Simporter Predictive Analytics backed up with machine learning algorithms can help retailers understand customer’s behavior and preferences. By studying browsing patterns and click-through rates of particular products, e-commerce companies can effectively place product recommendations and offer to maximize sales.

Personalized recommendations and reminders can also help retailers retain their customers, thus helping in creating a loyal customer base. Simporter Machine learning predictive analytics also makes it easier to manage supply chain processes. Using predictive algorithms, retailers can better manage inventory, avoid out-of-stock scenarios, and optimize logistics and warehousing.



There are many examples of machine learning in SAAS marketing. One of the most common use cases is identifying and acquiring prospects with attributes similar to existing customers. ML-based predictive analytics can also prioritize known prospects, leads, and accounts based on their likelihood to take action.

Simporter have been using predictive lead-scoring algorithms based on intricate data sets to radically improve their lead conversion rates. Machine learning predictive analytics creates a 360-degree view of the prospective customer by combining historical data points of customer behavior with market trends. Using predictive algorithms has helped companies achieve higher targets and streamlined their sales and marketing activities into a data-based undertaking instead of simply taking a shot in the dark.