SaaS & Marketing

We build the models that tighten the loop between spend and results: predicting campaign performance like clicks, ROAS, and conversions, distributing budget across platforms, detecting performance anomalies in real time, suggesting audiences and keywords, and auto-optimizing paid campaign parameters. For SaaS teams we also model churn, expansion, and lead scoring. Each model is scoped to your funnel and metrics, not a generic dashboard.

Google Ads, Meta, LinkedIn, programmatic platforms, and CRMs and marketing tools like HubSpot, plus your product analytics and data warehouse. We connect to these over APIs so models pull live performance data and push recommendations back into the tools your team uses. We extend this with broader AI workflow integration when you want decisions wired straight into your operations.

Yes. We use clustering and behavioral data to surface microsegments that convert, so you can target high-intent groups instead of broad averages. The segments update as behavior changes rather than staying fixed, and you can act on them in campaigns or feed them into lifecycle messaging.

Yes. Smarter budget allocation shifts spend toward the platforms, segments, and creatives that perform and pulls it from those that do not, so you get better outcomes from the same or lower spend. The models can pause or resume underperforming ads automatically within rules you set, and you keep approval control over major budget moves.

We baseline normal performance for each campaign and metric, then flag abnormal dips or spikes, for example a sudden CTR drop or a cost-per-acquisition jump, as they happen instead of after a weekly report. For always-on alerting at scale we serve these models through real-time inference engines so issues surface in minutes, not days.

Usually historical campaign and ad performance, conversion and revenue data, CRM records, and product usage events for SaaS use cases. The more history across platforms, the better the predictions. If the data is scattered across ad accounts, your CRM, and a warehouse, we handle the consolidation, cleaning, and feature engineering as part of the build.

We validate on held-out data using metrics that match the goal, for example RMSE for forecasting conversions or AUC for churn scoring, and we only ship a model that beats your current approach. After launch we monitor for drift and retrain on a schedule, because ad costs, audiences, and seasonality shift constantly. We avoid a single headline number because real accuracy depends on your accounts and history.

A focused build such as campaign forecasting, lead scoring, or anomaly detection typically runs about 4 to 10 weeks across data preparation, training, validation, and integration. Systems that combine forecasting, budget allocation, and automation take longer. We give a firm timeline after an initial scoping and data audit.

Cost depends on scope, data readiness, and how many platforms and models you need, so we scope and price after the audit rather than quote a vague range. Plan for ongoing monitoring and periodic retraining as a yearly cost, since marketing data drifts quickly. You own the models and logic, so there is no per-seat platform lock-in.

Teams we build for aim for lower cost per acquisition, stronger ROAS, automatic handling of underperforming ads, and clear visibility into which segments and campaigns drive results. We set the target metric with you up front and measure against your baseline so the impact is provable rather than a vague promise.