You have shipping data at your fingertips, even if you’re not yet using it. For retailers, each mouse click, each customer email, each item added to an ecommerce basket can help you sell more to your consumers and improve customer service. 

Are you already using this information to lower your costs and increase sales? For those in the maritime industry, are you using maritime data analytics to better plan your routes and operations? In both cases, if you’re not, join the club. 

An astounding 99.5% of all data collected isn’t analyzed or used. It can be overwhelming, but using shipping data analytics can improve your financials, whether that’s through your own business investing in the right software or having a third party leverage analytics to audit your invoices and optimize your contracts. Either way, it’s probably time that you learn a little bit more about big data.

What is big data?

Big data is a big category. It includes the data in both structured and unstructured forms, stored in the cloud. The data can also be divided into traditional and nontraditional data. 

Those in data science collect traditional data, which is also considered “look-back.” For ecommerce, those supply chain data sources might be order management systems, warehouses, payroll, inventory systems and carrier data. For the maritime analytics market, traditional data might come from dockyards, ships, vessel operations, and bills of lading. Traditional (fixed data) is used to analyze financials like profits and losses.

Nontraditional data is time-sensitive and not always quantifiable. Information can come via less structured sources and formats like audio, video, images, texts and internet of technology things (IoT) devices. Examples of nontraditional data are weather data, traffic and location data, and movement of freight via transportation. 

You can probably see how the maritime sector would find this helpful in the shipping industry, and how retailers would find this helpful in planning out transportation. Nontraditional data is helpful with forecasting and predicting problems so changes can be made before the problems happen.

An example of a nontraditional data source is IoT data, which can come from any node in the supply chain, making it an important method for gathering shipping analytics data. About 20% of shippers are using IoT, as are 30% of 3PLs, per an American Shipper study. The study also showed that 60% of respondents were not using any logistics technology for analytics. It’s never too late to start, and it’s a good company goal to have for 2021 and beyond. 

How can big data help?

Big data looks for patterns that can help with an actionable insight. The data will flow in continuously, and artificial intelligence is key in finding those patterns, which may show market trends, make predictions and help with operational efficiency. Your company may already use data for business intelligence and analytics solutions, but using big data in the shipping industry can help logistics companies with additional valuable insights. Analytics in the shipping industry is ripe for growth.

Due to the size, complexity and multiple data types, big data analytics doesn’t use the standard analytics programs. It needs advanced data processing tools including machine learning for effective data analysis.  

12 ways you can use big data in the shipping industry

Overall, big data enables ecommerce companies and shipping in the maritime industry to make better decisions, improve performance, predict and address problems, refine marketing and pricing, and prevent fraud. Here are 12 ways you can use big data for actionable insight and to gain a competitive advantage.

  1. Fraud detection: Analytics solutions can use shopping behavior and payment information to understand potentially fraudulent activity. This is a big activity for credit card companies, who can recognize when spending falls outside a typical pattern for that user. It can then flag the payment to potentially avoid fraud. The same can be done with ecommerce companies. The retailer can set up alerts if it detects multiple payment methods from the same IP address, for example. A series of maritime bookings that fall outside a pattern can also fall into fraud detection.
  2. Predictive analysis: Predictive analytics can be used in ecommerce as well as maritime data analytics. For ecommerce, shippers can track customer information, from their contact details to purchase history to what they clicked on. This information can be used to understand what products are most interesting to specific customers, and this can change the mix of what they offer, and to anticipate demand. It can also be used to understand customer wait times, so decisions can be made to improve that going forward. That might mean changing staffing levels proactively.
  3. Marketing and targeted advertising: As the big data shows personalized marketing trends, the retailer can use targeted advertising to give personalized experiences and ads. The retailer can segment their audience to send more personalized product and discount offers. That segmentation can include age, location, other demographic information, and socioeconomic information. It can also include style and size preferences. Personalization can influence buying decisions. One customer retention study showed that 86% of consumers said personalization was important in their purchasing decisions.  Acquiring a customer is more expensive than keeping a customer. Retailers can use more of their budget to retain their best customers rather than using the bulk of advertising campaign money to attract new customers. According to Forbes, Amazon’s predictive recommendations drive 35% of company revenue. 
  4. Spot trends: The shipping companies can use big data to spot trends. In the global maritime analytics market, that could be subtle changes in weather patterns which cause longer sailings, or shorter time periods in which shippers book cargo space for their freight. This can impact pricing and scheduling. For retailers, advanced analytics might show that some inventory is taking longer to arrive than others, and that’s because it’s coming from a specific country. Analyzing search results can help develop search engine optimization campaigns and inventory decisions.
  5. Pricing: Big data and shipping analytics can help tweak pricing based on supply and demand. Using information from prior years can help with inventory planning and discounts based on seasonality, timing and trends. The data can show how price management initiatives can increase business margins by 2%-7%, and grow ROI by 200%-350% on average in a one year period. This information can be used to set prices dynamically.
  6. Predictive maintenance: The transportation industry might use predictive maintenance for fleet management. Using big data can show when certain parts might need replacing or repair, even if it’s outside the manufacturer recommendations. Big data would take into account mileage, driving conditions, weather data, and even the driver.
  7. Ship design: Big data is being used in ship design, by analyzing sensors on ships already in the sea. That can include storage information, fuel efficiency, protecting cargo, and safer operation. 
  8. Route optimization: Whether for fleet management or maritime shipping, big data can help with route optimization to incorporate weather, traffic, timing and other pertinent factors. Automatic identification system data (AIS data) is used by ships and port authorities in real time, but it can also be used with predictive analytics to better plan routes.
  9. Tracking: While sensors, barcodes and RFID tags can be used in various ways to monitor and track shipments, combining those with weather and traffic information can help with predictions about arrival information, especially if there are hiccups in the process.
  10. Strategic decision making: Using prescriptive analytics, which recommends the best path forward, companies can rely on data and not intuition. Information is available in real time and can be analyzed quickly to make important decisions for their business strategy.
  11. Contract optimization: Carrier contracts can be optimized using big data as well. Your own carrier records and invoices contain a gold mine of information. That includes shipping spend, locations, package sizes and characteristics, residential versus commercial deliveries, timing, and accessorial fees paid. Combine this information with other shipping data, and you’ll have a good idea how your shipping compares to peers, and how your pricing compares as well. You will not have access to this comparison data on your own, but Shipware uses big data to make these comparisons and do deep analyses. The contract optimization process relies on big data and data analysis to better understand where your company can save money through process changes, and where you can negotiate discounts going forward.
  12. Invoice audit recovery: Your carrier invoices have mistakes on them and you may not see them with a manual review. Shipware’s invoice audit recovery service uses big data analytics to examine multi-point audit in invoicing where there are likely to be mistakes, based on your individual contract and data from other contracts. When service levels aren’t met or incorrect charges are made due to mistakes in applying shipping rules, Shipware can get your account credited with no effort on your part.

Big data is in its infancy, in the numerous ways it can be applied to help businesses meet their KPIs and operational goals. This data can help your company improve its margins, grow sales, and lower costs. Talk to Shipware about how we can help you use big data to optimize your carrier contracts and lower your carrier invoices. We can also offer tips on last mile delivery best practices and how to optimize your automated shipping. You can reach us online or at (858) 879-2020.