Focus on outliers: Those unexpected spikes or dips in your data often hold the most interesting insights. For example, why did sales suddenly surge in a specific region or for a particular product? Digging into anomalies can reveal untapped opportunities or hidden issues you’d otherwise miss.

One thing that works best is when you first identify the KPIs that matter. CLTV Churn CAGR NPS, etc. Once done start with a back integration and find out which of these analytics data are actually important and would happen to impact your most critical KPIs. In this way, you focus only on the most important data and weed out those that are not so important or can be even left out from your entire analysis.

To pinpoint key customer behaviour patterns, follow these steps: Data Preparation: Clean, integrate, and transform data for analysis. Customer Segmentation: Group customers based on demographics, behaviour, and psychographics. Key Behavior Metrics: Identify and calculate relevant metrics. Data Analysis: Use descriptive, predictive, and prescriptive analytics techniques. Tools and Technologies: Leverage data analysis tools, machine learning, and AI. Actionable Insights: Identify key patterns, understand customer journeys, segment customers, personalize experiences, and optimize marketing efforts.

Eu acredito que tudo come?a por entender o negócio e quebrar o problema em partes menores. Priorizar as análises e só ent?o escolher os dados necessários. N?o adianta olhar tudo de uma vez. é necessário estratégia até para analisar.