While working with data, we see it very often: customer information is scattered across different systems. Emails are stored in your email platform. Order history lives in a CRM or ERP. Support requests are tracked in a helpdesk tool. The information is fragmented, making it hard to get a complete picture of the customer.

Is using a client’s name in a newsletter really personalization? Technically, yes. But in reality, it barely scratches the surface. Most people don’t even notice it anymore. It’s become background noise.

Originally, predictive analytics was primarily for large enterprises aiming to make smarter, data-driven decisions. Companies wanted answers to questions like: Who will buy next? Who might leave? Which products should we recommend? Back then, using predictive analytics meant significant investments in infrastructure, data teams, and custom-built models.

While working with data, we see it very often: customer information is scattered across different systems. Emails are stored in your email platform. Order history lives in a CRM or ERP. Support requests are tracked in a helpdesk tool. The information is fragmented, making it hard to get a complete picture of the customer.

Is using a client’s name in a newsletter really personalization? Technically, yes. But in reality, it barely scratches the surface. Most people don’t even notice it anymore. It’s become background noise.

Originally, predictive analytics was primarily for large enterprises aiming to make smarter, data-driven decisions. Companies wanted answers to questions like: Who will buy next? Who might leave? Which products should we recommend? Back then, using predictive analytics meant significant investments in infrastructure, data teams, and custom-built models.