Stargo ROI simulator
Calculate the financial impact of stargo on your company
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bottom line impact of Stargo on your organization
Data is the lifeblood, of the logistics and freight world, it is at the core of every decision you make to promote the growth and profitability of your organization. Data empowers you to make precise decisions, based on facts and trends rather than speculations. Many forward-looking organizations are acknowledging that data has become diverse and unstructured.
Enriched data is valuable asset for any organization, it becomes more useful and insightful.
1. cost saving of data enrichment
2. data enrichment fosters meaningful customers relationships
3. data enrichment maximizes customers’ nurturing.
4. data enrichment boost successful targeted marketing
5. data enrichment facilitate greater sales
6. redundant data costs in company significantly, it results in revenue loss, customers loss and damaged reputation.
7. data enrichment improve customer’s experience
StarDox AI and machine learning engine deals with lump-sums, which are accrued sums, accumulated from several charge codes jointly, and are reflected within the operational and financial systems, as one sum. The StarDox engine is able to breakdown into the correct individuals’ charge code sums and accrue them automatically. The engine will search for relevant characteristics within the entire shipments data base, related to specific individual shippers’ information. The solution will eliminate wrong accruals, invoicing issues, and payment delays.
StarDox handles data duplications, within the freight and logistic operational and financial systems. Current freight and logistics companies’ problems, resulting in errors, missed opportunities associated with duplicated data. Having customers and suppliers, and other data elements duplicated in operational and financial systems, creates many challenges in an organization, whereby StarDox, through an AI and machine learning engine, would be resolving duplications dynamically.
Data duplication is a data quality problem, that is extremely pervasive in operational and financial systems. It means that a data source has multiple records, with different syntaxes for the same object. Our solution would deal dynamically with a data duplication problem.
Missing data are values that are not available, however, would be meaningful for your decision making. Missing data can be anything from missing sequence, incomplete shipments data element, missing files, information incomplete, data entry errors, etc.
missing data is part of any real-world data analysis, within the freight world, it can corps up in unexpected places, making analysis challenging to understand.
Our solution will assess the workflows in your organization, and deal with the missing data. We deal with special data structures and use them in your workflows for exploring missing data and creating better decision making.