Don't bet. SafeBet.
SafeBets Acquisition Strategy

From ad spend to users to investors

Two campaign tracks in parallel: investor acquisition (primary, the Pre-IPO raise) and user acquisition for the platform. The model below is built on the real numbers from our prior raise, and a content & authority-distribution layer can sit under both, capturing the 80%+ who research before they act.

The Strategy
Two funnels, one trust engine
Track 1 · Investor acquisition · primary
Reach + clickTargeted ad · CPC $2 to 5
→
Landing pageInvest page · a % start
→
6-step applicationEligibility → amount → form
→
PaymentSigns for an amount
→
Funded investorCapital raised
The 6-step application (Location → Eligibility → Personal Data → Application → Payment → Complete) is tracked stage by stage, so we see exactly where prospects drop off and fix it, and we retarget the drop-offs at every stage. The calculator below models the overall result; the per-step data is how we diagnose and improve it.
Track 2 · User acquisition
ClickConsumer ad: "predict the future"
→
Landing pageLands on the offer page
→
RegisterA % convert on the page
→
Active predictorEngaged, returning user
→
% → Track 1A share become investor prospects
Retargeting runs across both funnels, re-engaging drop-offs at every stage. The way into a higher gear: add a content layer that makes SafeBets the trusted answer everywhere buyers research, across every channel, every format, and the AI answers, building the trust signals that lift conversion on the same ad spend.
Campaign architecture
Organized by channel & funnel stage: the structure we've run before
LinkedIn, investor-led
TOFUBrand awareness, video & image
MOFUWebsite visits · engagement · video views
BOFULead-gen forms · website conversions
Google / YouTube
TOFUTrueView discovery · video action
MOFUDiscovery / display
BOFUSearch · smart display
Meta + multi-channel, users
BOFULead generation · conversions
USERSLower-cost user acquisition (not LinkedIn)
+Native lead forms (LinkedIn · Meta · Google)
Retargeting runs across every channel (MOFU/BOFU) to re-engage visitors and funnel drop-offs. Prior cost per investor by channel, historical estimates based on our previous data: LinkedIn ~$1,350 · Google ~$965 · Meta ~$4,500.
Measurement
Multi-source attribution: the aggregate is the accurate number
Ad libraries
Competitor + our own creative, straight from the platforms
Independent trackers
Several tracking sources run in parallel, none relied on alone
Platform + server-side
Analytics + conversion APIs + native lead forms across every channel
Reconciled
No single tool fully agrees, but the aggregate is the clearest, most accurate read
Every investor tied back to channel & creative → true cost-per-investor and ROAS. No single source is trusted on its own; the reconciled aggregate is what we optimize on.
The model · investor flow
Ad spend → investors → capital raised
The defaults are anchored on our proven outcome from the $11.3M we have already run on LinkedIn: a ~4.4x ROAS and the capital it raised at the default spend, at a ~0.14% blended CTR (our real overall ad CTR), which works out to ~$1.81 per click and ~$138 cost per lead. The CPM is back-solved so those proven outcomes hold at the real CTR. CTR is a real lever: the same budget buys a fixed number of impressions from the CPM, and a higher CTR turns more of those impressions into clicks, so more clicks reach the page and the effective cost per click drops. CPM, CTR and effective CPC are interlinked: edit any one and the model keeps the others consistent and recomputes clicks, so you can also drive from a target cost per click. Everything here is an estimate grounded in real performance, not a guarantee, and with the added strategies we layer on we believe we can beat it. We have watched this content-plus-paid approach help companies dominate entire categories. Type an exact number in any box, or drag any slider, and everything updates live. We recalibrate to SafeBets' own data in week 1.

Investor inputs

Media · calibrated to our LinkedIn actuals
Investor ad spend$
Model any window, not just a month. Based on the actual historical numbers from a similar capital raise we ran together, where these ratios held across various sizes. An estimate grounded in real performance, not a forward guarantee. Raised capital lands on an attribution delay, often a couple of weeks and sometimes faster, and we adapt the creative and targeting on a weekly cadence.
Cost per 1,000 impressions (CPM)$
What it costs to buy 1,000 impressions. Back-solved so the proven ~4.4x and capital hold at our real 0.14% CTR (effective CPC ~$1.81, shown on the right). Interlinked with CTR and effective CPC: editing one keeps the others consistent. Lower it (cheaper reach, more channels) and the same budget buys more impressions, so more clicks.
Ad CTR (impressions → clicks)%
Share of impressions that click. Our real overall ad CTR is ~0.14%. This is the lever: raise CTR at the same spend and you get more clicks reaching the page, more investors, and more capital, while the effective cost per click drops (shown on the right). CTR stays fixed when you edit CPM or effective CPC, so the back-solve runs against the real CTR.
Landing page CTR (clicks → landing page)%
Share of ad clicks that reach the landing page. Real default ~18.5%, a weighted aggregate across the campaign mix: some campaigns drive traffic to the landing page, others use native Lead Gen Forms instead. Not every remaining click is a Lead Gen Form either, some people click the ad, look, and do nothing. So a click can reach the landing page, go to a Lead Gen Form, or stop without proceeding. This is a lever: a mix weighted toward landing-page traffic raises the share, lifting landing visits and everything downstream to capital. Landing-page cost per click is shown on the right.
Application funnel · set each stage
1Landing page → starts the process%
Cost per start tracks our $137.99 lead-gen cost per lead.
2Eligibility, claims accredited (US)%
↳ Share of leads who are US%
US leads go through eligibility + accreditation documents. Non-US skip both.
3Personal data%
4Application completed%
5Accreditation documents verified (US)%
6Payment received%
7Finalized investor%
Investment mix → avg ticket$12,000
$10K90%
$25K8%
$50K2%
$100K0%
$250K0%
$1M0%
Total100%
Drag the mix. Most cluster at the $10K floor; a few large checks pull the average up. $10K is the SafeBets minimum.
◆ Investor funnel · number | rate
Impressions
Clicks
Effective CPC (editable · back-solves CPM)$
Landing-page clicks
1Starts the process
2Eligibility (US)
3Personal data
4Application completed
5Accreditation verified (US)
6Payment received
7Finalized investors
Capital raised
ROAS (capital ÷ spend)
Cost per investor
Cost per start (≈ lead)
Landing-page cost / click
CPM
Landing-click → investor
US vs Non-US investor breakdown (tap to expand)
The master funnel above is the blended total, fed by these two. US leads go through eligibility (Step 2) and accreditation documents (Step 5); non-US leads skip both.
◆ US investors
1 · Starts
2 · Eligibility
3 · Personal data
4 · Application
5 · Accreditation
6 · Payment
7 · Finalized
Capital
◆ Non-US investors
1 · Starts
2 · Eligibilityskipped
3 · Personal data
4 · Application
5 · Accreditationskipped
6 · Payment
7 · Finalized
Capital
Where every number comes from · sources & formulas
Every default is anchored to SafeBets' real prior-raise performance (the ~$11.3M already run on LinkedIn: ~4.4x ROAS, ~0.14% blended CTR, ~$138 cost per lead). Defaults are the starting point; every field is a live lever, and we recalibrate to SafeBets' own last 7 / 14 day numbers the moment the live ad-account feed connects. Sources: reporting/LinkedIn-Investor-Campaigns-Stats-Log.md · reporting/DATA-AVAILABILITY-MAP.md · EVM first-party pixel · GA4.
Investor inputs → source
Investor ad spend ($100,000)Modeled input. Live: ad-account APIs (LinkedIn costInUsd plus Meta / Google / X spend).
CPM ($2.53)Back-solved so the proven ~4.4x holds at the real 0.14% CTR. Live: spend ÷ impressions × 1000.
Ad CTR (0.14%)Real blended LinkedIn CTR. Live: clicks ÷ impressions (CTR is computed, not a returned field).
Landing-page CTR (18.5%)Weighted campaign-mix aggregate. Live: LinkedIn landingPageClicks ÷ clicks plus EVM pixel landing views.
Application funnel steps 1 to 7Prior-raise stage conversion rates. Live: EVM pixel conversion steps via evm_pixel_report_funnel (investor).
US share of leads (70%)Prior-raise geo mix. Live: LinkedIn MEMBER_COUNTRY_V2 plus pixel-captured geo.
Investment mix / avg ticket ($12,000)SafeBets actual ticket distribution ($10K minimum). Drag the tier sliders to re-weight.
Investor outputs → formula
Impressionsspend ÷ CPM × 1000
Clicks · Effective CPCclicks = impressions × CTR · CPC = spend ÷ clicks (editable; back-solves CPM)
Landing-page clicksclicks × landing-page CTR
Funnel steps 1 to 7chained stage rates; US path applies eligibility (2) + accreditation (5), non-US skips both
Finalized investorsUS step 7 + non-US step 7
Capital raisedinvestors × average ticket
ROAS · Cost per investorROAS = capital ÷ spend · CPI = spend ÷ investors
Cost per start (≈ lead) · Landing-click → investorspend ÷ starts · investors ÷ landing clicks
Reverse goal-seek & channel split
Total ad spend requiredtarget raise ÷ ROAS (default 4.4x, retuned to live last-7/14-day numbers)
Channel allocationproven historical split: LinkedIn 66% · Google/YouTube 31% · Meta 3%
User-acquisition model → formula
Impressions · Clicksspend ÷ CPM × 1000 · impressions × CTR (defaults $9 CPM, 0.6% CTR, multi-channel)
Registered users · Active predictorslanding × register% · registered × active% (live: EVM pixel sign_up / active_predictor)
User-acquisition ROI(active × user→invest% × avg ticket) ÷ spend
Content & distribution multiplier
Uplift (10% to 100%, default 30%)conservative to aggressive conversion lift applied to investors, capital, ROAS and users. Directional estimate, not a guarantee.
The model · reverse
Intelligent budgeting: name the raise, get the spend & the channel plan
The calculators above run forward (spend → capital raised). This one runs in reverse: type the capital you want to raise and it tells you the total ad spend required to hit it, then splits that budget across channels using the split we have already run. Goes up to billions, same clean formatting. Type any number or tap a preset.
Goal: raise → spend
Based on the proven ~4.4x today · auto-tuned to your last 7 / 14 day performance once the live feed connects.

Your target

Capital you want to raise
$
$50M $250M $500M $1B $2.1B $5B
Proven return on ad spend4.4×
Defaults to our blended ~4.4x. We recalibrate this to SafeBets' own last 7 / 14 day numbers the moment the live ad-account feed connects.
◆ Required to hit your goal
Total ad spend required$477M
Channel allocation · where the budget goes
LinkedInInvestor-led, primary channel $315M66%
Google / YouTubeSearch, discovery, video action $148M31%
MetaLead gen, lower-cost reach $14M3%
Estimated based on historical performance.
How to read it: spend the total on the left across the three channels on the right, at the proven split, and the ~4.4x carries that budget to your target raise. An estimate grounded in real performance, not a guarantee, and it gets sharper as the live feed tunes it to SafeBets' own data.
The model · user acquisition
Ad spend → registered users → user-acquisition ROI

User inputs

Media
User ad spend$
Cost per 1,000 impressions (CPM)$
Multi-channel, not just LinkedIn, so cheaper reach than the investor side: $9.00 CPM. Lower it and the same budget buys more impressions.
Ad CTR (impressions → clicks)%
Share of impressions that click. The lever: raise it at the same spend for more clicks, more users, lower effective cost per click.
Landing page CTR (clicks → landing page)%
Share of ad clicks that reach the landing page. A click can reach the page, take a different in-ad action, or stop without proceeding, so this is a weighted aggregate across the campaign mix. The user side routes most traffic straight to the page, default ~85%.
User funnel · set each stage
1Landing page → registers%
2Register → active predictor%
3Active user → invests%
Share of active platform users who become investors. Drives the value per user and the investors-from-users below.
◆ User funnel · number | rate
Impressions
Clicks
Effective CPC (editable · back-solves CPM)$
Landing-page clicks
1Registered users
2Active predictors
3Investors from users
Cost per active user
Cost per investor (from users)
Value per active user
Capital from users
CPM
User-acquisition ROI
The multiplier · content & distribution
Add the content engine: the upside is a range, and it can be significant
Conversion lift from content & distribution (conservative → aggressive)30%
◆ Investor flow · with content
More funded investors+11
More capital+$137,592
New funded investors50
New capital$596,232
New ROAS (capital ÷ spend)6.0×
◆ User acquisition · with content
More active users+1,122
New active users4,862
Cost per active user drops to$10
Same ad budget✓
This is the conservative end. Owned content compounds and returns roughly 10 to 50x on its own spend. By owning the research journey it can lift paid conversion from a meaningful percentage up to doubling results or more. Drag the slider to model conservative vs. aggressive. Illustrative, calibrated to SafeBets once live.
Under the hood
Technology & growth engine: how we actually run the program
LinkedIn API · Connected We run on a live wire into the ad accounts, an agent swarm that optimizes around the clock, and frameworks already proven at scale. The pill in the top bar is real: the developer API is connected and the live ad-account feed is in deployment.
The five capabilities Live API integration, AI agent optimization, custom tools, monitoring, viral frameworks A live data wire into the platforms, a specialized AI agent layer, custom tooling built on demand, always-on competitive monitoring, and viral frameworks proven at scale. See the five
Live API integration
LIVEWired directly into the ad-account APIs, so reporting and optimization run on live numbers, not screenshots.
NOWLinkedIn is connected first (the "LinkedIn API · Connected" status now in the top bar), with Meta, Google, and X to follow.
NEXTEvery decision is grounded in the same source of truth the platforms report, reconciled across channels.
AI agent optimization layer
TEAMA specialized team of AI agents monitors, optimizes, and checks in across the campaigns continuously, the kind of leverage that multiplies output 10 to 100X and beyond.
SCALEWe have already programmed 400+ autonomous agents, so the depth is there to spin up specialized agents for monitoring, optimization, and every aspect of these campaigns, and scale it as far as it needs to go.
DATAFed by sales-intelligence models refined across billions of dollars in sales.
Custom tools, built fast
BUILDThe agents build bespoke apps and tools on demand: real-time dashboards, rapid A/B/n test harnesses, scaling automations.
TESTWe test rapidly and scale efficiently on real-time numbers, instead of waiting on quarterly reports.
PROOFThis very portal is an example of that, built by the same engine that runs your campaigns.
Competitive + brand monitoring
WATCHContinuous monitoring of Polymarket, Kalshi, and many other brands.
SCANAd libraries, hooks, offers, and positioning, tracked as they move.
FEEDThat intelligence feeds our creative and targeting, so we react in days, not quarters.
Proven viral frameworks
VIRALWe apply proven viral hooks and formats, the kind that have driven tens of millions of views, around 30M views for a single channel in a few months.
SPREADAds and content are engineered to spread, not just convert paid impressions.
COMPOUNDEarned reach stacks on top of paid, so the same budget reaches further over time.
6 · Campaign reporting
Simple at a glance, extensive on demand. Live campaign reporting goes from a clean summary to a full campaign → ad-set → creative drill-down, fed by the same live API wire above.
Open full reporting →
Ad spend
$2,660
in the period
Leads
211
investor + user
ROAS
4.4×
capital ÷ spend
Cost / lead
$12.61
blended
Snapshot only. The full reporting page is filterable by date range and source, compares to the prior period, snapshots to PDF, and drills all the way down to the individual creative. Numbers shown are illustrative until calibrated to SafeBets' live accounts.
Put together: a live data wire into the platforms, a specialized AI agent optimization layer, custom tooling built on demand, always-on competitive monitoring, viral frameworks proven at scale, and live campaign reporting from summary to creative. This is the engine behind the numbers above, and it is the reason we believe we can beat the model, not just match it.
Free ad credits as we expand multi-channel
Every channel we add can bring platform credits that stretch the budget
As we expand into new ad channels beyond the LinkedIn connection above (TikTok, Reddit, Google, Microsoft, and more we are researching), we capture the new-advertiser promotions those platforms run. It is real money off the top, every credit is budget that goes to reach and conversions instead of cost.
TikTok Ads
up to ~$6,000
free ad credit for new advertisers
Reddit Ads
$500 → +$500
spend $500, get $500 in credit
Google · Microsoft · more
New-account credits
researching additional channels and their programs
These are platform promotions we capture as we onboard each channel, amounts and eligibility vary by platform and timing, so this is a benefit we pursue, not a guarantee. The point holds either way, the more channels we light up, the more capital-efficient the same budget becomes.
Proven together
Not theory. We've done this before.
$12M → $50M
LinkedIn ad spend → capital raised (~4.2×), confirmed by Alex
~$17M → ~$75M
blended total spend → raised across LinkedIn + Google/YouTube + Meta (~4.4×)
Prepared by EVM LLC for SafeBets Illustrative model, calibrated to SafeBets' actuals over time with enough statistically relevant data. Educational content only.