What
Start by refusing to trust either number on its own. Pull the raw geofence event log and the driver-confirmed delivery timestamp for the same delivery windows, then line them up into a matched-pair dataset. Everything after this depends on that pairing being clean, because a proof of delivery case study for better ROI planning is only as reliable as the timestamps feeding it. If you skip this step and analyze dashboards directly, you are reading someone else's rounding.Next, measure the delay distribution rather than the average. Calculate the time gap between geofence entry and driver confirmation for each event, then look at the median and the long tail. Averages hide the story. In fleet tracking data, most deliveries may show a tidy two-minute gap while a stubborn cluster of events sits twenty minutes out, and that cluster is usually where signal latency noise and genuine delivery confirmation timing problems get mixed together. Separate them before you attribute blame to anyone.Apply a threshold before drawing conclusions. When timestamp mismatches exceed five percent of total deliveries, treat that batch as a latency-contaminated sample. You can still study it, but flag it clearly so ROI figures are not quoted as if the underlying data were clean. This single flag often explains why two managers reviewing the same proof of delivery audit reach opposite conclusions about driver performance.Now check hardware and firmware. Compare buffer delays across device firmware versions in the fleet. Older firmware often introduces lag that never appears in standard dashboard reporting, so the visible numbers look stable while the recorded arrival times drift. If lag clusters around specific firmware versions, you have found a cause rather than a symptom. Signal jitter in tunnels or dense urban areas can corrupt arrival timestamps too, so pull route context for those events and see whether the same devices fail in the same spots.Customer disputes need a second reference point. When a customer cites a delivery window from their own shipment portal, that timestamp is usually built on cellular triangulation, not GPS. Compare their claim against your geofence log to isolate whether the discrepancy is customer-side or driver-side. This is the difference between a support ticket and a chargeback.Then test the ten-minute correction boundary. Count how many mismatches exceed ten minutes and estimate the manual correction cost per event. Multiply across the fleet. If that cost outruns the effort of reconfiguring geofence parameters and polling intervals, the case study data is not fit for ROI modeling yet. In many cases, this is a practical heuristic rather than a formal standard, but it separates one-off errors from a drain that compounds every shift.Run the two-second latency test next. Adjust geofence parameters and polling intervals, then measure again. If location data accuracy remains above roughly two seconds of lag, treat the hardware as the limiting factor before attributing failures to driver behavior or process gaps. Reconfiguration is the reasonable step up to that point. Beyond it, a platform change becomes the practical path to protecting fleet telematics ROI margins, and this is where a vendor like GPS Controller enters the conversation for teams comparing options.Finally, re-run the ROI model on the cleaned dataset. Strip latency-flagged events, then recalculate delivery success rates, fuel consumption estimates, and chargeback exposure. Corrupted timestamps can drive inefficient route recomputations and raise fuel use by an estimated eight percent in some cases. Once the latency artifact is removed, you can finally see how much of your original ROI gap was measurement error and how much was real. That split is the whole point of the exercise.