Case study 01 · ICGA
Eshita: from academic excellence to engineering, with an entrepreneurial vision
How scientific career guidance helps students look beyond marks, family expectations and conventional choices.
The starting point. When Eshita began her ICGA journey in 2025, her ICSE Class 10 results were exceptional: 100/100 in Physics, Chemistry and Biology, and 98/100 in Mathematics. Physics was her favourite subject, she was interested in engineering, and she loved making things. Her father, who runs a construction business, hoped she would choose Civil Engineering, an understandable reference point from his own experience.
The question was not whether Eshita could become an engineer. It was what kind of engineer, creator and professional she wanted to become.
What ICGA did. Through the CareerGraph assessment, ICGA examined her interests, cognitive strengths, behaviour and motivations. These results opened a better-informed conversation with her family about engineering specialisations, her practical interests and the possibility of building something of her own, rather than treating Civil Engineering as the automatic choice.
The outcome. Eshita has joined VIT (Vellore Institute of Technology) to study engineering. She plans higher studies abroad, with Singapore and Japan as targets, and wants to set up her own industry one day. Her next steps: choose the right specialisation, build technical and project experience, research postgraduate options, and learn the foundations of entrepreneurship.
- For students: high marks open doors; knowing your strengths shows which doors are worth opening.
- For parents: family experience is valuable, but a child’s interests deserve equal weight.
- For schools: guidance works best when it links academic results with individual strengths and long-term planning.
Case study 02 · ICGA
Samanyu: what after graduation? Turning a first job into a career plan
Graduation is not the end of career planning. It is the beginning of a much bigger decision.
The challenge. Samanyu had graduated and was working in Risk and Operations at Deloitte. Like many young graduates, he faced the question: what next? Keep building a corporate career, pursue an MBA, look abroad, or, in time, build something of his own? His interests ran well beyond his role, into business, history, geography, defence, archaeology and travel.
What ICGA did. The CareerGraph assessment examined his cognitive strengths, behaviour, motivations, interests and entrepreneurial orientation. It highlighted analytical thinking, logical reasoning, ethical orientation and teamwork as strengths, and decision-making, negotiation and professional communication as areas to develop.
1 · Build a corporate career
Use his Risk and Operations experience to move towards management, business operations or risk leadership.
2 · Higher education, strategically
An MBA or master’s tied to a clear goal, exploring international business, management, marketing and strategy rather than assuming a finance MBA was the only route.
3 · Keep entrepreneurship open
CareerGraph identified Solopreneur/Freelancer as his primary entrepreneurial fit and Hybrid Entrepreneur as his secondary fit, options he can test gradually alongside his job.
What it shows. “What after graduation?” deserves a better answer than peer pressure, a fashionable qualification or the first job offer. It should come from an honest look at strengths, interests, opportunities and long-term aspirations.
Case study 03 · ICGA
Krishna: is entrepreneurship the right career choice? Potential is not the same as readiness
When an assessment identifies entrepreneurial potential, should a student immediately start planning a business?
The student. Krishna, a Class 12 student from Coimbatore, was exploring Science and Engineering: aerospace, mechanical, automotive, marine and environmental. He wanted a science career that would benefit society and the country, ideally in defence research and development, with DRDO among his targets.
The discovery. CareerGraph’s two complementary assessments identified Social Entrepreneur as his primary fit and Startup Founder as his secondary fit. Strengths included openness (90%), team management (85%), numeracy (75%), workplace communication (75%) and vision (75%). His leading motivation was creativity and independence, followed by social acceptance and service.
The critical question. Krishna had no concrete startup idea, and a clear ambition: engineering, then defence R&D. A student can show entrepreneurial potential without wanting to be an entrepreneur today, and the ability to build a venture is not the same as readiness. The assessment was an input to guidance, not an instruction to abandon his chosen direction.
Pathway 1
Engineering → defence research and development: the right degree, strong technical and research fundamentals, projects and internships, then DRDO and other government scientific organisations.
Pathway 2
Engineering → aerospace and defence industry → R&D: specialist experience in industry, then research, design or advanced technology roles.
Three questions, not one
- Potential: what strengths and motivations does the assessment show?
- Preference: what does the student genuinely want?
- Readiness: what knowledge, experience and resources are needed to act on it?
The goal is never to push a student towards whatever an assessment suggests. It is to help them make a better-informed decision about their own future.
Case study 04 · CareerTests.in hiring
Two experienced sales candidates. One Business Development Manager role. Who is the better fit?
How structured assessment surfaced differences that CVs and interviews alone might not reveal.
The hiring challenge. A food and beverage business needed a Business Development Manager to open new markets, manage key accounts, lead a team, negotiate with customers and distributors, and follow through consistently. Two candidates made the shortlist. Both had relevant experience and both reported converting about seven in ten leads.
| Candidate A | Candidate B | |
|---|---|---|
| FMCG experience | 26 years (12 in food and beverage) | 12 years (7 in beverages) |
| Reported target achievement | ₹26 crore of ₹30 crore (about 87%) | About 100% |
| Reported lead conversion | 7 in 10 | About 7 in 10 |
| Availability | Immediate | 30-day notice |
| Temperament | Socially oriented, adaptable, expressive | Structured, methodical, execution-focused |
| TRIM: Goal / Responsibility | 75% / 41% | 95% / 95% |
| TRIM: Role / Integrity | 95% / 68% | 83% / 62% (below benchmark) |
The recommendation. Candidate B emerged as the stronger overall fit for the target-driven role, with one condition: his integrity score, below the report’s benchmark, needed validating through structured questions, references and background checks before any offer. Candidate A remained a strong option for relationship-led roles.
What assessment does, and doesn’t, do. CareerTests.in clarified the relative fit of the two candidates. It did not replace interviews, references, verification of sales results or the employer’s final decision. Scores are indicators to explore, not proof of conduct.
Case study 05 · Corporate training
Glenmark Pharma: building business analytics capability across locations worldwide
Business analytics, data literacy, Excel, AI-assisted analysis, decision intelligence.
The context. In a global pharmaceutical business, data drives decisions in sales and marketing, manufacturing, supply chain, R&D, finance and strategy. Glenmark’s training brought a shared understanding of business metrics, statistics, visualisation and evidence-based decisions to a worldwide, cross-location audience.
The analytics journey
Descriptive (what happened?), diagnostic (why?), predictive (what next?) and prescriptive (what should we do?).
Excel as a business tool
Descriptive statistics, PivotTables, charts, correlation, regression and scenario analysis on pharmaceutical scenarios.
AI-assisted analytics
Microsoft Copilot for natural-language queries, formulas, summaries and dashboard insights, with responsible use built in.
Subhashini’s role. Learning-content author and trainer: structuring the analytics learning journey; developing detailed training material and practical exercises; connecting statistical techniques to pharmaceutical business scenarios; integrating dashboard design and AI-assisted analytics; and supporting a consistent analytics foundation across locations.
The takeaway. Global organisations benefit when employees share a common understanding of data, metrics and analytical decision-making, whatever their function or location.
Case study 06 · Programme design
Royal Enfield: from telematics data to predictive intelligence
Advanced data science, machine learning, predictive maintenance, Generative AI, enterprise capability building.
The context. Connected motorcycles generate rich telematics data: riding patterns, trip frequency, speed, harsh acceleration, idle time, battery and vehicle-health indicators. Royal Enfield’s IT analytics team was exploring an Advanced Data Science and Generative AI programme. In discussions in April 2026, the team set out the opportunity: turn growing vehicle and customer data into practical intelligence for predictive maintenance and better operational decisions.
The challenge. The team mixed data analysts, data scientists with 3–4 years’ experience, BI developers, product owners and project managers, all on live projects. They needed learning that would build a self-sufficient internal analytics function, less reliant on external vendors.
Foundations
Python, data handling, exploratory analysis and statistics on real telematics data.
Predictive maintenance
Battery discharge and failure first, then brake pads, tyres, clutches and warranty analytics.
Generative AI
Interpreting user queries and a conversational interface for analysing telematics data.
Capstone hackathon
Teams solve different use cases and present to stakeholders.
About 70% hands-on, over roughly 9–10 days in a hybrid format. The takeaway: effective advanced analytics training starts with the business problem, not the technology syllabus.
What these cases say
Technical depth, joined to business relevance
Training should not end when participants understand a concept. It should help them see where, why and how to apply it in their work.