You don't need a data science team to start predicting customer behavior. At MIKAEL GLOBAL, we've built predictive models for businesses with as few as 50 customers.
The Framework
Step 1: Define What You're Predicting
Start with a clear business question: Which customers are most likely to churn next month? or What's the optimal price point for this product?
Step 2: Gather the Right Data
You likely have more data than you think. Transaction history, website behavior, support tickets — it's all valuable.
Step 3: Choose the Right Model
For most small businesses, a simple logistic regression or decision tree performs just as well as a complex neural network — and is far easier to explain.
Tools We Use
- Python with pandas, scikit-learn, and statsmodels
- R for statistical analysis
- Power BI for visualization
Case Study
We helped a Lagos-based retailer predict stock-outs 2 weeks in advance with 85% accuracy. Result: 30% reduction in lost sales and 20% less inventory holding cost.
Want to try this for your business? Get in touch.