How do you test and validate your assumptions and hypotheses with BI data for your KPIs?

1 Define your KPIs

Defining your KPIs clearly and precisely is the first step in achieving your strategic goals and objectives. KPIs should be aligned with your vision, mission, and values, and reflect the outcomes you want to achieve. Examples of KPIs include revenue, customer satisfaction, retention, and conversion rate. To define your KPIs, you need to consider the purpose of the KPI, how it will be measured, the target or benchmark you want to reach, how often it will be tracked and reported, and who is responsible for it.

2 Identify your data sources

The next step is to identify the data sources that will provide the information you need to measure your KPIs. Data sources are systems, platforms, or tools that collect and store data, such as databases, web analytics, CRM, or surveys. It is essential to ensure that your data sources are reliable, accurate, and consistent, and that they cover the relevant dimensions and metrics for your KPIs. When choosing your data sources, consider the type of data needed (quantitative or qualitative, structured or unstructured, historical or real-time), how you will access and integrate the data (API, ETL, or manual), how you will ensure data quality and integrity (validation, verification, or cleaning), and how you will secure and protect the data (encryption, authentication, or authorization).

3 Test your hypotheses

The third step is to test your hypotheses with the data you have collected and integrated. Hypotheses are statements that express your assumptions or expectations about the relationship between your KPIs and the factors that influence them. For instance, you may hypothesize that increasing your marketing budget will increase your revenue, or that improving your customer service will increase your retention. To test your hypotheses, you need to use analytical methods and techniques, such as descriptive, diagnostic, predictive, or prescriptive analytics, to explore, visualize, and model your data. When testing your hypotheses, consider questions such as: what is the question you want to answer with your data? What analytical approach will you use to answer it? What level of confidence or significance do you want to achieve with your results? How will you interpret and communicate your findings?

4 Validate your results

The final step is to validate your results with external sources and feedback. Validation is the process of verifying and confirming that your results are accurate, relevant, and actionable for your KPIs. You should compare your results with other data sources, such as benchmarks, industry standards, or best practices, to see how you are performing against your competitors or peers. Additionally, it is important to solicit feedback from your stakeholders, such as customers, employees, or managers, to see how they perceive and value your results. When validating your results, consider questions such as: how do your results compare with your expectations and goals, how do they compare with other data sources or benchmarks, and how do they align with your stakeholder's needs and preferences? Ultimately, you should use your results to improve your performance and decision-making.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?