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In this recipe, you are going to deploy a sample retail web application using automated deployment. This recipe demonstrates how Amazon Personalize AI service can be deployed and integrated with Twilio Segment to deliver real-time events from the web user interface to Amazon Personalize.

This recipe will explain what Facebook’s Conversions API is, why it’s worth focusing on now, and a step-by-step guide on using Twilio Segment to collect your first-party data and sync to Facebook for retargeting.

With this recipe, you’ll learn how to revive users who’ve become inactive. Reviving churned users is often the highest-leverage growth strategy your team can implement.

In this recipe, you’ll learn how to boost your conversion rates by personalizing landing pages based on your visitor’s traits, behaviors, purchase histories, and more.

In this recipe, you'll learn how how to use search terms collected from Google to personalize your website and improve conversion rates.

This recipe teaches you how to easily launch and optimize a referral and loyalty program using Twilio Segment and Friendbuy. The web and cloud mode destination integrations send information about your customers and their actions to Friendbuy to power your referral and loyalty programs.

In this recipe, you'll learn how to use location data to understand where your customers are when they engage with you.

In this recipe, you’ll learn how to save time complying with user deletion and suppression requests by using Twilio Segment’s deletion features.

Growth leaders today need to retarget their most valuable customers and know where they came from. By leveraging a customer data platform like Twilio Segment and a Mobile Measurement Partners (MMP) like Branch, you can identify your most engaged and high-LTV users, get accurate campaign attribution data, and discover your highest-ROI ad channels. In this recipe, you will learn how to identify and group your highest-value customers, as well as where they came from. You can then use this data to deliver targeted ads and capture more customers just like them in the same channels, with accuracy and at scale.

In this recipe, you’ll learn how to use Twilio Segment to connect Radar (a location data infrastructure platform) and Customer.io (a marketing automation software) to personalize your customer communications.

This recipe teaches you how to use customer behavioural events from Twilio Segment inside Movable Ink to achieve open-time email personalization and improve email engagement rates.

Welcome Series are an effective way to engage and activate new subscribers — especially when you have actions you want them to take following their initial conversion (like asking for more information). With step-by-step instructions and best practices outlined in this recipe, you'll be able to create a winning Welcome Series that drives action. A Welcome Series should help introduce your brand to your customers at the minimum, but can also be used to drive them to take action such as completing their profile or downloading your app. After completing this recipe, you should have created a Welcome Series that activates your new subscribers, establishes a strong relationship with them, and hopefully, drives business growth. So let's get started and make your Welcome Series a success!

You’ll build an order-ahead iOS app that simulates a user in the real world ordering food and traveling to a restaurant to pick up their order. The app will track the user’s various trip states and send a final SMS to notify the user’s order is ready. The app will use Radar to power the trip-tracking experience, send Radar events to Twilio Segment, and finally pipe the events to Twilio for SMS notifications.

In this recipe, you'll learn how to automatically identify and segment your website traffic, and then send that data to a heatmap tool to better understand their on-site behavior.

In this recipe, you learn how to use Twilio Segment and AWS Redshift ML together to generate predictive analytics without writing any code, spinning up any data pipelines, or requiring significant ML experience.