Tag Archives: Amazon Pinpoint

How Disney Streaming Services Uses Amazon Pinpoint to Send Personalized Messages to Millions of Users in Real Time

Post Syndicated from Hannah Nilsson original https://aws.amazon.com/blogs/messaging-and-targeting/how-disney-streaming-services-uses-amazon-pinpoint-to-send-personalized-messages-to-millions-of-users-in-real-time/

At AWS re:Invent 2018, Billy Liu and Jimmy Tam from Disney Streaming Services took the stage to talk about how they use Amazon Pinpoint to meet some of their unique digital user engagement needs. Disney Streaming Services supports several mobile apps, including MLB At Bat, Ballpark and Beat The Streak from Major League Baseball, along with the NHL mobile app. Billy and Jimmy shared their story on the re:Invent Launchpad stage, as well as during the Digital User Engagement Leadership Session with Simon Poile from AWS. In these sessions, they discussed how they use Amazon Pinpoint—along with other AWS services including Amazon Kinesis, AWS Lambda, Amazon S3, and AWS Glue—to target customers, monitor the performance of their campaigns in real time, and gain a deeper understanding of their users’ needs and desires.

Targeting the right customer at the right time 
When you consider the use cases for MLB’s suite of apps, you can quickly see why sending the right message to the right customer is a more complicated task than it might seem at first glance. For each of the 30 Major League Baseball teams, users can opt to receive eight different types of messages. Each of these eight message types is available in both English and Spanish. And on top of all that, each push notification sent has to target combinations of these segments when two teams play each other. There are thousands of possible segments and combinations of segments to consider with each message sent.

To address this issue, Disney Streaming Services uses Amazon Pinpoint to dynamically create unique segments and campaigns for every event in milliseconds. In the most demanding usage scenarios, Amazon Pinpoint scales to create over 300 segments and campaigns per hour, and over 20 segments and campaigns per minute. To learn more about how they solved this challenge, take a look at the recording of their session.

How Disney Streaming Services Targets The Right Customer
Monitoring campaign performance in real time
With the fast-paced nature of Disney Streaming’s notifications and the sheer number of campaigns and segments they are targeting, monitoring their performance directly in the Amazon Pinpoint console is not scalable for their use case. However, they must have real-time notifications to let them know if their campaigns are lagging or not reaching the expected number of recipients.

To meet this unique need, Disney Streaming developed a solution that uses AWS Step Functions, Amazon Cloudwatch, AWS Lambda, and Amazon Pinpoint. This solution monitors each campaign that is created. When a campaign is executed, their solution streams data about the execution and delivery of that campaign, and sends alerts when the team needs to take a closer look at how their campaigns are performing. You can learn more about the specifics of their monitoring solution in the recording of their session.

How Disney Streaming Services Monitors Campaign Performance

Understanding fans
After a campaign has been sent, Disney Streaming analyzes the performance of campaigns. By performing this analysis, they can better understand how customers engaged with notifications, and ensure fans are receiving a compelling experience.

To achieve this, Disney Streaming uses the event streaming and exporting features of Amazon Pinpoint. They stream engagement events by using Amazon Kinesis. These events let them know how fans interacted with the application, and allow them to drill down into various performance metrics on a per-team basis. They then store these metrics in S3, which are picked up by their data lake team for further processing. By using this solution, they can create near-real-time reports for the unique audiences.  How Disney Streaming Services Understands their Fans
They also use the Amazon Pinpoint API to export all of the details about the users to an S3 bucket using Lambda Triggers. An AWS Glue job processes the exported data and outputs the results to another S3 bucket. The data lake team then uses this data to glean additional insights about their audience.

How Disney Streaming Services Understands their Fans

Removing unengaged customers 
Disney Streaming also uses a custom solution to engage with customers who are still able to receive messages, and to ensure that reports only include engaged users. For example, if a customer uninstalls the MLB or NHL apps, re-installs an app but doesn’t set their messaging preferences, or starts using a device on a different platform, that customer might not be able to be contacted. Disney Streaming needs to remove these unreachable customers from campaigns so that they can maintain accurate reports on audience sizes, helping keep costs low, and reduce campaign latency.

To delete unreachable customers in real time, the Disney Streaming team uses Amazon Pinpoint to detect when they attempt to send a push notification to an unreachable customer. Their Kinesis Firehose stream then outputs campaign data to an S3 bucket, and an AWS Glue job filters out the customers who are unreachable. Finally, a Lambda function removes the endpoint by making a call to the Amazon Pinpoint API. You can find more details about how Disney Streaming Services implemented this solution in the recording of their session.

How Disney Streaming Services removes unengaged customers

You can learn more about the needs that Disney Streaming Service considered when they chose a Digital User Engagement solution by watching the recording of their discussion on the re:Invent 2018 Launchpad stage. You can also watch the Digital User Engagement Leadership Session to learn more about AWS’ Digital User Engagement solutions, information on recent feature launches, and to learn more about how Disney Streaming created solutions to their engagement challenges.

Your guide to AWS Digital User Engagement at re:Invent 2018

Post Syndicated from Hannah Nilsson original https://aws.amazon.com/blogs/messaging-and-targeting/your-guide-to-aws-digital-user-engagement-at-reinvent-2018/

Plan your Digital User Engagement agenda at re:Invent 2018

With roughly 50,000 attendees expected to descend upon Las Vegas next week for the seventh AWS re:Invent, our re:Invent 2018 agenda has unsurprisingly grown along with the crowd size. This year, we have added a full day of additional content, expanded our campus, added expo hours, and included over 1,000+ breakout sessions, hackathons, bootcamps, workshops, and more ways to get hands-on experiences with AWS. With so much going on, we wanted to create a quick guide for how you can connect with the AWS Digital User Engagement team to learn what’s new with the technology and tactics for effectively engaging your customers.

Meet us in the Expo Hall

Stop by the Mobile booth in the Venetian on Level 2 to learn more about our latest features (psst—did you know we just released a voice channel and event-based campaigns?), pick up some Amazon Pinpoint swag, and discuss strategies for transformative digital user engagement with our team of experts.

Expo hours are:

  • Monday, 11/26 from 4pm-7pm PT
  • Tuesday, 11/27 from 8am-8pm PT
  • Wednesday, 11/28 from 10am-6pm PT
  • Thursday, 11/29 from 10am-4pm PT

Livestream our Launchpad Session with Disney Streaming Services:

Unable to attend re:Invent in person? Check out our session with Disney Streaming Services live on Twitch. William Liu and Jimmy Tam from Disney Streaming will join us to discuss how migrating to Amazon Pinpoint from their internal digital engagement platform has allowed them to send billions of messages to their end users in real time.

  • Tuesday, 11/27, 5:15 PM – 5:35 PM  PT
  • Watch it live on Twitch, or stop by the Launchpad stage in the Expo Hall

Attend the Digital User Engagement Sessions:

Must-attend sessions:

(DIG302-L) Leadership Session: Overview of Amazon Digital User Engagement Solutions
Wednesday, 11/27, 3:15 PM – 4:15 PM PT – Venetian, Level 4, Delfino 4002
In this session, Disney Streaming Services will share how they utilize AWS Digital User Engagement platform to send billions of users relevant content in real time. Simon Poile, GM of AWS Digital User Engagement, will also describe how AWS provides the Amazon customer-centric culture of innovation, key technology building blocks, and a user engagement platform to help companies better engage their users. Plus, session attendees can claim a special piece of Amazon Pinpoint swag.

Enable Your Marketing Teams to Engage Users with Relevant & Personalized Content (DIG204)
Monday, Nov 26, 4:45 PM – 5:45 PM– Aria East, Level 2, Mariposa 8
This chalk talk is delivered by the innovation team from Claro, a major Brazilian telecommunications company that serves over 50 million customers with broadband, mobile, cable TV, and video-on-demand services. Members of the Claro innovation team describe how they enable their marketing departments to engage customers through campaigns that send personalized messages, such as billing reminders, updates on internet credits, and upcoming program alerts. They share how they then measure customer engagement and user behaviors, all using Amazon Pinpoint.

Game On! Building Hulu’s Real-Time Notification Platform for Live TV with Amazon Pinpoint (MOB304)
Wednesday, Nov 28, 1:45 PM – 2:45 PM– Venetian, Level 4, Marcello 4505
Notifying their viewers when their favorite teams are playing helps Hulu drive growth and improve viewer engagement, but building this feature was a complex process. Managing their live TV metadata while generating audiences in real-time in high scalability scenarios posed unique challenges for the engineering team at Hulu. In this session, Hulu will talk about their challenges in building their real-time notification platform, how Pinpoint helped them with their goals, and how they architected their solution for global scale and deliverability.

Full session list for re:Invent attendees:

Monday, November 26, 2o18

Perform Social Media Sentiment Analysis with Amazon Pinpoint & Amazon Comprehend (MOB314)
Monday, 11/26, 10:00 AM – 12:15 PM PT – MGM, Level 1, Grand Ballroom 124
In this workshop, we show you how to easily deploy an AWS solution that ingests all Tweets from any Twitter handle, uses Amazon Comprehend to generate a sentiment score, and then automatically engages customers with a dynamic, personalized message. The intended audience is developers and marketers who want to leverage AWS to create powerful user engagement scenarios. We highlight how quickly you can deploy a machine learning marketing solution. We cover Amazon Pinpoint, the AWS user engagement service, and Amazon Comprehend, the AWS natural language processing service that uses artificial intelligence and machine learning to find insights and relationships in text.

Delight your customers through natural language conversational experiences – powered by Amazon Lex and Amazon Pinpoint (AIM360)
Monday, 11/26, 11:30 AM – 12:30 PM PT – Mirage, St. Croix A
In this chalk talk, we first describe various use cases to engage customers through natural language conversations. We then showcase how to prepare, implement, and continuously improve such solutions. We use Amazon Lex to drive the chatbot interactions and capture user events in Amazon Pinpoint analytics. The event data is then used to measure user engagement and sentiment within conversations.

Understand & Remediate Message Deliverability Issues to Improve Customer Reach (DIGF202)
Monday, 11/26, 12:15 PM – 1:15 PM PT – Mirage, St. Thomas B
Several factors determine whether your messages reach your recipients. In this chalk talk, learn some of the critical factors that affect the deliverability of your messages across messaging channels, such as email and SMS. We walk you through some of the best practices, and we introduce you to deliverability tools, such as Phone Number Validate. With these tools, you can improve your customer reach, increasing the effectiveness of your campaigns and ultimately drive more revenue. The intended audience is developers and marketers who send outbound messages. We cover the AWS services Amazon Pinpoint and Amazon Simple Email Service (Amazon SES).

Effectively Engage Millions of Users in Seconds (MOB321 -R)
Monday, 11/26, 1:45 PM -2:45 PM PT – Aria East, Level 1, Joshua 4
In this session, we describe the challenges that companies face with high volume messaging-based engagement and the solutions AWS provides. Learn from current customers about how AWS can be used to message millions of users in seconds, while being cost effective and maintaining high deliverability rates. The intended audience is developers who are supporting their company’s growth or marketing activities using AWS services.

The repeat of this session (MOB321 -R1) will take place: Tuesday, 11/27, 5:30pm-6:30pm PT – Bellagio, Level 1, Grand Ballroom 1.

How to Use Amazon Pinpoint and Amazon Kinesis for Events-Based Messaging (MOB408-R)  
Monday, Nov 26, 2:30 PM – 3:30 PM – Aria West, Level 3, Starvine 10, Table 7
In this session, we demonstrate when and how to engage users in real time based on events and user behaviors to drive contextual and relevant user interactions. The intended audience is developers who are supporting marketing activities using AWS services.

The repeats of this session will take place:

Enable Your Marketing Teams to Engage Users with Relevant & Personalized Content (DIG204)
Monday, Nov 26, 4:45 PM – 5:45 PM– Aria East, Level 2, Mariposa 8
This chalk talk is delivered by the innovation team from Claro, a major Brazilian telecommunications company that serves over 50 million customers with broadband, mobile, cable TV, and video-on-demand services. Members of the Claro innovation team describe how they enable their marketing departments to engage customers through campaigns that send personalized messages, such as billing reminders, updates on internet credits, and upcoming program alerts. They share how they then measure customer engagement and user behaviors, all using Amazon Pinpoint.

Tuesday, November 27, 2018

Personalize User Targeting through ML & Measure User Engagement with Your Brand (DIG203)
Tuesday, Nov 27, 10:00 AM – 11:00 AM– Mirage, St. Croix A
In this session, we describe the critical role that multi-channel analytics plays in targeting users and how to use machine learning (ML) to predict the best content, channel, and time to engage. The intended audience is developers and marketers who need to target users and measure their engagement levels. We cover the AWS service Amazon Pinpoint.

Engage Users in Real-Time through Event-Based Messaging (MOB322-R)
Tuesday, Nov 27, 4:00 PM – 5:00 PM– Venetian, Level 4, Lando 4305
In this session, we describe when and how to engage users in real time, based on external events and user behaviors, to drive contextual and relevant user interactions. The intended audience is developers who support marketing activities using AWS services. We cover Amazon Pinpoint and Amazon Kinesis in this session.

The repeat of this session (MOB322-R1) will take place: Thursday, Nov 29, 1:00 PM – 2:00 PM– Venetian, Level 3, Murano 3202

Wednesday, November 28, 2018

Game On! Building Hulu’s Real-Time Notification Platform for Live TV with Amazon Pinpoint (MOB304)
Wednesday, Nov 28, 1:45 PM – 2:45 PM– Venetian, Level 4, Marcello 4505
Notifying their viewers when their favorite teams are playing helps Hulu drive growth and improve viewer engagement, but building this feature was a complex process. Managing their live TV metadata while generating audiences in real-time in high scalability scenarios posed unique challenges for the engineering team at Hulu. In this session, Hulu will talk about their challenges in building their real-time notification platform, how Pinpoint helped them with their goals, and how they architected their solution for global scale and deliverability.

Thursday, November 29, 2018:

Listen to Your Customers’ Social Voice & Engage Them with Delightful Experiences (DIG301)
Thursday, Nov 29, 12:15 PM – 1:15 PM– MGM, Level 1, Grand Ballroom 116
In this session, we demonstrate how to easily deploy an AWS solution that ingests all Tweets from any Twitter handle, uses Amazon Comprehend to generate a sentiment score, and then automatically engages customers with a personalized message. The intended audience includes developers and marketers who want to leverage AWS to create powerful user engagement scenarios. We highlight how quickly a machine-learning marketing solution can be deployed. We cover the AWS services Amazon Pinpoint, a digital user engagement service, and Amazon Comprehend, a natural language processing service that uses artificial intelligence and machine learning to find insights and relationships in text

Please note that session times and locations are subject to change. View the session catalogue for the most up-to-date information. For more helpful hints on how to make the most of your re:Invent experience, please visit our “How to re:Invent” guides.

Which session are you most excited about? Let us know in the comments!

Event-based campaigns let you automatically send messages to your customer when they perform certain actions

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/event-based-campaigns-let-you-automatically-send-messages-to-your-customer-when-they-perform-certain-actions/

Today, we added a new campaign management feature to Amazon Pinpoint: event-based campaigns. You can now use Amazon Pinpoint to set up campaigns that send messages (such as text messages, push notifications, and emails) to your customers when they take specific actions. For example, you can set up a campaign to send a message when a customer creates a new account, or when they spend a certain dollar amount in your app, or when they add an item to their cart but don’t purchase it. Event-based campaigns help you send messages that are timely, personalized, and relevant to your customers, which ultimately increases their trust in your brand and gives them a reason to return.

You can create event-based campaigns by using the Amazon Pinpoint console, or by using the Amazon Pinpoint API. Event-based campaigns are an effective way to implement both transactional and targeted campaign use cases. For transactional workloads, imagine that you want to send an email to customers immediately after they choose the Password reset button in your app. You can create an event-based campaign that addresses this use case in as few as four clicks.

For targeted campaign workloads, event-based campaigns are a great way to introduce cross-sale opportunities for complementary items. For example, if a customer adds a phone to their shopping cart, you can send an in-app push notification that offers them a special price on a case that fits the device that they’re purchasing.

When you use the campaign wizard in the Amazon Pinpoint console, you now have the option to create event-based campaigns. Rather than define a time to send your message to customers, you select specific events, attributes, and metric values. As you go through the event scheduling component of the wizard, you define the event that you want Amazon Pinpoint to look for, as shown in the following image.

In order to use this feature, you have to set up your mobile and web apps to send event data to Amazon Pinpoint. To learn more about sending event data to Amazon Pinpoint, see Reporting Events in Your Application in the Amazon Pinpoint Developer Guide.

A real-world scenario

Let’s look at a scenario that explores how you can use event-based campaigns. Say you have a music streaming service such as Amazon Music. You’ve secured a deal for a popular artist to record a set of live tracks that will only be available on your service, and you want to let people who’ve previously listened to that artist know about this exciting exclusive.

To create an event-based campaign, you complete the usual steps involved in creating a campaign in Amazon Pinpoint (you can learn more about these steps in the Campaigns section of the Amazon Pinpoint User Guide). When you arrive at step 4 of the campaign creation wizard, it asks you when the campaign should be sent. At this point, you have two options: you can send the campaign at a specific time, or you can send it when an event occurs. In this example, we choose When an event occurs.

Next, you choose the event that causes the campaign to be sent. In our example, we’ll choose the event named song.played. If we stopped here and launched the campaign, Amazon Pinpoint would send our message to every customer who generated that event—in this case, every customer who played any song in our app, ever. That’s not what we want, but fortunately we can refine our criteria by choosing attributes and metrics.

In the Attributes box, we’ll choose the artistName attribute, and then choose the attribute value that corresponds with the name of the band we want to promote, The Alexandrians.

We’ve already narrowed down our list of recipients quite a bit, but we can go even further. In the Metrics box, we can specify certain quantitative values. For example, we could refine our event so that only customers who spent more than 10 minutes listening to songs by The Alexandrians are sent notifications. To complete this step, we choose the timePlayed metric and set it to look for endpoints for which this metric is greater than 600 seconds. When we finish setting up the triggering event, we see something similar to the following example:

Now that we’ve finished setting up the event that will result in our campaign to be sent, we can finish creating the campaign as we normally would.

Transparent and affordable pricing

There are no additional charges associated with creating event-based campaigns. You pay only for the number of endpoints that you target, the number of messages that you send, and the number of analytics events that you send to Amazon Pinpoint. To learn more about the costs associated with using Amazon Pinpoint, see our Pricing page.

As an example, assume that your app has 50,000 monthly active users, and that you target each user once a day with a push notification. In this scenario, you’d pay around $55.50 per month to send messages to all 50,000 users. Note that this pricing is subject to change, and is not necessarily reflective of what your actual costs may be.

Limitations and best practices

There are a few limitations and best practices that you should consider when you create event-based campaigns:

  • You can only create an event-based campaign if the campaign uses a dynamic segment (as opposed to an imported segment).
  • Event-based campaigns only consider events that are reported by recent versions of the AWS Mobile SDK. Your apps should use the following versions of the SDK in order to work with event-based campaigns:
    • AWS Mobile SDK for Android: version 2.7.2 or later
    • AWS Mobile SDK for iOS: version 2.6.30 or later
  • Because of this restriction, we recommend that you set up your segments so that they only include customers who use a version of your app that runs a compatible version of the SDK.
  • Choose your events carefully. For example, if you send an event-based campaign every time a session.start event occurs, you might quickly overwhelm your users with messages. You can limit the number of messages that Amazon Pinpoint sends to a single endpoint in a 24-hour period. For more information, see General Settings in the Amazon Pinpoint User Guide.

Ready to get started?

You can use these features today in every AWS Region where Amazon Pinpoint is available. We hope that these additions help you to better understand your customers and find exciting new ways to use Amazon Pinpoint to connect and engage with them.


Introducing the Amazon Pinpoint Voice Channel

Post Syndicated from Kadir Rathnavelu original https://aws.amazon.com/blogs/messaging-and-targeting/introducing-the-amazon-pinpoint-voice-channel/

Today, Amazon Pinpoint added a new voice channel. This new channel enables you to deliver voice messages to your customers over the phone. When you use the voice channel, you provide a text script, and, optionally, instructions for converting that text to speech. Amazon Pinpoint converts your script into lifelike speech and then calls your customers over the phone to deliver your message. Pinpoint Voice is a great way to deliver transactional messages such as one-time passwords, appointment reminders, order confirmations, and more.

Phone calls offer several advantages over other channels; they’re highly reliable and very extensible, and just about everybody has a phone number. The addition of voice messages to Pinpoint’s existing email, SMS, and push notification channels provides you with even more flexibility in delivering the right message on the right channel for your customers.

For example, to send a One-Time Password (OTP) to your customer over the phone, you first provide the Amazon Pinpoint SMS and Voice API with a set of instructions, including the content of the message and the destination phone number. The content can be a plain-text or Speech Synthesis Markup Language (SSML) messages.

You can use SSML to personalize your messages by adding pauses, changing the pronunciation, emphasizing key words, and much more. Amazon Pinpoint takes your message and uses Amazon Polly to convert it into lifelike speech in one of the dozens of languages and voices available. Amazon Pinpoint then and calls the destination number and delivers the message.

      "Text":"<speak> Your one-time password is 1 <break time='0.5s'/> 4 <break time='0.5s'/> 5 <break time='0.5s'/> 7 </speak>",

As the call progresses, Amazon Pinpoint provides call metrics in real time to Amazon Kinesis Data Firehose and Amazon CloudWatch Logs. These metrics tell you when a call has been initiated, when it’s ringing, when it’s been answered, when it’s completed, and whether or not the recipient answered. You can use this information to measure the effectiveness of your messages and to optimize your future voice engagements.

In addition to delivering one-to-one transactional messages, you can also use voice messages as a backup channel when you aren’t able to deliver messages through email, SMS, or push notifications. For example, it’s not unusual for customers to provide you with phone numbers that aren’t able to receive text messages, such as land-line or VoIP numbers. You can use the Phone Number Validate feature of Amazon Pinpoint to determine the type of phone number provided. When you detect that a phone number can’t receive text messages, you can send voice messages instead. By using Amazon Pinpoint Voice, you can reach a segment of customers who you wouldn’t be able to reach by sending text messages.

To send voice messages using Amazon Pinpoint, you need to have at least one dedicated phone number. You can lease local phone numbers in a variety of countries directly from the Amazon Pinpoint console. In certain countries and regions, you can also use these dedicated phone numbers to send SMS messages to your customers. See Supported Countries and Regions for more information about leasing phone numbers.

To learn more, see the Amazon Pinpoint User Guide and the Amazon Pinpoint SMS and Voice API Reference.

Amazon Pinpoint’s Analytics Suite Now Includes Event Metric Visualizations

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/amazon-pinpoints-analytics-suite-now-includes-event-metric-visualizations/

Today, we’re enhancing Amazon Pinpoint’s analytics capabilities by giving you the ability to filter event data based on certain attributes and metrics. We’re also adding new analytics features help you visualize event data over time. These new features help you quickly understand how your customers are using your app, and make it easy to keep track of behavioral trends over time.

For example, if you want to understand how much time a customer spends performing a certain action within your app, such as watching a video, you can now visualize this in as few as three clicks. Start by signing in to the console and choosing the project that contains the data that you want to view. Next, in the navigation pane, under Analytics, choose Events. On the Events chart, you’ll see two new menus: Event Attributes and Metrics, and Event Attribute Values. Use these menus to refine the data in the chart to include only the events and attributes that you want to see.

When you select a metric value, Amazon Pinpoint shows a new time-series chart. This chart includes summary filters, so you can quickly see the total time an action occurs, the average, the minimum and maximum values over the time period, and the number of events. You can use this data when considering how to improve the user experience of your app, or when creating new engagement campaigns for certain groups of customers.

For example, you could see if the average time spent on your app is increasing or decreasing, or if the total price of goods sold is trending in the right direction. You can then create engagement campaigns that specifically target segments of users who spend less time in your app, or those who are making fewer in-app purchases.

As always, the data in Pinpoint is yours. You can easily export it to CSV format for analysis in external applications. For deeper analysis, we recommend that you set up Amazon Pinpoint Event Streams, which let you stream your data to nearly any destination. To learn more about setting up Event Streams, see Streaming Events from Amazon Pinpoint to Redshift on the AWS Messaging and Targeting Blog.

You can use these features now in all AWS Regions where Amazon Pinpoint is available. We hope that these additions help you to better understand your customers and find exciting new ways to use Amazon Pinpoint to connect and engage with them.

Exciting New Email Features Available Now in Amazon Pinpoint

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/exciting-new-email-features-available-now-in-amazon-pinpoint/

Yesterday, we launched several exciting new email-related features in Amazon Pinpoint. The biggest of these features is the ability to send transactional emails. Transactional emails are a great way to send one-to-one emails to your customers without creating segments or campaigns first. For example, when a customer makes a purchase in your app, or requests a password reset, you can send them an email immediately and directly with the information that they need. You can send transactional emails by using the Amazon Pinpoint API or an AWS SDK, or by using the Amazon Pinpoint SMTP interface.

Along with the ability to send transactional emails, we also added a dashboard that gives you deep insights into the performance of the emails you’ve sent. You can use this dashboard to view several response metrics related to the transactional emails you’ve sent, including your delivery, bounce, and complaint rates, the number of emails that were opened and clicked, and more. You can filter this dashboard to include data from 1 to 30 days.

We’ve also added the ability to lease dedicated IP addresses through Amazon Pinpoint. Dedicated IP addresses are IP addresses that are reserved for your exclusive use. Dedicated IP addresses give you complete control over the reputations of the IP addresses that you use to send email through Amazon Pinpoint. When you use dedicated IP addresses, you can create pools of your dedicated IP addresses. Dedicated IP pools are groups of IP addresses you use for different purposes. For example, you can create a pool of addresses to use when sending transactional messages, and another pool for sending marketing communications.

We also added the following email-related capabilities:

  • Content personalization: Generic, one-size-fits-all emails tend to have lower engagement rates than personalized messages. When you send a transactional email, you can include personalization tags, such as the recipient’s name or location. When Amazon Pinpoint sends the mail, it replaces the personalization tags with the appropriate values for the recipient.
  • Event destinations: You can use event destinations to send information about email events to various destinations, including Amazon CloudWatch, Amazon SNS, and Amazon Kinesis Data Firehose. Email events include email opens, clicks, bounces, complaints, and rejections.
  • Raw email support: You can use the Amazon Pinpoint API or SMTP interface to send raw, MIME-formatted emails. MIME emails can include custom headers and file attachments.

These features are available now in all AWS Regions where Amazon Pinpoint is available. We hope that these features help you find exciting new ways to use Amazon Pinpoint to connect and engage with your customers.

Amazon Pinpoint Offers Customers a New Console Experience with Richer Segmentation Capabilities to Drive End-User Engagement

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/amazon-pinpoint-offers-customers-a-new-console-experience-with-richer-segmentation-capabilities-to-drive-end-user-engagement/

Last night, we launched a significant update to the Amazon Pinpoint console. These updates make it easier to start sending campaigns with Amazon Pinpoint. In fact, you can start engaging with your customers in as few as three clicks!

This update also includes advanced segmentation capabilities, which enable you to filter based on user, endpoint, and channel attributes and bind those filters with certain conditions, such as AND, OR, and EQUAL TO operators. These features help you create finely-tuned segments, which result in better engagement and happier end-users. To learn more about creating segments in Amazon Pinpoint, see our tutorial in the Amazon Pinpoint User Guide.

Amazon Pinpoint’s new segmentation capabilities enable the following use cases:

  • Create new sub-segments from existing segments: You can now create sub-segments by selecting an existing segment and applying segmentation logic to it. For example, you could import a list of email addresses and filter out certain customers based on real-time endpoint attributes, such as the customer’s paid or free tier status. To learn more about creating segments, see Building Segments in the Amazon Pinpoint User Guide.
  • Advanced segmentation using smart operators: You can now create dynamic segments that incorporate certain operators, such as AND, OR, and EQUAL TO. For example, let’s say you classify your customers into tiers: tier 1 and tier 2. With today’s launch, you could create a new segment of customers whose tier status is equal to tier 1 OR tier 2, and then further refine your segment to include users who were not active in past 7 days.
  • Understand the impact of additional filters on the size of your segments, and see your audience size at a glance: The updated segmentation tool shows you the total size of your segment at all times. This size estimate is continuously updated as you add more filters to the segment, letting you see the impact of your filters in near real-time.

These new features are available to all Amazon Pinpoint users today at no additional cost.

Creating an AI-powered marketing solution for sentiment analysis and engagement

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/creating-an-ai-powered-marketing-solution-for-sentiment-analysis-and-engagement/

Note: Matt Dombrowski, one of our amazing Solutions Architects, wrote this article. He also developed the sample code that you can use to implement this solution.

Marketers know that it’s critical to understand the conversations that customers are having about their brands. The holy grail isn’t just to understand what’s happening on social media, but to distill those conversations into actionable insights. After that, you can scale, automate, and continuously improve your brand’s ability to engage.

In this blog post, we’ll demonstrate how your marketing department can use machine learning to understand social user sentiment and engage with users.

You’ll assume the role of a Marketing Manager at an up-and-coming retail company called Mountain Manhattan. Mountain Manhattan has seen strong growth in recent years, and is now looking for a better way to engage with its Twitter followers. Specifically, Mountain Manhattan wants to know who its advocates and distracters are, what the overall sentiment of the brand is, and who the key influencers are that need the white-glove treatment.

After that, we’ll show you how to quickly deploy a solution for real-time social media sentiment analysis and engagement. This process consists of three basic steps. First, you collect tweets that refer to your brand’s Twitter handle. Next, you use machine learning to assign a score to those tweets. And finally, you use Amazon Pinpoint to engage with your customers based on those scores.

Mountain Manhattan’s challenges

Like many companies, Mountain Manhattan has more data than they can act on. Mountain Manhattan receives over 1,000 tweets a day. That’s more than 365,000 tweets per year! Like most companies with a social media strategy, Mountain Manhattan thinks of each of these tweets as an ‘opportunity to engage.’ One of Mountain Manhattan’s challenges is that they need the tone, voice, response time, and candor of their responses to be clear and consistent—and they need to do so in several different languages.

They tried the brute force approach of reading tweets and manually responding to each one. However, this process quickly became unsustainable (not to mention very expensive) because of limited time and resources. Also, while Mountain Manhattan’s marketing team is rather tech-savvy, they don’t have the time or experience to worry about technical issues like ongoing development or security. Mountain Manhattan needs an engagement solution that’s affordable and effective, and that has industry-leading reliability, scale, and security.

The solution

Mountain Manhattan decided to use several AWS services to create an integrated social media monitoring and customer engagement solution. The marketing team spent about 30 minutes setting up the sophisticated solution described below, which enables testing and iteration on multiple use cases before going live.

This solution monitors a Twitter feed, and sends relevant tweets to an Amazon Kinesis data stream. Then it uses an AWS Lambda function to take the appropriate action. In this case, that action involves first calling Amazon Comprehend to provide a sentiment score, and then using Amazon Pinpoint to engage with the Twitter user. This solution has several benefits for Mountain Manhattan:

  • It’s scalable. Mountain Manhattan has flash and holiday sales, targeted campaigns, and various ad campaigns that can lead to spikes in customer tweets. This solution can handle nearly any workload in real time. Furthermore, ingesting every single tweet about their brand helps Mountain Manhattan get a holistic view of customer sentiment.
  • It’s easy to use. Mountain Manhattan needs to adapt to their customer needs. This means that they need a solution that’s customizable, user-friendly, and intuitive to use. By using Amazon Pinpoint, Mountain Manhattan’s marketing team was able to set up recurring campaigns based on certain customer characteristics. The daily, automated campaigns send notifications to an ever-updating dynamic segment. This ensures that customers never receive the same campaign message twice.
  • It’s cost-effective. Priorities for Mountain Manhattan can change quickly, and long-term contracts are no longer appealing to management. By using AWS services, there are no subscription fees, upfront costs, or long-term commitments. Mountain Manhattan pays only for they use, and they can adjust their marketing spend at any time.
  • It lets you own your data. Data is the lifeblood of modern marketing organizations. Companies need to own their data for use across many applications and systems. This solution gives Mountain Manhattan that ownership and flexibility. If it ever becomes necessary, they can change the destination of their Kinesis data streams to nearly any destination, and can export their customer data from Amazon Pinpoint.

How the solution works

The following architecture diagram shows the various AWS services that enable this AI-powered social sentiment marketing solution.

An image that shows the relationship between the various components used in this solution.

Let’s take a closer look at each of these components. This solution uses the following services and solutions:

  • Mobile client: Mountain Manhattan’s mobile app uses the Twitter SDK to authenticate users. The app is implemented in React Native for cross-platform compatibility, and because Mountain Manhattan’s developers are more familiar with JavaScript. Integrating the Twitter SDK enables Mountain Manhattan to map customers’ Twitter handles to specific mobile devices. There are a variety of ways to authenticate users—including authentication services from Facebook, Google, and Amazon. In this example, we focus on Twitter.
  • Amazon Kinesis Data Streams: This AWS service transfers tweets from Twitter into AWS Lambda (for sentiment analysis) and Amazon S3 (for long-term archival). Kinesis Data Streams can capture and store terabytes of data per hour from hundreds of thousands of sources. In the future, Mountain Manhattan could expand this solution to analyze data from Facebook, point-of-sale terminals, and website click streams.
  • Amazon ElasticSearch: Kinesis Firehose streams the tweets into an ElasticSearch cluster. By using ElasticSearch, Mountain Manhattan can easily search the data, and can visualize it by using Kibana.
  • AWS Lambda and Amazon Comprehend: Mountain Manhattan uses AWS Lambda to execute code without having to worry about deploying and maintaining servers. The AWS Lambda function looks at the tweets as they come in and determines the appropriate action to take. In Mountain Manhattan’s case, if the customer who tweeted is known, it calls Amazon Comprehend to perform AI-based sentiment analysis. Based on the results of that sentiment analysis, the Lambda function calls Amazon Pinpoint to begin the customer engagement process.
  • Amazon Pinpoint: This solution uses Amazon Pinpoint to handle two essential functions. First, it captures information about endpoints (the unique devices that use the app). Second, it sends targeted campaigns to those endpoints. The AWS Mobile SDK, which is integrated into Mountain Manhattan’s app, automatically associates the customer’s Twitter handle with their endpoint ID in Amazon Pinpoint. Mountain Manhattan also collects some custom attributes for each endpoint. For example, they place each endpoint into one of the following categories: Influencers, Supporters, Detractors, Loyal Shoppers, and CS Support Needed. By categorizing customers in this way, Mountain Manhattan can create more personalized messaging.

Mountain Manhattan’s solution in action

As Mountain Manhattan starts to ingest tweets, they get to know a lot more about their customers than just what they said. Mountain Manhattan can see the number of followers a user has, which is a good way to identify influencers. Additionally, Mountain Manhattan can see the Twitter user’s description, logo, picture, and location (if the user has exposed it). All of this data is fed into the AWS Lambda function, where Mountain Manhattan can take the customized action.

The screenshots in the following sections show the kinds of push notifications that Mountain Manhattan could automatically send to customers based on the content of their tweets.

Identifying influencers and early adopters

Mountain Manhattan’s AWS Lambda function determines how many Twitter followers each user has. If the number of followers is above a certain threshold, the function attaches the Influencer custom attribute to the user’s endpoint, and sends them a push notification.A push notification that says "We like you too! Tap here to join our Influencers Club and get free stuff!"

Tracking and engaging with consumers during events or promotions

During events, Mountain Manhattan can join the conversation with their customers by sending messages in real time based on customers’ tweets.A push notification that says "We're glad you're enjoying the sale! Tap here to subscribe to our events calendar."

Proactively engaging with customers having support issues

When Mountain Manhattan determines that the sentiment of a tweet is negative, they can send custom push notifications in an attempt to resolve the issue.A push notification that says "Sorry to hear you're having trouble! :( Tap here to talk to Jake, one of our best support team members."

Offering discounts or concessions to unhappy customers

Mountain Manhattan could monitor for certain words or phrases, such as “shipping delays”. When they detect these keywords, Amazon Pinpoint can automatically send a push notification that offers a discount on a future purchase.A push notification that says "We agree--delays are annoying! Tap here to get 20% off your next order."

Deploying the solution

Now that we’ve seen how this solution works, it’s time to implement it. The coolest part? You can use an AWS CloudFormation template to deploy all of the AWS components of this solution in a few clicks and about 10 minutes of your time.

Note: The procedures for deploying this solution might change over time as we continue to make improvements to it. For the latest procedures, see the Github page for this solution at https://github.com/aws-samples/amazon-pinpoint-social-sentiment/.


To complete these procedures, you have to have the following:

  • A mobile app that uses Twitter’s APIs or SDK for authentication and for ingesting tweets.
  • A macOS-based computer and physical iOS device (the Simulator that’s included with Xcode isn’t sufficient for testing this solution).
  • Xcode, Node.js, npm, and CocoaPods installed on your macOS-based computer.
    • To download Xcode, go to https://developer.apple.com/download/.
    • To download Node.js and npm, go to https://nodejs.org/en/. Download the latest Long-Term Support (LTS) version for macOS.
    • To download and install CocoaPods, type the following command at the macOS command line: sudo gem install cocoapods
  • The AWS Command Line Interface (AWS CLI) installed and configured on your macOS-based computer. For information about installing the AWS CLI, see Installing the AWS Command Line Interface. For information about setting up the AWS CLI, see Configuring the AWS CLI.
  • An AWS account with sufficient permissions to create the resources shown in the architecture diagram in the earlier section. For more information about creating an AWS account, see How do I create and activate a new Amazon Web Services account.
  • An Amazon EC2 key pair. You need this to log in to the EC2 instance if you want to modify the Twitter handle that you’re monitoring. For more information, see Creating a Key Pair Using Amazon EC2.
  • An Apple Developer account. Note that the approach that we cover in this post focuses exclusively on iOS devices. You can implement this solution on Android devices

Part 1: Create a Twitter application

The first step in this process is to create a Twitter app, which gives you access to the Twitter API. This solution uses the Twitter API to collect tweets in real time.

To create a Twitter application:

  1. Log in to your Twitter account. If you don’t already have a Twitter account, create one at https://twitter.com/signup.
  2. Go to https://apps.twitter.com/app/new, and then choose Create a new application.
  3. Under Application Details, complete the following sections:
    • For Name, type the name of your app.
    • For Description, type a description of your app.
    • For the Website and Callback URL fields, type any fully qualified URL (such as https://www.example.com). You’ll change these values in a later step, so the values you enter at this point aren’t important.
  4. Choose Create your Twitter application.
  5. Under Your access token, choose Create your access token.
  6. Under Application type, choose Read Only.
  7. Under Oauth settings, note the values next to Consumer key and Consumer secret. Then, under Your access token, note the values next to Access key and Secret access key. You’ll need all of these values in later steps.

Part 2: Install the dependencies

This solution requires you to download and set up some files from a GitHub repository.

To configure the AWS Mobile SDK in your app:

  1. Open Terminal.app. On the command line, navigate to the directory where you want to create your project.
  2. On the command line, type the following command to clone the repository that contains the source code that you’re using to configure this solution: git clone https://github.com/aws-samples/amazon-pinpoint-social-sentiment/
  3. Type the following command to change to the directory that contains the installation files: cd amazon-pinpoint-social-sentiment/mobile
  4. Type the following command to download the dependencies for this solution: npm install
  5. Type the following command to link the dependencies in the project: react-native link
  6. Type the following command to change into the ios directory: cd ios
  7. Type the following command to install CocoaPods into your project: pod install

Part 3: Set up your app to use the AWS Mobile SDK

To configure your app:

  1. From the /mobile directory, type the following command to create a backend project for your app and pull the service configuration (aws-exports.js file) into your project: awsmobile init. Press Enter at each prompt to accept the default response, as shown in the following example.
    Please tell us about your project:
    ? Where is your project's source directory: /
    ? Where is your project's distribution directory that stores build artifacts: /
    ? What is your project's build command: npm run-script build
    ? What is your project's start command for local test run: npm run-script start
    ? What awsmobile project name would you like to use: mobile-2018-08-16-03-16-39

  2. Open the file aws-exports.js. This file contains information about the backend configuration of your AWS Mobile Hub project. Take note of the aws_mobile_analytics_app_id key—you’ll use this value in a later step.
  3. In a text editor, open the file pinpoint-social-sentiment/mobile/App.js. Under TwitterAuth.init, next to twitter_key, replace <your key here> with the consumer key that you received when you created your Twitter app in step 1. Then, next to twitter_secret, replace <your secret here> with the consumer secret you received when you created your Twitter app. When you finish, save the file.
  4. In a text editor, open the file amazon-pinpoint-social-sentiment/mobile/ios/MobileCon/AppDelegate.m. Search for the following section:
    - (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions
      NSURL *jsCodeLocation;
      [[Twitter sharedInstance] startWithConsumerKey:@"<your-consumer-key>" consumerSecret:@"<your-consumer-secret>"];

    In this section, replace <your-consumer-key> with your Twitter consumer key, and replace <your-consumer-secret> with your Twitter consumer secret.

  5. In a text editor, open the file amazon-pinpoint-social-sentiment/mobile/ios/MobileCon/Info.plist. Search for the following section:

    Replace <your-API-key> with your Twitter consumer secret.

Part 4: Set up push notifications in your app

Now you’re ready to set up your app to send push notifications. A recent Medium post from Nader Dabit, one of our Developer Advocates, outlines this process nicely. Start at the Apple Developer Configuration section, and complete the remaining steps. After you complete these steps, your app is ready to send push notifications.

Part 5: Launch the AWS CloudFormation template

While your app is building, you can launch the AWS CloudFormation template that sets up the backend components that power this solution.

  1. Sign in to the AWS Management Console, and then open the AWS CloudFormation console at https://console.aws.amazon.com/cloudformation/home.
  2. Use the region selector to select the US East (N. Virginia) region.
  3. Choose Create new stack.
  4. Next to Choose a template, choose Specify an Amazon S3 template URL, and then paste the following URL: https://s3.amazonaws.com/mattd-customer-share/twitterdemo.template.yaml. Choose Next.
  5. Under Specify Details, for Stack Name, type a name for the CloudFormation stack.
  6. Under Parameters, do the following:
    1. For AccessToken, type your Twitter access token.
    2. For SecretAccessToken, type your Twitter access token secret.
    3. For AppId, type the app ID that you obtained in Part 3.
    4. For ConsumerKey, type your Twitter consumer key.
    5. For ConsumerSecret, type your Twitter consumer secret.
  7. Choose Next.
  8. On the next page, review your settings, and then choose Next again. On the final page, select the box to indicate that you understand that AWS CloudFormation will create IAM resources, and then choose Create.

When you choose Create, AWS CloudFormation creates the all of the backend components for the application. These include an EC2 instance, networking infrastructure, a Kinesis data stream, a Kinesis Firehose delivery stream, an S3 bucket, an Elasticsearch cluster, and a Lambda function. This process takes about 10 minutes to complete.

Part 6: Send a test tweet

Now you’re ready to test the solution to make sure that all of the components work as expected.

Start by logging in to your Twitter account. Send a tweet to @awsformobile. Your tweet should contain language that has a positive sentiment.

Your EC2 instance, which monitors the Twitter streaming API, captures this tweet. When this happens, the EC2 instance uses the Kinesis data stream to send the tweet to an Amazon S3 bucket for long-term storage. It also sends the tweet to AWS Lambda, which uses Amazon Comprehend to assign a sentiment score to the tweet. If the message is positive, Amazon Pinpoint sends a push notification to the Twitter handle that sent the message.

You can monitor the execution of the Lambda function by using Amazon CloudWatch Logs. You can access the CloudWatch Logs console at https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#logs. The log should contain an entry that resembles the following example:

On the Amazon Elasticsearch Service (Amazon ES) console, you can watch as Amazon ES catalogs incoming tweets. You can access this console at https://console.aws.amazon.com/es/home?region=us-east-1. For the Amazon ES domain for the tweets, choose the Kibana URL. You can use Kibana to easily search your incoming tweets, as shown in the following image:

Finally, you can go to your Amazon S3 bucket to view an archive of the tweets that were addressed to you. This bucket is useful for simple archiving, additional analysis, visualization, or even machine learning. You can access the Amazon S3 console at https://s3.console.aws.amazon.com/s3/home?region=us-east-1#.

Part 7: Create an Amazon Pinpoint campaign

In the real world, you probably don’t want to send messages to users immediately after they send tweets to your Twitter handle. If you did, you might seem too aggressive, and your customers might hesitate to engage with your brand in the future.

Fortunately, you can use the campaign scheduling tools in Amazon Pinpoint to create a recurring campaign.

  1. Sign in to the AWS Management Console, and then open the Amazon Pinpoint console at https://console.aws.amazon.com/pinpoint/home/?region=us-east-1.
  2. On the Projects page, choose your app.
  3. In the navigation pane, choose Campaigns, and then choose New Campaign.
  4. For Campaign name, type a name for the campaign, and then choose Next step.
  5. On the Segment page, do the following
    1. Choose Create a new segment.
    2. For Name your segment to reuse it later, type a name for the segment.
    3. For Filter by user attributes, choose the plus sign (+) icon. Filter by segment to include all endpoints where Sentiment is Positive, as shown in the following image:A screenshot that shows how to add the Sentiment = Positive attribute to a Pinpoint segment.
    4. Choose Next step.
  6. On the Message page, type the message that you want to send, and then choose Next step. To learn more about writing mobile push messages, see Writing a Mobile Push Message in the Amazon Pinpoint User Guide.
  7. On the Schedule page, choose the date and time when the message will be sent. You can also schedule the campaign to run on a recurring basis, such as every week. To learn more about scheduling campaigns, see Set the Campaign Schedule in the Amazon Pinpoint User Guide.

Final thoughts

In this blog post, we demonstrated how your marketing department can use machine learning to understand social user sentiment and engage with users.

In the interest of transparency, we calculated the total costs associated with running this solution. Our calculation includes a small Elasticsearch cluster, a small EC2 instance, storage costs, compute costs, and messaging costs. Assuming your app has 1 million monthly active users (MAUs), and assuming that 0.5% of those MAUs mention your brand every month on Twitter, running this solution would cost $28.66 per month, or just under four cents an hour.

We think this solution is one of the most affordable and capable social media sentiment analysis tools you’ll find on the market today. The best part about this solution is that it can be a complete solution—or the starting point for your own customized solution.

Need to send push messages on other platforms, such as Firebase Cloud Messaging (FCM) (for most Android devices)? No problem. Just set up your app to send endpoint data to Amazon Pinpoint and to send push notifications, and you’re ready to go! Want to send messages through different channels? If you have other endpoint data for your customers (such as email addresses or mobile phone numbers), you can add channels to your project in Amazon Pinpoint, and then use those channels to send messages.

We’re very excited about this solution, and we can’t wait to see what you build with it!

Streaming Events from Amazon Pinpoint to Redshift

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/streaming-events-from-amazon-pinpoint-to-redshift/

Note: This post was originally written by Ryan Idrigo-Lam, one of the founding members of the Amazon Pinpoint team.

You can use Amazon Pinpoint to segment, target, and engage with your customers directly from the console. The Pinpoint console also includes a variety of dashboards that you can use to keep track of how your customers use your applications, and measure how likely your customers are to engage with the messages you send them.

Some Pinpoint customers, however, have use cases that require a bit more than what these dashboards have to offer. For example, some customers want to join their Pinpoint data to external data sets, or to collect historical data beyond the six month window that Pinpoint retains. To help customers meet these needs, and many more, Amazon Pinpoint includes a feature called Event Streams.

This article provides information about using Event Streams to export your data from Amazon Pinpoint and into a high-performance Amazon Redshift database. Once your data is in Redshift, you can run queries against it, join it with other data sets, use it as a data source for analytics and data visualization tools, and much more.

Step 1: Create a Redshift Cluster

The first step in this process involves creating a new Redshift cluster to store your data. You can complete this step in a few clicks by using the Amazon Redshift console. For more information, see Managing Clusters Using the Console in the Amazon Redshift Cluster Management Guide.

When you create the new cluster, make a note of the values you specify for the Cluster Identifier, Database Name, Master User Name, and Master User Password. You’ll use all of these values when you set up Amazon Kinesis Firehose in the next section.

Step 2: Create a Firehose Delivery Stream with a Redshift Destination

After you create your Redshift cluster, you can create the Amazon Kinesis Data Firehose delivery stream that will deliver your Pinpoint data to the Redshift cluster.

To create the Kinesis Data Firehose delivery stream

  1. Open the Amazon Kinesis Data Firehose console at https://console.aws.amazon.com/firehose/home.
  2. Choose Create delivery stream.
  3. For Delivery stream name, type a name.
  4. Under Choose source, for Source, choose Direct PUT or other sources. Choose Next.
  5. On the Process records page, do the following:
    1. Under Transform source records with AWS Lambda, choose Enabled if you want to use a Lambda function to transform the data before Firehose loads it into Redshift. Otherwise, choose Disabled.
    2. Under Convert record format, choose Disabled, and then choose Next.
  6. On the Choose destination page, do the following:
    1. For Destination, choose Amazon Redshift.
    2. Under Amazon Redshift destination, specify the Cluster name, User name, Password, and Database for the Redshift database you created earlier. Also specify a name for the Table.
    3. Under Intermediate S3 destination, choose an S3 bucket to store data in. Alternatively, choose Create new to create a new bucket. Choose Next.
  7. On the Configure settings page, do the following:
    1. Under IAM role, choose an IAM role that Firehose can use to access your S3 bucket and KMS key. Alternatively, you can have the Firehose console create a new role. Choose Next.
    2. On the Review page, confirm the settings you specified on the previous pages. If the settings are correct, choose Create delivery stream.

Step 3: Create a JSONPaths file

The next step in this process is to create a JSONPaths file and upload it to an Amazon S3 bucket. You use the JSONPaths file to tell Amazon Redshift how to interpret the unstructured JSON that Amazon Pinpoint provides.

To create a JSONPaths file and upload it to Amazon S3

  1. In a text editor, create a new file.
  2. Paste the following code into the text file:
      "jsonpaths": [

  3. Modify the preceding code example to include the fields that you want to import into Redshift.
    Note: You can specify custom attributes or metrics by replacing my_custom_attribute or my_custom_metric in the example above with your custom attributes or metrics, respectively.
  4. When you finish modifying the code example, remove all whitespace, including spaces and line breaks, from the file. Save the file as json-paths.json.
  5. Open the Amazon S3 console at https://s3.console.aws.amazon.com/s3/home.
  6. Choose the S3 bucket you created when you set up the Firehose stream. Upload json-paths.json into the bucket.

Step 4: Configure the table in Redshift

At this point, it’s time to finish setting up your Redshift database. In this section, you’ll create a table in the Redshift cluster you created earlier. The columns in this table mirror the values you specified in the JSONPaths file in the previous section.

  1. Connect to your Redshift cluster by using a database tool such as SQL Workbench/J. For more information about connecting to a cluster, see Connect to the Cluster in the Amazon Redshift Getting Started Guide.
  2. Create a new table that contains a column for each field in the JSONPaths file you created in the preceding section. You can use the following example as a template.
    CREATE schema AWSMA;
      event_type VARCHAR(256) NOT NULL ENCODE LZO,
      event_timestamp TIMESTAMP NOT NULL ENCODE LZO,
      arrival_timestamp TIMESTAMP NULL ENCODE LZO,
      event_version CHAR(12) NULL ENCODE LZO,
      application_app_id VARCHAR(64) NOT NULL ENCODE LZO,
      application_package_name VARCHAR(256) NULL ENCODE LZO,
      application_version_name VARCHAR(256) NULL ENCODE LZO,
      application_version_code VARCHAR(256) NULL ENCODE LZO,
      application_title VARCHAR(256) NULL ENCODE LZO,
      application_cognito_identity_pool_id VARCHAR(64) NULL ENCODE LZO,
      application_sdk_name VARCHAR(256) NULL ENCODE LZO,
      application_sdk_version VARCHAR(256) NULL ENCODE LZO,
      client_cognito_id VARCHAR(64) NULL ENCODE LZO,
      device_model VARCHAR(256) NULL ENCODE LZO,
      device_make VARCHAR(256) NULL ENCODE LZO,
      device_platform_name VARCHAR(256) NULL ENCODE LZO,
      device_platform_version VARCHAR(256) NULL ENCODE LZO,
      device_locale_code VARCHAR(256) NULL ENCODE LZO,
      device_locale_language VARCHAR(64) NULL ENCODE LZO,
      device_locale_country VARCHAR(64) NULL ENCODE LZO,
      session_id VARCHAR(64) NULL ENCODE LZO,
      session_start_timestamp TIMESTAMP NULL ENCODE LZO,
      session_stop_timestamp TIMESTAMP NULL ENCODE LZO,
      monetization_transaction_id VARCHAR(64) NULL ENCODE LZO,
      monetization_transaction_store VARCHAR(64) NULL ENCODE LZO,
      monetization_transaction_item_id VARCHAR(64) NULL ENCODE LZO,
      monetization_transaction_quantity FLOAT8 NULL,
      monetization_transaction_price_reported VARCHAR(64) NULL ENCODE LZO,
      monetization_transaction_price_amount FLOAT8 NULL,
      monetization_transaction_price_currency_code VARCHAR(16) NULL ENCODE LZO,
      monetization_transaction_price_currency_symbol VARCHAR(32) NULL ENCODE LZO,
      - Custom Attributes
      a_campaign_id VARCHAR(4000),
      a_campaign_activity_id VARCHAR(4000),
      a_my_custom_attribute VARCHAR(4000),
      - Custom Metrics
      m_my_custom_metric float8
    SORTKEY ( application_app_id, event_timestamp, event_type);

Step 5: Configure the Firehose Stream

You’re getting close! At this point, you’re ready to point the Kinesis Data Firehose stream to your JSONPaths file so that Redshift parses the incoming data properly. You also need to list the columns of the table that your data will be copied into.

To configure the Firehose Stream

  1. Open the Amazon Kinesis Data Firehose console at https://console.aws.amazon.com/firehose/home.
  2. In the list of delivery streams, choose the delivery stream you created earlier.
  3. On the Details tab, choose Edit.
  4. Under Amazon Redshift destination, for COPY options, paste the following:
    JSON 's3://s3-bucket/json-paths.json'
    TIMEFORMAT 'epochmillisecs'

  5. Replace s3-bucket in the preceding code example with the path to the S3 bucket that contains json-paths.json.
  6. For Columns, list all of the columns that are present in the JSONPaths file you created earlier. Specify the column names in the same order as they’re listed in the json-paths.json file, using commas to separate the column names. When you finish, choose Save.

Step 6: Enable Event Streams in Amazon Pinpoint

The only thing left to do now is to tell Amazon Pinpoint to start sending data to Amazon Kinesis.

To enable Event Streaming in Amazon Pinpoint

  1. Open the Amazon Pinpoint console at https://console.aws.amazon.com/pinpoint/home.
  2. Choose the application or project that you want to enable event streams for.
  3. In the navigation pane, choose Settings.
  4. On the Event stream tab, choose Enable streaming of events to Amazon Kinesis.
  5. Under Stream to Amazon Kinesis, select Send events to an Amazon Kinesis Firehose delivery stream.
  6. For Amazon Kinesis Firehose delivery stream, choose the stream you created earlier.
  7. For IAM role, choose an existing role that allows the firehose:PutRecordBatch action, or choose Automatically create a role to have Amazon Pinpoint create a role with the appropriate permissions. If you choose to have Amazon Pinpoint create a role for you, type a name for the role. Choose Save.

That’s it! Once you complete this final step, Amazon Pinpoint starts exporting the data you specified into your Redshift cluster.

I hope this walk through was helpful. If you have any questions, please let us know in the comments or in the Amazon Pinpoint forum.

AWS Online Tech Talks – June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2018/

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:


Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.


AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.


June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.



June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.


Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new


AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.


Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.


Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.


June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.


Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.



June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.



June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

Practice Makes Perfect: Testing Campaigns Before You Send Them

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/practice-makes-perfect-testing-campaigns-before-you-send-them/

In an article we posted to Medium in February, we talked about how to determine the best time to engage your customers by using Amazon Pinpoint’s built-in session heat map. The session heat map allows you to find the times that your customers are most likely to use your app. In this post, we continued on the topic of best practices—specifically, how to appropriately test a campaign before going live.

In this post, we’ll talk about the old adage “practice makes perfect,” and how it applies to the campaigns you send using Amazon Pinpoint. Let’s take a scenario many of our customers encounter daily: creating a campaign to engage users by sending a push notification.

As you can see from the preceding screenshot, the segment we plan to target has nearly 1.7M recipients, which is a lot! Of course, before we got to this step, we already put several best practices into practice. For example, we determined the best time to engage our audience, scheduled the message based on recipients’ local time zones, performed A/B/N testing, measured lift using a hold-out group, and personalized the content for maximum effectiveness. Now that we’re ready to send the notification, we should test the message before we send it to all of the recipients in our segment. The reason for testing the message is pretty straightforward: we want to make sure every detail of the message is accurate before we send it to all 1,687,575 customers.

Fortunately, Amazon Pinpoint makes it easy to test your messages—in fact, you don’t even have to leave the campaign wizard in order to do so. In step 3 of the campaign wizard, below the message editor, there’s a button labelled Test campaign.

When you choose the Test campaign button, you have three options: you can send the test message to a segment of 100 endpoints or less, or to a set of specific endpoint IDs (up to 10), or to a set of specific device tokens (up to 10), as shown in the following image.

In our case, we’ve already created a segment of internal recipients who will test our message. On the Test Campaign window, under Send a test message to, we choose A segment. Then, in the drop-down menu, we select our test segment, and then choose Send test message.

Because we’re sending the test message to a segment, Amazon Pinpoint automatically creates a new campaign dedicated to this test. This process executes a test campaign, complete with message analytics, which allows you to perform end-to-end testing as if you sent the message to your production audience. To see the analytics for your test campaign, go to the Campaigns tab, and then choose the campaign (the name of the campaign contains the word “test”, followed by four random characters, followed by the name of the campaign).

After you complete a successful test, you’re ready to launch your campaign. As a final check, the Review & Launch screen includes a reminder that indicates whether or not you’ve tested the campaign, as shown in the following image.

There are several other ways you can use this feature. For example, you could use it for troubleshooting a campaign, or for iterating on existing campaigns. To learn more about testing campaigns, see the Amazon Pinpoint User Guide.

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.


May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.


May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.


May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.


May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.


May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PTGaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.


May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.


May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.


May 22, 2018 | 01:00 PM – 01:45 PM PTBuilding API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.


May 30, 2018 | 11:00 AM – 11:45 AM PTAccelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PTLearn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.





The intersection of Customer Engagement and Data Science

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/the-intersection-of-customer-engagement-and-data-science/

On the Messaging and Targeting team, we’re constantly inspired by the new and novel ways that customers use our services. For example, last year we took an in-depth look at a customer who built a fully featured email marketing platform based on Amazon SES and other AWS Services.

This week, our friends on the AWS Machine Learning team published a blog post that brings together the worlds of data science and customer engagement. Their solution uses Amazon SageMaker (a platform for building and deploying machine learning models) to create a system that makes purchasing predictions based on customers’ past behaviors. It then uses Amazon Pinpoint to send campaigns to customers based on these predictions.

The blog post is an interesting read that includes a primer on the process of creating a useful Machine Learning solution. It then goes in-depth, discussing the real-world considerations that are involved in implementing the solution.

Take a look at their post, Amazon Pinpoint campaigns driven by machine learning on Amazon SageMaker, on the AWS Machine Learning Blog.

The End of Google Cloud Messaging, and What it Means for Your Apps

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/the-end-of-google-cloud-messaging-and-what-it-means-for-your-apps/

On April 10, 2018, Google announced the deprecation of its Google Cloud Messaging (GCM) platform. Specifically, the GCM server and client APIs are deprecated and will be removed as soon as April 11, 2019.  What does this mean for you and your applications that use Amazon Simple Notification Service (Amazon SNS) or Amazon Pinpoint?

First, nothing will break now or after April 11, 2019. GCM device tokens are completely interchangeable with the newer Firebase Cloud Messaging (FCM) device tokens. If you have existing GCM tokens, you’ll still be able to use them to send notifications. This statement is also true for GCM tokens that you generate in the future.

On the back end, we’ve already migrated Amazon SNS and Amazon Pinpoint to the server endpoint for FCM (https://fcm.googleapis.com/fcm/send). As a developer, you don’t need to make any changes as a result of this deprecation.

We created the following mini-FAQ to address some of the questions you may have as a developer who uses Amazon SNS or Amazon Pinpoint.

If I migrate to FCM from GCM, can I still use Amazon Pinpoint and Amazon SNS?

Yes. Your ability to connect to your applications and send messages through both Amazon SNS and Amazon Pinpoint doesn’t change. We’ll update the documentation for Amazon SNS and Amazon Pinpoint soon to reflect these changes.

If I don’t migrate to FCM from GCM, can I still use Amazon Pinpoint and Amazon SNS?

Yes. If you do nothing, your existing credentials and GCM tokens will still be valid. All applications that you previously set up to use Amazon Pinpoint or Amazon SNS will continue to work normally. When you call the API for Amazon Pinpoint or Amazon SNS, we initiate a request to the FCM server endpoint directly.

What are the differences between Amazon SNS and Amazon Pinpoint?

Amazon SNS makes it easy for developers to set up, operate, and send notifications at scale, affordably and with a high degree of flexibility. Amazon Pinpoint has many of the same messaging capabilities as Amazon SNS, with the same levels of scalability and flexibility.

The main difference between the two services is that Amazon Pinpoint provides both transactional and targeted messaging capabilities. By using Amazon Pinpoint, marketers and developers can not only send transactional messages to their customers, but can also segment their audiences, create campaigns, and analyze both application and message metrics.

How do I migrate from GCM to FCM?

For more information about migrating from GCM to FCM, see Migrate a GCM Client App for Android to Firebase Cloud Messaging on the Google Developers site.

If you have any questions, please post them in the comments section, or in the Amazon Pinpoint or Amazon SNS forums.

The Amazon SES Blog is now the AWS Messaging and Targeting Blog

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/the-amazon-ses-blog-is-now-the-aws-messaging-and-targeting-blog/

Regular visitors to this blog may have noticed that its name has changed from the Amazon SES Blog to the AWS Messaging and Targeting Blog. The Amazon SES team has been working closely with the Amazon Pinpoint team in recent months, so we decided to create a single source of information for both products.

If you’re a dedicated Amazon SES user, don’t worry—Amazon SES isn’t going anywhere. However, as the goals of our two teams started to overlap more and more, we realized that we had lots of ideas for blog posts that would be relevant to users of both products.

If you’re not familiar with Amazon Pinpoint yet, allow us to make a brief introduction. Amazon Pinpoint was originally created to help mobile app developers analyze the ways that their customers used their apps, and to send mobile push messages to those users. Over time, the capabilities of Amazon Pinpoint grew to include the ability to send transactional messages (such as order confirmations, welcome messages, and one-time passwords), segment your audience, schedule campaign execution, and send messages using other channels (including SMS and email). In short, Amazon Pinpoint helps you deliver the right message to the right customers at the right time using the right channel.

In the past, this blog focused mainly on providing information about new features and common issues. Our new blog will include that same information, as well as practical tips, industry best practices, and the exciting things our customers have done using Amazon SES and Amazon Pinpoint. We hope you enjoy it!

If you have any questions, or if there’s anything you’d like to see us cover in the blog, please let us know in the comments section.

AWS Pinpoint Launches Two-Way Text Messaging

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aws-pinpoint-launches-two-way-text-messaging/

Last week Amazon Pinpoint launched AWS Global SMS two-way text messaging and we didn’t get an opportunity to cover the launch. AWS Pinpoint users can now programmaticaly respond to their end-users’ text messages. Users can provision both short codes and long codes (10-digit phone numbers) which send inbound messages to an SNS topic. Let’s take a look.

First I’ll navigate to the Pinpoint console where I’ll use the “Create a project in Mobile Hub” button in the top right corner. I’ll follow the steps in the wizard until the project is created.

Next, in the Pinpoint console I’ll click “Account Settings” in the top-right of the console window.

At the bottom of the Account Settings page there is a “Number Settings” section. If you don’t already have any short codes or long codes provisioned you’ll have to open a support ticket to request one. It can take multiple weeks for a short code to be approved by all carriers. Long codes are typically easier to provision.

Since I already have a few numbers provisioned I’ll use one of them now by clicking on it which brings me to that number’s configuration page.

Here I’ll enable 2-way SMS and create an SNS topic for messages.

I could create a quick lambda function to trigger on the SNS topic messages and then respond again with pinpoint.

import boto3
pinpoint = boto3.client('pinpoint')

def lambda_handler(event, context):
            'Addresses': {
                event['Records'][0]['Sns']['originationNumber']: {'ChannelType': 'SMS'}
            'MessageConfiguration': {
                'SMSMessage': {
                    'Body': 'Vim is the best!',
                    'MessageType': 'TRANSACTIONAL'

But pinpoint is extremely full-featured and you’re not limited to this simple message type. You can define rich messaging campaigns with various substitutions based on stored user data.

SMS is an area of continued investment for AWS so you can expect to see more advances and improvements as customers give us feedback on these new features. Let us know if you build anything cool with this!

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