---
id: "question-predicting-found-time"
type: "open-question"
source_timestamps: ["¶6", "¶7"]
tags: ["ad-tech", "predictive-analytics"]
related: ["action-build-exploration-playbook", "action-monitor-team-calendars"]
resolution_path: "Development of ad-tech integrations that utilize real-time behavioral signals (e.g., sudden shifts in GPS mobility data or calendar API integrations) to trigger programmatic ad delivery."
sources: ["commercial"]
sourceVaultSlug: "hbr-seg-commercial"
originDay: 5
articleStem: "hbr-foci-66-customers-willing-try-new-tech"
sourceUrl: "https://hbr.org/2025/11/research-when-are-customers-willing-to-try-a-new-technology"
sourceTitle: "Research: When Are Customers Willing to Try a New Technology?"
---
# How can brands algorithmically predict found time at scale?

**Open question:** The mandate is to 'be ready when unexpected time appears' (see [[quote-cannot-create-time]] and [[action-build-exploration-playbook]]). **Macro** events (weather, daylight saving) are easily trackable, but predicting *individual, idiosyncratic* [[concept-found-time|found time]] — like one specific consumer's cancelled meeting — at scale remains a significant technical challenge outside closed ecosystems (such as an internal manager viewing a team calendar, per [[action-monitor-team-calendars]]).

**Resolution path:** ad-tech integrations that use real-time behavioral signals — sudden shifts in GPS mobility data, or calendar-API integrations — to trigger programmatic ad delivery at the moment time is found.