PwC uses AI-driven send-time and audience scoring to improve campaign relevance

Overview

PwC France & Maghreb embedded AI models in Oracle Marketing to determine optimal send times, detect audience fatigue to avoid over-contacting, and score accounts by engagement potential and service-line interest, shifting from broad regional campaign lists to intent-based segments.

The challenge

At PwC, outreach relied on one-size-fits-many emails segmented mainly by geography or service line rather than by real-time behavior or intent: teams lacked tools to determine when a client or prospect was most likely to engage, send times were inconsistent, and marketing planners lacked automation to detect when audiences were over-contacted, increasing fatigue and wasted effort.

The solution

PwC turned to Oracle Marketing and embedded AI models to update its outreach: models analyze behavior and timing patterns to determine optimal send times, fatigue-analysis models identify when a contact has already received too many messages, and account-intelligence models profile and score accounts by engagement potential and service-line interest, supporting a shift from broad regional lists to intent-based segments.

Reported business value

Open and click-through rates have improved, pointing to more relevant content delivered at the right moment, and productivity increased as manual audience-preparation tasks dropped and workflows relied more on automation.