This article will explore how Amazon QuickSight‘s predictive analytics capabilities, powered by advanced machine learning, can unlock valuable insights and drive informed decision-making for your organisation.

Predictive analytics powered by machine learning

One of the key features of Amazon QuickSight’s predictive analytics capabilities is its seamless integration of machine learning technology. The platform’s built-in ML algorithms are designed to handle complex real-world scenarios, enabling you to create accurate, data-driven forecasts and predictive models for your business.

Predictive analytics use cases with Amazon QuickSight

Some of the key predictive analytics use cases you can explore with Amazon QuickSight include:

Sales forecasting: Leverage historical data, seasonality, and other relevant factors to predict future sales and revenue trends, helping you make informed decisions about inventory, pricing, and resource allocation.

Demand planning: Forecast customer demand to optimise your supply chain, production planning, and inventory management, ensuring you have the right products available at the right time.

Churn analysis: Identify customers at risk of churning and take proactive measures to retain them, improving customer loyalty and revenue.

Anomaly detection: Automatically detect unusual patterns or outliers in your data that may require further investigation, allowing you to quickly address potential issues or take advantage of emerging opportunities.

Seamless integration with Amazon SageMaker

To further enhance its predictive analytics capabilities, Amazon QuickSight seamlessly integrates with Amazon SageMaker, the fully managed machine learning service. This integration allows you to deploy your own custom machine learning models directly within the Amazon QuickSight platform, unlocking even more advanced predictive insights tailored to your specific business needs.

By combining the power of Amazon QuickSight’s intuitive data visualisation and BI capabilities with the flexibility of custom machine learning models, you can create a predictive analytics solution that delivers actionable insights and drives informed decision-making.

Forecasting in Amazon QuickSight Q

Amazon QuickSight Q is a powerful business intelligence (BI) service within Amazon QuickSight that allows users to ask questions about their data using natural language. With Amazon QuickSight Q, users can quickly get answers, insights, and visualisations without having to build complex dashboards or reports.

The forecasting feature in Amazon QuickSight Q allows users to generate predictions for up to three key business metrics simultaneously, simply by asking a natural language question starting with “forecast” followed by the measures they want to forecast.

For example, “Forecast sales, profit, and quantity” will trigger Amazon QuickSight’s forecasting algorithm, powered by the Random Cut Forest machine learning model, to analyse historical data and project future trajectories for those three metrics. Users can also apply filters to their forecasting questions, such as “forecast sales by region.”

The forecasts are presented in an easy-to-interpret visual format, and the time granularity can be adjusted to provide the most relevant view. This empowers business users, managers, and analysts to gain forward-looking insights without having to master complex forecasting models or parameters.

Uncovering the ‘why’ with contribution analysis

In addition to the powerful forecasting capabilities, Amazon QuickSight Q also supports a ‘why’ question type that enables users to instantly understand the key drivers behind changes in their data. By asking a question starting with ‘why’ followed by a numeric measure and a date/time range, Amazon QuickSight will perform an automated contribution analysis to identify the top factors contributing to the observed change.

For example, a user could ask ‘why did sales decrease in Q4 2022?’ Amazon QuickSight’s contribution analysis would then surface the specific values from dimensions like product category, sales region, or customer segment that had the biggest impact on the sales decline. The answers not only highlight these key drivers, but also quantify their relative influence, giving users clear, actionable insights to understand what’s behind the changes in their business metrics.

Users can further refine their “why” questions to zoom in on more specific time periods or dimensions, allowing them to progressively explore the underlying reasons behind data changes. This ‘why’ analysis capability in Amazon QuickSight Q empowers users to go beyond just seeing the numbers, and truly uncover the underlying reasons behind the data trends. It’s a powerful tool for data-driven decision making that complements the forecasting features discussed in this article.

Success stories: predictive insights in action

The following case studies demonstrate how organisations across various industries have successfully leveraged the predictive analytics and forecasting capabilities of Amazon QuickSight to gain insights and drive business impact.

GoDaddy: GoDaddy mentioned that Amazon Q in Amazon QuickSight allows them to ask contextual business questions about their data without having to constantly rely on ad-hoc dashboards. This has enabled them to much more easily discover and drill into anomalies in their business performance across the company, highlighting how they are using Amazon QuickSight’s predictive analytics features to uncover valuable insights.

Traeger Grills: Traeger Grills stated that Amazon QuickSight Q has shown them the power of natural-language experiences to accelerate their data work. By helping their business users get insights instantly, they are leveraging Amazon QuickSight’s predictive analytics and forecasting features, particularly the natural language querying capabilities, to quickly generate insights and respond to changing business needs.

Clinigence Health: Clinigence Health mentioned that with Amazon Q in Amazon QuickSight, they can identify insights and trends within their data in minutes, a process that previously took hours. By embedding these generative BI capabilities into their platform, Amazon Q in Amazon QuickSight has given their users the opportunity to ask their own questions using natural language, empowering them to explore the data more effectively.

To conclude

By seamlessly integrating advanced machine learning and natural language querying, Amazon QuickSight enables users to generate accurate forecasts, uncover key drivers behind data changes, and gain forward-looking insights that can transform their business. Whether it’s sales forecasting, demand planning, churn analysis, or anomaly detection, QuickSight’s predictive capabilities have helped organisations across industries unlock valuable insights and make strategic, impactful choices.

As an AWS Specialist Data and AI Partner, Firemind is uniquely positioned to help businesses leverage the power of predictive analytics in Amazon QuickSight to drive meaningful business outcomes.

To learn more about how Firemind can help you maximise the predictive insights of Amazon QuickSight, reach out to our team today, using the form below.