AI models learn from profile, behavior, and context to rank complementary products or upgrades and push them via CRM, journeys, web/app personalization, and agent UIs as the next best cross‑sell/upsell step. Decisioning systems manage an offer catalog with eligibility, frequency caps, and arbitration so each person sees a single best offer at the right time … Read more
AI‑powered SaaS turns cross‑sell/upsell from batch promos into a governed system of action. The effective pattern: ground recommendations in permissioned, fresh product, pricing, usage, and support data; use calibrated models that predict incremental lift (uplift) rather than mere propensity; simulate impact on revenue, margin, churn, fairness, and workload; then apply only typed, policy‑checked actions—offers, bundles, … Read more
Travel experiences feel personal when every touchpoint—search, booking, check‑in, in‑trip service, and post‑trip—adapts to traveler context and intent. Modern SaaS platforms make this practical by stitching fragmented data (CRS/PMS, GDS/NDC, loyalty, payments, support), running real‑time recommendations and dynamic pricing, and orchestrating offers, messaging, and service across web, app, email, WhatsApp, and airport/hotel ops. The winning … Read more
Email drips turn one-time signups into steady progress toward activation, adoption, and purchase. Done well, they deliver the right help and offers at the right time, lifting conversions without adding sales headcount. Why drips work for SaaS Core playbooks by lifecycle Design principles Triggers and segmentation to set up Copy and CTA patterns Measurement and … Read more
SaaS platforms sit on rich behavioral, technical, and commercial data. Converting that into revenue requires three things: precise segmentation, relevant offers tied to real value, and timely delivery inside the product and lifecycle. Done right, upsells feel like help, not hustle. Map the data you already have (and what it means) Design “next best offers” … Read more
Predictive analytics has moved from dashboards to decisions. In 2025, leading SaaS teams use ML models embedded in their stacks to forecast churn and expansion, optimize pricing, and trigger next‑best actions in real time—turning data into measurable growth levers across product, sales, and success. Why predictive matters now High‑impact use cases The data foundation Implementation … Read more