Show us how you think about real problems.
3-5 hours • Due in 5-7 days
There are hundreds of thousands of independent stores across the US not fully served. Bodegas, convenience stores, padel clubs, golf courses, gyms. Brands want to be there. Store owners want products their customers actually want.
The problem: these stores lack the intelligence to know what will sell. Should they stock protein bars? Which ones? Dubai chocolate? Baseball hats for the local team? The data and matching layer doesn't exist for independent retail.
We're building that layer. We connect brands with stores, match local demand, and place orders through our platform. That's how we make money. When stores reorder, we know something is working. We aim for great placements where customers and store owners want more.
We earned our footprint the hard way: physical reps opening thousands of locations, stocking shelves, learning what actually sells. That playbook gave us conviction. Today we're layering in technology-Store Portal, recommendation systems, dynamic tooling-so stores can self-serve while reps focus on flagship accounts and launches.
The more stores we work with, the more we know. The more we know, the better our matching gets. It's a data flywheel with execution. Think early Uber, but for physical retail. Our team built products at scale before. We know how to turn data into decisions and decisions into revenue.
Sales reps opening stores works. It scales. But we want to move faster. We're building the tech layer so the marketplace can grow without linear cost increases. Better unit economics, faster growth.
We have a solid data layer connecting everything. Business logic, tech infrastructure, and product experiences all built on top of that foundation. Here's the stack we're shipping:
Reps capture shelf data; system generates instant product suggestions. More scans, smarter recommendations.
Quick reorders, product discovery, market insights, trend analysis. Built for busy owners on their phones.
Predicts product performance by store using location, demographics, purchase patterns, and basket composition.
Self-onboarding for stores, smart market selection. Automation of what used to take weeks.
Dynamic matching between brand inventory and the right stores with pacing controls.
Adjusts prices in real-time to balance margins with competitiveness across markets.
Finance providers, POS systems, service platforms. Unlock data we couldn't see before.
Find highest-potential stores. Model growth trajectories. Optimize spend across markets.
Route planning for reps, delivery coordination, inventory flow. Offline operations running like software.
Intelligent rep routing and incentives to maximize growth across territories.
Phone conversations tracked, analyzed, turned into playbooks. Real-time guidance for remote reps.
Training for field and remote reps. Ramps up knowledge in weeks, not months.
Curiosity. You ask questions until you understand what's really happening. Not satisfied with surface answers.
Systems thinking. You see how pieces connect. Build things that scale, not one-off fixes.
Can build. You either write code, design products, or build financial models.
Understand people and the offline world. You get how small businesses work. How decisions get made. What actually matters when someone runs a store. You also understand brands-how they make decisions about where to invest money, what markets to prioritize, and what success looks like on their side of the marketplace.
Choose the challenge that fits your role, or do both if you want to show range.
This is how we get to know how you think.
The reorder moment is where retention lives. Store owners juggle inventory, customers, suppliers, family. They're on WhatsApp, Instagram, checking what's trending. How do we make reordering feel easy?
Meet Carlos: Manages two stores in Brooklyn. One's near a high school so he's always hunting for products his younger customers want. Juggling deliveries, staff, inventory across both spots. Lives on his phone
The context: Most independent stores don’t have POS data. We solve this by combining insights from in-store scans, market data from large retail chains, Google Maps, neighborhood demographics, and trend signals. This intelligence is then translated into product recommendations that our reps use today to pitch and sell to stores.
Now, we’re evolving this experience so stores can discover products and reorder directly themselves. The question is: how do we adapt what currently works for reps-who are used to presenting personalized recommendations-into a direct and intuitive communication experience for stores?
Your challenge: Design the reorder experience. Show us what you'd ship and why it works.
Show us:
Extra credit: Talk to a real store owner and use their insights. Leverage modern tech thoughtfully (AI, voice, smart notifications). Build a clickable prototype or mockup. Propose automation that reduces manual work.
Metroville is a fictional city-an early test market that became a playground for experiments. 6 months of data: 254 stores, 1,785 orders, $580K GMV.
What's in the data: Order history, GMV per store, seller assignments, store demographics, frequency patterns. Some stores crushed it, others didn't.
Your challenge: Diagnose what happened, model the opportunity, and design the operational system to scale.
What we need to see:
Extra credit: Talk to a store owner to understand their world. Build a functional dashboard or calculator we can use. Propose automation to reduce manual work. Find something unexpected we should know about.
A 2–3 hour take-home to show how you think about product, growth, and data - and how you'd turn a simple flow into something that feels immediately valuable and alive.
Context: We're onboarding new store owners across New Jersey and want them to feel the value of joining from the very first minute. They should see that they're part of a living ecosystem - surrounded by nearby stores, trends, and opportunities - and that we already have insight into what's working locally. The onboarding should also help us learn about them: what's selling, what they care about, and when and how it's best for us to reach out and continue the conversation.
Dataset: You'll use a New Jersey stores dataset containing information about store names, locations, types, ratings, and demographics. It doesn't include product data, so feel free to create light fictional or AI-generated insights (for example: trending categories by ZIP or local demand signals) to make the experience richer and more realistic.
Goal: Build a smooth, two-step onboarding flow where a store owner can:
What we care about:
Deliverables:
A short 2–3 hour exercise to see how you think about data, growth, and store economics. We want your framework — how you'd identify high-potential stores, design their first order, and decide where to invest to drive growth.
Context: RepRally connects local retailers with emerging brands. Each store carries 5–15 Brands from us . Each brand generates about $300–$1,000 in monthly revenue. Some products move fast with low margin, others move slow with higher profit. We can invest marketing budgets (samples, promos, rep time) to boost results. Your goal is to help us understand which stores are worth that extra push.
Your Task:
Hint: Keep it simple. Think in frameworks or light models. We care about your reasoning, not code or complex math.
Deliverable:
You'll work with 12 months of store activity: 6 stores, 106 orders, $115K in GMV, and 91 brands across 9 categories. Performance varies significantly - some stores order frequently and explore broadly, while others order sporadically with limited variety. Your task is to understand what drives these differences and design solutions to improve overall performance.
What's in the data: Purchase history, order dates, product categories, order values, and store-level patterns over time. All identifiers are anonymized. The dataset is clean and structured to enable straightforward analysis.
Your challenge: Analyze store performance and design a system to improve it.
Show us:
Think about: Many marketplaces use loyalty mechanics to shape behavior-tier systems, rewards for consistency, bonuses for trying new brands, recognition for top performers. How might similar approaches work for stores buying from us?
What we're looking for: Clear analytical thinking, well-reasoned recommendations, and practical implementation plans. We want to see how you approach ambiguous problems and translate insights into actionable solutions.
Deliverable: 3–5 pages (slides or document) covering your analysis, proposed solution, and implementation approach.
Email Lev your work. Questions? Just ask.