Ecommerce sellers can optimize their sales efforts using data, but only if they can capture useful information and properly apply it. One way to do that is with predictive analytics with data from the sales platform, but it can also include predictive analytics in the shipping industry. After all, shipping data can be used to improve the customer experience, which in turn can result in more sales. Instead of just reacting to customer activities, predictive analysis can be used to proactively anticipate and help shape customer demand. With some forethought, big data can improve your shipping company’s supply chain, increasing your market share.

First, let’s start with an explanation of predictive analytics, in which companies can accurately forecast elements of consumer sales, shipping costs, and transportation based on current and historical data. This may also incorporate outside factors such as weather, current events, and economics. External signals can add more granularity to the predictions. Taken together, data analytics can help an ecommerce or shipping company identify patterns to better plan for customer sales and all elements of their global supply chain.

This use of big data analytics for predictive analytics is growing in the shipping industry and in ecommerce. An MHI Industry report showed the tremendous growth of supply chain professionals using predictive analytics. The report indicated that this usage grew 76% between 2017 and 2019, including use in inventory management to lower cycle times and to improve customer service. In a Council of Supply Chain Management Professionals report, 93% of shippers said that it’s imperative to use data-driven decision making in the supply chain.

What industries use predictive analytics?

It’s not just ecommerce and logistics companies that use predictive analytics. Here are 8 other industries that find this type of data analysis helpful.

  1. Retail: Okay, so ecommerce is retail, but brick and mortar stores are using predictive analytics as well, and it’s doubly important in omnichannel retail. Retail stores need to ensure a steady flow of products their customers want, in the locations they want to buy them. Predicting when seasonal items will be needed in a specific location relies on weather trends in that area, past sales, current economic conditions, and customer demand.
  2. Healthcare: Healthcare supply chain management relies on predicting population needs as well as historic data about past illnesses and supply usage. With COVID-19, predictive analytics was used to help hospitals and organizations prepare for disease spread by incorporating testing data, positive rates in other parts of the country, factoring in local bed availability, and accounting for regional containment efforts. Healthcare organizations also use predictive analytics to predict chronic disease outcomes for specific populations.
  3. Financial institutions: Banks and credit card companies use data science to help prevent fraud, identifying outlier transactions that don’t fit into a customer’s normal pattern. They use customer information to predict what types of products they may want, whether it’s a credit card with specific rewards, or a bank account with certain benefits.
  4. Airlines, trains and buses: The transportation industry uses predictive analytics to plan for the number of vehicles or trips they need on a certain day or week, as well as potential capacity for specific destinations. They can use this information for staffing purposes, as well as optimizing for fuel, timing, and routes.
  5. Entertainment: When you log into Netflix or other entertainment platforms, you’ll get suggestions of other shows to watch, based on what you previously watched and what other similar customers enjoyed. These algorithms use artificial intelligence and are constantly updated based on continually changing data sets.
  6. Weather: While weather reports are often not 100% accurate, they do usually provide helpful information to predict weather events like hurricanes, snowstorms, temperature and other events. Weather forecasters use historic data in addition to trends relating to climate change, to better predict future weather.
  7. Manufacturing: Increasingly, manufacturers use prescriptive analytics, which works with predictive analytics, to determine future actions using data sets. Prescriptive analytics can help supply chain professionals understand the best course of action based on different choices. They also use it for predictive maintenance, to get the best understanding of when machines will need repairs or tune ups, to avoid them breaking down. This allows supply chain managers to increase their operational efficiency.
  8. Maritime analytics: The maritime industry uses analytics in a number of ways, and one is in ship design. Designers can anticipate future shipping needs based on past data and freight usage, to design a ship that is best suited for those uses. Maritime data analytics is also used to determine route optimization, so the ships use the least amount of fuel, avoid risky passages, and meet shipping deadlines. The maritime analytics market also uses predictive analytics to understand customer demands for shipping space and timing, so they can efficiently use the proper number of ships, and port stops to maximize efficiency. See our blog for more information on the benefits of route optimization.

What is predictive analytics used for?

It’s now more obvious how predictive analytics is used in various industries. So let’s focus on ecommerce and its supply chain operations. Predictive analytics is not mandatory to use in ecommerce, but it can boost a shipper’s margins and help them better serve customers. In ecommerce, it’s easy for customers to choose another company if the requested item isn’t in stock or shipping will take too long. An ecommerce company’s goal, then, is to anticipate their customers’ needs and then show customers they have the goods and can get them shipped quickly.

Ecommerce companies have the opportunity to use predictive capabilities to develop actionable insights, improving their operations and increasing both sales and customer satisfaction. This gives ecommerce companies an edge over their competitors. Here are some ways ecommerce companies can use advanced analytics to improve their businesses.

Customer data: Engage with customers in a more targeted manner, developing a predictive model. For example, your company can analyze a customer’s past purchases, clicks, and then group them with like-minded customers to better understand browsing and buying patterns. You can use this information to send email or SMS advertisements to them, and suggest items based on what they previously looked at or are looking at now. This can result in higher sales.

Customer trends: Trends change by region of the country as well as by customer segment. A predictive analytics solution can make it easier to focus on those trends to prepare stocked items for quantities ordered, arrival dates, and locations. It can also help prepare for warehouse staffing if you handle your own storage and fulfillment, or you can use the information to negotiate with your 3PL and ensure capacity.

Shipping data: By analyzing shipping data with machine learning, you can better understand where your customers live and what they will want to buy, before they buy it. That allows you to stock up on certain items, and ensure they can reach your customers in a time-sensitive manner. That might mean ordering items for warehouses around the country, rather than just one location, so the products are closer to your end consumer.

Predictive capabilities can also be used on shipping data to understand what shipping characteristics are most common, and how you might be able to shave down those costs. That might mean finding new carriers or varying which carriers deliver which parcels, to lower costs or increase service levels. It can also mean looking at repackaging options to find more cost-efficient ways to ship the same items. See our blog to learn more about “what is spend management”.

Carrier performance: In addition to costs, carriers have different performance levels. This is something you can monitor with your ecommerce shipping as well. You might see trends and predictions that could impact future costs, and you might see that late deliveries could cost you a percentage of your customers or income. On-time performance can decrease around holiday time, and if you’re already seeing issues, your predictive analytics solution might be able to model what that would look like in various situations.

Rate analysis: In addition to carrier performance and shipping data, you can better understand your future carrier and shipping rates with predictive analytics. This is especially true if your carrier is raising rates for specific locations, services or accessorial fees. Using the data and a predictive model, you can assess what future costs will be and whether it makes sense to look at additional shipping options. Running models based on various settings can give you both a big picture view and a granular view of future costs, so you can use that information to change your plan proactively.

Fleet management: If you are involved in your own fleet management, predictive analytics helps to manage shipments, staffing time, route optimization, fuel and trucking usage. Last mile delivery is usually the most expensive portion of the supply chain, and having the best data at this node means you can lower costs using planning and analysis. Transportation management software is important in this case, allowing visibility into this segment, and possibly into the movement of your goods via boat, train, and truck to the warehouse. Predictive analytics programs can look at the entire supply chain and use external factors to provide important and actionable insights.

What are the methods of predictive analytics?

Predictive analytics is often done with machine learning and applying that to shipping data or other data you want to optimize. Data is the key, because without high-quality data, it’s impossible to get helpful analysis to be proactive moving forward. You can use a predictive analytics platform for multiple software types. The platform might include transportation management software, audit recovery software, warehouse management programs, and contracting/procurement software.

The important thing is to use cleaned data to create a model or framework for analyzing the potential for certain outcomes or events. It means identifying patterns than can be updated as new information becomes available.

Parcel audit recovery technology is an easy way to save money using a predictive analytics platform. The software uses artificial intelligence fine-tuned over years, to accurately and automatically spot inconsistencies in carrier invoices. Running in the background, parcel audit recovery can catch carrier errors resulting in automatic credits to the shipper. That might mean the carrier charged an incorrect accessorial fee or delivered an item late when it qualified for guaranteed on-time delivery.

Combining a service like parcel audit recovery with a shipping or transportation management system is like a one-two punch to cut carrier and operations costs without negatively impacting your service. A shipping system can use predictive analytics to determine the best shipping method based on parcel or freight size, cost, carrier preferences, timing and other factors. The audit recovery service runs on the back end to ensure the shipper is paying the right amount of money for the agreed-upon services. The service costs nothing up front, instead only taking a portion of the recovered savings with no work on your part.

Reach out to us to Shipware for a demo of how our parcel audit recovery system can save you money, using predictive analytics. Call us at 858-870-2020.