3 Ways Big Data Can Help Small Businesses With Shipping Strategies
Heightened consumer shipping expectations mean small businesses must elevate their strategies in order to compete. Big data - while often reserved for larger companies -- is being recognized by small businesses as a resource to match customer demand, and compete with retail giants.
Big data can be analyzed to reveal patterns and trends, specifically related to a business’ behavior. Most companies already have big data at their fingertips - Excel records, Google Analytics and data stored in the cloud. But until recently, many businesses didn’t have the automated algorithms and solutions to analyze and interpret these data sets; form predictions; and make business decisions.
Although big data is often associated with powerhouse companies, small businesses have one major advantage over large companies - agility. Smaller businesses can pinpoint internal and external opportunities to take advantage of resources, improve efficiency and create a better customer experience. And considering their small size, they’re more enabled to quickly act on their findings than larger companies.
Here are a few ways that small businesses can use big data to smooth operations and improve their bottom lines.
1. Get smart about warehouse locations.
With a wealth of warehouse location and stock inventory options available, it can be difficult for companies to find the right mix to optimize cost and provide the best experience for customers. But, in order for businesses to save on costs, it’s crucial for them to determine the ideal spot to plant a warehouse and what items to stock where. Big data helps small businesses hone in on the best investment choice to avoid pouring funds into low-potential geographic areas, and instead direct their spending toward other areas, such as research and development.
Big data can give a company access to supply chain information so they can make educated business decisions. For example, Endicia uses package data and predictive analysis to guide ecommerce business owners’ decisions, ranging from where to plant a warehouse to how to improve delivery for the end-consumer through forecasted shipping costs and time in transit. By taking a look at where most products are shipped and how long they take to get there, business owners can determine the optimal location for a warehouse that will save the most time and money. On top of that, small businesses can make these high-impact decisions with their internal data, without the steep costs of external resources normally associated with intensive market research and implementation.
2. Optimize costs and delivery time expectations.
Customers are in the driver’s seat in ecommerce, and if businesses can’t meet expectations, they risk losing out on sales and profits. A RetailWeek and Shutl survey revealed that delivery performance expectations are 42 percent higher than what they were two years ago and that 91 percent of consumers want to know when a package will be delivered. Additionally, lengthy delivery times are becoming less tolerable. A 2016 comScore study revealed that 46 percent of consumers have abandoned a shopping cart due to shipping times that they considered to be too long. That’s where big data comes in.
With predictive pattern insights and forecasting abilities, big data provides more specific delivery time estimates; allows small businesses to provide customers with exact delivery time windows; and informs small businesses of the best time to ship out a package.
Businesses have generally offered a one to three day timeframe for expedited delivery, depending on the carrier selected. By closely examining past data, small businesses are able to identify a package’s average time in fulfillment and transit to present customers with narrower windows, ultimately improving their experience. For example, based on the data of hundreds of thousands of packages shipped in prior years, we can start to predict when the mail stream will become inundated with packages before it happens.
While we know that shipments spike during the holidays, predictive data can give us insight year round. It can let business owners know precisely when a delivery will take place. For example, while USPS Priority Mail provides a window of one to three business days, a business’ data may identify a particular route as faster, predicting delivery in one day and three hours instead. That way, businesses can save money using Priority Mail instead of Priority Mail Express and still ensure the package is delivered on time.
Predictive data also lets a small business know the best time to drop off a package at the Post Office in order to arrive at its destination in a timely manner. For example, if a small business drops off a package at the Post Office by 1:30 p.m. then it will get to the customer the next day, versus dropping it off at 4 p.m. and it gets to the customer in two days. All of these details are now accessible given the massive amounts of data at shippers’ fingertips.
From pinpointing prime warehouse placement to providing deeper insight into fulfillment methods, big data plays a vital role in small businesses’ ability to keep up with the growing, competitive ecommerce landscape and customers’ delivery demands.
3. Speed up order fulfillment.
Order fulfillment is rife with the potential for human error and lag time. Manually carrying out complex orders opens the door for details to be missed and mistakes to be made. These operations are also time-consuming, as workers have to manually select the shipping carrier and service for each and every package shipped out. Big data can speed up time in the warehouse by predicting the shipping carrier, service and add-ons to automatically create shipping labels.
ShippingEasy’s AutoShip predictive software exhibits how big data can mean big results. AutoShip utilizes machine learning to automate shipping, in turn, decreasing order processing and delivery times. Out of 2,422 orders, AutoShip predicted 2,417 accurately, and only five of these orders required human assistance. Services like AutoShip show how the integration of predictive data can help businesses drastically cut down on time and labor involved with the shipping and labeling process, and direct these resources to areas of growth, such as research and development.
From pinpointing prime warehouse placement to developing efficient fulfillment methods, big data plays a vital role in small businesses’ ability to keep up with the growing, competitive ecommerce landscape and customers’ delivery demands.