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Digital Themes

Commercial Analytics

What is Commercial Analytics?

Commercial Analytics involves a varied spectrum of analytics tools and techniques used to identify growth opportunities and actionable insights through hypothesis testing, scenario building, and a plethora of reporting capabilities.

Commercial analytics are used to monitor and report on operational status and competitive forces. Further, the application of these analytics tools and techniques allows for the rapid creation and execution of new products and strategies based on the most pertinent data.

Commercial analytics involves scientific marketing, as it facilitates real-time testing and measurement of campaigns. Once ideas and theories have been tested, adjusted, retested, and honed into profitable operations, the process is repeated over and over to capitalize on ever-emerging market anomalies and windows of opportunity.

Analytical insights can reveal gaps or inconsistencies in product offerings or customer service and can suggest optimization through various quantifiable actions like pricing strategies and product changes. Predictive analytics solutions link existing data sources to glean business insights using dashboards and simulation models.

Proficient commercial analytics includes data warehousing, statistics, risk management, big data, machine learning, and business intelligence. Mastering and leveraging these tools creates an opportunity for data visualization, credit risk modeling, marketing campaign optimization, fraud detection analytics, and more.

Commercial analytics involves examining customers and markets, understanding real or perceived risk, exploring cost reduction solutions, and testing or creating new products and services. The best uses of commercial analytics are based on business initiatives and their inspiration of lucrative strategies, tactics, and business decisions.

How can Commercial Analytics techniques be best applied to your business?

  • Data Visualization - Commercial analytics detects patterns and trends that may be overlooked by automated data mining or traditional pattern recognition software. Data visualization tools pull data from several data sources, including the Internet of Things (IoT), Big Data, statistics, data science, data warehousing, and operational databases.

  • Pricing and Yield Management - Commercial analytics tools and techniques provide data-based understanding and prediction of customer behavior to maximize revenue. Effective pricing and yield management require timely and relevant data on available resources, the sales window, and what customers are paying or will pay for a similar resource.

  • Marketing Campaign Optimization - Commercial analytics collects and analyzes the required data to inform new or revised marketing campaign strategies. Market campaign optimization involves data warehousing, integrated customer viewing, customer segmentation, modeling, and campaign measurement all to identify growth opportunities that can be quickly addressed but still generate a large payout.

  • Customer Value Analytics - Customer value analytics combines data warehousing, data mining, and predictive analytics to create models that test business theories related to customer behavior, possible behavioral changes, and potential outcomes. Customer Relationship Management (CRM) systems store the collected customer information and ultimately aid in predicting customer behavior.

  • Fraud Detection Analytics - Techniques for fraud detection include statistical analysis, artificial intelligence (AI), and data warehousing. Statistical approaches to fraud detection involve data pre-processing, profiling, clustering, classification, and matching to create informed fraud detention solutions.
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