सSaral Shiksha Yojna
Courses/Technology Product Entrepreneurship

Technology Product Entrepreneurship

CS9.424
Ramesh Loganathan + Prakash Yalla•Monsoon 2025-26•4 credits
Sample Papers/TPE end-sem mock paper (200 marks) · Paper 5

TPE end-sem mock paper (200 marks) · Paper 5

Duration: 180 min • Max marks: 200

Part 1 — Higher-Order Concepts (40 Marks Total, @5 each)

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  1. 1.Customer Segments for an Insurtech Fraud Detector A startup is building an AI-powered fraud-detection engine for motor-insurance claims. The engine flags suspicious claims (staged accidents, inflated repairs, identity reuse) by analysing photos, repair invoices, and historical claim patterns. Identify: a) One specific target customer; b) One key problem they face; c) Why they may pay for this solution; d) One way to test interest quickly.5 m
  2. 2.Problem–Solution Fit for a Sports-Tech Wearable A startup wants to help amateur cricketers improve their batting technique using a sensor that attaches to the bat handle and gives feedback through a mobile app. a) Who is the customer; b) What is the main problem; c) What is one simple MVP they can test first; d) What concern might *coaches and parents* have that the founders may underestimate?5 m
  3. 3.Idea Hexagon — Spatial Computing / AR-VR Using the Idea Hexagon, generate six startup ideas in the spatial-computing / AR-VR space (e.g., enterprise training, retail try-on, remote collaboration, immersive entertainment, spatial design tools). Each idea must clearly specify the target user and the core problem being solved.5 m
  4. 4.Business Model Basics — Account-Based Marketing AI A startup is building an AI platform that helps B2B SaaS sales teams run account-based marketing (ABM) campaigns — auto-personalising outbound email, LinkedIn, and ad creative for each target account. Identify: a) One target customer segment; b) The core value proposition for this segment; c) One possible revenue model; d) One channel to reach customers.5 m
  5. 5.Customer Segments for a Drone Delivery Service A startup is building a drone-based last-mile delivery service for high-value, time-critical items (lab samples, pharma, organ-transport-grade biologics, e-commerce in difficult terrain). Identify: a) One specific target customer; b) One key problem they face; c) Why they may pay for this solution; d) One way to test interest quickly.5 m
  6. 6.Problem–Solution Fit for a Warehouse-Picking Robot A startup is building autonomous mobile robots (AMRs) that pick and transport items inside e-commerce warehouses, replacing manual cart-and-trolley workflows. a) Who is the customer; b) What is the main problem; c) What is one simple MVP they can test first; d) What concern might *warehouse operators* have that the founders may underestimate?5 m
  7. 7.Idea Hexagon — Insurtech Using the Idea Hexagon, generate six startup ideas in the insurtech space (e.g., embedded insurance, parametric coverage, claims automation, distribution channels, risk pooling). Each idea must clearly specify the target user and the core problem being solved.5 m
  8. 8.Business Model Basics — Smart-Home IoT A startup is building an IoT controller that lets users manage smart lights, fans, ACs, locks, and cameras across brands from a single app, with energy-saving automations. Identify: a) One target customer segment; b) The core value proposition for this segment; c) One possible revenue model; d) One channel to reach customers.5 m
  9. 9.Your TPE Startup Idea (Bonus 5 Marks) Write down a one-line ad-lib for your idea as per the framework shared in class. State: a) Target user; b) Main customer pain; c) Product benefit; d) One gain for the customer. ---

Part 2 — Advanced Framework Application (80 Marks total, @20 each)

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  1. 1.Case: WealthSphere Robo-Advisor WealthSphere is building an AI-driven investment-advisory platform for retail investors with portfolios of ₹5L–₹50L. The product runs portfolio rebalancing, tax optimisation, and goal-based planning automatically. Users like the convenience but are reluctant to give the platform full discretion over their money. Wealth-management incumbents (private banks, advisors) have started bundling similar features into existing accounts. Questions: a) Identify the beachhead customer segment (5) b) List two customer pains and two gains the product addresses (5) c) Explain how WealthSphere can differentiate from private bank incumbents (5) d) Suggest one go-to-market strategy that handles the discretion concern (5)20 m
  2. 2.Case: TalentRadar AI Recruitment TalentRadar is a B2B SaaS that helps recruitment teams source, screen, and shortlist candidates using AI on resumes, LinkedIn profiles, and code samples. Talent acquisition heads love the time savings, but hiring managers often reject TalentRadar-shortlisted candidates as "too algorithmic." Two large competitors offer similar features. The founders have 9 months of runway. Questions: a) Identify two hypotheses the startup must test (5) b) Classify each as a problem risk or a solution risk (5) c) Suggest one MVP experiment to address the hiring-manager rejection rate (5) d) Propose one revenue model that aligns incentives across TA heads, hiring managers, and candidates (5)20 m
  3. 3.Case: PropMint AI for Brokers PropMint is building an AI co-pilot for real-estate brokers in tier-1 Indian cities. The product helps brokers shortlist properties for clients, auto-generate listings, and run virtual tours. Brokers love individual features, but as a paid tool, they are reluctant to subscribe — many already use 4–5 free tools. Two big property portals (99acres, MagicBricks) are starting to bundle similar AI features for their listed brokers. Questions: a) Identify the beachhead customer segment (5) b) List two customer pains and two gains the product addresses (5) c) Explain how PropMint can differentiate from the big property portals (5) d) Suggest one go-to-market strategy for early adoption (5)20 m
  4. 4.Case: AeroDock Drone Delivery AeroDock operates an inter-city drone delivery network connecting tier-2 logistics hubs with surrounding districts. The first 18 routes have been successful for transporting medical samples and pharma. Hospitals love the speed (3-hour vs 24-hour samples). However, regulatory clearance for each new route takes 6–8 months, and the cost of operating each drone hub is high. E-commerce players want to use the network for high-value items but volumes are unpredictable. Questions: a) Identify two hypotheses the startup must test (5) b) Classify each as a problem risk or a solution risk (5) c) Suggest one MVP experiment to validate e-commerce volumes (5) d) Propose one revenue model that handles the high fixed cost of drone hubs (5) ---20 m

Part 3 — Deep-Dive Case Studies (80 Marks total, @40 each)

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  1. 1.Case: SkyView Earth Observation SkyView is building a constellation of small Earth-observation satellites that capture high-frequency, high-resolution imagery and run AI analytics on top — detecting illegal mining, deforestation, crop health changes, illegal construction, and ship movements. Initial pilots in two regions showed promising results. Indian defence and intelligence agencies validated the imagery quality for surveillance use cases. Enterprise customers (mining companies, infrastructure developers, crop-insurance providers) said the analytics are 5–10× cheaper than alternatives. Climate research NGOs found it transformative but cannot pay enterprise prices. Meanwhile, hyperscalers (AWS Ground Station, Microsoft Azure Space) have proposed making SkyView's imagery available through their cloud marketplaces in exchange for a revenue share. SkyView is now considering three possible growth paths: - Sell directly to **defence and government agencies** as a long-term contracted satellite-imagery provider - Sell to **enterprise customers** (mining, infra, insurance) as an analytics SaaS - Partner with **hyperscaler cloud providers** as a data marketplace listing The startup has limited funding and only 15 months of runway. The founders must choose a path that balances impact, revenue, and scalability. a) **Strategic Evaluation (15 marks)** — Evaluate all three growth options using: a) Product–Market Fit; b) Revenue potential; c) Sales complexity; d) Scalability. b) **Recommendation (10 marks)** — Recommend the best path for SkyView and justify your choice. c) **Business Model Design (10 marks)** — For your recommended option, define: a) Revenue model; b) Key partners; c) Distribution channels; d) Cost structure. d) **Risk Analysis (5 marks)** — Identify: a) One market risk; b) One technology risk; c) One adoption risk — and propose mitigation for each.40 m
  2. 2.Case: ClickBin Quick Commerce ClickBin operates a network of urban dark stores in three Indian metros, delivering groceries and household essentials in 10–15 minutes via a consumer app. Initial pilots showed strong consumer demand — average order frequency of 3.2 orders per active customer per month — but consumer unit economics are tight (delivery cost, dark-store rent, and SKU breadth all pressure margins). At the same time, two D2C brands have approached ClickBin to use its dark-store network as a fulfilment layer for their own apps. Separately, three potential operator-partners (residential complex managers, kirana clusters) have asked whether they can franchise the dark-store kit and run it locally. ClickBin is now considering three possible growth paths: - Continue as a **B2C 10-minute delivery app** and expand dark stores into new metros - Pivot to **B2B fulfilment** — offer the dark-store network as a service to D2C brands (their app, ClickBin's backend) - **Franchise the dark-store kit** — license the operational playbook, software stack, and hub design to local operators for a royalty The startup has limited funding and only 15 months of runway. The founders must choose a path that balances impact, revenue, and scalability. a) **Strategic Evaluation (15 marks)** — Evaluate all three growth options using: a) Product–Market Fit; b) Revenue potential; c) Sales complexity; d) Scalability. b) **Recommendation (10 marks)** — Recommend the best path for ClickBin and justify your choice. c) **Business Model Design (10 marks)** — For your recommended option, define: a) Revenue model; b) Key partners; c) Distribution channels; d) Cost structure. d) **Risk Analysis (5 marks)** — Identify: a) One market risk; b) One technology risk; c) One adoption risk — and propose mitigation for each. ---40 m
  3. 3.Insurtech Fraud Detector **a) Target customer:** Claims-operations heads at mid-size general insurance companies (₹500–5,000 Cr motor premium) who own the claims-leakage P&L. **b) Key problem:** 8–12% of all motor claims are fraudulent or inflated, costing the company ₹50–₹200 Cr/year; manual claims teams catch fewer than half. **c) Why pay:** Even a 30% improvement in fraud detection saves 10–25× the SaaS fee in the first year, with a direct, auditable bottom-line impact. **d) Quick test:** Free "shadow scoring" of 5,000 historical claims — show how many already-paid claims the AI would have flagged as fraudulent; the gap between actual leakage and AI-flagged leakage is the value proof.
  4. 4.Sports-Tech Wearable for Cricketers **a) Customer:** Parents of school-age cricketers (10–16) enrolled at coaching academies, with monthly household income ₹1L+ and serious cricket aspirations for the child. **b) Main problem:** Coaching academies have 30–50 students per coach; individual technique feedback (bat angle, swing speed, point of impact) is impossible at scale. **c) Simple MVP:** A clip-on sensor + smartphone app deployed at one coaching academy for 4 weeks; 30 students; pre/post-test on key technique metrics; measure parent willingness-to-pay after the trial. **d) Underestimated concern (coaches/parents):** Coaches may feel **threatened** ("the app is replacing my judgment") and quietly discourage students from using it. Parents may feel guilt over not knowing what to do with the data. Both can kill adoption silently. Founders should treat coaches as the channel, not the target — design the dashboard for *coaches*, not parents.
  5. 5.Idea Hexagon — Spatial Computing / AR-VR 1. **Generalize:** AR factory-floor training built for manufacturing → extend to surgical training, military maintenance, oil-rig operations. Target: vocational trainers; problem: high-cost hands-on training. 2. **Fusion:** AR + AI shopping assistant → in-store AR overlay that recognises shelves and gives real-time price/quality comparisons. Target: shoppers; problem: information asymmetry. 3. **Find the Nails:** A high-fidelity AR rendering engine → applicable to retail try-on, real-estate virtual tours, museums, surgical planning, automotive design reviews. 4. **Find the Hammers:** Remote-collaboration problem → VR meeting rooms, AR shared whiteboards, holographic telepresence, real-time 3D model annotation, AI-summarised spatial meetings. 5. **Add an Adjective:** *Persistent* AR — overlays that stay anchored to physical objects across sessions. Target: construction PMs; problem: tracking on-site work against design specs. 6. **Do the Opposite:** Instead of bringing the digital world into physical space (AR), bring the physical world into digital space — a "digital twin marketplace" where physical assets, factories, and stores have live online avatars that anyone can interact with.
  6. 6.ABM AI Platform **a) Target segment:** Heads of demand generation at Series B–D B2B SaaS companies (₹50–200 Cr ARR), targeting 200–2,000 named accounts. **b) Value proposition:** Generates per-account personalised email + LinkedIn + ad copy in seconds, lifting outbound reply rates by 3–5× vs generic templated outreach. **c) Revenue model:** Per-seat SaaS (₹40K–₹80K/seat/month for SDRs) + a per-account add-on for high-priority enterprise accounts where personalisation depth justifies extra cost. **d) Channel:** Integrations with Salesforce, HubSpot, and Outreach.io — listed in their AppExchange/marketplaces, where the target buyer already shops.
  7. 7.Drone Delivery for High-Value Items **a) Target customer:** Diagnostic-lab chains running multi-city sample-collection networks (Dr. Lal Pathlabs, SRL, Metropolis) where current logistics use bikes/cars with 4–24 hour turnaround. **b) Key problem:** Time-sensitive samples (blood gas, biopsy, organ transport) degrade or fail QC after 4–6 hours; current logistics fails to meet SLA in 15–25% of cases, leading to repeat collections and patient delays. **c) Why pay:** Each saved repeat-collection costs the lab ₹400–₹1,500, plus the downstream revenue from the diagnostic itself; reliable sub-2-hour transport directly improves SLA and customer NPS. **d) Quick test:** Run a 30-day, single-route pilot between one collection centre and one central lab; measure samples delivered within 2 hours, SLA breach rate, cost per delivery vs current bike-based method.
  8. 8.Warehouse-Picking Robot **a) Customer:** Operations head at a mid-size 3PL or D2C fulfilment centre (50K–500K SKUs, 5K–50K orders/day). **b) Main problem:** Manual picking accounts for 40–55% of warehouse labour cost; peak-season scaling is hard because trained pickers can't be hired overnight. **c) Simple MVP:** Deploy 2 robots in one zone of one warehouse for 60 days; benchmark picks-per-hour, error rate, and labour-displacement cost vs the manual baseline. **d) Underestimated concern (warehouse operators):** They worry less about whether the robot works, and more about **what happens when it fails** during peak season. A robot that breaks down on the day of a major sale costs them more than the robot saves all year. Founders need a service-level commitment, fast on-site replacement parts, and a fallback manual mode that takes minutes (not hours) to switch into.
  9. 9.Idea Hexagon — Insurtech 1. **Generalize:** Parametric weather insurance built for crop → extend to event cancellation, retail footfall, solar-power generation, marine cargo. Target: SMBs; problem: indemnity insurance is too slow and dispute-prone. 2. **Fusion:** Insurance + lending — a co-underwritten product where insurance reduces lender risk on unsecured loans. Target: MSMEs; problem: credit access tied to risk-coverage gap. 3. **Find the Nails:** Real-time IoT/telematics data → usable for motor, health, home, marine, equipment, livestock, and workmen's comp insurance pricing. 4. **Find the Hammers:** Insurance fraud problem → solutions span AI photo analysis, blockchain claim history, social-graph identity verification, biometric ID at claim time, third-party adjuster networks. 5. **Add an Adjective:** *Embedded* insurance — coverage offered at the point of transaction (e.g., 1-tap travel insurance at airline checkout). Target: digital-platform users; problem: friction-heavy traditional purchase. 6. **Do the Opposite:** Instead of indemnifying losses *after* they happen, prevent losses *before* — risk-prevention services bundled into the premium (e.g., free CCTV for a discount on theft insurance).
  10. 10.Smart-Home IoT Controller **a) Target segment:** Urban Indian families in 2–4 BHK apartments who have bought 3+ smart devices across brands (Philips, Mi, Realme, Apple) and are tired of juggling 5+ apps. **b) Value proposition:** One app + voice control for all smart devices regardless of brand, plus automations that cut electricity bills by 8–15% by learning household patterns. **c) Revenue model:** Hardware sale (₹3,500–₹5,000 controller) + a ₹99/month premium tier for energy analytics, automation templates, and family-account management. **d) Channel:** Builder/developer tie-ups for new-construction apartments — bundle the controller with possession, and the developer markets it as a "smart home–ready" selling point.
  11. 11.Bonus — Ad-Lib for Your Own Idea *Sample format:* "For [vehicle-fleet operators of 5–100 trucks] who [lose 15–20% of margin to driver inefficiency and fuel pilferage], our [pay-as-you-save telematics platform] is a [fleet-intelligence service] that [delivers verified fuel savings + driver bonuses with zero upfront hardware cost]. Unlike [traditional GPS trackers], our product [is monetised on saved fuel, not subscription, so the fleet only pays when it saves]." **a)** Small-mid fleet owners. **b)** Margin erosion from fuel pilferage and driver inefficiency. **c)** Verified fuel savings at zero upfront cost. **d)** Higher net margin per truck + better-retained drivers.
  12. 12.WealthSphere Robo-Advisor **a) Beachhead segment:** Early-career professionals in tier-1 metros (28–38) with ₹15–50L investable assets, comfortable with apps, with no existing advisor relationship — they are *underserved* by private banks (too small to be priority clients). **b) Pains:** (i) Mutual-fund choice paralysis — 1,500+ schemes, no clear way to choose; (ii) Tax-planning anxiety — capital gains, ELSS timing, harvesting all done manually. **Gains:** (i) Confidence that money is working (rebalanced automatically); (ii) Tax savings of ₹15K–₹1L/year via automated harvesting. **c) Differentiation from private banks:** - *Pricing model* — flat fee (₹500–₹2,000/month) or % of AUM at 0.25% vs private banks' 0.75–1.5% + hidden commissions. - *Transparency* — show every fee, every rebalance, every tax impact on a clear dashboard; banks bury these. - *No relationship-manager rotation* — algorithm is always the same; bank RMs change every 18 months. - *Defensibility* — proprietary tax-optimisation engine built for Indian capital-gains structure (Tier-1 USP, patentable). **d) GTM that handles discretion concern:** Launch in **"advisory mode" first** — the platform recommends, but the user clicks to execute. Once trust builds over 90 days of accurate recommendations, offer a one-click upgrade to "auto-execute" with a clearly disclosed ₹X cap per transaction. Build trust incrementally rather than asking for it upfront.
  13. 13.TalentRadar AI Recruitment **a) Two hypotheses:** - H1: Hiring managers reject AI-shortlists because the AI optimises for keyword fit, not the qualitative cultural/team fit that hiring managers value. - H2: TA heads will continue to buy TalentRadar (and accept hiring-manager rejection) only as long as it speeds up TA-team throughput, not because of shortlist quality. **b) Classification:** - H1 is a **problem risk** — the founders may have misdiagnosed the problem: it isn't "speed of shortlisting" but "quality of cultural fit," which is a different product. - H2 is a **solution risk** — even if H1 is right, the current solution (algorithmic shortlists) is misaligned with the deeper job-to-be-done. **c) MVP experiment:** Take 50 active roles across 5 customers. Cell A: current AI shortlist (10 candidates). Cell B: AI shortlist of 30, with each candidate annotated with a "cultural-fit narrative" generated from public sources (blog posts, GitHub, talks). Measure hiring-manager acceptance rate, time-to-hire, and quality-of-hire at 90 days. **d) Revenue model:** Success-fee component — base subscription + a per-hire success fee (₹50K–₹2L per successful hire) where "success" is defined as the candidate accepting and clearing 3 months of probation. This aligns TalentRadar's incentives with hiring-manager outcomes, not TA-team throughput, and reframes TalentRadar from "AI sourcing tool" to "AI recruiter partner."
  14. 14.PropMint AI for Brokers **a) Beachhead segment:** **Solo and 2-3 person residential brokers** in tier-1 metros handling 5–30 transactions/year, who today juggle WhatsApp, MagicBricks, NoBroker, Google Sheets, and personal Rolodex. They are price-sensitive but spend 60–70% of their time on operational tasks. Big property portals will not build for this segment — too small for them. **b) Pains:** (i) Hours wasted matching client requirements to inventory across 5 platforms manually; (ii) Lost deals when slow listing responses; competitors close the lead first. **Gains:** (i) 50–70% time saved on shortlisting; (ii) Higher close rate via faster, more personalised property recommendations to clients. **c) Differentiation from 99acres/MagicBricks:** - *Brokers' tool, not portals' tool* — PropMint works *across* portals; it isn't trying to lock the broker into one inventory source. - *Pricing model* — ₹500–₹1,500/month for a solo broker vs portal bundles that cost ₹15K+/month in listing fees. - *Independence* — portals will always favour their own inventory; PropMint is broker-aligned, not portal-aligned. - *Defensibility* — proprietary database of broker-client interaction patterns over time (Tier-2 USP); the more brokers use it, the better it gets — a network effect competitors cannot quickly replicate. **d) GTM strategy:** Free-tier first, monetise the high-value features later. Offer a forever-free core tool (cross-platform property search + WhatsApp shareable listings) targeting solo brokers; charge for AI virtual tours, auto-generated listings, and CRM features. Distribution through broker WhatsApp groups in each city — community-led growth, near-zero CAC.
  15. 15.AeroDock Drone Delivery **a) Two hypotheses:** - H1: E-commerce demand is predictable enough at certain inter-city routes to justify route-dedicated drone hub costs. - H2: Hospital and pharma demand alone justifies the cost of an inter-city drone hub, with e-commerce as upside. **b) Classification:** - H1 is a **solution risk** — the current architecture (dedicated routes) may be wrong; the right solution might be dynamic routing or shared hubs. - H2 is a **problem risk** — the founders may have misidentified hospital/pharma as the anchor customer if volumes are too small to cover hub costs. **c) MVP experiment for e-commerce volumes:** Pick 2 routes where hospital demand already exists; signed a 90-day "shared hub" contract with one e-commerce player (Amazon, Flipkart) committing them to a minimum of 50 deliveries/day at a fixed rate; measure actual usage, fluctuation, and willingness to commit to a longer contract. **d) Revenue model:** **Hybrid anchor-tenant model.** Hospital/pharma customers act as anchor tenants paying a guaranteed monthly minimum (₹3–5L/route/month) that covers ~60% of hub fixed costs. E-commerce, climate research, and other secondary customers pay per-delivery at a lower price but contribute the marginal margin. This stabilises route economics while keeping the upside open.
  16. 16.SkyView Earth Observation **a) Strategic Evaluation ** | Option | PMF | Revenue potential | Sales complexity | Scalability | |---|---|---|---|---| | Defence / Government | Strong — already validated | **Very high** per deal (₹50–₹500 Cr multi-year) | **Very high** — clearances, multi-year cycles, compliance | **Low** — each contract is bespoke | | Enterprise analytics SaaS | Strong — 5–10× cost advantage confirmed | High — ₹10L–₹2Cr per customer/year, recurring | Moderate — 3–6 month enterprise cycles | **High** — productised, repeatable | | Hyperscaler marketplace | Unproven — revenue share unknown | Moderate per-image, but huge volume potential | Low — listing is simple; revenue depends on hyperscaler's marketing | **Very high** — instant global distribution | **b) Recommendation (10 marks)** With 15 months of runway, the **Enterprise analytics SaaS** route is the right primary path, with hyperscaler marketplace listing as a low-cost parallel and defence as a long-cycle bet running in the background. Reasoning: - *Runway fit*: enterprise cycles (3–6 months) close within the runway; defence (12–24 months) does not. - *PMF evidence*: validated 5–10× cost advantage = clear value prop; analytics is the most productisable use of the satellites. - *Capital efficiency*: enterprise SaaS uses the satellite assets the company already owns — no incremental capex. - *Hyperscaler parallel*: list on AWS/Azure marketplaces in month 3 with minimal effort; treat it as a long-tail acquisition channel for smaller customers (researchers, niche enterprises). - *Defence as background*: maintain one BD person engaging defence and intelligence agencies; if a contract closes inside the runway it's a windfall, not the plan. - *Pattern from cases*: this mirrors HubSpot's "anchor on the highest-LTV productisable segment first, let adjacent segments come via channels and time." **c) Business Model Design (10 marks)** — for the enterprise analytics SaaS beachhead - **Revenue model:** Tiered SaaS subscription (₹10L–₹50L/customer/year) by geography monitored + premium tier for custom analytics + per-API-call pricing for high-frequency users (insurance pricing engines). Multi-year contracts with annual escalation. - **Key partners:** (i) Satellite-launch providers (SpaceX, ISRO) for ongoing constellation expansion; (ii) Ground-station operators for downlink; (iii) AI/ML cloud infrastructure (AWS, Azure); (iv) Domain partners (mining-tech, agri-tech, insurance) who co-sell into thei15 m
  17. 17.ClickBin Quick Commerce **a) Strategic Evaluation ** | Option | PMF | Revenue potential | Sales complexity | Scalability | |---|---|---|---|---| | B2C 10-min delivery | Strong demand, weak unit economics | **High** at scale but margin is razor-thin | Low (B2C) but **very high** marketing + CAC | Moderate — limited by physical hubs | | B2B fulfilment for D2C brands | Plausible — 2 brands already approaching | High per-brand (₹50L–₹3Cr/year revenue) | Moderate — 1–3 month sales cycle | High once playbook is fixed | | Dark-store franchise | Untested but interest exists | Moderate (royalty 6–10% of franchisee revenue) but capital-light | Moderate — partner selection, training | **Very high** — asset-light, replicable | **b) Recommendation (10 marks)** With 15 months of runway, the **B2B fulfilment** path is the right primary lane, with a small B2C presence sustained for brand and operational scale, and franchise piloted in months 9–12. Reasoning: - *Unit economics fit the runway*: B2C 10-min delivery is structurally unprofitable at small scale; B2B fulfilment has fixed-cost amortisation (multiple brands share dark stores) and is margin-positive at modest scale. - *Existing infrastructure utilisation*: the dark stores already exist; B2B repurposes them for higher-margin uses without new capex. - *Customer acquisition*: D2C brands are easier to sell to (clear ROI, identifiable buyer, faster cycle) than millions of consumers (each with sub-₹500 LTV). - *Defensibility*: serving D2C brands' fulfilment is sticky once integrated; switching costs are high once their app is integrated with ClickBin's API. - *Franchise as later lever*: once the operations playbook is proven in B2B, franchise becomes the lower-risk geographic-expansion path (similar to McDonald's-style scale via franchisees). - *Pattern from cases*: avoiding the capital-hungry, slow-payback B2C path mirrors LoanMint's choice of marketplace over direct NBFC — preserve capital, prove the model, scale via capital-light channels. **c) Business Model Design (10 marks)** — for the B2B fulfilment beachhead - **Revenue model:** Per-order fulfilment fee (₹30–₹70 per order, depending on order size, distance, SLA) + monthly minimum commitment from each D2C brand (₹2–5L/month). Per-SKU storage fee for slow-moving inventory. - **Key partners:** (i) Last-mile delivery riders (gig workforce platforms or own); (ii) D2C brand technology platforms (Shopify, Magento) for plug-and-play integration; (iii) FMCG distribut15 m

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