Too Much Data, Don't Know What to Do With It.
Too Much Data, Don't Know What to Do With It.
Our consultancy is designed to unleash the potential of your information. We address your data challenges, from simple understanding to future optimization, following these 4 phases of analysis:
These problems aim to summarize and understand the current or past situation.
Performance and Trends: What was the sales growth in the last quarter and how does it compare to the previous year?
Customer Segmentation: How are our customers grouped or segmented based on their purchasing behavior and demographics?
Inventory and Stock: What is the current inventory level for a product and its historical turnover?
These problems aim to identify the root cause of a result, whether positive or negative.
Causes of Failure: Why did the website conversion rate drop last week?
Key Factors: What are the main factors contributing to customer satisfaction or dissatisfaction?
Cause-Effect Relationship: Is there a significant correlation between social media advertising investment and increased sales in a specific region?
These problems use historical data to project future outcomes or probabilities.
Customer Loyalty: Which customers are most likely to churn (abandon or stop using the service) in the next 6 months?
Demand Forecasting: How much product will customers purchase next month to avoid excess or shortages?
Risk and Fraud: Is a transaction or loan application likely to be fraudulent or defaulted on?
Customer Lifetime Value (LTV): What is the expected lifetime value of a newly acquired customer?
These are the most advanced problems, as they not only predict but also suggest the best course of action.
Price Optimization: What is the optimal price to maximize revenue given the price elasticity of demand for our product?
Recommendation: Which product should we recommend to a specific user in real time to increase the likelihood of a purchase?
Resource Allocation: How should we allocate the marketing budget across different channels to maximize return on investment (ROI)?
Scheduling: What is the most efficient maintenance schedule to prevent failures in production machinery (predictive maintenance)?