A higher CPAIL will produce less investment capital or require a higher spend
$/AI lead
Cost per accredited investor lead. Total ad spend ÷ accredited contacts created in the period. Lower = more efficient lead gen.
AI Lead → Discovery
Baseline: —
A higher booking rate will produce more investment capital or lessen the necessary spend
%
% of accredited leads that book a discovery call. Driven by setter outreach — dials, connections, and booking skill. Currently 93% of AI leads never reach a discovery. Every percentage point improvement here multiplies straight through to investors and capital.
Discovery → Held
Baseline: —
A higher hold rate will produce more investment capital or lessen the necessary spend
%
% of booked discoveries that actually happen. No-shows, reschedules, and cancellations reduce this. Higher = better confirmation/reminder process.
Held → New Funded
Baseline: —
A higher close rate will produce more investment capital or lessen the necessary spend
%
% of held discoveries that result in a first-time investment. The close rate — driven by closer skill, investor readiness, and fund attractiveness.
Avg First Check
Baseline: —
A higher average first check will produce more investment capital or lessen the necessary spend
$
Average dollar amount of a new investor's first investment. Larger checks = more capital per investor acquired.
Existing Investor Base (independent of ad spend)
Investors acquired before this projection reinvest at a historical run-rate. This capital flows regardless of what you spend on ads — it's the return on years of building the investor base. The default is computed from the trailing 15-month window (total repeat capital ÷ 15) for the smoothest average. If you believe the recent trend is more representative, override it below. This number × months is the single largest component of total capital raised — small changes here have a large effect on the bottom line.
$Baseline: —
Projected Funnel
Cost & Lifetime Value
How This Works
The Formula
This calculator models the capital-raising funnel from ad spend to funded investments. The spend-driven chain: ad spend produces accredited investor leads (AI Leads) at a blended cost (CPAIL). Setters convert a percentage of those into discovery calls. A percentage of booked discoveries are held. A percentage of held discoveries result in a first-time investment. Each new investor writes an average first check. New investors also begin reinvesting on a lagged schedule, adding new-investor repeat capital over time. Separately, the existing investor base (acquired before the projection) reinvests at a historical run-rate independent of any ad spend — this is context, not a product of the funnel.
Every slider adjusts one link in this chain. The green text below each slider shows the downstream impact: how many units that stage produces, the cost to produce each one, and the resulting capital. Below that, a lever line shows what that slider would need to move to in order to add $1M in capital. Drag any slider and watch the numbers cascade through every stage below it.
Forward Mode (Spend → Capital)
Enter a monthly ad spend and time horizon. The calculator pushes that spend forward through the funnel at the current conversion rates to project AI leads generated, discoveries booked and held, new investors funded, and total capital raised (new + repeat).
Reverse Mode (Capital Target → Spend)
Enter a capital target and time horizon. The calculator works backward through the funnel to determine the required ad spend, AI lead volume, and discovery volume needed to hit the target at the current conversion rates. Use the New Capital / Total Capital toggle to choose whether your target is just new investor capital or total capital including repeat. When targeting total capital, the calculator subtracts the base repeat (existing investors, independent of spend) and then divides the remainder by (1 + the effective kernel ratio for the term) to determine the new capital needed.
Slider Controls
Each conversion rate can be adjusted by dragging the slider or by clicking the value on the right and typing a number directly. The Reset to Defaults button reverts all sliders to the live-computed baseline values.
Inline Metrics (green text)
Below each slider, green text shows the computed output of that stage given the current inputs. For cost-bearing stages (Discovery, Held, New Funded), it also shows the cost per unit — total ad spend divided by the count at that stage. For Avg First Check, it shows cost as a percentage of new capital — how much of the raised capital went to ad spend. These metrics update live as any slider moves, making it immediately visible where improvement at one stage reduces cost at every downstream stage.
Lever Analysis
Below each slider's green metrics, a lever line shows what that lever would need to move to in order to add $1M in total capital (forward mode) or save $1M in spend (reverse mode). The percentage shown is the relative move from the current slider value to the required value.
The lever requiring the smallest relative move is highlighted in accent color and marked "smallest move." This tells you which lever the funnel is most responsive to right now — the one where a small adjustment produces the target gain.
Because the funnel is a multiplication chain, the required relative move is the same for all rate levers (AI Lead → Discovery, Discovery → Held, Held → New Funded, Avg First Check). CPAIL requires a slightly smaller move because it sits on the denominator — reducing cost by X% produces slightly more than X% more leads. The differentiation becomes meaningful when sliders are moved away from their baselines, because each lever's headroom (distance to its ceiling) differs.
The analysis measures mathematical sensitivity, not operational difficulty. A lever showing the smallest required move might involve coordinating multiple teams, while a lever showing a larger move might be a single process change. Use it to understand where the formula is most responsive, then apply judgment about which improvements are most achievable.
CPAIL (Cost Per AI Lead)
Total ad spend divided by the number of accredited investor contacts created in the period. This is the blended cost across all platforms (Meta, Google, LinkedIn) spread across all accredited leads — paid and organic — because the ad spend fuels the entire lead machine, not just directly-attributed contacts. Lower CPAIL means more efficient lead generation. The inline metric shows the total AI lead count and monthly run rate.
AI Lead → Discovery %
The percentage of accredited leads that end up booking a discovery call. This is primarily driven by setter outreach — dials, connections, and booking skill. Improving this rate means either generating more dials per lead, improving the connection rate, or converting more connections into booked discoveries. The inline metric shows discoveries booked, the monthly rate, and the cost per discovery.
Discovery → Held %
The percentage of booked discoveries that actually happen. Reduces when investors no-show, cancel, or reschedule. Improving this rate means better confirmation sequences, reminder cadences, and pre-call engagement. Meetings where the outcome isn't explicitly a failure (no-show, rescheduled, canceled, disqualified) count as held — this is generous to account for sparse dispositioning. The inline metric shows held discoveries, the monthly rate, and the cost per held discovery.
Held → New Funded %
The close rate — percentage of held discoveries that result in a first-time investment. This is the closer conversion rate. A 90-day maturity window is applied to the baseline calculation so recent discoveries that haven't had time to fund don't drag down the rate. The inline metric shows new investors, the monthly rate, and the cost to acquire each new investor.
Avg First Check
The average dollar amount of a new investor's first investment. This determines how much capital each new investor adds. Larger average checks mean fewer investors needed to hit a capital target. The inline metric shows total new capital, the monthly rate, and ad spend as a percentage of new capital raised — this is the present cost of acquiring that capital.
Existing Investor Base (independent of spend)
Investors acquired before the projection window continue reinvesting at a historical run-rate. This capital flows regardless of ad spend — it's the cumulative return on years of building the investor base. The default run-rate is computed from the trailing 15-month window (total repeat capital ÷ 15 months), regardless of which baseline window is selected for the funnel conversion rates. The 15-month average smooths out lumpiness from large individual reinvestments that can skew a shorter window.
The field is editable. If you believe the recent trend (e.g. last 6 months) is more representative of where repeat capital is heading, override it. This number × projection months is typically the single largest component of total capital raised — at the current default, it's roughly $3.5M/month or $42M/year. Small changes here have a disproportionate effect on the total raise figure.
This number does not appear in the four summary cards at the bottom — those show only capital produced by ad spend. It appears in the total raise line below the cards, which adds base repeat back in for the complete picture.
New-Investor Repeat (from the funnel)
Investors acquired during the projection begin reinvesting on a lagged schedule. This is computed using an empirical reinvestment curve calibrated from the full SCI investment history (917 investors, 357 repeat, 38.9% reinvestment rate). The average lag to reinvestment is ~25 months, and the lifetime reinvestment yield is $0.94 per $1 of first investment — but that $0.94 arrives over ~5 years, not instantly. The model accounts for staggered entry: investors acquired in month 1 of a 12-month projection have 11 months to reinvest, while investors acquired in month 12 have none.
This means shorter projections naturally show less new-investor repeat. A 12-month projection captures roughly 16% of the lifetime reinvestment from new investors; a 24-month projection captures ~30%. This component does appear in the summary cards as "New-Investor Repeat" because it's a direct product of the ad spend that acquired those investors.
When the projection horizon captures less than ~80% of the reinvestment curve, a flag appears noting that additional repeat capital would arrive beyond the window. This is informational, not an error — it's the nature of lagged reinvestment.
Output Bar (sticky at bottom)
Four cards showing what ad spend produces: Total Spend, New Capital, New-Investor Repeat, and Total from Spend. These stick to the bottom of the screen and update in real time. In reverse mode, Total Spend becomes "Required Spend." Base repeat from the existing investor base is not included in these cards — it's independent of spend.
Below the cards, a total raise line adds the base repeat back in: "Including reinvestment from those investors who had invested prior to N months ago ($X): $Y total capital raised over N months." This is the complete picture — spend-driven capital plus the independent base.
Cost & Lifetime Value (above the output bar)
This section shows cost perspective on the funnel output. Three cards side by side:
Cost / New Investor (present cost, accent border): the acquisition cost per new investor, shown as both a dollar amount and a percentage of their first check. This is the present cost — what you're paying today against what they invest today.
Cost / Lifetime Capital (future cost, green border): that same acquisition cost as a percentage of the investor's lifetime capital. The dollar cost doesn't change; the denominator grows as the investor reinvests over time. The LTV multiple is computed from all-time investment data — for each investor (grouped by email), total lifetime capital ÷ first investment. As the investor base matures, the LTV multiple increases and this percentage drops automatically.
Return on Spend: total capital from spend (new capital + new-investor repeat) divided by total spend. This measures the efficiency of the ad-driven funnel only — base repeat from the existing investor base is excluded because it flows independent of any spend.
Baseline Window & Live Data
Default slider values are computed from live cached data on each page load — not hardcoded. The baseline window ends 90 days ago (snapped to a complete calendar month) so every discovery has had a full maturity period to fund. As new data flows through the nightly cache rebuild (2 AM ET), the baselines update automatically.
Window Options (6 / 12 / 15 months)
The toggle above the baseline banner selects how far back the window reaches. Each option handles discovery call data differently because of a data quality issue: before January 2026, most discovery calls were not dispositioned (no outcome recorded). An undispositioned call counts as "held" — which inflates the held-discovery count and understates the close rate.
6 months — all metrics use the same window. The entire window falls after Jan 2026, so all discovery data is clean. Tighter sample but fully reliable.
12 months (default) — the full 12-month window is used for CPAIL, AI Lead → Discovery %, and Avg First Check. These metrics don't depend on discovery dispositions, so the longer window gives better statistical depth. Discovery → Held % and Held → New Funded % use a shorter sub-window starting Jan 2026 (the disposition floor). This means these two rates are computed from fewer months of data than the other metrics. Metrics using the shorter window are marked with ⁺ in the baseline tag. The base repeat run-rate always uses a fixed 15-month window regardless of this toggle — see the Existing Investor Base section above.
15 months — same split as 12 months, but with an even longer window for the non-disposition metrics. The disposition-dependent metrics still start at Jan 2026. As the team's dispositioning history grows, the clean sub-window expands automatically — once the trailing window start passes Jan 2026 (around April 2027), the floor becomes a no-op and all metrics use the full window.
The window snaps to complete calendar months — it ends at the last day of the month before the 90-day lookback point, and starts 12 months before that. This means the effective lag varies from 90 days (at the start of a month) to ~120 days (at the end). The tradeoff is intentional: partial months at the window edges would produce artificially low counts (half a month of leads against a full month of spend), distorting the computed rates. Complete months give cleaner rate calculations. The 30-day variability in the lag doesn't meaningfully affect 12-month conversion rates.
The green counts below each slider are model projections at the rounded default rates — they won't exactly match the raw baseline counts in the banner. This is expected: default rates round to one decimal place, and that rounding compounds through the six-stage formula. The banner shows what actually happened; the sliders show what the model projects at those rounded rates.
Meta spend loads from the R2 archive (fast, no API calls). Google Ads and LinkedIn spend are fetched asynchronously after the page renders — the CPAIL baseline updates silently when they arrive. If additional platform spend is less than $1,000 for the period, it's treated as negligible and CPAIL stays Meta-only.