5 Benefits of Data and Analytics for Positive Business Outcomes
Today, businesses can collect data along every point of the customer journey. This information might include mobile app usage, digital clicks, interactions on social media and more, all contributing to a data fingerprint that is completely unique to its owner. However, at some point not too long ago, the thought of customers sharing information such as what time they woke up, what they ate for breakfast, or where they went on holiday, would have been a bizarre consideration to say the least.
Customer social norms have certainly changed and as a result, expectations have escalated. This blog will outline five examples of benefits that businesses can reap from data and analytics in terms of driving positive outcomes for their own business and their customers, while still maintaining and facilitating the highest level of data protection.
Organisations are increasingly under competitive pressure to not only acquire customers but also understand their customers’ needs to be able to optimise customer experience and develop longstanding relationships. By sharing their data and allowing relaxed privacy in its use, customers expect companies to know them, form relevant interactions, and provide a seamless experience across all touch points.
Thus, companies need to capture and reconcile multiple customer identifiers such as cell phone, email and address, to one single customer ID. Customers are increasingly using multiple channels in their interactions with companies, hence both traditional and digital data sources must be brought together to understand customers’ behaviours. Additionally, customers expect and companies need to deliver contextually relevant, real-time experiences.
Security and fraud analytics aims to protect all physical, financial and intellectual assets from misuse by internal and external threats. Efficient data and analytics capabilities will deliver optimum levels of fraud prevention and overall organisational security: deterrence requires mechanisms that allow companies to quickly detect potentially fraudulent activity and anticipate future activity, as well as identifying and tracking perpetrators.
Use of statistical, network, path, and big data methodologies for predictive fraud propensity models leading to alerts will ensure timely responses triggered by real-time threat detection processes and automated alerts and mitigation. Data management alongside efficient and transparent reporting of fraud incidents will result in improved fraud risk management processes.
Furthermore, integration and correlation of data across the enterprise can offer for a unified view of the fraud across various lines of business, products, and transactions. Multi-genre analytics and data foundation provide more accurate fraud trend analyses, forecasts, and anticipation of potential future modus operandi and identification of vulnerabilities in fraud audits and investigations.
Products are the life-blood of any organisation and often the largest investment companies make. The product management team’s role is to recognise trends that drive strategic roadmap for innovation, new features, and services.
