A deployment like this targets marketing efficiency first - revenue per marketing dollar, measured against your own baseline - within the first six months. The rest of the working targets, all stated assumptions we validate during the audit rather than promised results: faster proposal turnaround, because teams stop rebuilding positioning from scratch and reference data-backed case study recommendations instead; better resource utilization, as managing directors see which service lines and consultant combinations generate repeatable, high-margin engagements; and fewer project write-offs, as marketing stops funding the campaigns that correlate with scope-creep-prone clients.
ROI compounds over 12 months as the attribution model matures. In the early months, marketing reallocates budget away from vanity-metric channels into campaigns that genuinely correlate with high-utilization engagements. As positioning informed by attribution data reaches proposal teams, sales cycles start to compress. By month twelve, the causal models are precise enough that managing directors use attribution insights for strategic planning - which service lines to expand, which client segments to target, which consultant expertise to hire next - extending the impact beyond Marketing into the entire P&L. We don't have a published case study measuring attribution modeling specifically yet, so we won't dress up a different result and call it proof. For a general sense of what Revenue Institute builds in professional services: Qualigence, a recruiting and talent firm, cut sourcing time 36.2% on a sourcing agent we built - a different kind of system than the attribution engine described here.