Director of Revenue Strategy & Economics, Coupa

• Exceptional quantitative modeling ability. You structure ambiguous questions into scenarios, and are rigorous about assumptions, sensitivities, and what the model cannot tell you. Anaplan experience strongly preferred
• Fluency in SaaS revenue mechanics: ACV versus bookings versus revenue, pipeline coverage and conversion, quota and capacity math, ramp, attainment distribution, CAC payback and sales efficiency
• Fluency in the mechanics of seller economics — quota credit and splits, overlay and specialist crediting, SKU-level bag definitions — and the ability to reconcile calculated payouts against finance actuals
• Judgment, not just math — you identify which question actually matters, take a position, and defend it with senior stakeholders
• 5–8 years in a quantitative, revenue-facing role, weighted toward RevOps or Sales Strategy in B2B SaaS — Strategy consulting, banking or PE, corporate strategy, and FP&A backgrounds fit if paired with real operational exposure to how GTM works (territory balancing, how sellers get credited and paid, etc)
• Expert Excel/Google Sheets — multi-scenario models with structure someone else can audit; SQL and a BI tool (Sigma, Tableau, Looker) used in practice, not just understood conceptually
• Can turn a model into a short executive argument — the recommendation, the two numbers that matter, and an articulation of what is still unknown
• Precision against a close calendar: while the commission calculation sits here, the output gets paid to people and reported upward, so it has to be right, reconcilable, and defensible line by line
• Comfort operating in ambiguity and owning an analytical agenda without a playbook already written

Desirable

• The Revenue Strategy & Economics Director is the quantitative backbone of how Coupa decides where to put its next go-to-market dollar
• This role builds and owns the scenario models behind the Revenue org's hardest investment questions: what does another ten AEs in a given segment actually return, and when; which segments and geos are earning their cost of coverage; whether a change pays for itself, and in what year
• Core scope is the Sales organization; however, it is rapidly expanding to serve other parts of the business (e.g., Partners, Value Solution Advisory, and Customer Success)
• It is a modeling and judgment role, partnering closely with RevOps Intelligence and FP&A for data and decision-making
• The deliverables are recommendations with the math behind them: sized, sensitivity-tested, and framed so the COO, CRO, and CFO can act on them
• You'll work within the Revenue Strategy & Planning team that maintains the roster, quota, and bookings infrastructure and serve as RevOps's primary quantitative POV in the annual planning cycle
• Your job is to make sure the investment decisions that flow into it are the right ones, and that the assumptions underneath the plan hold up
• Build and own the investment scenario models the Revenue org uses to evaluate GTM spend — headcount adds, segment and geo expansion, coverage-model changes, overlay and specialist roles, partner leverage
• Structure each question as a set of explicit, comparable scenarios — base, upside, downside — with stated assumptions, sensitivities, and the break-even conditions that would flip the answer
• Translate models into recommendations: what to fund, what to defer, what to stop, and the expected bookings and efficiency impact of each path
• Own the analysis — partnering with RevOps Intelligence to source the data, building the model, pressure-testing it, and presenting it to CRO- and CFO-level stakeholders who will push back on the math
• Model productive capacity by segment, geo, and role — ramp curves, attrition and backfill lag, quota coverage, and the gap between paper capacity and capacity that will actually carry a number
• Quantify the marginal return on the next hire: where the next AE, SE, SDR, or overlay dollar produces the most incremental bookings, and where coverage is already saturated
• Build the headcount investment case Revenue leadership takes into budget conversations — including what the plan does not fund and the quantified cost of that choice
• Stress-test capacity plans against hiring reality: what the number looks like if recruiting lands 70% of plan, or lands it a quarter late
• Own the productivity analytics that explain yield — time to first deal and to full productivity, attainment distribution, win rates and cycle length by segment, and the drivers behind each
• Quantify the bookings cost of attrition and territory disruption, and the return on retention, enablement, or ramp-acceleration investments
• Interpret the efficiency metrics leadership is held to — cost per dollar of bookings, sales efficiency, CAC payback — and explain what is actually moving them rather than restating that they moved
• Separate signal from noise: distinguish structural productivity differences from territory quality, deal mix, and single-deal effects
• Own Coupa's granular understanding of what a dollar of bookings actually costs us — by product, segment, and motion — once the coverage model, specialist overlays, and center-of-excellence SMEs behind that product are loaded in
• Compare cost per dollar of bookings across products and motions, and turn that into a point of view on where to invest coverage, which overlays earn their keep, and where the go-to-market around a product costs more than the product returns
• Hold the seller-level detail that makes this real: role from the sales roster, coverage from territory assignment, the SKUs each seller carries in their bag, and actuals from RevOps Analytics and Finance
• Because that detail lives here, this role produces the authoritative statement of who gets paid what at month- and quarter-end for the complex overlay and specialist cases standard comp plans don't cleanly resolve
• That calculation is an interim anchor, not a permanent one — the goal is to hand it to Sales Comp following their implementation of Anaplan for commissions calculations, and this role builds the methodology that makes the handoff clean
• Model coverage and route-to-market alternatives before they become decisions — new-business/expansion splits, accounts-per-rep ratios, overlay and specialist coverage, hybrid and partner-led motions
• Size addressable opportunity against realized yield by segment and vertical — where the market says invest versus where our own conversion says we can win
• Quantify the transition cost of a model change, not just its steady state: disruption, re-ramp, and attainment risk in the year of the change
• Build the analytical evidence base for segmentation, account-tiering, and specialization decisions
• Serve as the primary quantitative input into the annual territory and quota-setting cycle — capacity assumptions, productivity and ramp curves, coverage ratios, and quota-load sanity checks
• Pressure-test the plan before it goes out: does the bookings target reconcile to the capacity and productivity assumptions underneath it, and where is it fragile
• Partner closely with GTM Readiness, FP&A, and Comp — you supply and defend the analytical assumptions; they own cycle execution, roster, and quota allocation
• Reconcile the plan's math to FP&A's revenue model and surface divergence early, not in the QBR
• Build your own data — write the SQL, stand up the Sigma/Tableau views, and get to a trustworthy dataset without waiting in a reporting queue
• Establish reusable models and consistent definitions so recurring investment questions get answered in days rather than weeks, and the same question gets the same answer twice
• Document methodology so a model survives Finance-grade scrutiny and can be re-run by someone other than you
• Model the GTM impact of acquisitions — combined-org capacity and coverage, cross-sell and attach assumptions, and a realistic ramp on the synergy case
• Pressure-test deal-model GTM assumptions against what our own productivity data says is achievable
• Support integration decisions with analysis on territory consolidation, account mapping, and coverage design