "UNO Question2")
uno_q3 <- read_excel(excel_path, sheet = "UNO Question3")

conjoint <- read_excel(excel_path, sheet = "Conjoint_Long")
survey_full <- read_excel(excel_path, sheet = "Form Responses 1")

---

4 . Sample Profile (Demographics)

4.1 Age Distribution

ggplot(ageData, aes(x = .data[[names(ageData)[1]]], y = Count)) +
  geom_col(fill = primary_fill) +
  labs(title = "Age Distribution", x = "Age Range", y = "Number of Respondents") +
  theme_minimal()

4.2 Gender / Education / Income

plot_count_bar <- function(df, title, xlab) {
  ggplot(df, aes(x = .data[[names(df)[1]]], y = Count)) +
    geom_col(fill = primary_fill) +
    labs(title = title, x = xlab, y = "Number of Respondents") +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 20, hjust = 1))
}

plot_count_bar(genderData,    "Gender Distribution", "Gender")

plot_count_bar(educationData, "Education Distribution", "Education")

plot_count_bar(incomeData,    "Income Distribution", "Income Range")

Profile Summary

The respondent profile is concentrated in the 25–54 age range with a female-majority sample. Education levels are primarily Bachelor’s and Master’s, and a substantial share of respondents fall into higher-income brackets. Overall, the sample reflects urban, economically active consumers who are relevant for evaluating premium packaged bread positioning.

---

5 . Category Behavior Findings

5.1 Bread Usage Occasion

plot_count_bar(usageOccasion, "When Do Consumers Use Packaged Bread?", "Occasion")

Interpretation:

• The top occasions indicate whether packaged bread is mainly used for daily convenience vs special moments.
• Use this to connect to the focus group’s “daily habits + freshness” themes.

5.2 Motivation to Try a New Premium Bread

plot_count_bar(trialMotives, "Top Motivations to Try Premium Packaged Bread", "Motivation")

Key Insight – Motivation to Try Premium Packaged Bread

Premium or organic-looking packaging is the strongest motivation for trying a premium packaged bread, followed closely by the availability of unique or differentiated varieties. In contrast, viral social media promotion plays a relatively minor role, while free sampling has a moderate but secondary impact.

These findings suggest that consumers associate premium bread primarily with tangible quality cues—such as packaging and product differentiation—rather than promotional hype. Therefore, premium positioning should emphasize packaging design, ingredient quality, and variety, supported by in-store visibility rather than relying heavily on social media virality.

5.3 Purchase Severity

purchaseSev <- purchaseSev %>%
  mutate(pct = round(100 * Count / sum(Count), 1)) %>%
  arrange(desc(Count))

purchaseSev

purchaseSev %>%
  mutate(Purchase_Severity = fct_reorder(Purchase_Severity, Count)) %>%
  ggplot(aes(x = Purchase_Severity, y = Count)) +
  geom_col(fill = primary_fill) +
  geom_text(aes(label = paste0(pct, "%")), vjust = -0.3, size = 3) +
  labs(
    title = "Purchase Severity (Category Engagement)",
    x = "Purchase Frequency / Severity",
    y = "Number of Respondents"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 20, hjust = 1))

Interpretation:

The purchase severity distribution indicates the overall intensity of packaged bread consumption in our sample. This is important because it frames whether demand is driven by daily routines (supporting market penetration) or whether premium and specialty products would remain niche (supporting a more targeted product development strategy).

5.4 International Bread Usage Purpose

briochePurch <- briochePurch %>%
  mutate(pct = round(100 * Count / sum(Count), 1)) %>%
  arrange(desc(Count))

briochePurch

briochePurch %>%
  mutate(International_Bread_Usage_Purpose =
           fct_reorder(International_Bread_Usage_Purpose, Count)) %>%
  ggplot(aes(x = International_Bread_Usage_Purpose, y = Count)) +
  geom_col(fill = primary_fill) +
  geom_text(aes(label = paste0(pct, "%")), vjust = -0.3, size = 3) +
  labs(
    title = "Why Consumers Eat International Breads (Brioche/Baguette)",
    x = "Usage Purpose",
    y = "Number of Respondents"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 20, hjust = 1))

Interpretation:

International breads (e.g., brioche/baguette) are consumed mainly for specific occasions rather than as a daily staple. This aligns with our qualitative findings that brioche is often seen as an indulgent or “special moment” product (e.g., weekend breakfasts, guests, snacking), suggesting that a premium/international line would likely be an adjacent growth opportunity rather than a replacement for core daily breads.

6 . UNO Question 1 — Brand Perceptions

q1_item <- names(uno_q1)[1]   # first column = statement name (e.g., High_Quality)

uno_q1_long <- uno_q1 %>%
  pivot_longer(cols = where(is.numeric),
               names_to = "Scale",
               values_to = "Count") %>%
  mutate(Scale = as.factor(Scale))

ggplot(uno_q1_long, aes(x = Scale, y = Count, fill = Scale)) +
  geom_col(width = 0.7) +
  facet_wrap(~ UNO_Agreement_Level, scales = "free_y") +
  scale_fill_manual(values = likert_colors) +
  labs(
    title = "UNO Brand Perceptions (1 = Low, 5 = High)",
    x = "Agreement Level",
    y = "Number of Respondents"
  ) +
  theme_minimal(base_size = 11) +
  theme(
    legend.position = "bottom",
    strip.text = element_text(face = "bold"),
    plot.title = element_text(face = "bold")
  )

q1_topbox <- uno_q1_long %>%
  group_by(.data[[q1_item]]) %>%
  summarise(
    total = sum(Count, na.rm=TRUE),
    topbox_4_5 = sum(Count[Scale %in% c("4","5")], na.rm=TRUE),
    topbox_pct = round(100 * topbox_4_5 / total, 1),
    .groups = "drop"
  ) %>%
  arrange(desc(topbox_pct))

q1_topbox

UNO Brand Perceptions — Key Insights:

• Strongest association: New Product Success (55.4%)
• Solid core strengths: Reliable Everyday (45.8%), Value for Money (42.2%), Healthy Trust (41.0%)
• Weaker perceptions: Premium Brand (32.5%), Attractive Packaging (31.3%)

Overall, respondents believe that UNO can successfully develop new products and perceive it as a reliable everyday brand offering reasonable value and health-related trust. However, premium brand perception and packaging attractiveness are relatively weaker. This suggests that while UNO has the credibility to launch a premium line, premium cues—particularly packaging design and positioning—must be strengthened to justify higher pricing.

7 . UNO Question 2 — Action Likelihood for Premium Bread

q2_item <- names(uno_q2)[1]

uno_q2_long <- uno_q2 %>%
  pivot_longer(
    cols = where(is.numeric),
    names_to = "Scale",
    values_to = "Count"
  ) %>%
  mutate(Scale = as.factor(Scale))

ggplot(uno_q2_long, aes(x = Scale, y = Count, fill = Scale)) +
  geom_col(width = 0.7) +
  facet_wrap(as.formula(paste("~", q2_item)), scales = "free_y") +
  scale_fill_manual(values = likert7_colors) +
  labs(
    title = "Likelihood of Actions if UNO Launches Premium Bread (1 = Low, 7 = High)",
    x = "Likelihood Level",
    y = "Number of Respondents"
  ) +
  theme_minimal(base_size = 11) +
  theme(legend.position = "bottom")

q2_topbox <- uno_q2_long %>%
  group_by(.data[[q2_item]]) %>%
  summarise(
    total = sum(Count, na.rm=TRUE),
    topbox_6_7 = sum(Count[Scale %in% c("6","7")], na.rm=TRUE),
    topbox_pct = round(100 * topbox_6_7 / total, 1),
    .groups = "drop"
  ) %>%
  arrange(desc(topbox_pct))

q2_topbox

Behavioral Intentions — Funnel Interpretation:

The strongest intention is trying UNO’s premium bread at least once, indicating high trial potential. In contrast, willingness to pay a premium price is more moderate and purchase-on-promotion appears important, suggesting price sensitivity. Following product announcements online is low, implying that awareness-building may work better through in-store visibility, packaging, and sampling rather than relying on consumers actively tracking updates online.

Managerial Interpretation:

Trial intention for a premium UNO bread is high, indicating strong curiosity and openness among consumers. However, willingness to pay a higher price is more moderate, and buying on promotion appears important. This suggests that premium adoption will depend on clear value communication and controlled pricing rather than purely brand-driven willingness to pay.

8 . UNO Question 3 — Comparative UNO Ratings

q3_item <- names(uno_q3)[1]  # attribute name column (Taste, Freshness, etc.)

uno_q3_long <- uno_q3 %>%
  pivot_longer(cols = -all_of(q3_item),
               names_to = "Evaluation",
               values_to = "Count") %>%
  mutate(Evaluation = factor(Evaluation, levels = c("Very Bad","Bad","Same","Good","Very Good")))

ggplot(uno_q3_long, aes(x = Evaluation, y = Count, fill = Evaluation)) +
  geom_col(width = 0.7) +
  facet_wrap(~ Comparative_UNO_Ratings, scales = "free_y") +
  scale_fill_manual(values = eval_colors) +
  labs(
    title = "UNO vs Other Bread Brands (Comparative Evaluation)",
    x = "Evaluation",
    y = "Number of Respondents"
  ) +
  theme_minimal(base_size = 11) +
  theme(
    legend.position = "bottom",
    strip.text = element_text(face = "bold"),
    plot.title = element_text(face = "bold")
  )

Comparative Evaluation:

This section evaluates how UNO performs relative to other packaged bread brands across key attributes such as taste, freshness, price, packaging, healthiness, availability, innovativeness, and premium feel. Overall, UNO performs strongly on core functional attributes. Taste and availability receive a high concentration of “Good” and “Very Good” ratings, indicating that consumers perceive UNO as competitive or superior to other brands in everyday consumption contexts. Healthiness also shows a favorable distribution, supporting earlier qualitative insights that position UNO as a reliable and trusted option for daily bread needs. In contrast, attributes related to premium positioning—particularly premium feel and innovativeness—show a more mixed distribution, with many respondents selecting “Same” rather than “Good” or “Very Good.” This suggests that while UNO is not perceived as weak in these dimensions, it is not yet clearly differentiated as a premium or highly innovative brand compared to alternatives.

These results reinforce the interpretation that UNO’s current brand strength lies in functional performance and reliability rather than symbolic or premium differentiation. Consequently, any premium bread extension would need to rely on stronger innovation signals, packaging design, and product storytelling to shift consumer perceptions beyond parity with existing brands.

9 . Conjoint Regression

# Ensure categorical fields are treated as factors
conjoint <- conjoint %>%
  mutate(
    Bread_Type   = as.factor(Bread_Type),
    Slice_Type   = as.factor(Slice_Type),
    Package_Type = as.factor(Package_Type),
    Price        = as.factor(Price)
  )

rating_model <- lm(Rating ~ Bread_Type + Slice_Type + Price + Package_Type, data = conjoint)
summary(rating_model)

## 
## Call:
## lm(formula = Rating ~ Bread_Type + Slice_Type + Price + Package_Type, 
##     data = conjoint)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3940 -1.4284 -0.2546  1.3089  4.8860 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              4.10782    0.17067  24.069  < 2e-16 ***
## Bread_TypeTraditional   -0.01912    0.12146  -0.157  0.87496    
## Bread_TypeWhole grain    0.38851    0.12146   3.199  0.00141 ** 
## Slice_TypeThick         -0.29523    0.12207  -2.419  0.01570 *  
## Slice_TypeThin          -0.37151    0.12740  -2.916  0.00360 ** 
## Price150 TL             -0.78767    0.15431  -5.105 3.74e-07 ***
## Price25 TL              -0.10233    0.17102  -0.598  0.54969    
## Price35 TL              -0.17494    0.14914  -1.173  0.24098    
## Price50 TL              -0.35000    0.15710  -2.228  0.02604 *  
## Package_TypePlastic Bag -0.89179    0.10087  -8.841  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 1484 degrees of freedom
## Multiple R-squared:  0.08997,    Adjusted R-squared:  0.08446 
## F-statistic:  16.3 on 9 and 1484 DF,  p-value: < 2.2e-16

Conjoint Regression Results:

A rating-based conjoint regression was estimated to examine how different product attributes influence overall bread ratings. The model is statistically significant (F-test p < 0.001), indicating that the included attributes collectively explain variation in consumer preferences. While the R-squared value is modest, this is expected in consumer preference data and does not limit the interpretability of relative attribute effects.

The regression results reveal clear differences in how consumers evaluate product attributes:

• Bread Type: Whole grain bread has a positive and statistically significant effect on ratings, indicating a strong preference for healthier or more natural bread options. Traditional bread does not significantly increase ratings relative to the reference category.

• Slice Type: Both thick and thin slice options negatively affect ratings. This suggests that consumers prefer a standard slice thickness and penalize deviations from it.

• Price: Higher prices reduce ratings, particularly at the extreme level (150 TL), which has a strong negative effect. Lower price increases show weaker and mostly insignificant effects, suggesting some tolerance at moderate price levels.

• Package Type: Plastic packaging has the strongest negative impact on ratings. This highlights packaging as a critical driver of preference and suggests that plastic is perceived as inconsistent with quality or premium positioning.

9.1 What Drives Consumer Ratings for Packaged Bread?

coef_df <- tidy(rating_model) %>% filter(term != "(Intercept)")

ggplot(coef_df, aes(x = estimate, y = reorder(term, estimate))) +
  geom_point(size = 3) +
  geom_errorbarh(aes(xmin = estimate - 1.96*std.error, xmax = estimate + 1.96*std.error), height = 0.2) +
  geom_vline(xintercept = 0, linetype = "dashed") +
  labs(title = "Conjoint Regression: Effect of Attributes on Rating",
       x = "Estimated impact on Rating", y = "Attribute level") +
  theme_minimal()

## Warning: `geom_errorbarh()` was deprecated in ggplot2 4.0.0.
## ℹ Please use the `orientation` argument of `geom_errorbar()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `height` was translated to `width`.

Interpretation:

This analysis shows which product attributes matter most for consumer ratings. Packaging and price have the strongest impact, while bread type also plays an important role. High prices and plastic packaging reduce product appeal, whereas healthier bread options increase ratings. These results highlight the importance of premium packaging and disciplined pricing in any premium UNO bread concept.

10 . Utility-Based Segmentation Using Conjoint Results

To identify distinct preference-based consumer segments, we conducted a cluster analysis using individual-level part-worth utilities derived from the full-profile conjoint experiment. Each respondent’s utilities capture how strongly different product attributes influence their overall evaluations. These utilities were used as segmentation variables to group respondents with similar preference structures. This approach enables segmentation based purely on underlying preferences, independent of demographics or stated attitudes.

# Conjoint long format
conjoint_long <- conjoint

# Ensure correct data types
conjoint_long <- conjoint_long %>%
  mutate(
    Bread_Type   = as.factor(Bread_Type),
    Slice_Type   = as.factor(Slice_Type),
    Package_Type = as.factor(Package_Type),
    Price        = as.factor(Price)
  )

---

10.1 Estimate Individual-Level Utilities

individual_utilities <- conjoint_long %>%
  group_by(RespondentID) %>%
  do(
    tidy(
      lm(Rating ~ Bread_Type + Slice_Type + Package_Type + Price, data = .)
    )
  ) %>%
  ungroup() %>%
  filter(term != "(Intercept)") %>%
  select(RespondentID, term, estimate) %>%
  pivot_wider(names_from = term, values_from = estimate)

## Warning in summary.lm(x): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(x): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(x): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(x): essentially perfect fit: summary may be unreliable

# Replace missing utilities with 0 (standard practice)
individual_utilities[is.na(individual_utilities)] <- 0

Each respondent gets their own regression → coefficients = individual utilities.

---

utility_vars <- setdiff(names(individual_utilities), "RespondentID")

X <- individual_utilities %>%
  select(all_of(utility_vars)) %>%
  as.data.frame()

# Standardize utilities (critical for clustering)
X_scaled <- scale(X)

10.2 Decide Number of Clusters

Elbow method

fviz_nbclust(X_scaled, kmeans, method = "wss") +
  labs(title = "Elbow Method for Selecting Number of Preference Segments")

“Based on the elbow, a three-segment solution was selected.”

---

10.3 Run Cluster Analysis (k-means)

set.seed(123)

k <- 3   # choose based on diagnostics
kmeans_fit <- kmeans(X_scaled, centers = k, nstart = 50)

individual_utilities <- individual_utilities %>%
  mutate(Preference_Segment = factor(kmeans_fit$cluster))

# Segment sizes
individual_utilities %>% count(Preference_Segment)

---

10.4 Visualize Segments

pca <- prcomp(X_scaled)

pca_df <- data.frame(
  PC1 = pca$x[,1],
  PC2 = pca$x[,2],
  Segment = individual_utilities$Preference_Segment
)

ggplot(pca_df, aes(PC1, PC2, color = Segment)) +
  geom_point(size = 2, alpha = 0.7) +
  labs(
    title = "Preference-Based Consumer Segments (Conjoint Utilities)",
    x = "Principal Component 1",
    y = "Principal Component 2"
  ) +
  theme_minimal()

10.5 Segment Profile

segment_means <- individual_utilities %>%
  group_by(Preference_Segment) %>%
  summarise(across(all_of(utility_vars), mean), .groups = "drop")

segment_means

---

10.6 Identify What Differentiates Each Segment

segment_long <- individual_utilities %>%
  pivot_longer(
    cols = all_of(utility_vars),
    names_to = "Utility",
    values_to = "Value"
  ) %>%
  group_by(Preference_Segment, Utility) %>%
  summarise(MeanUtility = mean(Value), .groups = "drop") %>%
  arrange(Preference_Segment, desc(abs(MeanUtility)))

top_drivers <- segment_long %>%
  group_by(Preference_Segment) %>%
  slice_max(order_by = abs(MeanUtility), n = 6)

top_drivers

ggplot(top_drivers, aes(x = MeanUtility, y = reorder(Utility, MeanUtility))) +
  geom_col() +
  facet_wrap(~ Preference_Segment, scales = "free_y") +
  labs(
    title = "Key Preference Drivers by Segment",
    x = "Mean Utility (Direction & Strength)",
    y = "Attribute Level"
  ) +
  theme_minimal()

Segmentation Results:

Using individual-level conjoint utilities, three distinct preference-based segments were identified. The segments differ primarily in their sensitivity to price, packaging type, and bread type.

One segment shows strong negative utilities for higher prices and plastic packaging, indicating a group that is highly price-sensitive and quality-conscious. A second segment places positive utility on whole grain bread and non-plastic packaging, suggesting a health- and quality-oriented preference structure that is receptive to premium positioning. The third segment exhibits more moderate utilities across attributes, reflecting a balanced preference structure without a dominant driver.

These results demonstrate that the packaged bread market is not homogeneous in terms of preferences. A premium UNO bread concept is therefore likely to appeal more strongly to specific preference segments rather than the entire market.

11 . Hypothesis Evaluation

H1 (Market Penetration):
Supported. UNO is perceived as reliable, accessible, and competitive in taste and healthiness. These attributes support continued penetration in the core packaged bread market.

H2 (Product Development – Differentiation):
Partially supported. Consumers show strong willingness to try a premium UNO bread, but premium perceptions are not yet well established. Differentiation would require clear innovation and packaging cues.

H3 (Premium Adoption Potential):
Partially supported. While trial intent is high, sustained behaviors such as weekly purchase and willingness to pay a higher price are more moderate, suggesting selective rather than mass adoption.

H4 (Brand Stretch to Premium):
Conditionally supported. UNO has strong brand credibility, but premium positioning is weaker. A premium extension is feasible if supported by distinctive packaging, health-oriented positioning, and controlled pricing.

12 . Managerial Recommendation

This analysis identifies the product attributes that most strongly influence consumer ratings and therefore should guide managerial decision-making for a potential premium UNO bread launch.

The results clearly show that packaging type and price are the most influential drivers of consumer evaluations. Plastic packaging has the strongest negative effect on ratings, indicating that consumers associate plastic packaging with lower quality and weaker premium value. From a managerial perspective, this implies that any premium positioning effort would be undermined if plastic packaging is used, regardless of other product features.

Price sensitivity is also pronounced. While moderate price levels do not significantly reduce ratings, higher price points—especially at the upper end—lead to a substantial drop in consumer evaluations. This suggests that consumers are open to paying more for premium bread, but only within acceptable thresholds and when the premium value is clearly communicated.

Bread type plays a secondary but meaningful role. Whole grain options positively influence ratings, signaling that health-oriented positioning can enhance perceived value. In contrast, traditional bread types do not provide differentiation in a premium context.

Slice type has a relatively weak and negative impact on ratings, indicating that it is not a primary decision factor for consumers. This suggests that slice thickness should be treated as a hygiene attribute rather than a strategic lever.

Overall, the findings suggest that a successful premium UNO bread strategy should focus on non-plastic, premium packaging, health-oriented bread types, and carefully calibrated pricing. Investments in these areas are likely to yield higher returns than changes to secondary attributes such as slice type.

However, these attribute-level insights represent average effects across consumers. In addition, the preference-based segmentation derived from individual-level conjoint utilities indicates that these managerial implications do not apply uniformly across all consumers. The analysis reveals meaningful heterogeneity in how different segments evaluate price, packaging, and product attributes.

Specifically, one segment exhibits strong price sensitivity and places limited value on premium cues, suggesting that premium positioning would be less effective for this group. For these consumers, maintaining competitive pricing and reinforcing UNO’s role as a reliable everyday bread brand remains critical.

In contrast, another segment shows a stronger positive response to health-oriented bread types and non-plastic, premium packaging, while demonstrating greater tolerance for higher prices. This segment represents the primary target for a premium UNO bread offering, as the key drivers identified in the conjoint analysis align closely with their underlying preference structure.

A third segment displays more balanced utilities across attributes, indicating flexibility but weaker attachment to any single premium driver. For this group, overall product coherence and communication may matter more than specific attribute optimization.

Taken together, these findings suggest that a premium UNO bread strategy should be implemented as a targeted initiative rather than a mass-market repositioning. By leveraging preference-based segmentation, UNO can selectively address segments that value premium packaging, health-oriented products, and differentiated offerings, while continuing to serve price-sensitive segments through its core product portfolio. This segmented approach minimizes risk and maximizes the return on investment from premium product development.

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