Automated Programmatic Ad Bidding in Software

AI analyzes your Salesforce opportunity data, HubSpot lead profiles, and Stripe revenue events to identify which audience segments convert to the highest-LTV customers, then generates bid recommendations your team applies across channels to concentrate spend on those proven cohorts. Unlike generic DSPs, the system learns your 90-day sales cycle, understands your ICP precision, and correlates ad spend directly to ARR impact rather than last-click conversions. It continuously retrains on new customer data, discovering second-order signals - like free-trial seat expansion or feature-adoption depth in accounts already in a self-serve funnel - that predict deal size and retention.

Yes. We enforce zero-retention policies for AI model training; PII is pseudonymized before any analysis. Your Salesforce, HubSpot, and Stripe data never trains shared models.

Plan for a working system inside the first 100 days: weeks 1-3 cover data connectors and historical analysis, weeks 4-7 focus on model training and validation against your actual conversion data, weeks 8-10 involve pilot testing on 20-30% of ad spend with manual review gates, and weeks 11-14 scale to full production with your team's approval workflows embedded. A rollout like this is scoped to show measurable CAC reductions and bid efficiency gains within 60 days of go-live as the AI learns your baseline conversion patterns.

The core benefit is that spend recommendations follow revenue, not clicks. Suggested bids concentrate on the segments that historically become your highest-LTV customers - read straight from Salesforce, HubSpot, and Stripe, not inferred from platform metrics. Attribution respects your real sales cycle, so a prospect who clicked in January and closed in April counts as a win instead of a miss. And the model keeps retraining on new customer data, surfacing second-order signals that predict deal size and retention before your competitors' generic DSPs even register the account.

Three specifics your security team will ask about. Retention: nothing is kept for model training beyond your own deployment - zero-retention is the policy, not an upgrade tier. Identity: PII is pseudonymized before any analysis runs, so the optimization layer works with cohorts, not named contacts. Isolation: your Salesforce, HubSpot, and Stripe data never trains shared models, which means nothing your revenue data teaches the system ever benefits another company's bids.

The 100-day plan holds when your revenue data is already connected - and stretches when it is not. The honest variables: if Salesforce opportunity stages are inconsistently maintained, weeks 1-3 become a CRM cleanup before the model has anything trustworthy to learn from; if ad-attributed cohorts have never been mapped to Stripe revenue, that mapping is new plumbing, not configuration. The pilot phase in weeks 8-10 deliberately runs on only 20-30% of spend with manual review gates, so even a slower start risks a slice of budget, not the whole program.

A generic DSP sees your business through its own conversion pixel: a form fill is a win, silence is a loss, and the 90 days between click and contract are invisible. That blindness makes DSPs systematically underbid on enterprise segments (slow to convert, huge LTV) and overbid on self-serve signups (fast to convert, quick to churn). This system inverts that: attribution windows match your actual Salesforce funnel velocity, every cohort is scored by the ARR and retention it eventually produced, and patterns learned on one channel inform the recommendations for the others. Your team still applies the bids inside the DSP - this system just tells them what the numbers say those bids should be.