5 Lessons On-Demand Delivery Can Learn from Micromobility

by Anna Liu, Product Manager at Zoba. Anna enjoys transit-friendly cities, escape rooms, and previously worked at Google. She holds a B.A. in Computer Science from Harvard University.

Last-mile delivery is currently undergoing a radical transformation as shifting customer expectations push retailers, 3PLs, and parcel delivery firms to offer faster, more on-demand delivery options. Just as Uber and Bird disrupted the ride-hailing and micromobility sectors, quick commerce companies like Getir, goPuff, and Zepto are successfully leveraging technology to radically reshape customer expectations around delivery.

Gone are the days of two-day grocery delivery; customers want deliveries at the time and place most convenient for them. While customers won’t always want these items in minutes, the vast majority will be delivered same-day. A McKinsey study showed that 63% of American consumers prefer home delivery options, such as scheduled and on-demand delivery. To keep up, retailers and last-mile delivery providers need to embrace new technologies that enable them to shift from pre-planned and scheduled delivery models to on-demand and same-day models aligned with consumer expectations.

Zoba has spent years helping many of the largest shared mobility operators leverage demand forecasting to dynamically position thousands of vehicles when and where they are needed. We see a number of similarities between the emerging challenges facing last-mile delivery providers and those faced by micromobility operators.

In this blog, we’ll look at five lessons that micromobility has to offer delivery providers as they shift toward more dynamic, on-demand, and same-day operational models.

Lesson 1: Responding to Demand is Not Enough. You Need to Anticipate It.

Technological advancements in the last few decades have shifted consumer behavior away from scheduled trips to the store and toward an expectation that they can have what they want, whenever they want. We can read breaking news within minutes, stream our favorite songs, and binge the latest TV shows at the click of a button. Customers now expect their offline experiences to mirror those online: on-demand, personalized, and delivered at low cost. To keep up with consumer expectations, on-demand delivery providers need to be able to anticipate consumer needs and proactively position themselves to meet demand for physical goods.

Micromobility operators already recognize the importance of anticipating demand and pre-positioning physical assets. For a customer looking for a way to get to class or commute home from work, having a vehicle a few feet versus few blocks away can mean the difference in choosing to ride a bike or call a car. Micromobility operators have long leveraged technology like Zoba to anticipate demand and preposition their fleets. Every day, Zoba provides recommendations on where to position hundreds of thousands of vehicles so they are where they’re needed, when they’re needed.

On-demand delivery faces the same challenge. When an order needs to be delivered in 15 minutes, a delivery provider’s ability to service that order will be severely limited if the closest courier is currently across town. For retailers, this results in lost orders, and for couriers, it means losing the order to a competitor or failing to uphold their SLA.

By leveraging Zoba, which provides hyper-local demand forecasts that model how markets evolve in near real-time, delivery operators can ensure they have the best chance of capturing orders and meeting customer expectations by dynamically allocating resources not only to where they are needed now, but also where they are most likely to be needed in the near future.

Lesson 2: Like The Weather, Demand Can Change By The Hour

The Covid-19 pandemic has permanently altered our lives, and one of the most profound ways is in the breakdown of traditional daily routines. People no longer commute to work at the same times, eat lunch around the same hours, or work from the same places every day. Dynamic consumer behavior requires demand predictions that can keep up with the variability of consumer behavior and are unique to the time and location of interest. Just like weather, demand can vary widely block by block, hour by hour.

When predicting consumers’ transportation needs in the mobility sector, Zoba recognized that it’s not enough to group by time buckets, e.g. weekday mornings and weekend afternoons. A market may display different temporal patterns based on seasonality. To keep up with changing demands, we create custom demand profiles that are fine-tuned and updated in near real-time.

On-demand delivery providers also need the ability to respond quickly to everchanging consumer demand patterns. Understanding how order volumes change allows delivery providers to staff the appropriate number of couriers and assign them to the parts of the city they’re needed most.

Lesson 3: Task Stacking is Key to Profitability

Platforms like Zoba have long enabled micromobility operators to improve task efficiency and reduce travel times by combining vehicle deployments, battery swaps, and maintenance tasks into single routes. This saves time, reduces cost and improves vehicle utilization, often by as much as 20% to 30%.

On-demand delivery providers face similar challenges with operational efficiency. Given the pricing pressures in delivery, earning a profit on a single order can be challenging. Stacking orders and grouping tasks can have non-linear returns and greatly impact the business’s bottom line. Today, operators are caught between how to balance hitting tight 15–30 minute delivery promises while stacking as many orders as possible. On-demand delivery companies looking to improve margins should investigate adopting platforms like Zoba that combines demand-forecasting with decision-automation to provide real-time order stacking and dispatch recommendations.

Lesson 4: Automation Drives Speed & Efficiency

Delivery and micromobility are both operationally intense and margin-sensitive businesses. In micromobility, operators that under-invest in decision-automation technology lose money on 1 in 3 scooters deployed. On-demand delivery providers suffer similar challenges with order margins. Decision-automation technology can dramatically reduce costs by incorporating the latest information in recommended actions.

The impact of these operational decisions only grow with scale. In micromobility, operations started out fairly simple: an operator stores one hundred vehicles in a single warehouse and focuses on finding the best place to deploy scooters every day. Today, micromobility operators manage hundreds or thousands of vehicles across several warehouses. Fleets are now multimodal and swappable.

What started as a simple planning exercise now involves assessing the best options out of thousands of possibilities. As operations grow more complex, these tasks are well-suited to optimization systems that combine computing power with the latest data science and operations research.

Zoba’s optimization system automatically generates sequenced tasks, which integrates directly into the operator’s tooling and workflow. Fleet distribution analytics and task adherence can also be tracked through dashboards, removing the need to consolidate data from multiple places. An API-centric solution like Zoba’s plugs into a company’s technology stack behind-the-scenes, making it easier to manage decisions around courier placement, dispatch, and order stacking.

Lesson 5: Embrace and Adapt to Regulation

Certain types of delivery and labor models are facing increased scrutiny from government authorities, including the recent ban on dark stores in Barcelona and the New York City Council’s efforts to regulate 15-minute delivery companies by requiring them to notify delivery workers about routes and obtain specific licenses to operate. On-demand delivery providers should be proactive in adhering to regulatory changes like these to allow the delivery industry to flourish in the long term.

Early micromobility operators faced rapidly changing regulatory environments and needed to adapt quickly while growing their market presence. Zoba helped our operator partners ensure compliance through features such as zone constraints and equity minimums. Similarly, on-demand delivery providers could benefit from partnering with technology platforms with customizable features to fit each market’s regulatory environment and help them avoid unintended mistakes and fines.

In closing, we see many parallels between the challenges facing on-demand delivery providers today and those faced by micromobility operators over the past decade. As an early player in the micromobility ecosystem, Zoba has had a unique lens into these problems and the role technology can play in solving them. We look forward to playing a larger role helping delivery providers use demand-forecasting, predictive batching, and fleet optimization to shift from pre-planned to more dynamic, on-demand operational models.