Improves Customer Relationships

Implementing customer analytics software simplifies the job of managers and can reap various benefits for a business.  As they operate, companies collect raw data from internal and external affairs, including customer and purchase information, sales numbers, employee salaries, etc. 

Customer analytics software can simply take this information and transform it into useable, analytic reports for managers to apply their knowledge, in order to make better business decisions.

These data analysis and visualization tools provide managers with vital customer insights which can ultimately help to improve and maintain loyal relationships with customers.

Revenue Growth

Utilizing customer analytics software can yield many advantages for a company. Not only can it help you to satisfy more customers, it can also aid in overall revenue growth.

By using this system, customer data can be collected which enables managers to easily and efficiently monitor sales progression. In turn, a business can determine whether its promotion efforts are effective and headed in the right direction.

Typically, medium to large businesses will gain the greatest benefit from customer analytics. A greater sample of customers generates clearer patterns and trends which companies can better monitor and analyze. With a larger set of data, generated insights are significantly more accurate.

Why InetSoft

Upon choosing to implement customer analytics, it is important to decide which analytics software is best for the company. Gathering data is the easy part, but analyzing and applying that data is far more important. For that, companies need a business intelligence solution that facilitates the most robust reporting options.

InetSoft's unique interface consists of reporting options which are flexible and adjustable to the needs of the user. With the user in mind, the software was designed to be easily deployable and to require minimal training.

StyleBI provides businesses with a software solution that securely collects data from various data sources and efficiently compiles the information to produce high performance dashboards. These dashboards contain interactive graphs, complex charts, and detailed reports that are easy-to-read and ready for analysis.

Features and Capabilities of StyleBI:

Data Access and Mashup

• Real-time data mashup Data Block™ architecture
• Connectivity to relational databases (JDBC), multidimensional databases, XML, SOAP, Java beans (POJO), Microsoft Excel, flat files, OLAP cubes, SAP, PeopleSoft, JD Edwards, salesforce.com, Siebel CRM, Postgress, Google AdWords and Analytics, and Microsoft SharePoint
• High performance scalability for large data sets and large volumes of users via InetSoft's Data Grid Cache technology

Visualizations, Dashboards and Visual Business Analytics

• Dashboard and visualization annotations
• Unlimited multi-dimensional charting
• Brushing for data exploration
• Wide range of sophisticated chart types including custom geographic mapping
• Drag and drop design using only a Web browser

Enterprise Reporting

• Production reports with embedded business logic and parameterization
• Interactive reports and in-report exploration
• Ad hoc reporting wizards and free form editing
• Secure enterprise level development and deployment

Unified Business Analytics Software

• Mobile BI - access dashboards and analyses from Web-enabled devices including Android-based tablets, smartphones, iPads, and iPhones
• Target-based balanced scorecards
• Shared bookmarks - save annotations, selections, and chart edit settings of any dashboard or analysis for future retrieval and sharing among users
• OLAP access to applications such as Microsoft SQL Server Analysis Services, Hyperion ESSbase, Oracle OLAP, and SAP NetWeaver

What Customer Analytics Does a Professional Sports Team Do?

Professional sports teams increasingly rely on customer analytics to understand, engage, and grow their fan base, as well as to maximize revenue from ticket sales, merchandise, concessions, and sponsorships. By leveraging data across multiple touchpoints—both digital and physical—teams can build detailed profiles of their fans and develop personalized strategies that enhance loyalty and drive business outcomes. These analytics help teams transition from mass marketing to precision engagement.

• Ticketing and Attendance Analysis: Teams analyze ticket purchase behavior, attendance frequency, and game-day no-shows to identify loyal fans, casual attendees, and churn risks. Predictive models are used to forecast attendance for upcoming games and optimize ticket pricing strategies, such as dynamic pricing based on demand signals.
• Fan Segmentation: Using demographics, purchasing behavior, engagement levels, and location data, teams segment fans into groups such as season ticket holders, families, young professionals, and out-of-market fans. These segments guide targeted marketing, seat upgrades, and exclusive offers.
• Merchandise and Concession Analytics: Purchase data from stadium stores, mobile apps, and online shops help teams understand what fans buy, when they buy it, and how preferences change over time. Analytics optimize inventory, bundle offers, and promotional timing based on fan demand patterns.
• Digital Engagement Tracking: Teams track email open rates, website traffic, social media interactions, and app usage to measure digital campaign effectiveness and identify highly engaged users. These insights inform content personalization and campaign timing.
• Loyalty and Retention Analysis: Customer lifetime value (CLV) modeling helps identify the most valuable fans, while churn prediction flags at-risk season ticket holders or members. Teams use this data to trigger proactive retention campaigns, loyalty rewards, or surprise-and-delight moments.
• Geospatial Analytics: Location-based insights reveal where fans live, how far they travel, and which markets have growth potential. Teams may adjust marketing outreach or plan special events in regions with high concentrations of fans.
• Sponsorship Effectiveness: Teams use customer analytics to measure how fans interact with sponsor-branded content or activations. Data on fan affinity, brand recall, and engagement can be shared with sponsors to justify value and refine co-marketing efforts.
• In-Stadium Behavior Analysis: Wi-Fi, beacon, and app data track fan movement and dwell time within the venue. This supports queue management, concession placement, and gameday experience enhancements tailored to observed behaviors.
• Survey and Sentiment Analysis: Teams collect post-event feedback, online reviews, and social media sentiment to understand how fans feel about the team, experience, and brand. Natural language processing (NLP) tools help identify themes and pain points to improve fan satisfaction.
• Cross-Platform Identity Resolution: Through unified customer profiles, teams consolidate fan activity across email, ticketing, apps, and websites. This 360-degree view supports smarter targeting and consistent messaging across channels.