Defining analysis objectives is an exercise that must be deeply connected to the overall business strategy . To ensure this alignment, strategic goals must be translated into specific and measurable objectives for analysis.
For example, if the business strategy is focused on increasing customer retention, food and beverage email list the analysis objectives should focus on identifying churn patterns, loyalty-driving behaviors, and customer segments at higher risk of churn. Similarly, if the strategy is looking to maximize LTV (Lifetime Value), the analysis should explore upselling and cross-selling opportunities based on customer behavior.
A useful framework for this alignment is the OKR (Objectives and Key Results) method. This approach connects a general objective (e.g., “Improve user retention by 15%) with specific, measurable outcomes that guide the analysis (such as “Reduce churn among trial users from 25% to 15% over the next three months”).
Practical examples in SaaS: user retention, product optimization, conversion
Analysis objectives may vary depending on the company's priorities. Here are some practical examples in SaaS:
1. User retention:
Problem : Churn rate is increasing for users with less than 90 days on the platform. Analysis objective: Identify factors contributing to early churn. Key metrics: 90-day retention rate, cohort analysis by demographic segments.
2. Product optimization:
Problem : A product feature has low adoption, despite its potential to improve user experience. Analysis goal: Determine why users are not using the feature. Key metrics: Click-through rate on the feature, average duration of use, qualitative feedback collected through surveys.
3. Conversion:
Problem : The percentage of users who upgrade from free to paid plan is below the expected average. Analysis goal: Identify friction points in the conversion process and improve conversion rates. Key metric: Conversion rate between plans, behavioral analysis during the trial period.
Tools for mapping objectives and KPIs
Selecting tools to map objectives and KPIs for the SaaS data analysis process is critical to connecting strategic goals with relevant metrics and providing a clear framework for measuring progress.
Google Sheets or Excel: useful for creating basic maps of objectives and KPIs, organizing information in clear and accessible tables.
Notion or Trello: These project management platforms allow you to document and track goals, KPIs, and responsibilities, ensuring teams are aligned.
Tableau or Power BI: These visualization tools allow you to directly connect KPIs to data, providing interactive dashboards that show progress in real time.
Perdoo or Weekdone: specific solutions to manage OKRs, ensuring that the analysis objectives are aligned with the company's overall strategy.
Every data analysis process in SaaS starts with a specific question or problem
Step 2: Formulate analysis questions
Formulating analytical questions is a step that guides the entire data analysis process in SaaS and ensures that the approach is relevant. With so much vast and varied data, questions act as a filter that focuses efforts on the most valuable insights. This step improves the clarity and accuracy of the analysis, as well as facilitating the connection between data and strategic decisions.