Build Dynamic Pricing Engine for Omnichannel Retailer

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:

1. 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.
2. 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.
3. 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

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:

1. Define objectives and success metrics: What is the engine optimizing for, and how do you prevent “local” optimization that harms the brand?
2. 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).
3. 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).
4. Design the pricing engine approach: What inputs, segmentation, and decision logic would you use? Where would you start (which SKUs/categories/channels) and why?
5. 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.