Tag Archives: marketing communications

Building a generative AI Marketing Portal on AWS

2024-01-09 Tristan Nguyen

Post Syndicated from Tristan Nguyen original https://aws.amazon.com/blogs/messaging-and-targeting/building-a-generative-ai-marketing-portal-on-aws/

Introduction

In the preceding entries of this series, we examined the transformative impact of Generative AI on marketing strategies in “Building Generative AI into Marketing Strategies: A Primer” and delved into the intricacies of Prompt Engineering to enhance the creation of marketing content with services such as Amazon Bedrock in “From Prompt Engineering to Auto Prompt Optimisation”. We also explored the potential of Large Language Models (LLMs) to refine prompts for more effective customer engagement.

Continuing this exploration, we will articulate how Amazon Bedrock, Amazon Personalize, and Amazon Pinpoint can be leveraged to construct a marketer portal that not only facilitates AI-driven content generation but also personalizes and distributes this content effectively. The aim is to provide a clear blueprint for deploying a system that crafts, personalizes, and distributes marketing content efficiently. This blog will guide you through the deployment process, underlining the real-world utility of these services in optimizing marketing workflows. Through use cases and a code demonstration, we’ll see these technologies in action, offering a hands-on perspective on enhancing your marketing pipeline with AI-driven solutions.

The Challenge with Content Generation in Marketing

Many companies struggle to streamline their marketing operations effectively, facing hurdles at various stages of the marketing operations pipeline. Below, we list the challenges at three main stages of the pipeline: content generation, content personalization, and content distribution.

Content Generation

Creating high-quality, engaging content is often easier said than done. Companies need to invest in skilled copywriters or content creators who understand not just the product but also the target audience. Even with the right talent, the process can be time-consuming and costly. Moreover, generating content at scale while maintaining quality and compliance to industry regulations is the key blocker for many companies considering adopting generative AI technologies in production environments.

Content Personalization

Once the content is created, the next hurdle is personalization. In today’s digital age, generic content rarely captures attention. Customers expect content tailored to their needs, preferences, and behaviors. However, personalizing content is not straightforward. It requires a deep understanding of customer data, which often resides in siloed databases, making it difficult to create a 360-degree view of the customer.

Content Distribution

Finally, even the most captivating, personalized content is ineffective if it doesn’t reach the right audience at the right time. Companies often grapple with choosing the appropriate channels for content distribution, be it email, social media, or mobile notifications. Additionally, ensuring that the content complies with various regulations and doesn’t end up in spam folders adds another layer of complexity to the distribution phase. Sending at scale requires paying attention to deliverability, security and reliability which often poses significant challenges to marketers.

By addressing these challenges, companies can significantly improve their marketing operations and empower their marketers to be more effective. But how can this be achieved efficiently and at scale? The answer lies in leveraging the power of Amazon Bedrock, Amazon Personalize, and Amazon Pinpoint, as we will explore in the following solution.

The Solution In Action

Before we dive into the details of the implementation, let’s take a look at the end result through the linked demo video.

Use Case 1: Banking/Financial Services Industry

You are a relationship manager working in the Consumer Banking department of a fictitious company called AnyCompany Bank. You are assigned a group of customers and would like to send out personalized and targeted communications to the channel of choice to every members of this group of customer.

Behind the scene, the marketer is utilizing Amazon Pinpoint to create the segment of customers they would like to target. The customers’ information and the marketer’s prompt are then fed into Amazon Bedrock to generate the marketing content, which is then sent to the customer via SMS and email using Amazon Pinpoint.

In the Prompt Iterator page, you can employ a process called “prompt engineering” to further optimize your prompt to maximize the effectiveness of your marketing campaigns. Please refer to this blog on the process behind engineering the prompt as well as how to apply an additional LLM model for auto-prompting. To get started, simply copy the sample banking prompt which has gone through the prompt engineering process in this page.
Next, you can either upload your customer group by uploading a .csv file (through “Importing a Segment”) or specify a customer group using pre-defined filter criteria based on your current customer database using Amazon Pinpoint.

UseCase1Segment

E.g.: The screenshot shows a sample filtered segment named ManagementOrRetired that only filters to customers who are management or retirees.

Once done, you can log into the marketer portal and choose the relevant segment that you’ve just created within the Amazon Pinpoint console.

PinpointSegment

You can then preview the customers and their information stored in your Amazon Pinpoint’s customer database. Once satisfied, we’re ready to start generating content for those customers!
Click on 1:1 Content Generator tab, your content is automatically generated for your first customer. Here, you can cycle through your customers one by one, and depending on the customer’s preferred language and channel, an email or SMS in the preferred language is automatically generated for them.
- Generated SMS in English

PostiveSMS

- A negative example showing proper prompt-engineering at work to moderate content. This happens if we try to insert data that does not make sense for the marketing content generator to output. In this case, the marketing generator refuses to output (justifiably) an advertisement for a 6-year-old on a secured instalment loan.

NegativeSMS

Finally, we choose to send the generated content via Amazon Pinpoint by clicking on “Send with Amazon Pinpoint”. In the back end, Amazon Pinpoint will orchestrate the sending of the email/SMS through the appropriate channels.
- Alternatively, if the auto-generated content still did not meet your needs and you want to generate another draft, you can Disagree and try again.

Use Case 2: Travel & Hospitality

You are a marketing executive that’s working for an online air ticketing agency. You’ve been tasked to promote a specific flight from Singapore to Hong Kong for AnyCompany airline. You’d first like to identify which customers would be prime candidates to promote this flight leg to and then send out hyper-personalized message to them.

Behind the scene, instead of using Amazon Pinpoint to manually define the segment, the marketer in this case is leveraging AIML capabilities of Amazon Personalize to define the best group of customers to recommend the specific flight leg to them. Similar to the above use case, the customers’ information and LLM prompt are fed into the Amazon Bedrock, which generates the marketing content that is eventually sent out via Amazon Pinpoint.

Similar to the above use case, you’d need to go through a prompt engineering process to ensure that the content the LLM model is generating will be relevant and safe for use. To get started quickly, go to the Prompt Iterator page, you can use the sample airlines prompt and iterate from there.
Your company offers many different flight legs, aggregated from many different carriers. You first filter down to the flight leg that you want to promote using the Filters on the left. In this case, we are filtering for flights originating from Singapore (SRCCity) and going to Hong Kong (DSTCity), operated by AnyCompany Airlines.

PersonalizeInstructions

Now, let’s choose the number of customers that you’d like to generate. Once satisfied, you choose to start the batch segmentation job.
In the background, Amazon Personalize generates a group of customers that are most likely to be interested in this flight leg based on past interactions with similar flight itineraries.
Once the segmentation job is finished as shown, you can fetch the recommended group of customers and start generating content for them immediately, similar to the first use case.

Setup instructions

The setup instructions and deployment details can be found in the GitHub link.

Conclusion

In this blog, we’ve explored the transformative potential of integrating Amazon Bedrock, Amazon Personalize, and Amazon Pinpoint to address the common challenges in marketing operations. By automating the content generation with Amazon Bedrock, personalizing at scale with Amazon Personalize, and ensuring precise content distribution with Amazon Pinpoint, companies can not only streamline their marketing processes but also elevate the customer experience.

The benefits are clear: time-saving through automation, increased operational efficiency, and enhanced customer satisfaction through personalized engagement. This integrated solution empowers marketers to focus on strategy and creativity, leaving the heavy lifting to AWS’s robust AI and ML services.

For those ready to take the next step, we’ve provided a comprehensive guide and resources to implement this solution. By following the setup instructions and leveraging the provided prompts as a starting point, you can deploy this solution and begin customizing the marketer portal to your business’ needs.

Call to Action

Don’t let the challenges of content generation, personalization, and distribution hold back your marketing potential. Deploy the Generative AI Marketer Portal today, adapt it to your specific needs, and watch as your marketing operations transform. For a hands-on start and to see this solution in action, visit the GitHub repository for detailed setup instructions.

Have a question? Share your experiences or leave your questions in the comment section.

About the Authors

Tristan (Tri) Nguyen

Tristan (Tri) Nguyen is an Amazon Pinpoint and Amazon Simple Email Service Specialist Solutions Architect at AWS. At work, he specializes in technical implementation of communications services in enterprise systems and architecture/solutions design. In his spare time, he enjoys chess, rock climbing, hiking and triathlon.

Philipp Kaindl

Philipp Kaindl is a Senior Artificial Intelligence and Machine Learning Solutions Architect at AWS. With a background in data science and
mechanical engineering his focus is on empowering customers to create lasting business impact with the help of AI. Outside of work, Philipp enjoys tinkering with 3D printers, sailing and hiking.

Bruno Giorgini

Bruno Giorgini is a Senior Solutions Architect specializing in Pinpoint and SES. With over two decades of experience in the IT industry, Bruno has been dedicated to assisting customers of all sizes in achieving their objectives. When he is not crafting innovative solutions for clients, Bruno enjoys spending quality time with his wife and son, exploring the scenic hiking trails around the SF Bay Area.

Building Generative AI into Marketing Strategies: A Primer

2023-07-06 nnatri

Post Syndicated from nnatri original https://aws.amazon.com/blogs/messaging-and-targeting/building-generative-ai-into-marketing-strategies-a-primer/

Introduction

Artificial Intelligence has undoubtedly shaped many industries and is poised to be one of the most transformative technologies in the 21st century. Among these is the field of marketing where the application of generative AI promises to transform the landscape. This blog post explores how generative AI can revolutionize marketing strategies, offering innovative solutions and opportunities.

According to Harvard Business Review, marketing’s core activities, such as understanding customer needs, matching them to products and services, and persuading people to buy, can be dramatically enhanced by AI. A 2018 McKinsey analysis of more than 400 advanced use cases showed that marketing was the domain where AI would contribute the greatest value. The ability to leverage AI can not only help automate and streamline processes but also deliver personalized, engaging content to customers. It enhances the ability of marketers to target the right audience, predict consumer behavior, and provide personalized customer experiences. AI allows marketers to process and interpret massive amounts of data, converting it into actionable insights and strategies, thereby redefining the way businesses interact with customers.

Generating content is just one part of the equation. AI-generated content, no matter how good, is useless if it does not arrive at the intended audience at the right point of time. Integrating the generated content into an automated marketing pipeline that not only understands the customer profile but also delivers a personalized experience at the right point of interaction is also crucial to getting the intended action from the customer.

Amazon Web Services (AWS) provides a robust platform for implementing generative AI in marketing strategies. AWS offers a range of AI and machine learning services that can be leveraged for various marketing use cases, from content creation to customer segmentation and personalized recommendations. Two services that are instrumental to delivering customer contents and can be easily integrated with other generative AI services are Amazon Pinpoint and Amazon Simple Email Service. By integrating generative AI with Amazon Pinpoint and Amazon SES, marketers can automate the creation of personalized messages for their customers, enhancing the effectiveness of their campaigns. This combination allows for a seamless blend of AI-powered content generation and targeted, data-driven customer engagement.

As we delve deeper into this blog post, we’ll explore the mechanics of generative AI, its benefits and how AWS services can facilitate its integration into marketing communications.

What is Generative AI?

Generative AI is a subset of artificial intelligence that leverages machine learning techniques to generate new data instances that resemble your training data. It works by learning the underlying patterns and structures of the input data, and then uses this understanding to generate new, similar data. This is achieved through the use of models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.

What do Generative AI buzzwords mean?

In the world of AI, buzzwords are abundant. Terms like “deep learning”, “neural networks”, “machine learning”, “generative AI”, and “large language models” are often used interchangeably, but they each have distinct meanings. Understanding these terms is crucial for appreciating the capabilities and limitations of different AI technologies.

Machine Learning (ML) is a subset of AI that involves the development of algorithms that allow computers to learn from and make decisions or predictions based on data. These algorithms can be ‘trained’ on a dataset and then used to predict or classify new data. Machine learning models can be broadly categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Deep Learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to model and understand complex patterns. These layers of neurons process different features, and their outputs are combined to produce a final result. Deep learning models can handle large amounts of data and are particularly good at processing images, speech, and text.

Generative AI refers specifically to AI models that can generate new data that mimic the data they were trained on. This is achieved through the use of models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Generative AI can create anything from written content to visual designs, and even music, making it a versatile tool in the hands of marketers.

Large Language Models (LLMs) are a type of generative AI that are trained on a large corpus of text data and can generate human-like text. They predict the probability of a word given the previous words used in the text. They are particularly useful in applications like text completion, translation, summarization, and more. While they are a type of generative AI, they are specifically designed for handling text data.

Simply put, you can understand that Large Language Model is a subset of Generative AI, which is then a subset of Machine Learning and they ultimately falls under the umbrella term of Artificial Intelligence.

What are the problems with generative AI and marketing?

While generative AI holds immense potential for transforming marketing strategies, it’s important to be aware of its limitations and potential pitfalls, especially when it comes to content generation and customer engagement. Here are some common challenges that marketers should be aware of:

Bias in Generative AI Generative AI models learn from the data they are trained on. If the training data is biased, the AI model will likely reproduce these biases in its output. For example, if a model is trained primarily on data from one demographic, it may not accurately represent other demographics, leading to marketing campaigns that are ineffective or offensive. Imagine if you are trying to generate an image for a campaign targeting females, a generative AI model might not generate images of females in jobs like doctors, lawyers or judges, leading your campaign to suffer from bias and uninclusiveness.

Insensitivity to Cultural Nuances Generative AI models may not fully understand cultural nuances or sensitive topics, which can lead to content that is insensitive or even harmful. For instance, a generative AI model used to create social media posts for a global brand may inadvertently generate content that is seen as disrespectful or offensive by certain cultures or communities.

Potential for Inappropriate or Offensive Content Generative AI models can sometimes generate content that is inappropriate or offensive. This is often because the models do not fully understand the context in which certain words or phrases should be used. It’s important to have safeguards in place to review and approve content before it’s published. A common problem with LLMs is hallucination: whereby the model speaks false knowledge as if it is accurate. A marketing team might mistakenly publish a auto-generated promotional content that contains a 20% discount on an item when no such promotions were approved. This could have disastrous effect if safeguards are not in place and erodes customers’ trust.

Intellectual Property and Legal Concerns Generative AI models can create new content, such as images, music, videos, and text, which raises questions of ownership and potential copyright infringement. Being a relatively new field, legal discussions are still ongoing to discuss legal implications of using Generative AI, e.g. who should own generated AI content, and copyright infringement.

Not a Replacement for Human Creativity Finally, while generative AI can automate certain aspects of marketing campaigns, it cannot replace the creativity or emotional connections that marketers use in crafting compelling campaigns. The most successful marketing campaigns touch the hearts of the customers, and while Generative AI is very capable of replicating human content, it still lacks in mimicking that “human touch”.

In conclusion, while generative AI offers exciting possibilities for marketing, it’s important to approach its use with a clear understanding of its limitations and potential pitfalls. By doing so, marketers can leverage the benefits of generative AI while mitigating risks.

How can I use generative AI in marketing communications?

Amazon Web Services (AWS) provides a comprehensive suite of services that facilitate the use of generative AI in marketing. These services are designed to handle a variety of tasks, from data processing and storage to machine learning and analytics, making it easier for marketers to implement and benefit from generative AI technologies.

Overview of Relevant AWS Services

AWS offers several services that are particularly relevant for generative AI in marketing:

Amazon Bedrock: This service makes FMs accessible via an API. Bedrock offers the ability to access a range of powerful FMs for text and images, including Amazon’s Titan FMs. With Bedrock’s serverless experience, customers can easily find the right model for what they’re trying to get done, get started quickly, privately customize FMs with their own data, and easily integrate and deploy them into their applications using the AWS tools and capabilities they are familiar with.
Amazon Titan Models: These are two new large language models (LLMs) that AWS is announcing. The first is a generative LLM for tasks such as summarization, text generation, classification, open-ended Q&A, and information extraction. The second is an embeddings LLM that translates text inputs into numerical representations (known as embeddings) that contain the semantic meaning of the text. In response to the pitfalls mentioned above around Generative AI hallucinations and inaccurate information, AWS is actively working on improving accuracy and ensuring its Titan models produce high-quality responses, said Bratin Saha, an AWS vice president.
Amazon SageMaker: This fully managed service enables data scientists and developers to build, train, and deploy machine learning models quickly. SageMaker includes modules that can be used for generative AI, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
Amazon Pinpoint: This flexible and scalable outbound and inbound marketing communications service enables businesses to engage with customers across multiple messaging channels. Amazon Pinpoint is designed to scale with your business, allowing you to send messages to a large number of users in a short amount of time. It integrates with AWS’s generative AI services to enable personalized, AI-driven marketing campaigns.
Amazon Simple Email Service (SES): This cost-effective, flexible, and scalable email service enables marketers to send transactional emails, marketing messages, and other types of high-quality content to their customers. SES integrates with other AWS services, making it easy to send emails from applications being hosted on services such as Amazon EC2. SES also works seamlessly with Amazon Pinpoint, allowing for the creation of customer engagement communications that drive user activity and engagement.

How to build Generative AI into marketing communications

Dynamic Audience Targeting and Segmentation: Generative AI can help marketers to dynamically target and segment their audience. It can analyze customer data and behavior to identify patterns and trends, which can then be used to create more targeted marketing campaigns. Using Amazon Sagemaker or the soon-to-be-available Amazon Bedrock and Amazon Titan Models, Generative AI can suggest labels for customers based on unstructured data. According to McKinsey, generative AI can analyze data and identify consumer behavior patterns to help marketers create appealing content that resonates with their audience.

Personalized Marketing: Generative AI can be used to automate the creation of marketing content. This includes generating text for blogs, social media posts, and emails, as well as creating images and videos. This can save marketers a significant amount of time and effort, allowing them to focus on other aspects of their marketing strategy. Where it really shines is the ability to productionize marketing content creation, reducing the needs for marketers to create multiple copies for different customer segments. Previously, marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food). Generative AI can automate this process, providing the opportunities to dynamically create these contents programmatically and automatically send out to the most relevant segments via Amazon Pinpoint or Amazon SES.

Marketing Automation: Generative AI can automate various aspects of marketing, such as email marketing, social media marketing, and search engine marketing. This includes automating the creation and distribution of marketing content, as well as analyzing the performance of marketing campaigns. Amazon Pinpoint currently automates customer communications using journeys which is a customized, multi-step engagement experience. Generative AI could create a Pinpoint journey based on customer engagement data, engagement parameters and a prompt. This enables GenAI to not only personalize the content but create a personalized omnichannel experience that can extend throughout a period of time. It then becomes possible that journeys are created dynamically by generative AI and A/B tested on the fly to achieve an optimal pre-defined Key Performance Indicator (KPI).

A Sample Generative AI Use Case in Marketing Communications

AWS services are designed to work together, making it easy to implement generative AI in your marketing strategies. For instance, you can use Amazon SageMaker to build and train your generative AI models which assist with automating marketing content creation, and Amazon Pinpoint or Amazon SES to deliver the content to your customers.

Companies using AWS can theoretically supplement their existing workloads with generative AI capabilities without the needs for migration. The following reference architecture outlines a sample use case and showcases how Generative AI can be integrated into your customer journeys built on the AWS cloud. An e-commerce company can potentially receive many complaints emails a day. Companies spend a lot of money to acquire customers, it’s therefore important to think about how to turn that negative experience into a positive one.

GenAIMarketingSolutionArchitecture

When an email is received via Amazon SES (1), its content can be passed through to generative AI models using GANs to help with sentiment analysis (2). An article published by Amazon Science utilizes GANs for sentiment analysis for cases where a lack of data is a problem. Alternatively, one can also use Amazon Comprehend at this step and run A/B tests between the two models. The limitations with Amazon Comprehend would be the limited customizations you can perform to the model to fit your business needs.

Once the email’s sentiment is determined, the sentiment event is logged into Pinpoint (3), which then triggers an automatic winback journey (4).

Generative AI (e.g. HuggingFace’s Bloom Text Generation Models) can again be used here to dynamically create the content without needing to wait for the marketer’s input (5). Whereas marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food), generative AI provides the opportunities to dynamically create these contents on the fly given the above inputs.

Once the campaign content has been generated, the model pumps the template backs into Amazon Pinpoint (6), which then sends the personalized copy to the customer (7).

Result: Another customer is saved from attrition!

Conclusion

The landscape of generative AI is vast and ever-evolving, offering a plethora of opportunities for marketers to enhance their strategies and deliver more personalized, engaging content. AWS plays a pivotal role in this landscape, providing a comprehensive suite of services that facilitate the implementation of generative AI in marketing. From building and training AI models with Amazon SageMaker to delivering personalized messages with Amazon Pinpoint and Amazon SES, AWS provides the tools and infrastructure needed to harness the power of generative AI.

The potential of generative AI in relation to the marketer is immense. It offers the ability to automate content creation, personalize customer interactions, and derive valuable insights from data, among other benefits. However, it’s important to remember that while generative AI can automate certain aspects of marketing, it is not a replacement for human creativity and intuition. Instead, it should be viewed as a tool that can augment human capabilities and free up time for marketers to focus on strategy and creative direction.

Get started with Generative AI in marketing communications

As we conclude this exploration of generative AI and its applications in marketing, we encourage you to:

Brainstorm potential Generative AI use cases for your business. Consider how you can leverage generative AI to enhance your marketing strategies. This could involve automating content creation, personalizing customer interactions, or deriving insights from data.
Start leveraging generative AI in your marketing strategies with AWS today. AWS provides a comprehensive suite of services that make it easy to implement generative AI in your marketing strategies. By integrating these services into your workflows, you can enhance personalization, improve customer engagement, and drive better results from your campaigns.
Watch out for the next part in the series of integrating Generative AI into Amazon Pinpoint and SES. We will delve deeper into how you can leverage Amazon Pinpoint and SES together with generative AI to enhance your marketing campaigns. Stay tuned!

The journey into the world of generative AI is just beginning. As technology continues to evolve, so too will the opportunities for marketers to leverage AI to enhance their strategies and deliver more personalized, engaging content. We look forward to exploring this exciting frontier with you.

About the Author

Tristan (Tri) Nguyen

How to Manage Global Sending of SMS with Amazon Pinpoint

2023-06-28 Tyler Holmes

Post Syndicated from Tyler Holmes original https://aws.amazon.com/blogs/messaging-and-targeting/how-to-manage-global-sending-of-sms-with-amazon-pinpoint/

Amazon Pinpoint has a global SMS reach, of 240 countries and regions around the world, enabling companies of all sizes to send SMS globally. Unlike the process of sending a personal message from your phone to someone in another country, sending Application to Person (A2P) messages, also known as bulk SMS, involves many more regulations and requirements that vary from country to country. In this post we will review best practices for sending Global SMS and share a selection of AWS resources to help you send SMS globally.

The first thing to understand about delivering SMS around the world is that it takes a vast network of components working seamlessly together around the globe to deliver an SMS globally. The image below gives a simple example of delivering an SMS in the United States. Mobile devices are at the center of this, connecting to mobile carriers or operators, who operate the infrastructure necessary for SMS transmission. Once you hit that send button from AWS, your message travels to an Aggregator, who has connections to Operators, Partners, and/or other Aggregators. The reason for this is that there is no one vendor who delivers globally. AWS uses many Aggregators that both enable us to send globally as well as improve resiliency and deliverability of your messages. The last stop on the journey is the Short Message Service Center (SMSC), a central hub that receives, stores, and forwards text messages. The SMSC acts as a gateway, routing your message to the recipient’s carrier or operator through a series of interconnected networks, thanks to agreements between different carriers known as interconnection agreements. The entire process is facilitated by the Signaling System 7 (SS7), a set of protocols that enables the exchange of information between telecommunication networks, ensuring messages reach their intended recipients.
Diagram showing how SMS is delivered using aggregators
Every country has its own regulations and processes that you need to comply with in order to successfully deliver SMS to handsets that are registered to a particular country. There are some countries with little regulation and others that will block all SMS traffic unless it has been registered with the proper authorities.

Each country’s requirements include the origination identities (OIDs) that their networks support, some of these include long codes (standard phone numbers that typically have 10 or more digits), short codes (phone numbers that contain between four and seven digits), and Sender IDs (names that contain 6–11 alphanumeric characters). Each of these types of origination identities has unique benefits and drawbacks and you will need one for each use case and country you plan on supporting. Here is a list of the countries that AWS currently sends to and the OIDs that are supported.

Pre-Planning and Country Selection
The first step to planning a global roll out of SMS is to know what countries you want to send to and what each of your use cases are. Put together a spreadsheet for each unique use case you have and the countries you plan on sending to with the below key details:

The volumes you expect to send to each country
The throughput (Also referred to as Messages per Second, MPS, Transactions per Second, or TPS) at which you expect to deliver these messages
Whether your use case is one-way or two-way
- Not all countries support 2-way communications, which is the ability to have the recipient send a message back to the OID. Sender ID also does not support 2-way communication so if you are planning on using Sender ID you will need to account for how to opt recipients out of future communications.
Leave a column for the Origination Identity you will use for each country
Leave a column for whether this country requires advanced registration
Leave a column for any country specific limitations or requirements such as language limitations
Leave a column for the estimated time it takes to register
- This chart has estimates for common countries but there are others that also have lead time in procuring an OID so please open a support case for review

Selecting an Origination Identity

Now that you have these details all in one place consult this table to determine what OIDs each country supports, and, if your use case requires it, which countries support two-way.

In countries where there are multiple options for OIDs there are several guidelines to consider when you’re deciding what type of origination identity to use:

Sender IDs are a great option for one-way use cases. However, they’re not available in all countries and if you are needing to opt-out your customers you will need to provide a way for them to do so since they are only one-way.
- In some countries (such as India and Saudi Arabia), long codes can be used to receive incoming messages, but can’t be used to send outgoing messages. You can use these inbound-only long codes to provide your recipients with a way to opt out of messages that you send using a Sender ID.
Short codes are a great option for two-way use cases and have the highest throughput of all OIDs.
- While short codes have a higher throughput they also come at a much higher cost than other OIDs so weigh your cost against your use case requirements.
In some countries, we maintain a pool of shared origination identities. If you send messages to recipients in a particular country, but you don’t have a dedicated origination identity in that country, we make an effort to deliver your message using one of these shared identities.
- Shared identities are unavailable in some countries, including the United States and China.
- Shared identities cannot be 2-way so make sure you have a way of opting customers out of communication

With these in mind consult this guide to help you decide which OID to use for each country and use case. Update your sheet as you review each country. Many of our customers opt for a phased roll-out, enabling SMS for the countries that do not require registration and can be put into production swiftly while working through the registration process for those countries that require it and bringing those to production as they are approved. A phased approach is also preferred as it allows customers to monitor for any problems with deliverability with a smaller volume than their full production workload.

Procurement and Registration of Origination Identities

In countries where registration is onerous it is important to have a few things about your process all in one place. Some registrations are very similar in the information that they ask for while others have special processes that you need to follow. Examples include:

US
- Short Code
  - The information you gather from this post is useful for many of the other registrations
- 10DLC
- Toll-Free
India
Singapore
China

Once you have decided on your OIDs for each of your countries you can begin the process of procuring them. Depending on where you plan on sending you may need to open a case to procure them. Short codes you also need to open a case but the process is slightly different so review the documentation here. If you are having trouble making a decision on OIDs you may have the option of engaging with AWS support or your Account Manager dependent on the support level you have opted for on your account.

Testing SMS Sending

Once you have procured OIDs and are ready to begin testing, it is essential that you set up a way of monitoring the events that Pinpoint generates. Pay attention to the Delivery Receipts (DLRs) that are returned back into the event stream. These provide you details on the success or failure of your sends. Pinpoint delivers all events via Amazon Kinesis, which needs to be enabled within each Project you are using. This is a common solution among our customers. It enables the stream, sends it to a user-specified S3 Bucket, and sets up Tables and Views within Amazon Athena, our serverless SQL query engine.. Kinesis can stream to many different destinations, including Redshift and HTTP endpoints, among many others. This gives you flexibility in how you deliver the events to their required locations. Monitoring SMS events is an important part of sending globally, these are the SMS Events that are possible to receive in your stream.

TPS limits can vary depending on the countries you’re sending to and the OIDs you’re using. If there’s a risk of exceeding these limits and triggering rate limiting errors, it’s crucial to devise a strategy for queuing your messages. Keep in mind, Amazon Pinpoint doesn’t offer queueing capabilities. Therefore, message queueing must be incorporated at your application level or by leveraging AWS services. For instance, you could deploy this commonly used architecture that’s adjustable according to your specific use case.

Once you have your monitoring solution in place, you are read to begin testing sends to real destination phone numbers. Keep in mind that at this point you are likely still in the Sandbox for SMS. This means you have much lower quotas for sending and can only send to verified phone numbers or the SMS Simulator numbers. Pinpoint includes an SMS simulator, which you can use to send text messages and receive realistic event records to 51 commonly sent to countries. Messages sent to these destination phone numbers are not sent over the carrier network but do incur the standard outbound SMS messaging rate for the country that the simulated phone number is based in.

Best Practices for Sending
Before beginning There are two common ways of sending SMS via Pinpoint. The first option is the Pinpoint API using the SendMessages Action, which you can send a direct message to as many as 100 recipients at a time. The second option is to use the SMS and Voice v2 API and the SendTextMessage Action, which has more options available to configure your sends and can send to a single recipient with each call. The V2 API is the preferred way of sending as it allows for more fine grained control over your messages and is the API upon which new functionality will be built. Keep in mind that sending via the API does not attribute any metrics back to an endpoint unless you are specifying an endpoint ID in your call, so if you are using other features of Pinpoint such as campaigns or journeys or sending via other channels such as email you will need to consider your strategy for measuring success and how you will tie all of your communication efforts together.

When sending SMS Pinpoint includes logic for selecting the best OID to send from based on the country code. If there are multiple OIDs available to send to a particular country Pinpoint will default to the highest throughput OID available in your Account/Region. If there are not OIDs specific to the country being sent to Pinpoint will default to SenderID or to a shared OID owned by Pinpoint in that order, if the country allows these OIDs to be used. Given this functionality the best practice for sending SMS is to not specify the OID needed to send to a specific country and to allow Pinpoint to select. You can restrict Pinpoint to send to only those countries that you have OIDs for by using Pools, and turning off Shared Routes, more on this below.

If you have multiple use cases and need to specify the correct OID for each, this is where the V2 API is useful. OIDs can be attached to Pools, which can be configured to serve a particular use case, and the pool can be specified in your SendTextMessage call. Sending using a PoolID and allowing Pinpoint to select the right OID from that pool for the destination phone number simplifies your sending process. This blogpost details the process for creating Pools and using them to send SMS.

As mentioned above Pools also serve an additional use case, which is to limit message sending to specific countries. Some countries allow messages without an OID. If you don’t modify your settings to disable this feature, Pinpoint will attempt to deliver messages to these countries, even if you don’t have an explicit OID for them. Restricting SMS sends only to countries that you have OIDs for can be accomplished by using Pools and configuring “SharedRoutesEnabled“ to false by using the UpdatePool Action. Once configured you will receive an error back if attempting to send to a destination phone number that you do not have an OID for in the Pool. This configuration gives you the ability to control your costs while simplifying your process.

Managing Opt-Outs

As we have seen, managing SMS in an environment of increasing global regulation is challenging. An area of importance that needs to be configured is how you plan on managing the ability for recipients to opt out of your communications. Pinpoint can automatically opt your customers out of SMS communications using predefined keywords such as, “stop” or “unsubscribe.” However, this would make for an Account wide opt-out, and not ideal for customers that have multiple use cases such as OTP and Marketing communications. This blogpost details the process of managing opt-outs for multiple use cases. The configuration is enabled through the V2 API and is another reason to standardize your process on this API.

Monitoring Sending

The last step in ensuring success for SMS sending is having a solid platform for monitoring your sending. SMS is not a guaranteed delivery channel. You will always receive an event for a successful send in the event stream but there is no guarantee of a return status event, if a DLR from a carrier is not sent. A list of SMS Events and possible statuses can be found here.

The first Event you should see returned when watching the Event Stream for an SMS send activity is the “PENDING” event. This means we’ve sent the message to the carrier, where it’s buffered, and we’re waiting for the carrier to return a status message. There are no status messages between the “PENDING” state and the “whatever happens next” state, so if the carrier is retrying, we simply stay in PENDING and do not create more events. If a message is successfully delivered and a DLR is sent back from the carrier then a new event will be generated with a status of “SUCCESSFUL/DELIVERED.”

Make sure to review all of the possible values for the record_status attribute so that you are aware of varying issues with your sending that can arise. For example, statuses such as “Blocked,” “Spam,” and “Carrier_Blocked“ can indicate systemic issues that should be investigated.

Updates sent from a carrier via a DLR can be delayed for up to 72 hours or never sent at all. This varies based on the carrier and the country being sent to. Should you require a higher level of reliability, you need to establish business logic around monitoring SMS messages. If messages remain in a PENDING status longer than your business requirements permit, you must make a decision on how to handle them. You need to consider whether missed or duplicated messages are acceptable, or if it’s preferable to retry messages that are stuck in pending. The following is an example architecture for failed SMS retries that you can adjust to your needs.

Conclusion

This post covers the general process for getting started with Global SMS but as you have learned each country presents a different challenge and the regulatory environment is constantly evolving. It’s important to make sure that you are receiving messages from AWS that detail new regulations, new feature launches, and other major announcements to continually improve your process and make sure your SMS are delivering at the highest rate possible.

Take the time to plan out your approach, follow the steps outlined in this blog, and take advantage of any resources available to you within your support tier.

Decide what origination IDs you will need here
Review the documentation for the V2 SMS and Voice API here
Review the Pinpoint API and SendMessage here
Check out the support tiers comparison here

Resources:
https://docs.aws.amazon.com/pinpoint/latest/userguide/channels-sms-countries.html
https://aws.amazon.com/blogs/messaging-and-targeting/how-to-utilise-amazon-pinpoint-to-retry-unsuccessful-sms-delivery/
https://datatracker.ietf.org/doc/html/draft-wilde-sms-uri-20#section-4
https://docs.aws.amazon.com/pinpoint/latest/developerguide/event-streams-data-sms.html
https://docs.aws.amazon.com/pinpoint/latest/userguide/channels-sms-limitations-opt-out.html
https://docs.aws.amazon.com/pinpoint/latest/userguide/channels-sms-simulator.html

How to send geofenced marketing messages using Amazon Pinpoint

2023-06-12 Zach Elliott

Post Syndicated from Zach Elliott original https://aws.amazon.com/blogs/messaging-and-targeting/send-geofenced-marketing-messages-using-amazon-pinpoint/

Introduction

Geofencing, which creates a virtual geographical boundary that triggers a marketing action to a mobile device when a user enters or exits that boundary, can be used in marketing messages to drive more traffic and increase conversions. Amazon Pinpoint, AWS’ multichannel communication tool, can be used to create mobile notifications using geofencing technology, so customers receive notifications about a business when they’re close by that physical location.

Ways retailers can use geofencing:

There are a number of different use cases that retail or location-based businesses can use geofencing to drive customer conversions:

Target the customer with real-time offers and promotions when the customer is near the store: Detecting and establishing an interaction with the customer while in the store improves the customer experience. Using geofencing, retailers will be able to detect the presence and will be able to send coupon or promotional notifications.
Improve product search in the store: As the consumer enters the geofenced store, activate the product search for the store to help the consumer to search and navigate easily within the store.
Get more information about the customer in the store: Retailers will be able to collect more accurate consumer behavior inside the store by recording the interaction between the consumers and product search, and using geofencing and position to calculate the dwell time inside the store or how long the consumer is waiting in the queue.

In this blog we will talk about how you can use Amazon Location Service to trigger a notification using Amazon Pinpoint when a consumer enters a geofenced store.

Architecture Overview

Fig. 1: Geofencing and Pinpoint – Sample Architecture

Figure 1 depicts the solution architecture and resources deployed by the AWS CloudFormation Template, described in more detail in later sections. In the solution workflow:

Store Management defines a Geofence around store locations they wish to enroll using Amazon Location Service Geofencing and circular geofences.
A customer who has opted into location tracking using the app will update an Amazon Location Service Tracker Resource. This tracker will be evaluated against the store geofences.
If a geofence ENTER event is triggered, a message is sent to Amazon EventBridge.
EventBridge will trigger an AWS Lambda function.
The Lambda function looks up the Store Information in an Amazon DynamoDB table that matches the geofence ID in order to enrich the email.
Event is sent to a Pinpoint Journey with information from the Geofence event as well as store info.
Personalized email is sent to customer via Pinpoint

Configuring AWS Cloudformation

To deploy the Amazon Location Service resources as well as EventBridge, DynamoDB, and Lambda, we have created an AWS Cloudformation Template.

Use this link to launch the CloudFormation stack in the US-West-2 region. Selecting the button next to “I acknowledge that AWS CloudFormation might create IAM resources.” click Create stack

Fig. 2: AWS CloudFormation Console showing stack options.

Once the stack is complete. We can begin configuring Pinpoint.

Configuring Pinpoint

Our project was created for us via the CloudFormation template, but we still need to configure some items in Pinpoint. First, we’ll set up our email identity to send and receive messages from; for the purposes of this blog, you’ll use the same email address for sending and receiving the email, but in a production environment, your sending identity could either be a specific email address you’ve verified for messaging, or an entire email domain you’ve verified via DNS.

Configuring email channel

Adding an email

On the left-side Pinpoint menu, expand the Email option and choose Email identities
Select Verify email identity
Enter an email address you have access to for the confirmation step
Select Verify email address

Fig. 3: AWS Console showing email verification

Fig. 4: Email verification options

Now, check your inbox for a verification email. It should look something like this:

Fig. 5: Email Verification message from Amazon Pinpoint

Click the link to verify your email address. Now we can begin sending and receiving messages at this address.

Now that we have a verified email, we can configure the email channel.

Configuring the email channel

On the left-side Pinpoint menu, navigate to All projects and select CoffeeShop
Navigate to Settings and select Email
Select Edit next to Identity details
Select the checkbox for Enable the email channel for this project
Select Use an existing email address and select the address you verified in the previous step.
Select Save

Fig. 6: Configuring the email channel

Configuring email template

Next, we need to define what our email looks like that is sent to our customers when they enter a geofence. We’ve provided HTML code for a basic Coffee Shop template here

Configure email template

On the left-side Pinpoint menu, navigate to Message templates, select Create template
Name the template CoffeeShopGeoTarget and set the subject to “We haven’t seen you in a while”
Paste the contents of the HTML template into the Message field.
Select Create

Fig. 7: Configuring the email template

You can see multiple attributes are used in the template. These attributes come from our segment in the case of FirstName, and DynamoDB in the case of the store name and address.

Configuring email segment

Now we need to define who we are going to send an email to. For this, we need to set up our segment within Pinpoint. We’ve provided a sample segment file here. Download this file and open it in a text editor.

Fig. 8: Configuring the email segment

Replace all the values with your own information . The email needs to be the same email we verified in an earlier step. Create a UserID for the user that can be used to uniquely identify them. Leave ChannelType as “EMAIL” to indicate we are using the email channel in Pinpoint, and leave OptOut as “NONE” which indicates the user would like to receive all communications and has not opted-out of receiving notifications. Once the information is edited, save the file.

Importing the segment

On the left-side Pinpoint menu, navigate to All projects, and select your CoffeeShop Project
Navigate to Segments and select Import a segment
Drag the downloaded csv file into the Drop files here box.
Select Create Segment

Fig. 9: Importing a segment

Configuring Journey

In this post, we will be setting up a very simple Journey that sends an email anytime a user enters a geofence. If we wanted to go a step farther, we could add additional activities later in the Journey such as determining if the customer purchased something based on receiving the email, and sending them targeted emails based on the drink they ordered.
Now that we’ve added the email channel, we can set up our journey.

Configuring journey entry

On the left-side Pinpoint menu, navigate to the CoffeeShop Project and select Journeys
Select Create journey
Name the journey “CoffeeShopGeoTarget”
Set the entry condition to “geofence enter”
Select Save

Fig. 10: Journey event configuration

Configuring journey activity

Select the Add activity icon
Select Send an email from the dropdown
Choose the email template we created earlier
Enter the verified email we configured earlier
Select Save

Fig. 11: Journey email destination configuration

Reviewing Journey

Select Review
Select Mark as reviewed
Select Publish

Fig. 12: Reviewing the Journey

Once we publish our journey, a 5 minute timer will start, which will give us time to set up our tracking environment.

Configuring Amazon Location Resources

Now that we’ve configured Pinpoint to send geotargeted emails, we need to set up our Geofences as well as emulate a person passing nearby our coffee shops. To do that, we will use the AWS CLI and AWS Cloudshell .

To open AWS CloudShell, select it in the upper right near the region selection.

Fig. 13: Location of AWS Cloudshell in the AWS Console

AWS CloudShell will now open in the bottom half of the AWS Console , note it may take up to a minute on first launch. First, we’ll create our geofences. For this, we will use Circular geofences around a point location. In this case, we will create two geofences, one for a Coffee shop at Amazon’s Doppler office, and one for a shop at Amazon’s Nitro North office. These correlate with the DynamoDB store information table.

aws location put-geofence --collection-name StoreCollection --geofence-id store_1508 --geometry 'Circle={Center=[-122.33826293063228, 47.61530011310656], Radius=100}'

Successful Geofence creation will create output similar to the below:

{
"CreateTime": "2023-04-21T19:31:57.807000+00:00",
"GeofenceId": "store_1508",
"UpdateTime": "2023-04-21T19:31:57.807000+00:00"
}

Next we create our second geofence:

aws location put-geofence --collection-name StoreCollection --geofence-id store_1509 --geometry 'Circle={Center=[-122.34051934099395, 47.61751544952795], Radius=100}'

Successful Geofence creation will create output similar to the below:

{
"CreateTime": "2023-04-21T19:32:41.980000+00:00",
"GeofenceId": "store_1509",
"UpdateTime": "2023-04-21T19:32:41.980000+00:00"
}

Now that our geofences are created, we can emulate a person walking by and triggering a geofence. We will do this using Amazon Location Service Trackers. In CloudShell, enter the following command:

aws location batch-update-device-position --tracker-name CustomerDevices --updates Accuracy={Horizontal=0},DeviceId=111,Position=-122.33811005706218,47.61541094771129,SampleTime=$(date +%s)

When this command is issued, a geofence is then evaluated which will trigger an event sent to Amazon EventBridge. This event then triggers a Lambda, which creates an event with Pinpoint. This triggers the Journey, which sends an email.

Now check your email, you should see a customized email with the store you were close to and your name . Note because we are not using domain verification, you may receive a warning on the email message. See our documentation on how to use domain verification.

Fig. 14: Email received from Amazon Pinpoint

Next Steps

For this blog, we used the default Journey configuration. However, we can further optimize our Journey by following Tips and best practices for journeys. You can also set up push notifications or in-app notifications to further optimize the customer experience to catch them in the moment they walk by, instead of when they may check their email next. You can read more about push notifications here.

Clean up

Deleting CloudFormation template

In the AWS Console, navigate to the AWS CloudFormation console. Select the PinpointGeotarget stack
Select Delete Stack

Deleting Pinpoint resources

In the AWS Console, navigate to the Pinpoint Console
Select Message templates
Select the CoffeeShop template
Select Delete then confirm you wish to delete it

Removing email identity

In the AWS Console, navigate to the Pinpoint Console
Navigate to Email, and select Email identities
Select the radio button next to the verified email you configured
Select Remove email identity
Type Delete to confirm the removal

Conclusion

In this post, we explored how you can detect the presence of the customer whenever they cross near the geofenced physical store, using Amazon Location Service in which Amazon EventBridge receives the event, triggers an AWS Lambda function, and then triggers a Journey in Amazon Pinpoint to send a notification to the customer with a coupon.

Further more, integrating this solution with your customer data platform and with Amazon Personalize will help you to personalize the promotions and vouchers to fit the tastes and tendencies of customers

Zach Elliott works as a Solutions Architect focusing on Amazon Location Service at AWS. He is passionate about helping customers build geospatial solutions on AWS. He is also part of the IoT Subject Matter Expert community at AWS and loves helping customers develop unique IoT-based solutions.

With an illustrious track record as a technology thought-leader, Anshul joined AWS in 2016 and is the EMEA technology leader for retail. He is responsible for defining and executing the company’s retail technology strategy, which includes building retail-focused solutions with services like Amazon Forecast and Amazon Personalize, as well as experiences like Frictionless Shopping with AI/ML and IoT services from AWS. Anshul also works very closely with AWS global retail customers to help transform their businesses with cutting-edge AWS technologies.

How to create a WhatsApp custom channel with Amazon Pinpoint

2023-03-30 Sparsh Wadhwa

Post Syndicated from Sparsh Wadhwa original https://aws.amazon.com/blogs/messaging-and-targeting/whatsapp-with-amazon-pinpoint/

How to add WhatsApp as an Amazon Pinpoint Custom Channel

WhatsApp now reports over 2 billion users in 180 countries, making it a prime place for businesses to communicate with their customers. In addition to native channels like SMS, push notifications, and email, Amazon Pinpoint’s custom channels enable you to extend the capabilities of Amazon Pinpoint and send messages to customers through any API-enabled service, like WhatsApp. With these new channels, you have full control over the message delivery to the endpoints associated with each custom channel campaign.

In this post, we provide a quick overview of the features and capabilities of using a custom channel as part of campaigns. We also provide a blueprint that you can use to build your first sandbox integration with WhatsApp as a custom channel.

Note: WhatsApp is a third-party service subject to additional terms and charges. Amazon Web Services isn’t responsible for any third-party service that you use to send messages with custom channels.

How to add WhatsApp as a custom channel:

Prerequisites

Before creating your new custom channel, you must have the integration ready and an Amazon Identity and Account Management (IAM) User created with the necessary permissions. First set up the following:

Create an IAM administrator. For more information, see Creating your first IAM admin user and group in the IAM User Guide. Specify the credentials of this IAM User when you set up the AWS Command Line Interface (CLI).
Configure the AWS CLI. For more information about setting up the AWS CLI, see Configuring the AWS CLI.
Follow the steps at Meta documentation – https://developers.facebook.com/docs/whatsapp/cloud-api/get-started to register as a Meta Developer and getting started with WhatsApp Business Cloud API provided directly by Meta. By completing step 1 and step 2 of the above documentation, you should be able to
1. Register as a Meta Developer,
2. Claim a test phone for sending messages on WhatsApp,
3. Verify a recipient phone number (since, currently you’re in Sandbox, you can send WhatsApp messages only to the verified phone numbers. You can verify upto 5 phone numbers)
4. and finally send a test message on Whatsapp using a provided sample POST request. Remember to review the terms of use for WhatsApp.
In the test message sent above, you have used temporary Access Token credentials which expires in 23 hours. In order to get permanent Access Token, generate a ‘System User Access Token’ by following the steps mention here – https://developers.facebook.com/docs/whatsapp/business-management-api/get-started/

Procedure:

Step 1: Create an Amazon Pinpoint project.

In this section, you create and configure a project in Amazon Pinpoint. Later, you use this data to create segments and campaigns.

To set up the Amazon Pinpoint project

Sign in to the Amazon Pinpoint console at http://console.aws.amazon.com/pinpoint/.
On the All projects page, choose Create a project. Enter a name for the project, and then choose Create.
On the Configure features page, under SMS and Voice, choose Configure.
Under General settings, select Enable the SMS channel for this project, and then choose Save changes.
In the navigation pane, under Settings, choose General settings. In the Project details section, copy the value under Project ID. You need this value for later.

Step 2: Create an endpoint.

In Amazon Pinpoint, an endpoint represents a specific method of contacting a customer. This could be their email address (for email messages) or their phone number (for SMS messages) or a custom endpoint type. Endpoints can also contain custom attributes, and you can associate multiple endpoints with a single user. In this step, we create an SMS endpoint that is used to send a WhatsApp message.

To create an endpoint using AWS CLI, at the command line, enter the following command:

aws pinpoint update-endpoint –application-id <project-id> \
–endpoint-id 12456 –endpoint-request “Address='<mobile-number>’, \
ChannelType=’SMS’,Attributes={username=[‘testUser’],integrations=[‘WhatsApp’]}”

In the preceding example, replace <project-id> with the Amazon Pinpoint Project ID that you copied in step 1.

Replace <mobile-number> with your phone number with country code (for example, 12065550142). For the WhatsApp integration to work, you must use the mobile number that are registered on WhatsApp and are already verified on Meta Developer Portal (since your Meta account is currently in sandbox).

Note: WhatsApp Business Cloud message API doesn’t require ‘+’ symbol in the front of the Phone number. So in case you plan to use this segment for both SMS and Custom Channel, you may configure Phone Number in E.164 format (for example, +12065550142) and remove ‘+’ symbol in the Lambda function code that we create in the step 4.

Step 3: Storing WHATSAPP_AUTH_TOKEN, and WHATSAPP_FROM_NUMBER_ID in AWS Secrets Manager.

We can securely store the WhatsApp Auth Token and WhatsApp From Number Id which we have received in the previous steps in AWS Secrets Manager.

Open the AWS Secrets Manager console at https://us-east-1.console.aws.amazon.com/secretsmanager/listsecrets?region=us-east-1 (in the required AWS region), and then click on “Store a new Secret”.
Under “Secret Type”, choose Other type of secret.
Under Key/value Pair, add the following Key-Value pairs:
1. WHATSAPP_AUTH_TOKEN: <Pass the Auth Token generated previously>
2. WHATSAPP_FROM_NUMBER_ID : <Pass the From Number Id>.
Click Next
Provide the Secret name “MetaWhatsappCreds” and provide a suitable description.
Click Next twice and finally click “Store” button.

Step 4: Create an AWS Lambda.

You must create an AWS Lambda that has the code that calls Meta WhatsApp Business Cloud API and sends a message to the endpoint.

Open the AWS Lambda console at http://console.aws.amazon.com/AWSLambda, and then click on Create Function.
Choose Author from scratch.
For Function Name, enter ‘WhatsAppTest’.
For Runtime, select Python 3.9.
Click Create Function.
For the function code, copy the following and paste into the code editor in your AWS Lambda function:

import base64
import json
import os
import urllib
from urllib import request, parse
import boto3
from botocore.exceptions import ClientError

WhatsApp_messageAPI_URL = "https://graph.facebook.com/v15.0/" 

def get_secret():

    secret_name = "MetaWhatsappCreds"
    region_name = "us-east-1"
    # Pass the required AWS Region in which Secret is stored

    # Create a Secrets Manager client
    session = boto3.session.Session()
    client = session.client(
        service_name='secretsmanager',
        region_name=region_name
    )

    try:
        get_secret_value_response = client.get_secret_value(
            SecretId=secret_name
        )
    except ClientError as e:
        # For a list of exceptions thrown, see
        # https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html
        raise e

    # Decrypts secret using the associated KMS key.
    secret = get_secret_value_response['SecretString']
    return secret
   
def lambda_handler(event, context):
    credentials = get_secret()
    WhatsApp_AUTH_TOKEN = json.loads(credentials)["WHATSAPP_AUTH_TOKEN"]
    WhatsApp_FROM_NUMBER_ID = json.loads(credentials)["WHATSAPP_FROM_NUMBER_ID"]
    if not WhatsApp_AUTH_TOKEN:
        return "Unable to access WhatsApp Auth Token."
    elif not WhatsApp_FROM_NUMBER_ID:
        return "Unable to access WhatsApp From Number Id."
    # Lets print out the event for our logs 
    print("Received event: {}".format(event))

    populated_url = WhatsApp_messageAPI_URL + WhatsApp_FROM_NUMBER_ID + "/messages"

    for key in event['Endpoints'].keys(): 
        to_number = event['Endpoints'][key]['Address']
        # Example body and using an attribute from the endpoint
        username = event['Endpoints'][key]['Attributes']['username'][0]
        body = "Hello {}, here is your weekly 10% discount coupon: SAVE10".format(username)
        post_params = {"messaging_product":"whatsapp","to": to_number ,"recipient_type": "individual","type": "text", "text":{"preview_url": "false","body": body}}
        # encode the parameters for Python's urllib 
        print(post_params)
        data = parse.urlencode(post_params).encode('ascii') 
        req = request.Request(populated_url)
        req.add_header("Authorization", WhatsApp_AUTH_TOKEN ) 
        req.add_header("Content-Type","application/json")
        try:
            # perform HTTP POST request
            with request.urlopen(req, data) as f:
                print("WhatsApp returned {}".format(str(f.read().decode('utf-8')))) 
        except Exception as e:
            # something went wrong!
            print(e)

    return "WhatsApp messages sent successfully"

Add permissions to your AWS Lambda to allow Amazon Pinpoint to invoke it using AWS CLI:

aws lambda add-permission \
–function-name WhatsAppTest \
–statement-id sid \
–action lambda:InvokeFunction \
–principal pinpoint.us-east-1.amazonaws.com \
–source-arn arn:aws:mobiletargeting:us-east-1:<account-id>:apps/<Pinpoint ProjectID>/*

Step 5: Create a segment and campaign in Amazon Pinpoint.

Now that we have an endpoint, we must add it to a segment so that we can use it within a campaign. By sending a campaign, we can verify that our Amazon Pinpoint project is configured correctly, and that we created the endpoint correctly.

To create the segment and campaign:

1. Open the Amazon Pinpoint console at http://console.aws.amazon.com/pinpoint, and then choose the project that you created in step 1.
2. In the navigation pane, choose Segments, and then choose Create a segment.
3. Name the segment “WhatsAppTest.” Under Segment group 1, include all audiences in the Base Segment and add the following Criteria:
4. For Choose an endpoint attribute, choose integrations, then for values, choose WhatsApp.
5. Confirm that the Segment estimate section shows that there is one eligible endpoint, and then choose Create segment.
6. In the navigation pane, choose Campaigns, and then choose Create a campaign.
7. Name the campaign “WhatsAppTest.” Under Choose a channel for this campaign, choose Custom, and then choose Next.
8. On the Choose a segment page, choose the “WhatsAppTest” segment that you just created, and then choose Next.
9. In Create your message, choose the AWS Lambda function we just created, ‘WhatsAppTest.’ Select SMS in the Endpoint Options. On the Choose when to send the campaign page, keep all of the default values, and then choose Next. On the Review and launch page, choose Launch campaign.

Within a few seconds, you should receive a WhatsApp message at the phone number that you specified when you created the endpoint and verified on the Meta Developer portal.

Your Custom channel solution for WhatsApp is now ready to use. But first, review and upgrade your WhatsApp sandbox. This post is simply a walkthrough to show you how quickly you can prototype and start sending WhatsApp messages with Pinpoint and Meta. However, for production usage, you need to make sure to review all of the additional terms and charges. Start here to understand more: https://developers.facebook.com/docs/whatsapp/cloud-api/get-started

As a next steps, you can go ahead and claim a Phone number for sending WhatsApp messages in production. You can further configure a Webhook which can help you in receiving WhatsApp message delivery status and other WhatsApp supported events.

There are several ways you can make this solution your own.

Customize your messaging: This post used an example message to be sent to your endpoints within the AWS Lambda. You can customize that message to fit your needs. See the various ways in which you can send WhatsApp messages here.
Expand endpoints in your application: This post only used one endpoint for the integration. You can use your WhatsApp integration with new endpoints by importing a segment that can be used with a new campaign. Learn how to import a segment here: https://docs.aws.amazon.com/pinpoint/latest/userguide/segments-importing.html
Use new integrations: This post focused on integrating your custom channel with WhatsApp but there are many other integrations that are possible when using AWS Lambda.

Amazon Pinpoint is a flexible and scalable outbound and inbound marketing communications service. Learn more here: https://aws.amazon.com/pinpoint/

Customize marketing messages and promotions for personalized outreach

2023-03-28 binpazho

Post Syndicated from binpazho original https://aws.amazon.com/blogs/messaging-and-targeting/customize-marketing-messages-and-promotions-for-personalized-outreach/

Introduction

Amazon Pinpoint is widely used by many customers for their various user engagement use cases like marketing campaigns, scheduled communications (newsletters, reminders, etc.), and transactional messaging. By using the message template feature in Amazon Pinpoint, customers can design messages personalized to the specific end users, by using variable attributes. While Amazon Pinpoint enables customers to include up to 250 attributes for each user, often times there might be need to pick and choose from a wide range of attributes about a user, that can lead to needing more than the allowed number of attributes.

The CampaignHook feature of Amazon Pinpoint can come to rescue for a situation like this. Using the CampainHook feature, we can filter out attributes that are not applicable to a specific user, while allowing to add new attributes, right before of sending the message. In this blog, I will walk you through how I have implemented the CampaignHook feature for a similar use case.

Sample Use-Cases

When setting up your Pinpoint campaign, following are the use cases where a CampaignHook can be enabled:

Retrieving data and perform custom compute logic in real time from third party data stores.
Filter endpoints out of the send: This is useful if you need to do some type of custom logic that you can’t do in Segmentation (custom opt-out, quiet time, campaign prioritization, etc.)
Avoid costly and time consuming Extract, Transform & Load (ETL) processes by accessing the data sources directly and applying custom compute logic in real-time.

Solution overview

CampaignHook Demo Architecture

The diagram above shows the solution that we will setup in this blog. As you can see, the Campaign event will trigger the Amazon Pinpoint Campaign. The event can be triggered from your web or mobile app that are accessed by your end-users, and can be setup to be triggered when the user performs a certain action. You can read more about setting up Amazon Pinpoint campaign in the user guide. By having the CampaignHook enabled on your Amazon Pinpoint campaign, the Lambda function that is configured with the CampaignHook will be triggered. This function will have access to the endpoint attributes passed by the Campaign event, and perform additional logic to derive new attributes for the user. Once all the new fields are derived, the function will update the user endpoint. Amazon pinpoint will then perform the next steps in the Campaign, and substitute the variables in the message template, before the personalized message is sent to the end user.

Prerequisites

AWS Account with Console and Programmatic access
Access to AWS CloudShell
Email channel enabled in Amazon Pinpoint

Building the demo

Build the Amazon Pinpoint Project

From the AWS Management console, go to Amazon Pinpoint and create a new project called “PinpointCampaignHookDemo”, and choose the option to enable the email channel. For more information about creating a project see the user guide, and follow the instructions here to setup your email channel.

If your account is in the Sandbox account, you will need to verify the email address, before you can send the email. You can follow the steps here to upgrade your account to a Production status if you are ready to deploy this solution to production.

Create the segment.

A segment is a group of your users that share certain attributes. For example, a segment might contain all of your users who use version 2.0 of your app on an Android device, or all users who live in the city of Los Angeles. You can send multiple campaigns to a single segment, and you can send a single campaign to multiple segments.

For this demo, let’s create a Dynamic Segment. Let’s call it ‘CampaignHookDemoSegment’. Follow the steps here to create your Dynamic Segment.

Create a Segment

Setup message template

Let’s create our first template and call it “CampaignHookDemoTemplate”. You can read more about Amazon Pinpoint templates in the user guide.

For this demo, I have used the HTML template shown below, and I have 3 endpoint attribute variables: 2 that are passed from the campaign event trigger, and the third one (Company) that will be generated by the CampaignHook lambda function. For the subject of the email, I used “Campaign Hook Demo Campaign“.

Create eMail Template

The email template can be found in this GitHub repository.

Create Campaign

Next, create your campaign and use the Segment and email Template that you created in the previous steps by following the instructions here.

Select the ‘when an event occurs’ option to trigger the campaign when an event occurs. (This option will trigger the campaign when a specific event occurs). Yoy may also schedule your campaign to run on a scheduled bases as available in the setup screen. I used ‘CampaignHookTrigger’ as my event name.

Create a campaign

Set your Campaign Start date, time and end date. I have left all the other settings to default and saved the campaign. Now that you have successfully created your first Campaign, you are ready for the next steps.

Set Campaign Start and End Times

Create the Lambda function

This is the function that we will configure to trigger the Amazon pinpoint campaign event . From the Lambda console page, create a new function by clicking on the ‘Create function’ button. You can then pick the following options and create the function.

Name: Campaign_event_trigger_function

Runtime: Python 3.9 or higher.

Replace the default script with the code from the GitHub repository, and then deploy your code by clicking on the “Deploy” button.

Assign permissions

In-order for the Lambda function trigger to trigger the Pinpoint Campaign, you will need to add an inline policy to the IAM role that is attached to your Lambda function, by selecting Pinpoint as the service and PutEvents from the Write options. You can select the Lambda function as the resource to which the access will be granted.

{

"Version" :"2012-10-17",

"Statement":[

{

"Sid": "VisualEditor0",

"Effect": "Allow",

"Action": [

"mobiletargeting:PutEvents"

],

"Resource":"ARN of your Lambda function goes here."

}

]

}

Create the CampaignHook Lambda function

This is the function that we will triggered from the CampaignHook. From your Lambda console, click on “Create function” and enter the basic information as shown below to create your function.

Name: CampaignHookFunction

Runtime: Python 3.9 or higher.

Next replace your default code with the sample GitHub code, and then deploy your code by clicking on the “Deploy” button.

Assign permissions

Next add permissions for Amazon Pinpoint to invoke the Lambda function by running the command below from your Command Shell. Replace the Lambda function name and Account number with yours.

aws lambda add-permission \

--function-name [YourCampaignHookLambdaFunctionName] \

--statement-id my-hook-id1 \

--action lambda:InvokeFunction \

--principal pinpoint.us-east-1.amazonaws.com \

--source-arn 'arn:aws:mobiletargeting:us-east-1:[YourAccountNumber]:apps/*'

You can also do this from the Lambda console, by clicking on “Configuration” and then scrolling down to “Resource based Policy” and by clicking on “Add permissions“.

Update Campaign settings to add the Campaign Hook

Now that you have the Lambda function that needs to act as the hook is created, and granted Amazon Pinpoint service to invoke that function, run the command below to update the Campaign settings to add the Campaign Hook. You can also set a default CampaignHook for ALL campaigns in the project by setting the CampaignHook property on the Project Settings via this API.

Replace the application-id (project id), campaign-id, and the arn of the Campaign Hook lambda function and run the command below. (You can find the Project ID by clicking on All Projects at the top-left of the Pinpoint Console. The Campaign ID can be found by opening your Pinpoint Project and then clicking Campaigns in the Pinpoint Console.)

aws pinpoint update-campaign --application-id /

[your-application-id-goes-here] –campaign-id /

[your-campaign-id-goes-here] --cli-input-json '{"ApplicationId": /

"","CampaignId": "","WriteCampaignRequest": {"Hook": {"LambdaFunctionName": /

"your-CampaignHook-Function-goes-here","Mode": "FILTER","WebUrl": ""}}}'

You can optionally run the command below to make sure that the campaign settings have been updated:

aws pinpoint get-campaign –application-id [your-application-id-goes-here] –campaign-id [your-campaign-id-goes-here]

Test your Campaign.

Go back to your Lambda function that you have created to trigger the Campaign in the “Create the Lambda function” step above. I have used the test event as shown below. Update the Application id to reflect your Project id and change the email address to the email you verified earlier and click on “Test” button.

{

"application_id": "your application id",

"endpoint_id": "223",

"event_type": "CampaignHookEvent",

"nextTestDate": "12/15/2025",

"FirstName": "Jack",

"email": "[email protected]",

"userid": "Jack123"

}

You should now receive an email with the variables replaced with the values that was passed from your json payload. Further you can see the Company name was added to the endpoint from the CampaignHook Lambda, which is passed to the email template. If you have not received the email, please check the following:

The Lambda function ran without any errors
The LambdaHook function has the proper rights assigned to be invoked from Pinpoint
The From and To email id that you have used are verified in SES.

Verify email identity

Clean up resources

Once you are satisfied with your setup and testing, you can now clean up the resources by following the steps below:

Delete your Amazon Pinpoint Project, Campaign and Segment.
- aws pinpoint delete-campaign –application-id [your appl id] –campaign-id [your campaign id]
- aws pinpoint delete-segment –application-id [your app id] –segment-id [your segment id]
- aws pinpoint delete-app –application-id [your app id]
Delete you Lambda functions
- aws lambda delete-function –function-name CampaignHookFunction
- aws lambda delete-function –function-name Campaign_event_Trigger_Function

Conclusion

By dynamically generating the attributes in real-time, customers can now add greater levels of personalization within a single user message template. By invoking a Lambda function, you can perform custom compute logic, calculate new attribute values, and access external data stores, to modify the campaign’s segment, right before Amazon Pinpoint sends the message. Campaign Hook feature makes this possible as explained in this blog by running few basic CLI commands to enable the feature on your Amazon Pinpoint Campaign. You can read more about Amazon Pinpoint Campaign from the user guide documentation”.

How to build LINE messaging into business communications

2023-03-20 nnatri

Post Syndicated from nnatri original https://aws.amazon.com/blogs/messaging-and-targeting/how-to-build-line-messaging-into-business-communications/

In today’s interconnected world, businesses need to communicate with their customers through multiple channels. This means using a variety of messaging apps, social media platforms, and other communication tools to reach customers where they are. One such platform that has gained immense popularity in select Asian markets is LINE. As the biggest social network in Japan, LINE offers businesses a unique opportunity to connect with customers in this region. Within Japan alone, LINE’s 2021 data shows 86 million users, constituting approximately 85% of Japan’s adult population. However, managing communication through multiple channels can be challenging for businesses.

That’s where Amazon Pinpoint comes in. Amazon Pinpoint is a flexible communication service for businesses that simplifies the process of sending targeted messages to customers across multiple channels. In this blog post, we’ll focus on how to integrate LINE with Amazon Pinpoint. This post is part of a series on integrating different communication channels with Amazon Pinpoint, and it is intended for both marketing operations and communication developers.

If you are already using LINE, this blog post will help you centralize management within Amazon Pinpoint. Additionally, if you are looking to integrate another messaging service with an open API, the steps outlined here will provide a helpful guide. Finally, if you’re a business looking to tap into Asian markets, this blog post is essential reading. By integrating LINE with Amazon Pinpoint, you’ll be able to reach your customers on the platform they are already using, providing seamless end-to-end customer engagements that will greatly enhances customer experience.

Note
Line is a third-party service that is subject to additional terms and charges. Amazon Web Services isn’t responsible for any third-party service that you use to send messages with custom channels.

Why Integrate LINE with Amazon Pinpoint?

Integrating LINE with Amazon Pinpoint has several benefits for businesses:

Centralized communication management: With LINE integrated into Amazon Pinpoint, businesses can centralize the management of outbound communication channels and simplify their communication workflows.
Increased flexibility for marketing campaigns: With LINE added as a custom channel in Amazon Pinpoint, businesses can create targeted messaging campaigns and reach customers through multiple channels, including LINE. Along with Pinpoint journeys, businesses can craft end-to-end customer engagement journeys that start from one channel and end in another.
Access to LINE’s popular messaging platform: With LINE integrated into Amazon Pinpoint, businesses can tap into the app’s massive user base in select Asian markets and engage with their customers through a popular and widely used messaging platform. Having access to LINE’s demographics of approximately 50% office workers with high penetration into 20s-30s age band, brands can tap into this high-spending power segment to drive revenue for their products.

Architecture

This solution uses Amazon Pinpoint,AWS Lambda, Amazon API Gateway, Amazon Simple Storage Service (Amazon S3), AWS Secrets Manager and LINE Messaging API

The solution architecture can be broken up into two main sections:

Steps 1-4 cover handling inbound user events and managing user data within Amazon Pinpoint.
Steps 5-8 cover how to send outbound campaigns via Amazon Pinpoint Custom Channel.

The customer subscribes to the business’ LINE channel.
The subscribe/unsubscribe event is received and checked via Amazon API Gateway.
The edge-optimized Amazon API Gateway passes valid requests via a proxy integration to the backend Lambda.
The backend Lambda compares the request body with the x-line-signature request header to confirm that the request was sent from the LINE Platform, as recommended by LINE API document. Afterwards, the Lambda function processes the user events:
1. If the user subscribes to the channel, a new endpoint will be added to Amazon Pinpoint’s user database.
2. If the user unsubscribes from the channel, the corresponding endpoint (identified by the LINE User ID) is deleted from Amazon Pinpoint’s user database.
Amazon Pinpoint initiates a call to a Lambda function via Custom Channel with a payload. Of particular importance would be the Data field contained within the payload, which can be specified within the Amazon Pinpoint console to modify the content of the message.
If the message contains image/audio/video files, the Lambda will request the file from the corresponding Amazon S3 buckets to be included for step 7. Amazon S3 then sends back the presigned URL containing the requested file(s).
The Lambda function puts the message in the correct format expected by the LINE Messaging API and sends it over to the LINE Platform.
The LINE Messaging API receives the request and processes the message content. If necessary, it will retrieve and download the file from Amazon S3 using the presigned URLs generated in step 6 then finally send the message to the corresponding user on the LINE Mobile App.

Step-by-Step Deployment Guide

Prerequisites

To deploy this solution, you must have the following:

An AWS account, with the appropriate AWS CLI profile.
- Named Profile: Run aws configure with the --profile option. The following steps assumed you have created a profile called line-integration to use with AWS CDK.
Minimum Python v3.7, with pip and venv
AWS CDK v2 installed.
Docker Engine installed. You can download and install the appropriate Docker Desktop Distribution for your system via this link
A LINE Account.
- If you have never worked with LINE Messaging API before, you should login to to LINE Developers Console using one of the following accounts.
  - LINE account
  - Business account
- Afterwards, you should create a new provider.
- Within the provider page, you can then choose to create a new channel. For our Integration purposes, we will be choosing Messaging API channel type.

Preparation

The source code can be found in this GitHub Repository.

Fork the GitHub Repo into your account. This way you can experiment with changes as necessary to fit your workload.
In your local compute environment, clone the GitHub Repository and cd into the project directory.
Run the following commands to create a virtual environment, activate it and install required dependencies.

python3 -m venv env \
&& source env/bin/activate \
&& python -m pip install -r requirements.txt

Deploy the CDK

We can set the AWS CLI profile in CDK commands by adding the --profile flag. Run the following commands to bootstrap your AWS environment, synthesize the CDK template and deploy to your environment.

cdk bootstrap --profile LINE-integration \
&& cdk synth --profile LINE-integration  \
&& cdk deploy --profile LINE-integration

Note
Enter y when prompted with Do you wish to deploy these changes (y/n)?

After the deployment is done, the CDK template will output the API Gateway endpoint URL which takes the form of https://[********].execute-api.[region].amazonaws.com/prod/. Copy down this information as you will need it to set up the webhook connection later on.

Getting LINE Official Account Credentials

Log in to LINE developer console.
Once inside, choose the channel you’d like to have integrated with Amazon Pinpoint. This assumes that you’ve created a provider and a channel as mentioned in the Prerequisite section.
In the Basic settings tab, scroll down and note down the Channel Secret.
In the Messaging API tab, scroll down and click on Edit under Webhook URL and enter the API Gateway endpoint URL you have noted down in step 5. Click on Update to save the changes.

NOTE Once you have finished entering your Channel Secret token in step 14, you can return to this page to Verify your webhook URL is set up correctly).
Finally, issue a Channel Access Token (at the bottom of the Messaging API tab) and note it down.

Registering Secrets in AWS Secrets Manager

Navigate to the AWS Secrets Manager console. Make sure you’re in the same region as your CDK deployment region.
Click on Secrets in the left side pane. You should find a secret with the name LINE_secrets
Click on Retrieve Secret Value.
Then click on Edit:
- Replace YOUR_CHANNEL_SECRET secret value with the channel secret you issued in step 10.
- Replace YOUR_CHANNEL_ACCESS_TOKEN secret value with the access token you issued in step 10

Marketing Operations Demonstration

Once you’ve successfully deployed the CDK and configured your secrets, you can immediately get started sending communications campaign to your customers.

LINE supports multimedia messaging formats, meaning that you can choose to send texts, images, audio and even video files to your customers as part of your campaigns. You just need to make sure that your customers have subscribed to your channel.

Create a segment of subscribed users

The deployed solution has integrated user database management with Amazon Pinpoint so once users start subscribing to your LINE channel, they will be added as endpoints. To start filtering out who we should send to, you can create segments of your subscribers.

Navigate to the Amazon Pinpoint console.
On the All projects page, a project named Line-Pinpoint-Project has been created for you.
On the left-side pane, choose Segments and then Create a segment.
Give your segment a descriptive name and add the appropriate criteria to filter down to your target audience (E.g.: filter down to customers who have Custom channel type).
Confirm the number of endpoints that you will be sending in the Segment estimate section matches your expectations and then choose Create segment.

Upload media files for campaign

If you’d like to use your own image, audio and video files for the campaign, follow along with this section. Otherwise, proceed to the Create Campaigns section (step 9).

Note
Depending on the media type, there are restrictions imposed such as maximum file size and file format extensions. You can find more information here.

Navigate to the Amazon S3 console.
Here you will find a list of buckets which corresponds to the type of media files you want to upload:
- part-1-stack-images3bucket...: contains image files.
- part-1-stack-audios3bucket...: contains audio files.
- part-1-stack-videos3bucket...: contains both video and image cover files.
Upload the corresponding files that you want to use for your campaign by choosing Upload.

Create campaigns

In the navigation pane, choose Campaigns, and then choose Create a campaign.
Give your campaign a descriptive name. Under Campaign Type choose Standard campaign and under Channel, choose Custom. Click Next to confirm.
On the Choose a segment page, choose the segment that you created in step 5, and then choose Next.
In Create your message, depending on the type of message that you want to send, choose the corresponding Lambda function. Your function should be named part-1-stack-send[text/image/audio/video]lambda...
In the custom data section, you can choose to leave it blank, which will trigger the campaign to send the sample message.
Otherwise, depending on the type of message, you can customize your campaigns to send the content that you want by inputting the following values into Custom Data.
- Text Campaign: Enter the Text Message that you want to send.
- Image Campaign: Enter the name of the image file you’ve uploaded in step 8 including the extension name (E.g.: sample_image.png)
- Audio Campaign: Enter the name of the audio file you’ve uploaded in step 8 including the extension name and the duration of the audio file in milliseconds separated by a comma (E.g.: sample_audio.mp3,5000)
- Video Campaign: Enter the name of the video file you’ve uploaded in step 8 including the extension name and the name of the image file you’ve uploaded in step 8 including the extension name, separated by a comma (E.g.: sample_video.mp4,sample_image.png)
Choose Next and configure when to send the campaign depending on your needs. Once done, choose Next again.
On the Review and launch page, verify all your information is correct and then click on Launch campaign.

That’s it! Your message will be sent through LINE to the designated recipients.

Cleanup

To delete the sample application that you created, use the AWS CDK.


cdk destroy

You’ll be asked:


Are you sure you want to delete: part-1-stack (y/n)?

Hit “y” and you’ll see your stack being destroyed.

What’s Next?

In conclusion, integrating LINE with Amazon Pinpoint provides businesses with a powerful tool to centralize their communication management, create more flexible marketing campaigns, and tap into LINE’s massive user base. With the step-by-step guide and demo provided in this blog post, you can easily get started with integrating LINE with Pinpoint and start leveraging its benefits for your business.

The solution presented in this blog post serves as a template that you can develop and customize to make it your own:

Adding additional message types: The LINE messaging platform is famous for its rich messaging types and format. The deployed solution only utilized a fraction of what is available. You can add additional Lambda functions to send Stickers, Locations, Image Maps, Buttons or Carousel and more.
Orchestrate LINE with other channels: Using Amazon Pinpoint Journeys, you can now meet the customer where they are most likely to see and respond to your message. Create a journey that starts with an SMS, send targeted communications based on yes/no or multivariate splits via emails and seal the deal with LINE. With Pinpoint and journey custom channel input and response support, you can craft the perfect omni-channel journey for your customers.
Watch this space: Do stay tuned for the next blog post in this series, where we’ll show you how to manage inbound communications through LINE using Amazon Connect and Amazon Lex bots.

AWS Communication Developer Services (CDS) recognized in the 2022 Gartner Market Guide for CPaaS

2022-10-12 laurcudn

Post Syndicated from laurcudn original https://aws.amazon.com/blogs/messaging-and-targeting/aws-communication-developer-services-cds-recognized-in-the-2022-gartner-market-guide-for-cpaas/

2022 Gartner Market Guide for CPaaS recognizes AWS Communication Developer Services (CDS)

Every business needs to communicate with customers through channels like email, SMS, notifications, voice, and even video. CPaaS, or Communications Platform as a Service, provers combine those capabilities into a single offering. Communication developers exploring new CPaaS solutions can read the 2022 Gartner Market Guide for CPaaS to learn about AWS Communication Developer Services’ (CDS) capabilities in these channels.

What is CPaaS?

Gartner defines CPaaS, or Communication Platform as a Service, as “cPaaS offer[ing] application leaders a cloud-based, multilayered middleware on which they can develop, run and distribute communications software. The platform offers APIs and integrated development environments that simplify the integration of communications capabilities (for example, voice, messaging and video) into applications, services or business processes.” CPaaS offerings allow companies to integrate communication capabilities into their apps, websites, and technologies in a flexible and customized manner. Companies can choose to add channels and features, like SMS, email, voice, notifications, and more through APIs and SDKs. CPaaS also describes the layers of target audience segmentation, message orchestration and vertical solutions in communication technologies.

2022 Gartner CPaaS Market Guide

The 2022 Gartner CPaaS Market Guide provides key considerations to keep in mind when selecting a communications platform partner to increase customer engagement across channels. Gartner defines the market in three levels, Foundational, Emerging, and Potential Differentiation.

Foundational: Foundational requirements are common communication APIs, and are estimated by Gartner to represent about 85% of current market spend. These fall in to channel-based capabilities like SIP trunking, phone numbers, long and short codes, network connections, SMS, voice, notifications, orchestration, inbound and outbound email, and basic security like 2-factor authorization (2FA) via SMS, email, voice, or other channels. AWS Communication Developer Services (CDS) are built on flexible APIs that support all of the channels listed above. CDS allows builders to use APIs as simple configuration out of the box, or customize APIs through more advanced coding.

Emerging: Emerging offerings include ML capabilities like messaging and voice bots or sentiment analysis, advanced message options, reputation and delivery managers, and campaign managers. AWS Communication Developer Services (CDS) is built using AWS’ leading ML capabilities, and makes it easy for developers to implement advanced machine learning and artificial intelligence features like voice and messaging bots, speech to text, and sentiment analysis. AWS Communication Developer Services (CDS) has orchestrated email and message capabilities. Campaign management is simple through designated campaign and journey features.

Potential differentiation: Gartner identifies potential long-term opportunities for differentiation in areas like vertical solutions and integration with contact centers. CDS has helped many healthcare, financial services, and entertainment customers modernize their customer communications. Our customers work closely with leading partners like Accenture, Local Measure, and many more for seamless integration into current technology and communication stacks. Through CDS’ close ties with the Amazon Connect contact center solution, it’s easy to integrate contact center capabilities with messaging and multichannel communications.

Market Direction and Trends:

Gartner predicts continued growth of the CPaaS category. Gartner reports that most new CPaaS customers enter the category with a specific channel need, such as push notifications, then expands their offerings to include additional channels and functionalities as new needs arise.
Trends include conversational everything and the rise of messaging and voice bots, predictive intelligence for personalization, and local global expansion through Channel Partners.

About AWS Communication Developer Services

AWS Communication Developer Services (CDS) are a set of SDKs and APIs that allow developers to embed customer communications directly into their applications. Developers use AWS CDS to improve communication with their customers across different use cases, including application-to-person (A2P) communications, asynchronous communications, and real-time communications. AWS CDS supports channels like email, SMS, push notifications, chat, audio, video, and voice over the PSTN. Send messages to your customers with the scale of AWS and pay only for what you use.

Use Cases

Alerts + Notifications

Send customers alerts or notifications through email, SMS, push, in-app notifications, or via voice API

Promotional Messages

Send promotions and marketing messages with top email inbox deliverability and segmented messaging

In-App Video

Build your application or website with scalable video technology that can support telehealth, field support, education, and more

Interactive Voice Response (IVR)

Build an IVR system that can support touch-tone, conversation voice bots, and text-to-speech

Want to read the full Market Guide?
Download the 2022 Gartner CPaaS Market Guide for free here.