AI-Powered Data and Analytics

The importance of data in running a successful business cannot be overstated. To stay ahead of the competition, optimize operations, and unlock growth, organizations are willing to invest in a data strategy that aligns with their business goals.

Data analytics works like magic when utilized properly. It has not only refined the customer experience but also revolutionized the customer-marketer relationship while multiplying sales and enhancing customer satisfaction. Paul Gaynor, a partner at PwC, states that advanced business analytics has the potential to predict everything from customer interaction to financial transactions, anywhere in the world. This equates to data as no less than a superpower for businesses today.

The COVID-19 pandemic is creating unprecedented disruptions around industries and insurance is not an exception. A pandemic coupled with economic uncertainty poses an opportunity as well as a major challenge to quantify risk and customer engagement for insurance companies. The main reason behind this disruption is the mandatory social distancing measures, signifying that digital transformation is not an optional creative idea anymore but a requirement. It will affect all aspects of the insurance business from underwriting to claims processing. Using rapidly changing alternative data and advanced big data analytics for insurance is no longer a nice-to-have option but will be a strong pillar of the insurance business strategy.

As digital services permeate every aspect of the world around us, data has begun to assume a central role. With increasing customer demands, growth expectations, and the need to evolve rapidly, data is an organization’s biggest source of competitive advantage. Real-time insights gathered by data are crucial to support digital initiatives and allow organizations to make better operational choices.

The quality of a business’s customers determines everything from its valuation to its longevity. CMOs and marketing experts can’t stress this enough. In recent times, we have started seeing a shift from standard revenue metrics to customer-centric metrics to evaluate companies. Customer metrics can be broken down into three broad categories: acquisition, retention, and quality (which is AOV for a customer along with several average customer orders or average customer revenue).

Manufacturing industries face unprecedented challenges in the post-COVID world. Although the post-pandemic effects are onerous, manufacturers can leverage data produced via procurement, production, and delivery processes and analyze it to gain insights. These insights can help manufacturers build data-driven strategies that can help them successfully navigate modern world challenges.

The spreadsheet-based manual forecasting approach that is still used in many retail organizations is not suitable for staying ahead of the competition and improving their bottom line. Manual adjustments of forecasts in this method leave all inventory spend decisions to human judgement, which pushes towards generalized decisions rather than going with product, vendor, and market-specific indicators.

Data warehouses and data lakes are critical elements used by many organizations to support modern data platforms. However, the outdated architectural design of present-day data platforms poses considerable challenges and have inevitable drawbacks that require attention.

Data-driven email marketing campaigns have redefined the way organizations engage with potential and existing customers. Email marketing not only costs less as compared to other contemporary tools but is also more effective and has a wider reach. However, organizations now have large volumes of data at their disposal which they can leverage to drive data-backed marketing campaigns.