Planning delivery routes with multiple stops is impossible without analytical insights.

Even when your routing and optimization is entrusted to error-free algorithms of route planning software, analytics is what helps you fine-tune your routing machine.

What do we talk about when we talk about predictive analytics in Track-POD route planning? And how can you make the most of it?

This post has all the answers.

What is predictive analytics?

Let's start with a generic definition of predictive analytics.

Predictive analytics encompasses methods, tools, and techniques used to analyze real-time and historical data to make predictions concerning future events.

If we observe and analyze any process over a period of time, we can identify trends and anticipate how similar processes will unfold.

From there, we can make predictions and build plans addressing the gaps and laying the groundwork for more efficient utilization of resources and frameworks.

Predictive analytics

Predictive analytics for route planning

Predictive analytics in delivery route planning refers to real-time and historical data analytics and performance insights that power future routing and optimization.

On this blog, we've talked about the North Start metric of delivery operations - Delivery In Full, On Time aka On Time and In Full Delivery (DIFOT or OTIF).

While DIFOT is a very good metric for a top-level assessment of your operational efficiency, there are other insights you can dive into using shipment analytics in Track-POD.

By looking back at your drivers' performance over a period of time, you can not only run data-based delivery driver performance evaluation but also make adjustments to your route planning strategy.

What kind of data are we talking about? And where can you find it? Let's take a closer look.

Predictive analytics in Track-POD

Track-POD is a delivery ecosystem that offers real-time and historical analytics both on desktop and mobile.

As a route manager, you can trace your team's performance up to 2 years back. At the same time, you can get live or historical (up to 2 months back) performance insights at any point during the day with a tap on your mobile screen.

Download Track-POD Route Manager app for Android (Beta)

In addition to route analytics and KPIs easily accessible in your web dashboard, Track-POD offers ready-made as well as customizable shipping reports.

By switching to the new version of Track-POD analytics, you can retrieve the latest added report - Cost Savings by Time/Distance. This is where your predictive analytics for routing live.

Track POD shipping reports

Note that the report is meant to provide you with cost savings insights, i.e. how much money you saved with route optimization depending on how your drivers' planned vs actual distance and time compare.

Cost Savings by Time/Distance report is available for all depots or any one of your depots so you can segregate data and analyze it at the depot level.

Track POD cost savings report

If you're interested in the same analytics but without the insights into cost savings, you can access it in your Track-POD Route Manager app for Android.

predictive analytics in Track POD Route Manager app

Planned vs actual distance

Once you've downloaded the Cost Savings by Time/Distance report in the new version of Track-POD analytics, you'll see a breakdown per driver.

The data points for cost savings calculation is # routes, #stops, planned vs actual distance, and planned vs actual time.

shipping report cost savings Track POD

Planned vs actual distance is exactly what it sounds like. On the one hand, you have the distance planned by the system. Depending on whether you use the Track-POD route optimization algorithm or not, your planned times will vary.

On the other hand, you have actual distance, i.e. what the driver traveled based on GPS tracking enabled via the Track-POD delivery driver app.

Cost savings by distance formula

Planned vs actual time

Planned vs actual time insights work in the same way.

When you plan delivery routes with Track-POD, the system calculates how much time it will take to complete routes based on how long it takes the drivers to get to each site in the route, how long it takes to carry out the delivery service, as well as drivers' break time.


By assessing the difference between the planned and actual time it took your drivers to complete delivery routes, together with your route rates, Track-POD can calculate how much money you've saved or lost.

Now, based on how your planned and actual distance/time compare and how significant your cost savings are, you can make better routing decisions.

routing optimization settings Track POD

Average service duration

Going back to the old version of Track-POD analytics and KPIs specifically, you can retrieve another predictive analytics insight - average service duration.

KPIs in Track POD analytics

Average service duration is a metric that will help your future route planning because you can compare it to the service time you indicate in your Track-POD routing & optimization settings.

By adjusting your service time to the actual average time your drivers spend per site, you feed more accurate data to Track-POD's route optimization algorithm. In turn, you get more precise with your route scheduling.

Wrapping up

Delivery operations need to rely on performance data. When you have reliable historical data, you can maximize the usage of route optimization algorithms and AI.

Predictive analytics in route planning is how you get consistently better with route planning. I hope this post gives you an overview of what kind of data you can retrieve from Track-POD to make the most of its route planning.

If you'd like to know how you can boost your efficiency with Track-POD delivery software, book a free demo and we'll give you a personalized tour.