Indian Institute of Management
Kozhikode
MARKETING RESEARCH
Marketing Research to Launch
EDUKONE
(Data & AI Training Business)
April 16, 2025
Author:
EPGP11KC021 - Anish Bapna
EPGP11KC023 - Ankit Kourav
EPGP11KC040 - Dashrath Gadhvi
EPGP11KC105 - Rupa kumari
EPGP11KC144 - Vedartham venkata sesha Harish
EPGP11KC149 - Vishvaraj Chandradeepsingh Rajput
EPGP11KC151 - Vivek Joshi
Marketing Research to Launch Edukone
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Contents
1 Executive summary
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2 Problem definition
2.1 Background to the problem . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Statement of the marketing problem . . . . . . . . . . . . . . . . . . . . . .
2.3 Statement of the research objectives – information needs . . . . . . . . . . .
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3 Approach to the problem and research design
3.1 Type of research design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Exploratory Research . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2 Descriptive Research . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.3 Causal Research (to be decided) . . . . . . . . . . . . . . . . . . . . .
3.2 Data collection from secondary sources . . . . . . . . . . . . . . . . . . . . .
3.2.1 Industry Reports & Market Research . . . . . . . . . . . . . . . . . .
3.2.2 Competitor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3 Consumer Trends & Social Media Analytics . . . . . . . . . . . . . .
3.3 Data collection from primary sources . . . . . . . . . . . . . . . . . . . . . .
3.3.1 Qualitative Research . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2 Quantitative Research . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.3 Sampling Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 Data analysis
4.1 Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Results
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6 Conclusions and recommendations
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7 Limitations and caveats
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A Letter of authorization
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B Questionnaire development and pre-testing
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C Questionnaires, forms, interview guides
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D Sampling techniques, including error and confidence levels
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E Fieldwork
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F Lists including contact individuals and organization
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G Research Project Plan
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H References
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Acknowledgment
We would like to deeply and sincerely thank Professor Joshy Joseph for his guidance
throughout the course without which it would have been very difficult to understand the
subject in such detail. We’d also like to thank Niograph Technology Consulting LLP.
and their management for providing insights on their training services business Edukone and
providing company’s confidential information about their initiative.
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Executive summary
This marketing research project explores the urgent need for accessible, industry relevant
trainings in Data, AI, GenAI and Cloud technologies in India. With the rapid digital transformation across sectors, there is a growing demand for professionals skilled in data analytics,
data science machine learning, and AI. So There is a significant gap exists between the skills
taught in traditional academic programs and those required in the job which is present in
market.
This marketing research project aims to support Edukone’s launch the live online training
platform by understanding the needs and preferences of its target audience which includes
students, fresh graduates, and working professionals. The core challenges include identifying
market demand, determining relevant course content, selecting optimal delivery formats,
crafting competitive positioning, and designing effective pricing strategies.
To solve these challenges, a mixed-methods research design has been adopted. Exploratory
research through expert interviews and focus groups helps understand learner needs and
industry expectations, while descriptive research via structured surveys quantifies demand,
pricing sensitivity, and format preferences. Secondary data from industry reports and competitor platforms complement primary data collection.
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Problem definition
2.1
Background to the problem
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The Problem Background of the Edukone project can be understood through the context
of the growing need for specialized training in the rapidly evolving fields of Data, AI, and
Cloud Technologies. The detailed background of the problem is as follows:
1. Increasing Demand for Data & AI Skills: With the ongoing digital transformation
across industries, organizations are increasingly adopting AI and data-driven decisionmaking to stay competitive. As a result, there is a surge in demand for professionals
with expertise in data analytics, machine learning, artificial intelligence, and related
technologies. However, there is a gap between the supply of qualified individuals and
the demand for these skills in the workforce. This creates a significant challenge for
both students and professionals who need to acquire these specialized skills to stay
relevant in the job market.
2. Skill Gap in the Market: While there is a growing recognition of the importance of
Data & AI, many educational institutions are not able to keep pace with the rapidly
changing demands of the tech industry. This leads to a mismatch between the skills
imparted through traditional academic programs and the practical, job-centric skills
that employers need. As a result, there is a pressing need for specialized, hands-on
training that focuses on current technologies, real-world applications, and the specific
requirements of the job market.
3. Lack of Accessible, Industry-Relevant Training: Many existing training programs in the field of Data & AI are either outdated or too theoretical, with little
connection to actual industry needs. This leaves many students and working professionals struggling to gain the practical knowledge that is required to succeed in a
data-driven economy. Furthermore, the availability of such training is limited or not
easily accessible, particularly for working professionals who may not have the time or
flexibility to attend traditional classroom-based courses.
4. Geographical Limitations in Accessing High-Quality Training: Many individuals, particularly those in smaller cities and towns, face geographical barriers in
accessing high-quality education and training in advanced technology fields like Data
& AI. Edukone seeks to bridge this gap by offering online live training, providing a
flexible and accessible solution for learners across India, regardless of their location.
5. Lack of Tailored Content for Different Audiences: There is a need for training
programs that cater to different segments, such as fresh graduates, students, and working professionals. Each group has different needs and expectations in terms of learning
outcomes, pace, and content focus. Current training offerings may not address these
diverse needs, making it crucial for Edukone to create customized, job-oriented training
programs that can appeal to both early-career individuals and seasoned professionals
looking to transition into Data & AI fields.
6. Growing Competition and Market Saturation: The online training market is
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becoming increasingly competitive, with many players offering similar programs. To
differentiate Edukone from other providers, it is essential to understand the specific
preferences of the target audience, ensuring that the content, delivery methods, and
pricing are aligned with market expectations. The market research is aimed at uncovering these insights, ensuring Edukone’s offerings stand out in a crowded marketplace.
In summary, the problem background is rooted in the growing demand for Data & AI skills,
the mismatch between academic learning and industry needs, and the lack of accessible,
relevant, and practical training. The project aims to address these gaps by offering live
online training programs that are industry-focused, customized to different learner needs,
and accessible to a broad audience across India. The research will help Edukone design
programs that meet the evolving demands of both students and professionals.
2.2
Statement of the marketing problem
Edukone, a specialist training brand under Niograph, seeks to launch an online live training
platform focused on Data & AI to meet the growing demand for these skills in the technology
sector. However, before launching, Edukone faces a critical marketing problem that must be
addressed to ensure the success of the platform. The marketing problem can be outlined as
follows:
1. Identifying Market Demand: There is a need to understand the exact demand for
Data & AI training across various target segments, including students, fresh graduates,
and working professionals. The challenge is to accurately gauge the interest levels,
preferred topics, and the willingness to pay for such training programs. Without this
knowledge, Edukone risks offering courses that may not align with the current needs
or expectations of the market.
2. Curriculum and Content Relevance: Edukone must determine the specific skills
and training modules that are most in demand among different learner segments. The
challenge is to design a curriculum that is practical, up-to-date, and job-oriented, ensuring that it meets both industry standards and the skill requirements of the learners.
The marketing problem is to ensure that the content offered addresses the real-world
applications of Data & AI, filling the knowledge gap between what is taught in traditional education and what is needed in the workforce.
3. Understanding Preferred Learning Formats and Delivery Methods: Given
that Edukone aims to offer online live training, it is essential to understand the preferred formats (such as live sessions, webinars, hands-on labs) and engagement strategies (such as interactive Q&A, real-time problem-solving) that will resonate best with
the target audience. The marketing problem here is to identify what types of delivery
methods will ensure the highest levels of engagement, completion rates, and learner
satisfaction.
4. Competitive Positioning: The online training market for Data & AI is becoming
increasingly crowded, with many players offering similar services. Edukone must find
a way to differentiate itself and establish a unique value proposition. The market5
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ing problem is to develop a clear brand identity and market positioning that emphasizes Edukone’s strengths—such as industry-specific training, flexibility, and job-centric
learning outcomes—while addressing any perceived gaps in the current market offerings.
5. Pricing Sensitivity and Market Segmentation: Pricing is a critical factor in determining the success of Edukone’s training offerings. The marketing problem involves
determining the right pricing model that will appeal to different learner groups, while
also ensuring profitability. It is essential to understand the price elasticity of different segments (students, fresh graduates, working professionals) to tailor the pricing
strategy effectively.
6. Building Awareness and Generating Leads: The marketing challenge is to develop effective strategies for reaching potential learners, building awareness of Edukone’s
offerings, and generating qualified leads. Given the target demographic’s widespread
use of platforms like WhatsApp, social media, and professional networks, it is essential
to leverage these channels effectively. The challenge is to create engaging content that
resonates with potential students and professionals, encouraging them to enroll in the
courses.
Therefore, the core marketing problem Edukone faces is understanding and addressing the demand, content relevance, preferred learning methods, competitive positioning, pricing strategy, and lead generation for their online live training platform. The success of Edukone’s
platform hinges on gathering the right insights through market research, ensuring that the
courses offered align with market needs and effectively engage the target audience while
positioning Edukone as a leader in specialized Data & AI training.
2.3
Statement of the research objectives – information needs
To address the marketing problem and ensure the successful launch of Edukone’s live online
training platform in Data & AI, the research objectives must focus on gathering critical
insights that will inform key aspects of the platform’s design, delivery and positioning. The
research is structured to uncover specific information needs across various dimensions to
support decision-making. Below are the key research objectives and the corresponding information needs.
These research objectives and information needs aim to gather actionable insights that will
help Edukone design a training platform that meets the needs of students and professionals
in India. By addressing these key areas, the research will provide valuable data to refine
course content, optimize marketing strategies, and ensure Edukone’s offerings are aligned
with the expectations of potential learners. This will ultimately guide decisions related to
curriculum design, pricing, delivery format, and overall market positioning.
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# Objective Statement
1 To understand the level of
interest and demand for
Data & AI training across
different target segments
(students, working
professionals, etc.)
2 To determine which
specific skills and training
modules are most in
demand within the Data &
AI fields
3 To understand the
preferred learning formats
(live online sessions,
recorded webinars, etc.)
and engagement strategies
for training delivery
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To gauge the willingness to
pay for Data & AI training
programs and determine
optimal pricing strategies
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To understand how
learners currently view
existing training providers
and what they expect from
Edukone as a new entrant
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To assess the importance of
certification, job placement
assistance, and other
career support features as
part of the training offering
To determine the most
effective marketing
channels for reaching
potential learners and
generating leads
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Information Needs
- What is the overall demand for Data & AI training in India?
- Which segments show the highest demand?- How urgently do
learners need to acquire these skills?
- What are the perceived barriers (cost, time, accessibility)?
- What are the key skills learners are most interested in (e.g.,
ML, Data Science, AI, Cloud)?
- What are the top course modules (basic vs. advanced)?
- What skill gaps do learners feel are not being addressed?
- What type of hands-on learning is considered valuable?
- What delivery formats do learners prefer (live, recorded,
hybrid)?
- How important are interactive elements (Q&A, group
discussions, feedback)?
- What are the preferred session durations and frequency?
- How do learners view certification and post-course support
(mentorship, job assistance)?
- What is the price sensitivity of students vs. professionals?
- What are the expected price ranges for training?
- How do learners perceive the value in terms of content and
career impact?
- What pricing models are most attractive (pay-per-course,
subscription, bundled packages)?
- What are the current preferences for providers (reputation,
expertise, content)?
- How familiar are learners with Edukone and Niograph?
- What are their expectations on quality, flexibility, credibility?
- How do they perceive Edukone’s USPs (job-focused, industry
involvement)?
- How important is certification when choosing a provider?
- Are learners seeking job placement or career advancement
services?
- What role do industry-recognized credentials play in course
enrollment decisions?
- What are the most effective outreach channels (WhatsApp,
social media, LinkedIn)?
- What marketing messages resonate most (promotions,
previews, success stories)?
- How do learners prefer to receive course info (messages,
videos, brochures)?
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3
Approach to the problem and research design
3.1
Type of research design
The research design for launching Edukone will follow a mixed-methods approach, integrating
both exploratory and descriptive research:
3.1.1
Exploratory Research
• Used to gain insights into market demand for Edukone’s AI and data training business.
• Involves qualitative techniques such as expert interviews and focus group discussions.
3.1.2
Descriptive Research
• Helps quantify findings from exploratory research.
• Includes structured surveys and secondary data analysis to measure market size, competitor pricing, and customer preferences.
3.1.3
Causal Research (to be decided)
• If testing the impact of price changes or promotional strategies, causal research using
A/B testing or quasi-experimental designs may be applied.
3.2
Data collection from secondary sources
Secondary data will be collected to understand industry trends, customer behaviors, and
competitor strategies. Key sources include:
3.2.1
Industry Reports & Market Research
• Reports from NASSCOM, McKinsey, IBISWorld, and Statista on the EdTech industry.
• Government/academic papers on AI and data science education demand.
3.2.2
Competitor Analysis
• Pricing models and course structures from platforms like Coursera, Udemy, and upGrad.
• Customer reviews and feedback from TrustPilot, Reddit, and LinkedIn.
3.2.3
Consumer Trends & Social Media Analytics
• Keyword analysis using Google Trends.
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Data collection from primary sources
Primary data collection will include both qualitative and quantitative methods:
3.3.1
Qualitative Research
• Expert Interviews: Conduct interviews with industry professionals, educators, and HR
managers in AI-driven companies to understand skill gaps.
• Focus Groups: Engage with target users (students, professionals) to gauge learning
preferences and perceived value of Edukone’s courses.
3.3.2
Quantitative Research
• Surveys: Structured surveys to collect insights on pricing sensitivity, preferred course
formats, and brand perception. Distribution via email campaigns, LinkedIn, and Telegram groups.
• Experimental Testing: Conduct A/B testing with different pricing models and promotional messages.
3.3.3
Sampling Strategy
• Target Audience: College students, working professionals, and AI enthusiasts.
• Sampling Method: Stratified random sampling to ensure representation across different
demographics.
• Sample Size: Determined using Cochran’s formula for statistical validity.
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Data analysis
• We analysed data based on the survey responses.
• One way ANOVA is done between 2 groups – Gender (Independent variable) and Which
Certification you are Interested In based on the Webinar Discussion? (Dependent
variable)
• These variable data is converted to numerical format for ANOVA analysis
• Marketing Research survey response and ANONA analysis are available in attached
excel.
• ANOVA result indicates that there is a significant difference between the two groups,
which implies that the factor in question (interest in certification from the discussion
on the webinar) differs significantly across gender.
• The information reveals that gender positively impacts the level of interest for certifications as being discussed in the webinar
• Recommendation: Additional examination can involve studying what gender is more
interested in certificates and investigate the reason why the difference exists.
4.1
Data Visualization
Below fields are work in progress and will be shared as part of the final report.
Figure 1: Data Visualization of Data
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Results
This is Work in Progress.
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Conclusions and recommendations
This is Work in Progress.
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Limitations and caveats
This is Work in Progress.
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A
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Letter of authorization
This is Work in Progress.
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B
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Questionnaire development and pre-testing
This is Work in Progress.
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C
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Questionnaires, forms, interview guides
This is Work in Progress.
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D
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Sampling techniques, including error and confidence
levels
This is Work in Progress.
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E
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Fieldwork
This is Work in Progress.
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F
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Lists including contact individuals and organization
This is Work in Progress.
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Research Project Plan
Phase
Project
Initiation
Task
Define Research
Objectives
Problem
Definition
Identify Key
Research
Questions
Select Research
Methodology
Develop Survey &
Focus Group
Guide
Market &
Competitor
Analysis
Conduct Surveys
& Interviews
Select Target
Audience
Research
Design
Data Collection
Strategy
Secondary
Research
Primary
Research
Sampling
Strategy
Data Collection
Execution
Data Analysis
Report
Preparation
Presentation
Preparation
Administer
Surveys
Conduct
Quantitative &
Qualitative
Analysis
Compile Findings
& Draft Report
Create
Presentation
Description
Identify research goals: market
demand, pricing, competitors,
customer preferences.
Develop primary and secondary
research questions for analysis.
Timeline
Completed
Choose between exploratory,
descriptive, and causal research.
Create structured surveys and focus
group discussion points.
Completed
Gather industry reports, competitor
pricing, student demand trends.
Completed
Collect survey responses and
conduct in-depth interviews.
Define sample population (webinar
attendees, social media users,
students).
Distribute surveys via email, social
media, and website.
Analyze survey data using
statistical tools, qualitative insights
from interviews.
Work in Progress
Organize findings, insights, and
recommendations.
Prepare slides for final presentation
to the professor.
2nd Week of
April
Last Class in
2nd Week of
April
Completed
Completed
1st Week of
April
1st Week of
April
2nd Week of
April
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References
• HBS Online: How to Conduct Market Research for a Startup
• Webinar — Market Research for Startups - The How & Why
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