Key indicator monitoring is an essential business activity in understanding the effectiveness of its processes, customers behaviors and the reasons of success or failure of business decisions. The indicators capture business and customer information of past and present activities enabling the application of descriptive analytics to support better decision making.
SERVICES
Descriptive Analytics
Knowing your business helps you better plan for the future
Descriptive Analytics includes many techniques that help businesses describe what has happened in the past and what is happening today. Fidelis descriptive analytics approach uses advanced data queries, descriptive statistics, correlation analysis, advanced reporting and data visualization techniques within an interactive, flexible and intuitive environment. Using Fidelis descriptive analytics approach, customers can create dashboards with ease to both help formulate and communicate effective decision making.
Business data sources can be structured like data found in databases, or unstructured such as those found in documents, email and social networks. We use techniques to consolidate all relevant data sources for analysis and reporting, providing the tools to process and integrate large amounts of data. This results in highlighting valuable insights in the operations of your organization.
We give you various tools to visualize
and analyze your data in real time
We understand that the right visualization requires an understanding of the client’s needs, nature of the data, and the many tools and techniques available to present data. With data visualization we help you tell a story in an accurate, complete and simple way, providing insights and even revealing unknown patterns and relationships between the data.
In addition to traditional descriptive analytics methods such as statistical analysis, data queries and reports, we use AI techniques including unsupervised learning techniques, those that seek to identify patterns without the need for the user to provide any prior information, and, therefore, help to discover hidden patterns, new structures or useful relationships within your data.
We work with the two most popular unsupervised learning techniques which are:
Cluster Analysis
Clustering techniques identify similarities between elements using some measure of similarity. For example, you can use clustering to identify customer segments with similar purchasing behaviors, or product groups with similar purchase characteristics.
Association Rules
Association rules discover elements that occur frequently together. They are widely used for Market Basket Analysis to identify set of items that are frequently sold together. Fidelis used these techniques to identify customer purchase patterns in supermarkets based on the day of the week, special events days, impact of shopper advertisement in product sales, impact of shopper products in other products not included in the shopper, products representing majority of sales, forecasting the likelihood of products to be purchased together, and analysis of most profitable products purchased by customers.
Let’s work together
We are more than thrilled to work with your business. Fidelis Engineering offers a variety of services that meet your needs.