Over the past few years, Out-of-Home delivery through Pickup Points and Lockers has become one of the cornerstones of eCommerce logistics. It is no longer simply an alternative to home delivery, but a logistics model capable of reshaping the rules of last-mile delivery.
However, theoretical analyses alone are not enough to understand how this model will evolve. What is needed is real behavioral data: when consumers collect their parcels, how long they take to do so, where shipments are concentrated, and which habits emerge over time.
If you are an eCommerce manager, you are probably thinking that this type of information is rarely accessible. And you would be right. Gathering it requires extensive logistics infrastructure and technology systems capable of analyzing millions of interactions between users, pickup points, and logistics operators.
We have good news for you.
We have this data. And that’s not all—we want to share it.
Why?
Because we believe the future of eCommerce logistics depends on sharing actionable operational insights. Understanding how people actually use pickup points helps eCommerce businesses and logistics operators design more efficient delivery networks, reduce failed deliveries, and improve the customer experience.
Thanks to our network of more than 500,000 Pickup Points and Lockers across Europe, we have analyzed collection patterns to gain a deeper understanding of how consumers behave when choosing an Out-of-Home delivery location.
The results paint a very clear picture: parcel collection follows precise, predictable patterns that can be leveraged to optimize eCommerce deliveries.
And that’s exactly where we begin.
Why Pickup Behavior Data Is Becoming Strategic for eCommerce
In a previous article, we explored how artificial intelligence automatically selects the best delivery location. One key insight emerged: algorithms do not rely solely on geographical proximity.
To identify the most effective pickup point, modern systems simultaneously analyze multiple behavioral patterns, including preferred collection times, average distance traveled by customers, frequency of failed home deliveries, and average parcel dwell times.
This enables a shift from a purely geographical approach to a behavioral one. In other words, the system does not simply identify the nearest pickup location—it identifies the location that the customer is most likely to use.
The challenge is that this type of data is difficult to obtain. It requires high shipment volumes, a large network footprint, and analytics systems capable of aggregating logistical, geographical, and behavioral data.
This is precisely why the insights generated by the GEL Proximity Network provide a unique window into real consumer behavior.
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From Data to Decisions: How These Insights Improve Delivery Performance
Behavioral data becomes truly valuable when it is translated into operational decisions.
Knowing when most collections take place, for example, allows businesses to optimize the pickup points suggested during checkout. Algorithms can prioritize locations situated along a customer’s daily routes rather than simply recommending the closest option to their home.
Likewise, understanding average collection times helps operators better manage locker capacity and parcel storage periods, reducing the risk of network congestion.
These insights also help address one of the most expensive challenges in eCommerce logistics: failed home deliveries.
When a system can predict that a customer is more likely to collect a parcel from a specific location, proactively recommending that option can improve delivery success rates while enhancing the overall customer experience.
This is where technology platforms such as GEL Proximity come into play, combining network data, behavioral analytics, and optimization algorithms to help eCommerce businesses and logistics providers identify the most effective pickup point for every shipment.
The Future of eCommerce Delivery Is Increasingly Out-of-Home
The growth of pickup points is more than just a logistics trend—it is a response to broader changes in urban mobility, delivery sustainability, and consumer expectations.
European cities are progressively restricting traffic while delivery volumes continue to increase. In this context, the Out-of-Home model offers a clear advantage: it consolidates deliveries, reduces failed attempts, and improves last-mile efficiency.
However, expanding the number of lockers or pickup points alone is not enough. To fully unlock the potential of this model, businesses must understand how consumers actually use these networks.
Behavioral data is therefore becoming one of the most strategic assets for the future of eCommerce logistics.
Discover the Full eCommerce Pickup Point Dataset
The data presented in this article is based on analyses conducted across GEL Proximity’s Italian network, a leading Out-of-Home delivery technology provider with access to more than 500,000 Pickup Points and Lockers.
If you would like to learn how pickup points can help improve your eCommerce delivery performance, explore the services and technology solutions offered by GEL Proximity.
