In this second of our series of eight data engineering and insights articles, we move away from the strategic why questions that should be asked at the beginning of your data journey to the more tactical challenges around analytics tools.

Insight 2: Choosing the Right Analytics Tools to Drive Business Growth

In today's data-driven world, selecting the right digital analytics tools is critical. With a vast range of tools available, many digital leaders face the challenge of identifying which will best meet their strategic goals and deliver the insights they need. Choosing the wrong tools can result in wasted time, resources, and poor outcomes.

In this article, we'll explore how to navigate the diversity of analytics tools available and select the right tool for the right job to drive business growth.

More than just web analytics

Historically, businesses have relied heavily on platforms like Universal Analytics for web performance tracking. However, this often led to data overload and a lack of actionable insights. Google Analytics 4 (GA4) has improved this by offering deeper insights, but even the best web analytics tools cannot address every business need.

Success in today’s landscape requires a more comprehensive approach—one that leverages a suite of analytics tools designed to handle a diverse range of data and provide actionable insights.

Types of analytics and their benefits

Selecting the right analytics tools begins with understanding the five key types of analytics and how each one can create value for your business.

By choosing tools that match these types, you can ensure you're unlocking the insights needed to gain a competitive edge.

Here's an overview of each:

1. Descriptive analytics

Helps us summarise historical data to understand what has happened in the past. It relies on data aggregation and mining to provide insights into past performance. Common tools include dashboards, scorecards, and reporting platforms that visualise data through charts, graphs, and tables. These can be built in Microsoft Excel or more advanced business intelligence tools like Tableau and Power BI. Descriptive analytics is essential for tracking Key Performance Indicators (KPIs), monitoring performance, and identifying trends. Well-configured web analytics excels in this area.

2. Diagnostic analytics

Goes further by helping us understand why something happened. This involves deeper analysis to identify the root causes of past outcomes. Tools often include statistical analysis software, data visualisation platforms, and data mining techniques. This type of analytics is invaluable for troubleshooting, identifying causality, and understanding the factors influencing performance. Google Analytics 4’s Exploration Reports, which allow us to perform funnel analysis to identify user journey pain points, are an excellent diagnostic tool. Combining these with UX analytics tools like Microsoft Clarity or Hotjar, which offer heatmaps and session replays, provides deeper insights into specific obstacles.

3. Predictive analytics

Uses historical data and statistical models to forecast future events and trends. It aims to predict what is likely to happen based on past data. Tools often include machine learning algorithms, forecasting software, and advanced statistical techniques. In web analytics, Google Analytics 4 offers predictive audience tools that use machine learning to identify likely purchasers, top spenders, and users at risk of churning—assuming sufficient website traffic. Adobe Analytics adds powerful predictive features like cohort tables for churn analysis, anomaly detection, intelligence alerts, and machine learning-driven contribution analysis to explain factors behind data trends.

4. Prescriptive analytics

Builds on predictive insights by recommending actions to achieve desired outcomes. It doesn’t just forecast what will happen but also suggests the best course of action. Tools include optimisation software, decision analysis platforms, recommendation systems, and simulation models. This type of analytics is critical in areas like supply chain optimisation, resource allocation, and strategic planning. An excellent example of prescriptive analytics is A/B testing software like Optimizely Experimentation, which tells us which variations are statistically likely to outperform others.

5. Cognitive analytics

Leverages artificial intelligence (AI) and machine learning to simulate human thought processes, helping us generate insights by understanding natural language, recognising patterns, and learning from data. It's used in various applications, such as chatbots, virtual assistants, generative AI, fraud detection, and personalised customer experiences.

Key considerations for choosing analytics tools

• Business objectives: Clearly define your business objectives and analytical goals. Select tools that provide insights that directly support your objectives.
• Data integration: Choose tools that integrate seamlessly with your existing data sources and systems. Breaking down data silos is key to more accurate and timely insights.
• Ease of use: Prioritise user-friendly tools that your team can adopt quickly without extensive training. Consider external technical expertise to support the effective use of the tools.
• Scalability: Select tools that will grow with your business and handle increasing data volumes without requiring costly re-platforming.
• Cost: Evaluate both the upfront and ongoing costs, such as subscriptions, maintenance and training, to ensure they fit your budget and deliver a strong return on investment.
• Support and training: Evaluate the level of support and training available, both from the vendor and publicly. Access to reliable, tailored support and extensive training resources enhances the effectiveness of any tool.
• Security and compliance: Confirm that the tools meet data protection regulations and feature robust security measures to safeguard sensitive information.
• Performance and reliability: Review the tool's ability to handle large datasets and complex analyses efficiently. A dependable tool will operate without frequent downtimes or errors.
• Vendor reputation: Investigate the vendor's reputation and track record. Positive reviews, case studies, and testimonials offer valuable insights into the tool's performance and the vendor's reliability.

Conclusion

Navigating the complex landscape of digital analytics tools can be challenging. 

However, by understanding the distinct types of analytics and aligning tool selection with your business goals, you can ensure your digital strategy is built on a solid foundation of actionable insights.

Let us help you craft a digital vision that leverages the right analytics tools to drive meaningful business outcomes. Get in touch with us today to explore how we can support your journey.