Retarget the customers who have put items in their carts but did not complete the checkout through emails, push notifications etc. in an automated and personalized manner.
Behavioral targeting engine monitors browsing and purchase behavior of users. Based on this, customized product recommendations can be served to potential buyers on the website, email, push notifications etc.
Multi-channel activities of consumers like their online browsing history, personal data, purchase data, campaign response data etc. are available in a single view for marketers. This enables you to analyze and precisely target consumers with no need to manually merge data from different sources.
Identify each customer’s preferred time & channel to receive communication using statistical modeling techniques and machine learning algorithms. This enables marketers to optimize their email’s send time and drive higher open rates along with figuring out the best channel to communicate with each user.
Predict who is going to uninstall your app within the next 7 days and prevent them from Churning. The predictive analytics model calculates uninstall probability of every App user based on multiple prediction models through available data like last 30-day activities on the App, time spent, etc.
Do end-to-end analysis of your campaign performance. Understand which marketing communication of yours is driving the customer back to your website/App, which channel is performing the best, what content is helping the user convert etc.