Sara lands on an ecommerce website through a paid ad on her Facebook news feed. She browses through few dresses but decides to leave the website without purchasing anything. As she reaches the close button, she sees a lead form banner offering 10% off on first purchase if she registers. She fills in her details and starts browsing again to use her discount.
Two weeks after her last visit, Sara receives an email showcasing all new styles launched on the website in the genre of dresses she purchased last time along with some personalized recommendations. She clicks on one of the styles she likes, lands back on the website and start scrolling through the new collection.
Sara adds few products to the cart but leaves the website without checking out. After 15 minutes, she receives a browser push notification asking her to complete her purchase in the next 10 minutes to enjoy additional 5% discount. She clicks on the browser notification and completes the purchase using her extra discount.
Sara hasn’t visited the website again for almost two months now. She is retargeted through email as well as an SMS offering 10% off on her next three consecutive purchases. She reaches the landing page to check out the new styles and utilize her exclusive discount.
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.
