Director of Revenue Strategy & Economics

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 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 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. 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. Can turn a model into a short executive argument — the recommendation, the two numbers that matter, and an articulation of what is still unknown. Comfort operating in ambiguity and owning an analytical agenda without a playbook already written. 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). Judgment, not just math — you identify which question actually matters, take a position, and defend it with senior stakeholders. 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. 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. Working familiarity with Salesforce data — opportunity, account, and hierarchy structures as they actually behave. Experience with planning tools (Anaplan, Pigment, or similar), particularly capacity and quota modeling in them. Python, R, or dbt for heavier data work. Exposure to M&A diligence or GTM integration. Experience presenting to a CRO, CFO, or board audience. #J-18808-Ljbffr