Blogs
Geospatial Data Analysis for Machine Learning-based Customer Clustering
Both descriptive analytics and machine learning models can benefit greatly from using geographic data analysis to solve segmentation use cases.
Mosaic designs and deploys custom AI & ML solutions for a range of professional services firms; whether your specialty is management consulting, accounting, financial, or technical services, Mosaic can help your company make data-driven decisions.
Both descriptive analytics and machine learning models can benefit greatly from using geographic data analysis to solve segmentation use cases.
As our world becomes more and more digitized, organizations need to leverage behavior data to provide a better customer experience and detect potential problems before they arise. This can be achieved by analyzing behavior patterns from digital platforms customers operate.
The business was investigating how to incorporate modern data science techniques into their budgeting decisions. Since they didn’t have this expertise in-house, they needed an analytics partner that could provide the right capabilities to accurately model and forecast revenue and serve these results to users in the proper context.
Text data presents a tremendous opportunity to benefit all stakeholders of an organization – investors, employees, processes, and the all-important customer – if the organization can find a way to sift through this data in an automated way to extract key information and solve specific challenges. In that case, they could learn about their firm and start optimizing the way they operate.
Deep learning, specifically computer vision and natural language processing, can be designed to identify defects during the product packaging process. These deep learning models can verify that a label on a package is present, correct, straight, and readable.
Successfully developing machine learning tools to provide value in a business environment requires much more than algorithmic knowledge and tuning. Machine learning cannot provide value in a vacuum.
Traditional lending practices are a prime candidate for machine learning improvements. Lenders can make more accurate and faster decisions by shifting decision-making from analysis of individuals to analysis of trends and patterns.
Root cause and diagnostic analytics is a great approach for identifying computer failures.
Applying statistical inference is a great way for businesses to get more value out of there email marketing programs.
Applying predictive AI services to identify the factors behind dreaded Blue Screen of Death.