CRM Analytics. Sync frequency and history. Fresh pipeline without API burn.

What this means for your store

Sync schedule and history window decide how fresh - and how expensive - your datasets are. Too short a history window and YoY lenses break after a fiscal calendar change. Too long and every refresh reprocesses years of closed opps. High-volume B2B ecommerce orgs often run hourly incremental sync on open pipeline and nightly full recompute on trend datasets with a bounded lookback.

Scenario on a real storefront

A UK electrical wholesale group splits workloads: hourly open_pipeline, daily closed_won_history with a 36-month window:

# Data Manager → Dataflow → Schedule

open_pipeline_dataflow:
  type: incremental
  schedule: every 1 hour
  objects: Opportunity (IsClosed = false), OpportunityTeamMember

closed_won_history:
  type: full / incremental per object docs
  schedule: daily 02:00 Europe/London
  filter: CloseDate >= addmonths(now(), -36)

# Monitor → Jobs: duration and rows added
# Setup → CRM Analytics Integration User → API usage
# Trend lens assumes history refresh before 08:00 stand-up

What to do next

• Hourly sync on a massive custom object can hit API caps - filter to open records or recent LastModifiedDate.
• Put an “as of” timestamp on dashboard subtitles so reps do not compare CRMA to live list views refreshed in real time.
• After a trade-show bulk import, trigger a manual dataflow run before exec reviews stand-up numbers.

Bottom line

Match sync frequency to how stale each KPI can be. Refresh open pipeline often; bound history lookback; show last-sync time on the dashboard.