Logistics analytics fortifies the operations with best-in-class security and enables collaboration across departments.
FREMONT, CA: Transportation and logistics firms create and consume more data than almost every other sector. Despite this, they find themselves lagging behind other verticals in their potential to turn a profit from data. With thinning profit margins and new competitors entering the logistics sector, the only way to outperform other firms is through the brain. Logistics analytics provides the edge over the competition. Innovative data analytics doesn't just promise better ROIs but also offers logistics firms a way to optimize their business to cut costs, reach more customers, and find new revenue streams to grow. Here is a look at the areas of logistics analytics and how they drive business growth.
Logistics firms that strive to enhance their business processes need to keep their finger on their performance pulse. Performance analytics is conceptually simple. The logistics firms set up metrics that measure KPIs, which tell how different business aspects are doing. But the majority are still battling to understand the basic unit economics of their operations. Working with several distributors within the supply chain comes with many reporting variability and inconsistencies, making accurate data collection hard to attain.
Customer-facing analytics enable logistics firms to enhance their user experience and delight their customers, thus increasing retention. Helping avoid customer churn always helps the bottom line. Sometimes, it's as easy as increasing visibility through freight tracking. Set up sensors or GPS telemetry to enable customers to track their packages and cargo shipping, sending real-time data to devices. But customer data analytics can be more innovative. By joining data across CRMs, sales, and financials, logistics firms create customer profiles shared with sales and support teams.
With the increase of big data analytics and machine learning, the logistics sector has tapped into predictive analytics's potential. Predictive analytics looks at historical data and uses that data to build predictive models to forecast future trends.