Company Background
You’ve joined Northstar Retail Group (NRG) as an interim Strategy & Data lead reporting to the CFO and Chief Merchandising Officer. NRG is a mid-to-large US omnichannel retailer focused on home essentials and small appliances (think blenders, air fryers, bedding, storage, cleaning devices). NRG operates 420 stores across 38 states and an e-commerce site/app that together generate $6.2B annual revenue. Roughly 58% of revenue is in-store, 42% online; however, online is growing faster (12% YoY) than stores (1% YoY).
NRG historically used a traditional retail pricing approach: a base “everyday” price set by category managers, seasonal promotions planned quarterly, and manual competitive checks on a limited set of SKUs. Over the last 18 months, NRG’s performance has weakened: traffic is flat, conversion is down online, and gross margin has compressed. The CEO believes pricing is a major lever because competitors have become more dynamic and algorithmic.
NRG’s gross margin is currently 31.5%, down from 33.8% two years ago. The CFO has set a target to recover 150 bps of margin over the next 12 months without materially harming revenue growth. At the same time, the CMO is worried about brand perception: NRG positions itself as “fair price, reliable quality,” not a discount chain.
Strategic Situation: Why a Pricing Engine, Why Now
Three external forces are converging:
- Marketplace transparency: Customers increasingly check prices across Amazon, Walmart.com, Target, and niche DTC brands before buying. NRG’s internal surveys show 47% of online shoppers price-check at least one competitor before purchase.
- Competitor algorithmic pricing: Amazon and Walmart adjust prices frequently (sometimes multiple times per day) on high-velocity items. NRG’s manual process can’t keep up.
- Cost volatility and supply constraints: Freight costs have stabilized, but vendor costs still fluctuate. NRG has seen COGS changes of ±6–10% on some imported small appliances over the last year.
The CEO has approved a program to build a pricing engine that recommends (and eventually automates) prices across channels. The engine must balance margin, revenue, inventory health, and price perception.
You are asked to propose a strategy for building and rolling out this pricing engine, including market/competitive context, economic sizing, operating model, and a phased go-to-market plan.
Current Business & Data Snapshot
NRG sells ~180,000 active SKUs annually, but volume is concentrated.
- Top 5,000 SKUs drive ~62% of revenue
- Top 20,000 SKUs drive ~84% of revenue
Category mix and economics
| Category | Revenue Share | Avg Gross Margin | Notes |
|---|
| Small Appliances | 28% | 26% | Highly price transparent; many identical UPCs across retailers |
| Home Organization & Storage | 22% | 36% | More private label; lower direct comparability |
| Bedding & Bath | 18% | 39% | Seasonal promo heavy |
| Cleaning & Floor Care | 14% | 29% | Mix of branded and private label |
| Kitchenware | 10% | 34% | Moderate price transparency |
| Other | 8% | 33% | Long tail |
Competitive and customer behavior indicators
- NRG’s internal “price index” (100 = parity with Amazon on matched items) is 104 on the top 1,000 matched branded SKUs.
- Online conversion rate is 3.2%, down from 3.6% last year.
- Cart abandonment attributed to “found better price elsewhere” is 18% of abandonment reasons (from exit survey).
- Promotions account for 22% of units but 38% of gross profit dollars (because promos are used to move high-margin private label and bundles).
Operational constraints
- Price changes must be communicated to stores; shelf labels are updated weekly today.
- E-commerce prices can change daily, but customer service and marketing teams require at least 24 hours notice for major price moves on advertised items.
- Legal/compliance requires guardrails to avoid discriminatory pricing and to comply with state-level price display rules.
Your Task (Deliverables)
As the candidate, you should walk through how you would approach building the pricing engine and the surrounding strategy. Address the following:
- Define objectives and success metrics: What is the engine optimizing for, and how do you prevent “local” optimization that harms the brand?
- Size the value opportunity: Provide a back-of-the-envelope estimate of profit impact over 12 months, including where it comes from (e.g., price increases on inelastic items, promo optimization, markdown efficiency).
- Competitive analysis: How do Amazon/Walmart/Target and specialty retailers price in these categories, and what does that imply for NRG’s pricing posture? Use a structured lens (e.g., Porter’s Five Forces or a tailored competitive dynamics view).
- Design the pricing engine approach: What inputs, segmentation, and decision logic would you use? Where would you start (which SKUs/categories/channels) and why?
- Go-to-market and operating model: How do you roll this out across merchandising, stores, marketing, and finance? What governance and controls are needed?
Constraints
- Timeline: Show measurable impact within 16 weeks; full rollout target within 12 months.
- Budget: $4.5M for the first year (people + tooling + data acquisition).
- Team: 1 product manager, 6 data scientists/ML engineers, 4 data engineers, 2 analysts; merchandising team is already at capacity.
- Risk: Avoid a visible “race to the bottom” on price; maintain NRG’s brand promise.
- Systems: Legacy pricing system supports batch uploads once per day; store POS updates weekly unless upgraded.
You may ask clarifying questions, but assume you must present a coherent plan with reasonable assumptions and explicit trade-offs.
