How Big Data Can Improve E-commerce Fulfilment

Big data is often used to help e-commerce marketers who want to discover which of their products sell best over time and which factors drive those sales. However, as digital branding and ad-tech writer Kayla Matthews explains here, it’s also useful when fulfilling e-commerce orders, which is another essential step in the process of keeping products in stock and making customers happy.

Big data is an excellent e-commerce resource and is having some incredibly positive impacts on e-commerce fulfilment. As technology improves, and platforms get adopted in the industry on an even broader basis, even more benefits will become apparent.

Here are four ways that big data makes e-commerce fulfilment go more smoothly than ever.

  1. Reduction of inventory management barriers

Many young entrepreneurs have ideas about starting e-commerce businesses, from small apartments or their dorm rooms, but feel overwhelmed by the thought of having to keep their inventory with them and manage it themselves.

Big data gives them another option by allowing them to track their orders and get merchandise to customers all over the world without taking care of inventory management needs themselves.

Often, the owners of these e-commerce businesses work with drop-shipping companies and depend on big data to make inventory management more cost-effective and streamlined.

  1. Faster, more responsive last-mile deliveries

Last-mile deliveries are those that are in transit from the transportation hubs to their final destinations, which are often customers’ doorsteps. E-commerce providers face ongoing challenges with that last, but all-important, stage of the journey.

For example, delivery drivers may encounter road work that causes slowdowns, or realise too late that a particular area in an urban community is extremely crowded and lacks sufficient parking space.

Using big data allows e-commerce companies to become aware of the factors that may reduce the success rates of their last-mile deliveries and proactively make changes. Then, because they respond to obstacles quickly, customers get their goods more efficiently than they otherwise would.

Although big data is undeniably useful in e-commerce fulfillment for that reason, many logistics managers are still unaccustomed to using it.

There must be a culture shift away from the now-outdated ways; and people working in the field need to have access to educational outlets that can help them apply big data analytics to their business models.

  1. More accurate analysis of trends over the years

One of the most helpful aspects of big data is that it allows people to look at enormous amounts of historical evidence and use it to identify trends to see how those patterns emerged. For example, people involved in e-commerce fulfilment could examine industry data from 2016 and 2017 and discover how some things have changed and other aspects have remained relatively constant.

Looking at massive amounts of data from the past is immensely valuable when determining the reasons behind noticeable shifts that affect an e-commerce business. However, before big data platforms became prominent and user-friendly, people had to rely on their memories or small amounts of paper records. Those two resources often did not allow them to retrieve insights from far enough in the past to be worthwhile.

  1. Improved demand forecasting and lead time estimates

Having products in stock when customers want them is especially important in the world of e-commerce. When people solely shopped in brick-and-mortar stores, they may not have had the time or energy to drive to other locations if the first ones they tried didn’t have the items they wanted. But the nature of the internet means people can visit dozens of online stores in a few minutes to check availability.

Big data benefits the retail supply chain by aiding fulfilment experts in realising that certain products are selling faster than expected, allowing them to place replenishment orders before the items run out. These platforms can also factor in things like events in pop culture that might stimulate higher than usual demand.

For example, if a celebrity made headlines related to an internationally recognised awards ceremony for carrying a purse on the red carpet that anyone could buy from a well-known website, people might be abuzz about that accessory and flock to purchase it. A big data platform could analyse keywords used in social media posts to indicate such a flurry of activity.

Even with the advancements in demand forecasting that big data brings, some people inevitably miss out on being able to immediately buy the products they want. Fortunately, big data is also useful in giving accurate lead time estimates. Statistics can tell e-commerce retailers how soon they’ll get new shipments based on past arrivals of ordered stock.

Then, companies don’t have to merely say a product is “back ordered”. They could say something as specific as “next shipment to arrive between August 5-10”. Such information allows people to plan their e-commerce shopping experiences and set expectations to avoid disappointment.