We are getting a 413 Entity Too Large error when querying the analytics API for our long-term forecasting model.
Our WFM forecast requires 180 days of conversation data. When we set the interval to 180 days with all segment and metric filters, the response payload exceeds the API’s maximum response size. How do we structure the query to avoid this?
For outbound campaigns, this is even worse.
Each outbound interaction generates significantly more segment data than inbound calls (preview, dialing, connected, wrap-up, compliance hold segments). A single day of campaign data can exceed the 413 threshold if you run multiple concurrent campaigns with high attempt volumes.
The solution is to chunk your requests into smaller time intervals.
Instead of querying 180 days in one request, break it into daily intervals. Each daily query stays well under the payload limit.
From a statistical modeling perspective, daily granularity is actually preferable for seasonal pattern detection. Monthly aggregates smooth out the weekly cyclical patterns that my Erlang models depend on. Daily data preserves the Monday-spike and Friday-dip patterns that are critical for accurate staffing forecasts.