Operand

What do they actually do

Operand sells a paid, high‑touch AI system that ingests a company’s internal data alongside live market signals to recommend concrete commercial actions — starting with pricing, discounts, and related demand/margin levers for retail and e‑commerce brands Operand YC profile. It monitors competitor prices, promotions, stock levels, and other signals, and trains models on the client’s data to forecast demand and surface price‑elasticity and promotion effects Operand.

Customers typically onboard by connecting internal sources and enabling external web data, then Operand builds forecasting and elasticity models and continuously collects market data. Outputs — such as dashboards, reports, and specific price/markdown recommendations — are reviewed and deployed with help from Operand’s team, which the company notes includes ex‑MBB consultants; the system then runs continuously with alerting (e.g., MAP violations, undercutting, promotion impact) Operand. The company positions this as an end‑to‑end delivery rather than a self‑serve tool, with pricing & discount strategy as the first capability live in market YC profile Operand.

Who are their target customer(s)

• Pricing manager at a mid‑market retail or e‑commerce brand: Struggles to predict customer response to price changes, defaults to reactive discounting that erodes margin, and cannot continuously track competitor prices or MAP violations without heavy manual work Operand.
• Head of revenue/operations at a consumer brand: Needs reproducible answers across pricing, promotions, and inventory but currently relies on slow, expensive consultant projects or ad‑hoc spreadsheets for decisions Operand.
• Finance director / FP&A lead: Promotions and price changes disrupt forecasts and margins; lacks auditable models and fast scenario analysis to justify recommendations to executives Operand.
• Growth or performance marketing lead: Can’t reliably choose between ad spend and discounting without integrated measurement of promotion lift, margin impact, and cross‑channel ROI Operand.
• Merchandising / inventory manager: Faces stockouts or overstocks because demand forecasts don’t factor live competitor moves, promotions, and elasticity, forcing manual repricing or emergency markdowns Operand.

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led, bespoke paid pilots via YC/founder networks and warm intros into mid‑market retail/e‑commerce (pricing, revenue/ops buyers). Each pilot is tightly staffed, integrates data + market signals, and is scoped to a single KPI (e.g., margin or promo ROI) to create referenceable wins Operand YC profile.
• First 50: Convert early pilots into reference accounts and package a repeatable “pricing & discounts” offer. Use targeted outbound to similar verticals, small industry roundtables/webinars, and referrals; add a light channel with e‑commerce/analytics partners to speed onboarding Operand.
• First 100: Move to an inside‑sales motion with SDR/AE playbooks selling modules (pricing first, then ad spend/engagement). Standardize connectors and implementation templates, offer a lower‑touch SaaS tier for simpler brands while keeping a high‑touch enterprise track with expert review, and formalize partnerships with consultancies/platforms Operand YC profile.

What is the rough total addressable market

Top-down context:

Research estimates price‑optimization software at about $1.2B in 2024 and retail analytics around $10.4B in 2024, with overlap between the two categories TBRC IMARC. If Operand expands into broader BI/analytics, that category is roughly $32B (2024), while the consulting spend it aims to replace is in the hundreds of billions annually in the U.S. alone Fortune BI IBISWorld.

Bottom-up calculation:

Near term, assume ~5,000 mid‑market and enterprise retail/e‑commerce brands are serviceable in the next 2–4 years; at ~$100k average ACV for a pricing/discounts module, that implies ~${500}M SAM. With two additional modules (e.g., ad‑spend and engagement) lifting average ACV to ~$250k, the medium‑term SAM for the same cohort is ~$1.25B–$2.5B.

Assumptions:

• Serviceable customer count of roughly 5,000 mid‑market/enterprise retail and e‑commerce brands in reachable geographies within 2–4 years.
• Average ACV of ~$100k for the initial pricing/discounts module, rising to ~$250k with two additional modules due to broader impact and continued expert support.
• Adoption will be constrained initially by integrations, trust/auditability, and data controls, which is why the near‑term SAM focuses on a subset of the broader market.

Who are some of their notable competitors

• Pricefx: Enterprise price‑optimization and management platform used by brands to model elasticity, optimize list and promo pricing, and operationalize price changes — overlaps directly with Operand’s initial pricing focus.
• Revionics (Aptos): Retail AI pricing and promotion optimization suite; widely used by large retailers for base pricing, markdown, and promo planning, competing head‑to‑head on pricing and discount strategy.
• Zilliant: B2B pricing and revenue optimization with deal guidance and analytics; overlaps on price optimization and elasticity modeling for enterprises seeking data‑driven pricing.
• Competera: AI‑driven price optimization and competitor price monitoring for retail and e‑commerce; similar to Operand’s use of live market signals and pricing recommendations.
• o9 Solutions: Integrated business planning platform (demand forecasting, revenue growth management, retail/CPG) that can compete as a broader planning system alternative for large enterprises.