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Solving data challenges for hyperlocal delivery startups

(Image credit: Image source: Shutterstock/wk1003mike)

A few years ago, there was a boom in on-demand delivery startups which subsequently shut down their operations. With 2020 around the corner, the spotlight has returned to the hyperlocal delivery space. An improved e-commerce experience on mobile devices is enabling companies to quickly and more effectively deliver items to consumers at home, and consumers are embracing the convenience. 

Investors are also embracing these platforms, and companies such as Seamless, Postmates, Doordash, among others, are growing and securing significant funding. According to a Statista report, online food delivery alone will be a $28 billion market in the US by 2023, and online platforms make up the fastest-growing segment. What’s more, many consumers are willing to pay up to 30 per cent more for same-day delivery. And those numbers are increasing as some companies offer delivery for items even within a few hours.

Still, uncertainties remain about whether or not hyperlocal delivery startups can keep up with ever-increasing consumer demands, compounding logistics costs, and the ever-looming threat of delivery giants like Amazon Prime. Here’s a look at some of the biggest challenges for hyperlocal delivery startups right now, and the latest technology helping to overcome them.

Consumer demand for faster fulfilment

Delivery expectations are changing. Consumers want fast fulfilment and expect businesses to deliver it. Consequently, hyperlocal delivery startups are in a position to meet these demands. However, time and money are wasted when couriers have to drive back and forth along the same route to complete deliveries. Unlike traditional third-party logistics providers, which accept next-day delivery orders up until a recent cut-off time, hyperlocal delivery services require careful planning and the flexibility to change routes in nearly real-time.

Route optimisation software, which relies on machine learning, is helping to tackle this problem. Algorithms, which use historical data to make future predictions, can identify the optimal times for delivery and help to streamline delivery routes. For instance, the Beijing-based site JD.com promises to deliver orders in as little as 30 minutes across China using an algorithm that determines the proximity of JD.com’s warehouses and distribution centres, as well as offline retailers, to a customer. If an offline retailer is closer to the customer, then the AI system will request that retailer to complete the order directly, decreasing the total route time or the number of stops on a route.

Many hyperlocal delivery companies are using these features, and even letting the customer know that the delivery person has a stop or so to make before they’ll receive their item. Customers appreciate the real-time tracking this software allows, and companies can still meet the demand of delivering items quickly.

Scaling in new markets

The success of a hyperlocal delivery service in one market does not mean that it can be replicated easily in another market. Outside of the Western world, hyperlocal delivery services face many challenges to scale efficiently and effectively. Language barriers, differing government regulations, and a lack of infrastructure are among the most obvious obstacles.

These problems are compounded by a lack of hyperlocal data to utilise. Necessary consumer data, such as addresses, may be written in a native language or in a manner that doesn’t translate to data systems, making it difficult to standardise delivery processes. Brick and mortar businesses may also lack an online presence, creating a barrier for hyperlocal delivery startups seeking to track their inventory, business hours, and location. For delivery services to even begin, these small stores must have all records digitised, which is not always the case.

This is all critical data necessary to identify new partners in order to grow their services and delivery zones. And collecting this hyperlocal data to make the unit economics work in emerging markets is a real challenge. In smaller markets, there still remains traditional shops that rely on loyal customers and do not necessarily need an online presence. Of course, these stores already do or soon will find it difficult to compete with newer stores that utilise digital marketing campaigns and offer easy online delivery.

While collecting data via crowdsourcing is nothing new, it is becoming easier due to the prevalence of Internet-connected devices in emerging markets. For instance, there are already a number of services that rely on crowd-sourced data from individuals around the world to report traffic and weather conditions.

Waze is one of the most famous success stories of using crowd-sourced data to reveal real-time traffic conditions. These same data collection methods can be used to collect and verify data on local businesses as well. When delivery startups have access to better hyperlocal data, then they can analyse and identify opportunities for growth more quickly.

Where hyperlocal delivery is headed

Hyperlocal delivery is growing in complexity and scope. Startups in this space must prepare to implement the latest technology to handle the rising demand for instant delivery. What’s more, innovation is needed to compete with e-commerce giants like Amazon and Walmart, who are spending heavily on logistics development and creating a consumer expectation of same-day for everything. These companies are also investing in technologies such as autonomous vehicle technology and self-driving drones and robots to make last-mile deliveries more cost-effective in the not so distant future.

The ability to compete will somewhat rely on data. Hyperlocal delivery companies that can access to utilise this data will likely better meet consumer needs. While consumers are demanding faster deliveries, they are open to companies outside of the tech giants to take a piece of this market.

Hyperlocal delivery is a complicated undertaking, and growing competition requires hyperlocal startups to innovate constantly. Given this, we might see more of these smaller companies merging in order to share data, resources, and, more importantly, technology talent that knows how to create and maintain systems to take a larger piece of the market.

It is, however, becoming easier for all companies to improve delivery times and collect critical data that can help them scale without decreasing the speed or quality of their services. And as more and more consumers demand two-day, same-day, and two-hour delivery, this data will play an integral role in creating loyal customers.

Geoffrey Michener, CEO and Founder, dataPlor