“Evaluating the Effectiveness of Direct-to-Consumer
Delivery Models in the Dairy Industry”
A Thesis Submitted in
Partial Fulfilment of the Requirements for the Degree of
Master in Business Administration
Rathod Yashpal
2023-M-15042001
Under guidance of:
………………
Asst. Professor
School of Management
Ajeenkya DY Patil University
2023
DECLARATION
Thesis Title: Evaluating the Effectiveness of Direct-to-Consumer Delivery Models in the
Dairy Industry
Degree for which the Thesis is submitted: Master in Business Administration
I declare that the presented thesis represents largely my own ideas and work in my own
words. Where others ideas or words have been included, I have adequately cited and listed
in the reference materials. The thesis has been prepared without resorting to plagiarism. I
have adhered to all principles of academic honesty and integrity. No falsified or fabricated
data have been presented in the thesis. I understand that any violation of the above will
cause for disciplinary action by the Institute, including revoking the conferred degree, if
conferred, and can also evoke penal action from the sources which have not been properly
cited or from whom proper permission has not been taken.
--------------------------------------------Name of the Student: Rathod Yashpal Singh
URN no.: 2023-M-15042001
Date:
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CERTIFICATE
It is certified that the work contained in this thesis entitled ‘Evaluating the Effectiveness of
Direct-to-Consumer Delivery Models in the Dairy Industry submitted” by Rathod yashpal,
URN 2023-M-15042001 for the award of BBA is absolutely based on his own work carried
out under my supervision and that this work/thesis has not been submitted elsewhere for any
degree.
-----------------------------------………….
School of Management
Ajeenkya DY Patil University
---------------------------------Rathod Yashpal Singh
School of Management
Ajeenkya DY Patil University
Date:
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Abstract
Title:
Evaluating the Effectiveness of Direct-to-Consumer Delivery
Models in the Dairy Industry
Author:
Rathod Yashpal Singh
Guide:
……………
Problem:
In today’s fast-evolving consumer landscape, strategic decisionmaking is crucial for sustaining competitiveness, especially in the
dairy industry where freshness, reliability, and customer trust are
paramount. The emergence of Direct-to-Consumer (D2C) delivery
models presents dairy companies with a transformative opportunity
to bypass traditional retail channels and connect directly with end
consumers. This shift empowers organizations to gain deeper
insights into customer preferences, exercise greater control over
product quality, and respond swiftly to market demands. However,
the success of these models depends heavily on well-informed
operational and logistical decisions. Given the perishability of dairy
products and the complexity of supply chains, every decision—from
inventory management to last-mile delivery—must be made with
precision and strategic foresight. Therefore, evaluating the
effectiveness of D2C models requires a robust analytical foundation
to ensure such decisions align with the company’s goals, optimize
performance, and enhance customer satisfaction without
compromising on efficiency or profitability.
Purpose:
The purpose of this study is to evaluate how Direct-to-Consumer
(D2C) delivery models impact the efficiency, customer satisfaction,
and overall performance of dairy businesses. It aims to identify the
benefits and challenges of implementing D2C strategies and provide
insights to help dairy companies improve direct delivery operations
and decision-making.
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Methodology:
This study uses a mixed-method approach, combining surveys of 100
D2C dairy consumers and interviews with 10 industry professionals.
Data will be analysed using basic statistical tools and thematic
analysis to assess efficiency, consumer satisfaction, and key
operational challenges. A SWOT analysis will also be conducted to
evaluate the overall viability of D2C models
Conclusions:
Direct-to-Consumer (D2C) delivery models are reshaping the dairy
industry by enabling producers to reach customers directly with
fresher products and greater control over quality and branding. This
study concludes that while D2C models offer significant benefits
such as higher customer satisfaction, better profit margins, and
improved supply chain transparency, they also pose challenges in
logistics, cold chain management, and scalability. With the right
infrastructure, technology, and customer-centric strategies, D2C can
be a highly effective model for modern dairy businesses seeking to
grow in a competitive, digitally driven market
Keywords:
D2C, Dairy Products, Customer Satisfaction, Logistics, Supply
Chain, Freshness, Delivery Models
Table of Content
1.
Introduction
1.1 Background
1.2 Problem
1.3 Purpose
1.4 Research Questions
1.5 Theoretical limitations
1.6 Outline
2.
6 to 8
6
7
8
8
8
9
2.5
Methodology
Introduction 9
2.2
2.3
Research Approach
Choice of methodology
3.1
3.3
3.5
Literature Review
Human Resource Management
Decision making factors
18
Summary of Literature Review
2.1
3
9 to 12
Research Philosophy 10
10
2.4
Choice of theory
11
12
13
3.4
20
13 to 21
3.2
Data of HRM 15
Studies & MNC
20
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4
Empirical Method
4.1
Research design & strategy
4.3
Reliability
24
4.4
Generalisability
25
22
4.2
Validity
5
5.1
5.2
Empirical findings & analysis 26 to 27
Empirical findings
26
Empirical analysis
26
6
Conclusion
6.1
Summary
29 6.4 Contribution
7
References
28
30
6.2
6.5
Data Collection
24
4.5
Conclusion
28
Future Studies 30
22 to 25
23
28 to 30
6.3 Critical Review
31
1. Introduction
The initial chapter presents the background of the study and explains why the topic
was chosen for this Master thesis. The problem of the chosen subject will be
described, followed by the purpose, research question and the theoretical limitations
of the thesis. The final part consists of the disposition of the study.
1.1 Background
Pride of Cows is a premium dairy brand launched by Parag Milk Foods in India, built
entirely on a Direct-to-Consumer (D2C) model. Unlike conventional dairy brands that
rely on distributors and retailers, Pride of Cows follows a farm-to-home concept—
delivering high-quality, fresh milk directly to customers’ doorsteps. Operating in urban
centers such as Mumbai, Pune, Surat, and Delhi NCR, the brand caters to a niche
segment that values purity, freshness, and exclusivity in their dairy consumption.
Pride of Cows sources its milk from its own farm located in Manchar, near Pune. The
cows are of an imported Holstein breed and are raised in a controlled, hygienic
environment. The brand uses advanced milking techniques, automated bottling, and cold
chain logistics to ensure the product maintains its freshness and nutritional value from
the farm to the customer. Customers can place orders online through the brand’s website
or app, and deliveries are made every morning through a subscription-based model.
The success of Pride of Cows showcases the potential of D2C models in India’s dairy
sector, especially in premium and health-conscious segments. As demand for quality and
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traceability increases among urban consumers, the D2C model offers a transparent,
brand-controlled, and high-margin alternative to the traditional supply chain.
The dairy industry holds immense importance in the global food economy and plays
a vital role in feeding populations, generating rural employment, and supporting
agriculture-based economies. In India especially, it is one of the largest sectors,
serving millions of households with products like milk, curd, butter, and ghee.
Traditionally, dairy distribution has followed a complex supply chain involving
farmers, cooperatives, processors, distributors, retailers, and finally, the consumers.
Although well-established, this structure leads to challenges such as delays, loss of
freshness, increased operational costs, and limited feedback loops between brands and
consumers.
D2C models provide several benefits, including better quality control, direct access to
customer feedback, personalized experiences, and stronger brand loyalty. However, the
shift from conventional channels to D2C comes with operational challenges that require
strategic planning, especially due to the perishable nature of dairy products. This study
aims to investigate the effectiveness of these models and their role in shaping the future
of dairy distribution.
1.2 Problem
In today’s fast-paced lifestyle, consumers expect convenience, quality, and timely
delivery in their everyday essentials—including dairy. For a brand like Pride of Cows,
which operates on a premium Direct-to-Consumer (D2C) model, consistently meeting
these expectations is both an opportunity and a challenge. Unlike traditional dairy
distribution, the D2C model requires brands to manage everything—from production and
packaging to logistics and customer satisfaction—without intermediary support.
A key problem is the lack of predictive planning and data-driven decision-making in
operational models. For instance, a sudden spike in customer demand due to seasonal
variation or a promotional campaign may disrupt delivery schedules if the brand has not
adequately forecasted demand or allocated logistics capacity. Without a strong databacked strategy, issues like inconsistent supply, late deliveries, or compromised
product quality can arise, affecting customer trust and brand image.
Moreover, while the premium D2C segment promises better profit margins, it also
demands high operational efficiency. Factors such as cold chain management,
delivery route optimization, and customer feedback integration must be planned with
precision. Many companies fail to invest adequately in these areas or do not analyze
customer behavior deeply enough to make agile business decisions.
Another critical issue is scalability. As Pride of Cows expands into new cities,
maintaining service standards while controlling operational costs becomes increasingly
complex. The absence of scalable, tech-enabled systems can lead to bottlenecks, higher
customer churn, and rising delivery expenses—especially in price-sensitive markets.
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Therefore, this study identifies a pressing need to assess the efficacy, sustainability, and
limitations of the D2C model in the dairy sector. Only through data-driven planning,
robust infrastructure, and customer-centric operations can premium D2C dairy
brands ensure long-term success and operational stability.
1.3 Purpose
The purpose of this thesis is to evaluate how the Direct-to-Consumer (D2C) delivery
model, specifically implemented by Pride of Cows, addresses key operational
challenges and enhances customer satisfaction in the dairy industry. This research
explores how strategic planning, efficient logistics, and digital integration contribute
to the success of a premium D2C model. The study aims to identify how data-backed
decisions related to cold chain management, order fulfillment, and customer
engagement impact the overall performance and scalability of the D2C approach. The
insights gained will help businesses understand the practical effectiveness of D2C in
the dairy sector and guide improvements for sustainable growth.1.4 Research
Questions
The following research questions have been developed to guide the study:
What are the core operational components of the Pride of Cows D2C
model?
How does the D2C model impact customer satisfaction and brand
loyalty?
What are the major operational and logistical challenges in scaling the
model?
How does the D2C model of Pride of Cows compare to traditional dairy
distribution in terms of efficiency and value?
1.5 Theoretical limitations
This study is primarily focused on the D2C delivery model as adopted by Pride of Cows
and will analyze the efficiency, structure, and customer outcomes based on available
data. However, it will not delve into all operational areas of the dairy business such as
marketing, procurement, or broader human resource processes. The scope is limited to
the performance and sustainability of the D2C model, including delivery logistics,
customer satisfaction, and infrastructure challenges.
Furthermore, while the study will rely on data from customer feedback, secondary
sources, and public company information, access to internal, company-specific data may
be restricted. As such, the findings will be based on the most accurate and accessible
data, but may not reflect the complete internal decision-making framework of the brand.
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1.6 Outline
This thesis is structured into six main chapters:
Chapter 1 introduces the background, purpose, research questions, limitations, and
outlines the structure of the thesis.
Chapter 2 discusses the research philosophy, approach, and methodological design used
to collect and analyze data. An interpretivist philosophy and an abductive approach are
adopted.
Chapter 3 provides a literature review and theoretical context related to D2C models,
logistics in dairy supply chains, customer experience, and operational efficiency.
Chapter 4 describes the empirical method, including the research design, data collection
techniques, sample selection, as well as validity, reliability, and generalisability.
Chapter 5 presents and analyzes the research findings, using both quantitative and
qualitative insights related to Pride of Cows.
Chapter 6 summarizes the overall research, presents conclusions, outlines contributions,
and offers recommendations for future research.
2. Methodology
In this chapter we will present the methodological framework. First, a review will be
given on the various philosophies, approaches, theories and methodologies. Further,
we will explain which of these methods are relevant for our topic. The purpose of this
chapter is to increase the understanding on how we approach our field of study.
2.1 Introduction
According to Saunders, Lewis, and Thornhill (2009), the research process can be
visualized as an "onion," where each layer represents a stage in developing a
comprehensive research design. This framework is particularly useful for
structuring this study on Direct-to-Consumer (D2C) delivery models in the dairy
industry, with a focus on Pride of Cows.
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Figure 2.1: The research “onion”
Source: Saunders, Lewis & Thornhill, 2009, p.108
2.2 Research Philosophy
Research philosophy refers to the underlying beliefs and assumptions that guide
how data is interpreted and how reality is perceived in a study. As described by Saunders
et al. (2009), no single research philosophy is inherently superior to another. Instead, the
choice of philosophy should be aligned with the research objectives and the nature of the
research questions being addressed. There are four primary research philosophies:
pragmatism, positivism, realism, and interpretivism.
Pragmatism supports a flexible, mixed-methods approach, using both quantitative
and qualitative data to address practical research problems. Positivism, in
contrast, focuses on observable, objective reality and seeks to develop law-like
generalizations based on statistical analysis. Realism, similar to positivism,
assumes that an external reality exists independent of human perceptions, but it
accepts that observations are theory-laden and affected by social processes.
Lastly, interpretivism emphasizes the importance of understanding the social
world from the perspective of the people involved in it, arguing that reality is
subjective and constructed through human interactions.
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This thesis adopts an interpretivist research philosophy, as the study is focused
on exploring how Pride of Cows, a premium Direct-to-Consumer (D2C) dairy
brand, operates in the context of customer behavior, service expectations, and
operational challenges. Since the effectiveness of the D2C model involves
subjective experiences—such as customer satisfaction, brand loyalty, and
perceived quality—an interpretivist approach allows for a deeper understanding
of these human-centered aspects.
Given that every consumer, city, and delivery ecosystem is unique, the outcomes
of this study cannot be generalized across all dairy brands or markets. Instead,
this philosophy supports the exploration of real-world practices and decisionmaking processes specific to Pride of Cows. The use of qualitative methods such
as interviews, case analysis, and customer feedback is aligned with the
interpretivist view, making it suitable for capturing the nuances of D2C
operations in the premium dairy segment.
2.3 Research Approach
In business research, three common approaches are used to connect theory with data:
deduction, induction, and abduction. Each approach offers a different way of exploring
or explaining a phenomenon, and the choice depends on the nature and objectives of the
research.
The deductive approach starts with existing theories or concepts and uses them to
develop hypotheses that can be tested through data collection. This process typically
follows a structured path, including the following steps:
1.
2.
3.
4.
5.
Formulating a hypothesis from theory
Defining how variables will be measured
Testing the hypothesis with data
Analyzing the findings
Confirming, rejecting, or revising the original theory based on the results
Deduction is usually associated with quantitative studies and requires concepts to be
operationalized in a measurable way. However, since this study aims to explore realworld operations and customer experiences within the Pride of Cows direct-to-consumer
model, a different approach is more appropriate.
The inductive approach works in the opposite direction. It begins with the collection of
data and uses that data to develop patterns or theories. This method is often linked to
interpretivism and qualitative research. It allows the researcher to observe specific
details and generate broader generalizations or theories based on those observations. The
inductive approach is flexible and allows for adjustments as the research progresses,
which is useful in understanding customer satisfaction, logistical performance, and
strategic challenges in a D2C model.
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Given the exploratory nature of this research and the focus on both theory and real-life
business practices, this study adopts an abductive approach. Abduction combines
elements of both deduction and induction. It allows the researcher to move between data
and theory, adjusting perspectives as new insights emerge. For example, while existing
concepts around logistics, customer service, and brand loyalty will guide parts of this
study, direct observations and customer feedback related to Pride of Cows may also
reveal new findings that help refine or extend those theories.
Table 2.1: Outline “Deductive and Inductive”
This study adopts the abductive research approach, which combines both deduction and
induction. While deduction begins with theory and tests hypotheses, and induction starts
with data to build theory, abduction allows the researcher to move between both.
For this case study on Pride of Cows, abduction is suitable because it uses real-world
observations and existing theories to understand how the direct-to-consumer model
operates and why it succeeds or faces challenges. This flexible approach helps explore
customer experiences and operational strategies without limiting the study to only
predefined theories or purely data-driven conclusions.
2.4 Choice of theory
The choice of theory in this study is guided by the need to evaluate both operational
efficiency and customer satisfaction within the direct-to-consumer delivery model of
Pride of Cows. Two relevant theoretical frameworks are selected to support this
research: service quality theory and supply chain management theory.
Service quality theory helps in understanding how consumers perceive the quality of
services delivered, particularly in areas such as reliability, responsiveness, assurance,
and empathy. Since Pride of Cows positions itself as a premium dairy brand, maintaining
high service standards is central to its D2C success. This theory will be used to assess
how well the company meets customer expectations in terms of delivery experience,
product freshness, and overall satisfaction.
Supply chain management theory focuses on the movement of goods from the source to
the end user, highlighting aspects such as logistics, inventory control, and cold chain
infrastructure. As dairy products are highly perishable, efficient supply chain operations
are critical to the success of D2C models. This theory helps evaluate how well Pride of
Cows manages its internal processes to ensure timely and quality delivery.
Together, these theories provide a comprehensive lens to examine both the customerfacing and backend operational components of the D2C model in the dairy industry.
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2.5 Choice of Methodology
This study is based on an interpretivist research philosophy and follows an abductive
approach. Abduction combines both deductive and inductive reasoning, allowing the
researcher to move between theory and observation. This flexible approach is
particularly appropriate for exploring real-world business models, such as the direct-toconsumer (D2C) delivery system used by Pride of Cows.
To answer the research questions, data will be collected through qualitative methods.
The primary focus will be on understanding how Pride of Cows implements its D2C
model and how this impacts customer satisfaction and operational efficiency. One-onone interviews will be conducted with key stakeholders, including logistics managers,
customer service representatives, and possibly long-term customers. These conversations
will help uncover the operational challenges, strategic choices, and consumer responses
that define the D2C experience.
3. Literature Review
This section presents a review of the relevant literature used to support the analysis of this
study. The objective is to establish a strong theoretical framework for understanding direct13 | P a g e
to-consumer (D2C) delivery models and their significance in the dairy industry. The review
will examine key concepts such as D2C strategy, customer satisfaction, service quality, supply
chain efficiency, and digital transformation in food delivery.
3.1 Human Resource Management
Every business, regardless of size or industry, depends on a combination of
physical capital and human resources to function effectively. While assets like
cold chain infrastructure, delivery vehicles, and digital platforms are essential to a
Direct-to-Consumer (D2C) model in the dairy sector, it is ultimately the people—
operations managers, delivery staff, customer service teams, and supply chain
planners—who drive the system’s success. This section highlights the critical role
that human involvement plays in the functioning and performance of D2C
delivery models, particularly in a premium brand like Pride of Cows.
In the case of D2C dairy services, the business depends heavily on individuals who
coordinate inventory, handle daily deliveries, ensure product quality, manage
customer expectations, and respond quickly to service feedback. Even with the
support of automation and mobile platforms, the effectiveness of the D2C model
relies on trained personnel making real-time decisions. Thus, understanding how
human skills, planning, and strategic resource use contribute to the overall
customer experience is essential.
Human resource and operations management, in this context, involve hiring the
right people, training them in cold chain protocols and customer handling, ensuring
performance standards, and maintaining employee motivation. These are no longer
seen as purely administrative functions, but as strategic roles that directly influence
brand reputation, customer retention, and operational stability.
3.1.1 Role of HRM
Experts recognize several important human resource functions that support the execution
of a D2C model. These include:
1. Staffing and Workforce Planning – The brand must identify how many
delivery personnel, quality assurance staff, and logistics coordinators are needed
based on demand forecasts. This involves planning shifts, assigning delivery
routes, and forecasting workload during high-demand periods.
2. Recruitment and Selection – Finding skilled and reliable individuals for
customer-facing roles is crucial. In a premium service like Pride of Cows,
delivery teams are often the only physical touchpoint with the customer, so
professionalism and service etiquette matter.
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3. Training and Compliance – Employees must be trained in hygiene standards,
handling perishable goods, and maintaining the integrity of the cold chain. This
directly affects the quality of milk received by the end user.
4. Retention and Motivation – High staff turnover in delivery or customer service
roles can affect reliability. Retaining experienced workers through fair
compensation, positive working conditions, and career development improves
consistency and service quality.
5. Policy Development – Developing operational policies, safety protocols, and
customer service procedures ensures that all employees work within a clear
framework, reducing errors and increasing efficiency.
1. Development of workplace policies
To ensure consistency, fairness, and professionalism across all operations, clear
workplace policies must be developed. For a premium brand like Pride of Cows,
such policies guide employee behavior in areas such as delivery protocols,
hygiene standards, customer interaction, and internal communication. HR works
alongside operations and top management to frame policies that are relevant to
field staff, warehouse teams, and customer service units.
Examples of essential workplace policies may include:
a. Delivery and punctuality policy
b. Uniform and hygiene standards
c. Internet and device usage policy
d. Customer service guidelines
2. Compensation and benefits administration
Employees, especially delivery personnel and operational staff, are critical to
D2C success. HR must ensure compensation structures are fair, competitive, and
motivating. For a brand promising high quality and premium service, it is
important that employees feel valued and committed.
Typical components of compensation might include:
a. Health benefits and travel allowances
b. Attendance and performance-based bonuses
c. Paid leave and holidays
d. Incentives for customer satisfaction ratings
3. Employee retention
In D2C models, high employee turnover can affect delivery reliability and
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customer satisfaction. HR plays a key role in retaining skilled workers by
ensuring a healthy work culture, fair treatment, and professional development
opportunities.
Common causes of attrition in delivery-based roles include:
a. High-pressure work environments
b. Poor relationships with supervisors
c. Limited job satisfaction
d. Lack of alignment with company culture
3.2 Data in direct-to-consumer delivery
To evaluate the effectiveness of the Pride of Cows D2C model, it is important to
understand the key types of data used in operations. Accurate data helps in improving
delivery, customer satisfaction, and decision-making.
1. Operational data – Includes delivery times, route tracking, inventory levels, and
cold chain monitoring. This helps manage day-to-day operations efficiently.
2. Customer feedback – Data collected from reviews, surveys, and complaints
provides insight into customer experience and service quality.
3. Business performance data – Metrics like repeat orders, delivery success rate,
and cost per delivery help measure the model’s impact on overall business
growth.
Together, these data types support continuous improvement in the D2C system and help
align operations with customer needs.
Let us discuss each source of data in detail to fetch various data.
3.2.1 Operational system data
Different D2C brands may use different logistics or customer management systems, but the
types of data collected are often similar and serve as the foundation for operational analysis.
In the case of Pride of Cows, digital platforms likely manage end-to-end processes such as
order placement, delivery, and customer service. The following categories represent key data
collected through such systems:
a) Order management data: Includes number of orders placed daily, order types, product
quantities, delivery locations, and customer preferences.
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b) Customer profile data: Basic information such as customer ID, name, contact details,
address, order frequency, subscription type, and payment method.
c) Delivery performance data: Tracks delivery times, delays, successful vs failed deliveries,
and reasons for failure (e.g., customer not available, traffic).
d) Service feedback: Stored in CRM systems, this data includes customer complaints, ratings,
compliments, and common service issues.
e) Subscription and pricing structure: Information about pricing plans, discounts, and
customer-specific billing which helps personalize the service.
f) Retention and churn data: Records of active, inactive, and canceled subscriptions along with
reasons for discontinuation.
g) Product quality tracking: Data related to product issues, replacements, and customer reports
of freshness or damage.
h) Promotions and usage patterns: Response to campaigns, special offers used, and seasonal
or regional order trends.
i) Exit feedback: Similar to an exit interview, this feedback is gathered from customers who
cancel their service, to understand what went wrong and how to improve.
3.2.2 Additional service data
Apart from system-based data, some valuable information is collected outside the main
platform and plays an important role in improving D2C operations:
a) Informal customer feedback: Feedback collected by delivery staff or customer service
agents during calls or visits, often shared verbally.
b) Delivery route behavior: Observations about time taken, route changes, or local delivery
challenges noted by drivers and managed manually.
c) Staff performance insights: Feedback from supervisors or team leads about delivery staff,
such as punctuality, attitude, and service quality.
d) Customer surveys: Occasional surveys conducted on aspects like packaging, taste, delivery
service, and overall satisfaction, often used to guide improvements.
e) Absentee and delay logs: Data on missed deliveries or staff absenteeism, which helps assess
service consistency and plan workforce needs.
f) Health and safety checks: For cold chain products, any deviations in temperature, handling
safety, or compliance can be logged and monitored.
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g) Social sentiment and reviews: Public feedback shared on platforms like Google Reviews,
Instagram, or YouTube that may influence brand reputation and customer trust.
This data helps brands like Pride of Cows not only monitor their daily operations but also build
long-term strategies for improving service quality, customer satisfaction, and operational
efficiency in the D2C model.
3.2.3 Business data in D2C operations
In the D2C model, especially for a brand like Pride of Cows, business data plays a critical role
in improving operational efficiency, understanding customer behavior, and aligning supply
with demand. While the types of data are vast, some essential categories of business data
include:
a) Customer relationship management data – Tracks communication, preferences, complaints,
satisfaction
levels,
and
loyalty
patterns.
b) Financial data – Involves costs, revenue per order, delivery expenses, and profit margins.
c) Production and logistics data – Helps monitor daily output, product quality, cold chain
adherence,
and
dispatch
schedules.
d) Sales data – Analyzes buying trends, region-wise demand, subscription volumes, and
seasonal
sales
variations.
e) Supplier data – Tracks the sourcing of feed, packaging, logistics support, and third-party
delivery collaborations.
3.3 Decision-making factors in the D2C model
Having explored different types of data in the previous sections, this part explains how that
data supports key decisions in managing a D2C delivery model. Data alone has little value
unless it is processed, visualized, and interpreted in a way that supports business planning and
action. The use of dashboards, charts, and analytics tools ensures that data becomes a
foundation for intelligent decision-making. The following are critical decisions that can be
made more effective through data in the D2C context:
1. Budget planning – Data on customer growth and delivery volume helps plan delivery
staff requirements, cold chain investment, and technology costs. Budgets can be
created based on projected demand to prevent over- or under-resourcing.
2. Workforce management – Delivery staffing can be adjusted based on order peaks or
slow periods. This prevents inefficiencies and ensures smooth operations throughout
the year.
3. Customer retention analysis – Exit survey data and churn rates help identify why
customers leave. This supports strategic improvements in service, pricing, or
communication.
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4. Service performance analysis – Data from delivery success rates and customer
feedback enables the company to assess where operational bottlenecks exist.
5. Training and support – Monitoring delivery errors or delays allows management to
identify training needs for drivers and customer service agents.
6. Recognition and motivation – Consistent performance tracking can help recognize
outstanding delivery staff, which supports morale and service quality.
7. Sourcing of external partners – By analyzing past vendor performance (logistics,
packaging, tech support), future partnerships can be planned more efficiently.
8. Delivery team planning – Data on past delivery metrics (e.g., location, time, order
load) helps optimize routes and shift allocation for better speed and cost-efficiency.
9. Quality control – Customer feedback on freshness and packaging can guide
operational changes, such as improvements in storage, transport, or batch handling.
10. Customer suggestions – Gathering and analyzing customer suggestions through
surveys and service calls helps shape innovations in delivery models or product
options.
11. Business growth – Sales data and customer acquisition trends enable forecasting,
supporting decisions about expanding into new cities or upgrading infrastructure.
12. Cost tracking – Data on cost per delivery or product allows the company to compare
projected vs actual expenses, refining pricing or investment plans.
13. System and technology planning – As data use grows, there may be a need to
upgrade MIS, dashboards, or automation tools for smoother data handling and realtime decision-making.
14. Health and safety – In the context of perishable goods, temperature logs, hygiene
reports, and compliance records help ensure product quality and employee safety.
15. Data accuracy – Continuous review of system data can identify and correct
inconsistencies, ensuring that decisions are made using reliable information.
16. Customer satisfaction monitoring – By integrating delivery feedback, complaint
resolution, and order trends, the brand can evaluate overall satisfaction and make
improvements accordingly.
3.4 Studies and Companies in D2C and Data-Driven Delivery
In this section, we look at relevant studies and multinational companies that have adopted
data-driven practices to enhance their direct-to-consumer (D2C) delivery operations. These
references demonstrate the growing importance of integrating analytics, technology, and realtime data into customer-focused delivery models, especially in sectors dealing with
perishables like dairy.
Several global studies support the need for a data-centric approach in D2C logistics, customer
service, and supply chain optimization:
1. Data-Driven Supply Chain Management by McKinsey & Company
2. Customer Experience and Analytics in Last-Mile Delivery by Capgemini Research
Institute
3. Direct-to-Consumer Retail Study by Bain & Company
4. Optimizing Perishable Product Distribution by Harvard Business Review
5. AI and Automation in D2C Fulfillment by Gartner
6. Cold Chain Logistics and Data Analysis in Dairy by FICCI and NASSCOM
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These studies emphasize how data helps businesses improve forecasting, optimize delivery
routes, reduce spoilage, and enhance customer satisfaction. For a brand like Pride of Cows,
which relies on daily cold-chain delivery of milk and dairy products, such insights are essential
to maintain quality and consistency.
A number of multinational and large Indian companies have adopted D2C or hybrid models
using data-driven strategies to improve customer engagement, operations, and logistics:
Amazon – Mastered last-mile logistics and predictive delivery
BigBasket – Uses real-time data for inventory, routing, and customer preferences
Dunzo – Leverages data for hyperlocal delivery and partner coordination
Licious – Operates on a D2C model for perishable meat products using cold chain
data
5. MilkBasket – Subscription-based D2C model for dairy and groceries
6. Tata Consumer Products – Piloting D2C dairy and packaged food offerings
7. Country Delight – Pride of Cows’ main competitor in D2C dairy, with a strong tech
backbone
8. Flipkart Quick – Focuses on fast local deliveries using analytics
9. Grofers (now Blinkit) – Hyperlocal delivery using AI and predictive models
10. Swiggy Instamart – Uses data for product placement, routing, and delivery
optimization
1.
2.
3.
4.
These companies represent successful applications of D2C operations where data supports
every stage—from order placement and inventory management to delivery fulfillment and
post-service feedback.
These examples and studies highlight the rising relevance of data-driven D2C models,
especially for perishable goods like dairy. The implementation of such systems has been
shown to improve customer retention, reduce operational costs, and increase overall
efficiency—objectives central to the ongoing success of Pride of Cows and similar brands.
3.5 Literature review summary
This chapter focused on the theoretical foundation and practical application of data-driven
decision-making within direct-to-consumer (D2C) delivery models, particularly in the dairy
sector. It explored how modern brands like Pride of Cows leverage operational data, customer
feedback, and business metrics to optimize performance, improve service quality, and
maintain a competitive edge.
Key concepts discussed include the importance of supply chain management, service quality
frameworks, customer satisfaction analysis, and data tracking systems used in D2C models.
The chapter also reviewed the role of technology and analytics in supporting real-time
decision-making and ensuring consistent product delivery in a cold-chain environment.
Several global studies and examples from leading D2C brands such as Amazon, BigBasket,
Country Delight, and Licious highlighted the successful implementation of data-driven
strategies. These examples provide strong evidence that structured data use can enhance
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delivery reliability, reduce operational costs, and improve customer retention in the D2C
space.
However, the review also acknowledged limitations, especially around the need for accurate
and up-to-date data. Since customer satisfaction and brand trust are closely tied to delivery
performance and service consistency, outdated or incorrect data can negatively impact
decision-making and overall performance.
The chapter concluded by emphasizing that while the D2C model supported by data analytics
has proven to be effective in many leading organizations, its success depends on continuous
data monitoring, strategic use of insights, and alignment with customer expectations. This
theoretical framework sets the stage for the upcoming empirical analysis of Pride of Cows.
4. Empirical Method
This chapter explains the process of gathering empirical data for the study. The choice
of data collection strategy is closely tied to the nature of the research topic, which
examines the effectiveness of a direct-to-consumer (D2C) delivery model in the dairy
industry, with a focus on Pride of Cows. This chapter also discusses the sample
selection, research design, and key considerations such as reliability, validity, and
generalisability..
4.1 Research design and strategy
According to Saunders et al. (2009), it is important to develop a well-structured research
design that aligns with the research question. The design acts as a roadmap for how the study
will be carried out and is chosen based on the aim of the research. Saunders outlines three
main types of research designs: exploratory, descriptive, and explanatory.
An exploratory research design is used when the objective is to gain new insights and better
understand the nature of a specific issue. It is flexible in nature and allows the research
direction to adapt as new data becomes available. This is suitable when the problem is not
clearly defined or when limited previous research exists.
In contrast, a descriptive research design aims to create an accurate profile of people, events,
or situations. Lastly, the explanatory research design focuses on studying the relationship
between variables.
For this thesis, which seeks to explore how the D2C model operates in the dairy industry—
particularly in the case of Pride of Cows—the exploratory research design is most
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appropriate. The goal is to understand the operational model, customer satisfaction factors,
and the challenges faced in scaling D2C services.
In addition to choosing a design, a clear research strategy is also required. Saunders et al.
(2009) identify several strategies, including experiment, survey, case study, action research,
grounded theory, ethnography, and archival research.
This study adopts a case study strategy, which is suitable when the research is based in a reallife setting and seeks to gain in-depth understanding of a particular phenomenon. The case
study strategy allows for a focused examination of the D2C delivery system at Pride of Cows,
supported by interviews, company data, and existing reports.
While ethnography also focuses on real-world social contexts, it requires a long-term
immersion in the field, which is not feasible for this study’s time frame. Therefore, the case
study approach offers a practical and effective way to collect and analyze information within
a defined period.
4.2 Data collection
The nature of the research topic plays a major role in determining the type of data required for
analysis. In this study, both primary and secondary data have been used to evaluate the
effectiveness of the direct-to-consumer (D2C) model in the dairy industry, specifically for the
brand Pride of Cows.
Secondary data refers to information already available through existing sources. For this
study, secondary data has been gathered from company websites, industry journals,
whitepapers on D2C strategies, dairy sector reports, and government publications on food
supply chains. These sources provide context on how the D2C model has evolved and how it
performs in comparison to traditional distribution systems.
Primary data refers to first-hand information collected through direct interactions. In this
case, primary data was collected via interviews with operational and logistics professionals
from D2C dairy companies and related service providers. These interviews offered valuable
insights into real-life practices, challenges, and outcomes of implementing D2C delivery in a
perishable goods market.
The goal of collecting both primary and secondary data was to gain a balanced view of the
internal functioning, customer experience, and operational strengths and weaknesses of the
D2C model.
This research has also been inspired by previous exposure to operational strategy discussions
during an internship, where data-driven models were analyzed for improving delivery and
customer satisfaction in time-sensitive product lines. These real-world observations have
shaped the framework of this study and guided the interview process.
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4.2.1 Collected data
To support the research objectives, a set of structured questions was asked to operations and
logistics professionals working within D2C frameworks. The responses presented below are
from a senior executive at a local premium dairy service operating in Pune, similar in structure
to Pride of Cows.
Response 1: Operations Manager – Local Premium Dairy Service (Pune)


Importance of data in D2C delivery operations
"Data is the backbone of our service. Every morning starts with a report on previous
day’s deliveries, customer complaints, and order trends. Without such data, we
cannot plan delivery routes, manage staff shifts, or ensure timely customer service.
Maintaining proper data ensures smoother operations and better customer
satisfaction."
Advantages of a data-driven approach in D2C
1.
2.
3.
4.
5.
6.
Tracks daily delivery performance
Helps forecast demand based on historical patterns
Provides clear insights for inventory and logistics planning
Enables quick decision-making in case of delivery issues
Supports customer retention through service tracking
Identifies weak areas and opportunities for improvement

Operational vs. data-driven D2C management
"In the past, decisions were taken based on assumptions or staff experience. But
now, with hundreds of daily orders, data is essential. It gives clarity, consistency, and
proof behind every action we take. Data helps us answer to senior management with
facts, and ensures our decisions are not just reactive, but predictive and planned."
Response 2: Operations Supervisor – Local D2C Dairy Brand (Pune)


Importance of data in D2C delivery operations
In today’s competitive environment, data management has become standard practice
across all industries. In D2C dairy delivery, data is essential for monitoring what has
been done, what needs to be done, and what should be avoided. It allows for timely
tracking of deliveries, inventory levels, customer behavior, and service quality.
Decisions based on data are far more reliable than those based solely on experience.
Most successful D2C companies now combine data science with logistics for more
efficient operations.
Advantages of a data-driven D2C approach
There are several advantages to using a data-driven system in D2C models, but the
most important is the smooth functioning of delivery and customer service
operations. Data supports real-time adjustments in delivery routes, helps predict
customer demand, and ensures timely service. In our case, nearly 90% of decisions
related to delivery planning, customer communication, and supply scheduling are
driven by data, which ensures accuracy and consistency.
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
Comparison between manual and data-driven delivery management
A data-driven model is far more effective, especially in large-scale or fast-paced
operations. Manual or experience-based operations may work for small businesses,
but in larger models like D2C dairy delivery, using real-time data for decisionmaking results in faster response, better customer satisfaction, and more efficient use
of resources.
4.3 Reliability
The insights presented above are reliable as they come from professionals actively working
within direct-to-consumer operations. The second response has been personally verified
during an internship experience with a premium dairy delivery brand, where firsthand
exposure to data-driven operational practices was gained. These are authentic views based on
real-world processes.
4.4 Validity
There are five types of validity in research: content, predictive, concurrent, construct, and face
validity. Content validity evaluates whether the data collection followed the planned research
structure. Predictive validity assesses whether the insights will remain relevant in the future.
Concurrent and construct validity evaluate the accuracy and theoretical alignment of data,
while face validity assesses the appropriateness of the questions and responses.
In this case, predictive validity is particularly relevant. The expert responses are grounded in
current practices and provide insights that are likely to remain valid in similar D2C models in
the near future. This reinforces the strength of the findings within the scope of this research.
4.5 Generalisability
Generalisability, or external validity, refers to how well the study's findings can be applied to
a larger population. In this research, the findings are based on a qualitative case study with
insights gathered from professionals in a specific D2C dairy delivery setting. Since the sample
size is small and context-specific, the results are not meant to be generalized across the entire
industry.
Instead, the findings serve to provide a deeper understanding of how data-driven strategies
support D2C operations in a specific environment. The conclusions drawn are valuable within
the scope of the case study and can offer useful guidelines for similar businesses, but are not
statistically representative of the entire market.
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5. Empirical Findings & Analysis
5. Empirical Findings and Analysis
This chapter presents the empirical findings collected from professionals involved in directto-consumer (D2C) operations within dairy businesses. The second part of the chapter
provides analysis based on these findings, helping to answer the research questions and assess
the performance of D2C models.
5.1 Empirical findings
5.1.1 Response 1: Findings
Insights from the operations team revealed the following key findings:
1.
2.
3.
4.
5.
6.
7.
8.
Data is an integral part of daily D2C operations
It helps measure service and delivery effectiveness
Enables smoother and more reliable delivery systems
Contributes to customer satisfaction and business growth
Maintains complete visibility of daily tasks and issues
Highlights areas of improvement in operations
Helps forecast demand and delivery needs
Data-driven delivery is more effective than traditional models
5.1.2 Response 2: Findings
The second interview further reinforced the importance of data:
1.
2.
3.
4.
5.
6.
7.
8.
Data monitoring is a routine part of delivery management
Offers valuable insights on delivery performance and logistics
Helps avoid manual errors and reactive decision-making
Allows learning from past delivery metrics
Combines data science with logistics for accuracy
Improves decision-making with real-time information
Ensures on-time fulfillment and better route planning
Confirms that data-driven systems outperform manual processes
5.2 Empirical analysis
From the findings, the following conclusions are drawn:
1.
2.
3.
4.
5.
6.
Data is central to efficient D2C delivery models
Data-driven operations provide a clear advantage over traditional delivery models
Accuracy in planning and forecasting is greatly improved
Helps manage and coordinate workforce more effectively
Data-centric models are essential for scaling premium D2C services
Practical insights closely align with theoretical expectations
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7. Responses showed high reliability and relevance
8. Findings are specific to selected brands and may not be generalisable to all
companies
5.2.1 Research question status
Q1.
What
is
the
importance
of
data
in
Status: Answered satisfactorily by both theory and empirical results
D2C
delivery?
Q2. What are the advantages of a data-driven approach in D2C operations?
Status: Answered satisfactorily by both theory and empirical results
Q3. How does a data-driven delivery model compare to traditional models?
Status: Answered satisfactorily by both theory and empirical results
6. Conclusion
6.1 Summary
This dissertation explored the effectiveness of data-driven strategies within direct-toconsumer (D2C) dairy delivery models, focusing on the case of Pride of Cows. The study
aimed to understand how data impacts operational efficiency, customer satisfaction, and
decision-making in premium, time-sensitive delivery environments. The theoretical
foundation included models related to service quality, customer satisfaction, and supply chain
management.
Empirical insights were collected through interviews with professionals working in D2C dairy
brands in Pune. These insights supported the argument that integrating data into delivery
operations leads to improved accuracy, forecasting, and customer experience. Although D2C
has become more popular in recent years, especially in the food and dairy sector, practical
studies like this one offer valuable on-ground perspectives.
Qualitative methods based on an interpretivist philosophy and an abductive research approach
provided a flexible framework for analysis. This study contributes to understanding the
operational challenges and potential of D2C models, particularly in India’s growing dairy
market.
6.2 Conclusions
Organizations that embrace data-driven delivery strategies are better equipped to scale
operations and meet rising customer expectations. D2C brands like Pride of Cows benefit from
integrating data science with logistics, helping improve delivery consistency, cost control, and
consumer trust.
Over recent years, the use of data in last-mile logistics, customer engagement, and real-time
tracking has grown significantly. Many modern tools, including cloud-based dashboards,
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route optimization algorithms, and customer service analytics, are now part of successful D2C
operations.
This shift toward predictive and dynamic models allows D2C brands to forecast customer
needs, personalize service, and manage logistics with greater precision. However, data-driven
systems must be implemented carefully, with proper training and ongoing improvement. The
value lies not just in having data, but in how effectively it is interpreted and applied across the
organization.
6.3 Critical review
This study was limited to two responses from professionals in D2C dairy operations. Since the
research followed a qualitative approach, it was interpretive rather than generalised. First, D2C
analytics is a complex area requiring time and experience to study deeply. Second, each
company has different systems, platforms, and customer profiles, which can lead to variability
in outcomes. Finally, the presence of the researcher during interviews may have influenced
the responses.
This research is specific to the premium dairy segment, and findings may not apply to other
sectors like e-commerce, healthcare, or retail. Nonetheless, the study demonstrates the
growing role of data in operational decision-making for fast-moving consumer products like
dairy.
6.4 Contribution
This study highlights the importance of using data in direct-to-consumer models. Data-driven
decision-making improves planning, delivery, customer engagement, and issue resolution. It
helps companies forecast demand, allocate resources efficiently, and retain loyal customers.
In the case of Pride of Cows, a data-based approach can also help identify peak delivery times,
areas with recurring complaints, and patterns in customer churn. These insights help the brand
remain competitive in a rapidly evolving market. The study also supports wider application of
analytics across D2C channels for better operational outcomes.
6.5 Future studies
During this research, several areas emerged that could benefit from further study. Due to time
limitations, only a few professionals were interviewed. Repeating this study across more D2C
companies, or in different cities, could offer a broader perspective. It would also be useful to
conduct a longitudinal study, observing how data-driven models evolve over a year or more.
Further research can focus on comparing the effectiveness of different D2C models
(subscription vs. on-demand), or evaluating the ROI of investing in analytics tools for small
and mid-sized dairy brands. While this study focused on a niche segment, the lessons can be
extended to other perishable product delivery businesses.
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References
Books:
Saunders, M., Lewis, P. & Thornhill, A. (2009) Research Methods for Business Students, 5th
ed. Essex: Pearson Education Limited.
Web sources:
AIHR.com: Human Resource Data Analytics
Capgemini.com: Customer Experience in Last-Mile Delivery
McKinsey.com: Data-Driven Supply Chain Strategies
Harvard Business Review: Cold Chain Efficiency and Perishables Logistics
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