Monthly Archives: September 2022

Earlier this year, Amazon Comprehend, a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text, launched the Targeted Sentiment feature. With Targeted Sentiment, you can identify groups of mentions (co-reference groups) corresponding to a single real-world entity or attribute, provide the sentiment associated with each entity mention, and offer the classification of the real-world entity based on a pre-determined list of entities.

Today, we’re excited to announce the new synchronous API for targeted sentiment in Amazon Comprehend, which provides a granular understanding of the sentiments associated with specific entities in input documents.

In this post, we provide an overview of how you can get started with the Amazon Comprehend Targeted Sentiment synchronous API, walk through the output structure, and discuss three separate use cases.

Targeted sentiment use cases

Real-time targeted sentiment analysis in Amazon Comprehend has several applications to enable accurate and scalable brand and competitor insights. You can use targeted sentiment for business-critical processes such as live market research, producing brand experience, and improving customer satisfaction.

The following is an example of using targeted sentiment for a movie review.

“Movie” is the primary entity, identified as type movie, and is mentioned two more times as “movie” and the pronoun “it.” The Targeted Sentiment API provides the sentiment towards each entity. Green refers to a positive sentiment, red for negative, and blue for neutral.

Traditional analysis provides sentiment of the overall text, which in this case is mixed. With targeted sentiment, you can get more granular insights. In this scenario, the sentiment towards the movie is both positive and negative: positive in regards to the actors, but negative in relation to the overall quality. This can provide targeted feedback for the film team, such as to exercise more diligence in script writing, but to consider the actors for future roles.

Prominent applications of real-time sentiment analysis will vary across industries. It includes extracting marketing and customer insights from live social media feeds, videos, live events, or broadcasts, understanding emotions for research purposes, or deterring cyberbullying. Synchronous targeted sentiment drives business value by providing real-time feedback within seconds so that you can make decisions in real time.

Let’s take a closer look at these various real-time targeted sentiment analysis applications and how different industries may use them:

• Scenario 1 – Opinion mining of financial documents to determine sentiment towards a stock, person, or organization
• Scenario 2 – Real-time call center analytics to determine granular sentiment in customer interactions
• Scenario 3 – Monitoring organization or product feedback across social media and digital channels, and providing real-time support and resolutions

In the following sections, we discuss each use case in more detail.

Scenario 1: Financial opinion mining and trading signal generation

Sentiment analysis is crucial for market-makers and investment firms when building trading strategies. Determining granular sentiment can help traders infer what reaction the market may have towards global events, business decisions, individuals, and industry direction. This sentiment can be a determining factor on whether to buy or sell a stock or commodity.

To see how we can use the Targeted Sentiment API in these scenarios, let’s look at a statement from Federal Reserve Chair Jerome Powell on inflation.

As we can see in the example, understanding the sentiment towards inflation can inform a buy or sell decision. In this scenario, it can be inferred from the Targeted Sentiment API that Chair Powell’s opinion on inflation is negative, and this is most likely going to result in higher interest rates slowing economic growth. For most traders, this could result in a sell decision. The Targeted Sentiment API can provide traders faster and more granular insight than a traditional document review, and in an industry where speed is crucial, it can result in substantial business value.

The following is a reference architecture for using targeted sentiment in financial opinion mining and trading signal generation scenarios.

Scenario 2: Real-time contact center analysis

A positive contact center experience is crucial in delivering a strong customer experience. To help ensure positive and productive experiences, you can implement sentiment analysis to gauge customer reactions, the changing customer moods through the duration of the interaction, and the effectiveness of contact center workflows and employee training. With the Targeted Sentiment API, you can get granular information within your contact center sentiment analysis. Not only can we determine the sentiment of the interaction, but now we can see what caused the negative or positive reaction and take the appropriate action.

We demonstrate this with the following transcripts of a customer returning a malfunctioning toaster. For this example, we show sample statements that the customer is making.

As we can see, the conversation starts off fairly negative. With the Targeted Sentiment API, we’re able to determine the root cause of the negative sentiment and see it’s regarding a malfunctioning toaster. We can use this information to run certain workflows, or route to different departments.

Through the conversation, we can also see the customer wasn’t receptive to the offer of a gift card. We can use this information to improve agent training, reevaluate if we should even bring up the topic in these scenarios, or decide if this question should only be asked with a more neutral or positive sentiment.

Lastly, we can see that the service that was provided by the agent was received positively even though the customer was still upset about the toaster. We can use this information to validate agent training and reward strong agent performance.

The following is a reference architecture incorporating targeted sentiment into real-time contact center analytics.

Scenario 3: Monitoring social media for customer sentiment

Social media reception can be a deciding factor for product and organizational growth. Tracking how customers are reacting to company decisions, product launches, or marketing campaigns is critical in determining effectiveness.

We can demonstrate how to use the Targeted Sentiment API in this scenario by using Twitter reviews of a new set of headphones.

In this example, there are mixed reactions to the launch of the headphones, but there is a consistent theme of the sound quality being poor. Companies can use this information to see how users are reacting to certain attributes and see where product improvements should be made in future iterations.

The following is a reference architecture using the Targeted Sentiment API for social media sentiment analysis.

Get started with Targeted Sentiment

To use targeted sentiment on the Amazon Comprehend console, complete the following steps:

1. On the Amazon Comprehend console, choose Launch Amazon Comprehend.
   
2. For Input text, enter any text that you want to analyze.
3. Choose Analyze.
   

After the document has been analyzed, the output of the Targeted Sentiment API can be found on the Targeted sentiment tab in the Insights section. Here you can see the analyzed text, each entity’s respective sentiment, and the reference group it’s associated with.

In the Application integration section, you can find the request and response for the analyzed text.

Programmatically use Targeted Sentiment

To get started with the synchronous API programmatically, you have two options:

• detect-targeted-sentiment – This API provides the targeted sentiment for a single text document
• batch-detect-targeted-sentiment – This API provides the targeted sentiment for a list of documents

You can interact with the API with the AWS Command Line Interface (AWS CLI) or through the AWS SDK. Before we get started, make sure that you have configured the AWS CLI, and have the required permissions to interact with Amazon Comprehend.

The Targeted Sentiment synchronous API requires two request parameters to be passed:

• LanguageCode – The language of the text
• Text or TextList – The UTF-8 text that is processed

The following code is an example for the detect-targeted-sentiment API:

{
"LanguageCode": "string", 
"Text": "string"
}

The following is an example for the batch-detect-targeted-sentiment API:

{

"LanguageCode": "string", 
"TextList": ["string"]

}

Now let’s look at some sample AWS CLI commands.

The following code is an example for the detect-targeted-sentiment API:

aws comprehend 
--region us-east-2 
detect-targeted-sentiment  
--text "I like the burger but service was bad" 
--language-code en

The following is an example for the batch-detect-targeted-sentiment API:

aws comprehend 
--region us-east-2 
batch-detect-targeted-sentiment 
--text-list "We loved the Seashore Hotel! It was clean and the staff was friendly. However, the Seashore was a little too noisy at night." "I like the burger but service is bad" 
--language-code en

The following is a sample Boto3 SDK API call:

import boto3
import subprocess

session = boto3.Session()
comprehend_client = session.client(service_name='comprehend', region_name='us-east-2')

The following is an example of the detect-targeted-sentiment API:

response = comprehend_client.detect_targeted_sentiment(
LanguageCode='en',
Text = "I like the burger but service was bad"
)
print(response)

The following is an example of the batch-detect-targeted-sentiment API:

response = comprehend_client.batch_detect_targeted_sentiment(
    LanguageCode='en',
    TextList = ["I like the burger but service was bad","The staff was really sweet though"]
)

For more details about the API syntax, refer to the Amazon Comprehend Developer Guide.

API response structure

The Targeted Sentiment API provides a simple way to consume the output of your jobs. It provides a logical grouping of the entities (entity groups) detected, along with the sentiment for each entity. The following are some definitions of the fields that are in the response:

• Entities – The significant parts of the document. For example, Person, Place, Date, Food, or Taste.
• Mentions – The references or mentions of the entity in the document. These can be pronouns or common nouns such as “it,” “him,” “book,” and so on. These are organized in order by location (offset) in the document.
• DescriptiveMentionIndex – The index in Mentions that gives the best depiction of the entity group. For example, “ABC Hotel” instead of “hotel,” “it,” or other common noun mentions.
• GroupScore – The confidence that all the entities mentioned in the group are related to the same entity (such as “I,” “me,” and “myself” referring to one person).
• Text – The text in the document that depicts the entity.
• Type – A description of what the entity depicts.
• Score – The model confidence that this is a relevant entity.
• MentionSentiment – The actual sentiment found for the mention.
• Sentiment – The string value of positive, neutral, negative, or mixed.
• SentimentScore – The model confidence for each possible sentiment.
• BeginOffset – The offset into the document text where the mention begins.
• EndOffset – The offset into the document text where the mention ends.

For a more detailed breakdown, refer to Extract granular sentiment in text with Amazon Comprehend Targeted Sentiment or Output file organization.

Conclusion

Sentiment analysis remains crucial for organizations for a myriad of reasons—from tracking customer sentiment over time for businesses, to inferring whether a product is liked or disliked, to understanding opinions of users of a social network towards certain topics, or even predicting the results of campaigns. Real-time targeted sentiment can be effective for businesses, allowing them to go beyond overall sentiment analysis to explore insights to drive customer experiences using Amazon Comprehend.

To learn more about Targeted Sentiment for Amazon Comprehend, refer to Targeted sentiment.

About the authors

Raj Pathak is a Solutions Architect and Technical advisor to Fortune 50 and Mid-Sized FSI (Banking, Insurance, Capital Markets) customers across Canada and the United States. Raj specializes in Machine Learning with applications in Document Extraction, Contact Center Transformation and Computer Vision.

Wrick Talukdar is a Senior Architect with Amazon Comprehend Service team. He works with AWS customers to help them adopt machine learning on a large scale. Outside of work, he enjoys reading and photography.

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This post was co-written by Marius Cealera, Senior Partner Solutions Architect at AWS, Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, Cloud Architect at Accenture.

Machine learning (ML) is a high-stakes business priority, with companies spending $306 billion on ML applications in the past 3 years. According to Accenture, companies that scale ML across a business can achieve nearly triple the return on their investments. But too many companies aren’t achieving the value they expected. Scaling ML effectively for the long term requires the professionalization of the industry and the democratization of ML literacy across the enterprise. This requires more accessible ML training, speaking to a larger number of people with diverse backgrounds.

This post shows how companies can introduce hundreds of employees to ML concepts by easily running AWS DeepRacer events at scale.

Run AWS DeepRacer events at scale

AWS DeepRacer is a simple and fun way to get started with reinforcement learning (RL), an ML technique where an agent, such as a physical or virtual AWS DeepRacer vehicle, discovers the optimal actions to take in a given environment. You can get started with RL quickly with hands-on tutorials that guide you through the basics of training RL models and testing them in an exciting, autonomous car racing experience.

“We found the user-friendly nature of DeepRacer allowed our enablement sessions to reach parts of our organizations that are usually less inclined to participate in AI/ML events,” says Zdenko Estok, a Cloud Architect at Accenture. “Our post-event statistics indicate that up to 75% of all participants to DeepRacer events are new to AI/ML and 50% are new to AWS.”

Until recently, organizations hosting private AWS DeepRacer events had to create and assign AWS accounts to every event participant. This often meant securing and monitoring usage across hundreds or even thousands of AWS accounts. The setup and participant onboarding was cumbersome and time-consuming, often limiting the size of the event. With AWS DeepRacer multi-user account management, event organizers can provide hundreds of participants access to AWS DeepRacer using a single AWS account, simplifying event management and improving the participant experience.

Build a solution around AWS DeepRacer multi-user account management

You can use AWS DeepRacer multi-user account management to set usage quotas on training hours, monitor spending on training and storage, enable and disable training, and view and manage models for every event participant. In addition, when combined with an enterprise identity provider (IdP), AWS DeepRacer multi-user account management provides a quick and frictionless onboarding experience for event participants. The following diagram explains what such a setup looks like.

The solution assumes access to an AWS account.

To set up your account with AWS DeepRacer admin permissions for multi-user, follow the steps in Set up your account with AWS DeepRacer admin permissions for multi-user to attach the AWS Identity and Access Management (IAM) AWS DeepRacer Administrator policy, AWSDeepRacerAccountAdminAccess, to the user, group, or role used to administer the event. Next, navigate to the AWS DeepRacer console and activate multi-user account mode.

By activating multi-user account mode, you enable participants to train models on the AWS DeepRacer console, with all training and storage charges billed to the administrator’s AWS account. By default, a sponsoring account in multi-user mode is limited to 100 concurrent training jobs, 100 concurrent evaluation jobs, 1,000 cars, and 50 private leaderboards, shared among all sponsored profiles. You can increase these limits by contacting Customer Service.

This setup also relies on using an enterprise IdP with AWS IAM Identity Center (Successor to AWS Single Sign-On) enabled. For information on setting up IAM Identity Center with an IdP, see Enable IAM Identity Center and Connect to your external identity provider. Note that different IdPs may require slightly different setup steps. Consult your IdP’s documentation for more details.

The solution depicted here works as follows:

1. Event participants are directed to a dedicated event portal. This can be a simple webpage where participants can enter their enterprise email address in a basic HTML form and choose Register. Registered participants can use this portal to access the AWS DeepRacer console. You can further personalize this page to gather additional user data (such as the user’s DeepRacer AWS profile or their level of AI and ML knowledge) or to add event marketing and training materials.
2. The event portal registration form calls a customer API endpoint that stores email addresses in Amazon DynamoDB through AWS AppSync. For more information, refer to Attaching a Data Source for a sample CloudFormation template on setting up AWS AppSync with DynamoDB and calling the API from a browser client.
3. For every new registration, an Amazon DynamoDB Streams event triggers an AWS Lambda function that calls the IdP’s API (in this case, the Azure Active Directory API) to add the participant’s identity in a dedicated event group that was previously set up with IAM Identity Center. The IAM Identity Center permission set controls the level of access racers have in the AWS account. At a minimum, this permission set should include the AWSDeepRacerDefaultMultiUserAccess managed policy. For more information, refer to Permission sets and AWS DeepRacer managed policies.
4. If the IdP call is successful, the same Lambda function sends an email notification using Amazon Pinpoint, informing the participant the registration was successful and providing the AWS Management Console access URL generated in IAM Identity Center. For more information, refer to Send email by using the Amazon Pinpoint API.
5. When racers choose this link, they’re asked to authenticate with their enterprise credentials, unless their current browser session is already authenticated. After authentication, racers are redirected to the AWS DeepRacer console where they can start training AWS DeepRacer models and submit them to virtual races.
6. Event administrators use the AWS DeepRacer console to create and manage races. Race URLs can be shared with the racers through a Lambda-generated email, either as part as the initial registration flow or as a separate notification. Event administrators can monitor and limit usage directly on the AWS DeepRacer console, including estimated spending and training model hours. Administrators can also pause racer sponsorship and delete models.
7. Finally, administrators can disable multi-user account mode after the event ends and remove participant access to the AWS account either by removing the users from IAM Identity Center or by  disabling the setup in the external IdP.

Conclusion

AWS DeepRacer events are a great way to raise interest and increase ML knowledge across all pillars and levels of an organization. This post explains how you can couple AWS DeepRacer multi-user account mode with IAM Identity Center and an enterprise IdP to run AWS DeepRacer events at scale with minimum administrative effort, while ensuring a great participant experience.

The solution presented in this post was developed and used by Accenture to run the world’s largest private AWS DeepRacer event in 2021, with more than 2,000 racers. By working with the Accenture AWS Business Group (AABG), a strategic collaboration by Accenture and AWS, you can learn from the cultures, resources, technical expertise, and industry knowledge of two leading innovators, helping you accelerate the pace of innovation to deliver disruptive products and services. Connect with our team at accentureaws@amazon.com to engage with a network of specialists steeped in industry knowledge and skilled in strategic AWS services in areas ranging from big data to cloud native to ML.

About the authors

Marius Cealera is a senior partner solutions architect at AWS. He works closely with the Accenture AWS Business Group (AABG) to develop and implement innovative cloud solutions. When not working, he enjoys being with his family, biking and trekking in and around Luxembourg.

Zdenko Estok works as a cloud architect and DevOps engineer at Accenture. He works with AABG to develop and implement innovative cloud solutions, and specializes in Infrastructure as Code and Cloud Security. Zdenko likes to bike to the office and enjoys pleasant walks in nature.

Selimcan “Can” Sakar is a cloud first developer and solution architect at Accenture Germany with focus on emerging technologies such as AI/ML, IoT, and Blockchain. Can suffers from Gear Acquisition Syndrome (aka G.A.S.) and likes to pursuit new instruments, bikes and sim-racing equipment in his free time.

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Every company, regardless of its size, wants to deliver the best products and services to its customers. To achieve this, companies want to understand industry trends and customer behavior, and optimize internal processes and data analyses on a routine basis. This is a crucial component of a company’s success.

A very prominent part of the analyst role includes business metrics visualization (like sales revenue) and prediction of future events (like increase in demand) to make data-driven business decisions. To approach this first challenge, you can use Amazon QuickSight, a cloud-scale business intelligence (BI) service that provides easy-to-understand insights and gives decision-makers the opportunity to explore and interpret information in an interactive visual environment. For the second task, you can use Amazon SageMaker Canvas, a cloud service that expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface that allows you to generate accurate ML predictions on your own.

When looking at these metrics, business analysts often identify patterns in customer behavior, in order to determine whether the company risks losing the customer. This problem is called customer churn, and ML models have a proven track record of predicting such customers with high accuracy (for an example, see Elula’s AI Solutions Help Banks Improve Customer Retention).

Building ML models can be a tricky process because it requires an expert team to manage the data preparation and ML model training. However, with Canvas, you can do that without any special knowledge and with zero lines of code. For more information, check out Predict customer churn with no-code machine learning using Amazon SageMaker Canvas.

In this post, we show you how to visualize the predictions generated from Canvas in a QuickSight dashboard, enabling intelligent decision-making via ML.

Overview of solution

In the post Predict customer churn with no-code machine learning using Amazon SageMaker Canvas, we assumed the role of a business analyst in the marketing department of a mobile phone operator, and we successfully created an ML model to identify customers with potential risk of churn. Thanks to the predictions generated by our model, we now want to make an analysis of a potential financial outcome to make data-driven business decisions about potential promotions for these clients and regions.

The architecture that will help us achieve this is shown in the following diagram.

The workflow steps are as follows:

1. Upload a new dataset with the current customer population into Canvas.
2. Run a batch prediction and download the results.
3. Upload the files into QuickSight to create or update visualizations.

You can perform these steps in Canvas without writing a single line of code. For the full list of supported data sources, refer to Importing data in Amazon SageMaker Canvas.

Prerequisites

For this walkthrough, make sure that the following prerequisites are met:

• Run through the solution in Predict customer churn with no-code machine learning using Amazon SageMaker Canvas. Make sure that, during the model building phase, you don’t drop the State and Phone attributes. You use these later.
• Sign up for a QuickSight subscription. For this post, we only use QuickSight features included in the Standard subscription.

Use the customer churn model

After you complete the prerequisites, you should have a model trained on historical data in Canvas, ready to be used with new customer data to predict customer churn, which you can then use in QuickSight.

1. Create a new file churn-no-labels.csv by randomly selecting 1,500 lines from the original dataset churn.csv and removing the Churn? column.

We use this new dataset to generate predictions.

We complete the next steps in Canvas. You can open Canvas via the AWS Management Console, or via the SSO application provided by your cloud administrator. If you’re not sure how to access Canvas, refer to Getting started with using Amazon SageMaker Canvas.

2. On the Canvas console, choose Datasets in the navigation pane.
3. Choose Import.

4. Choose Upload and choose the churn-no-labels.csv file that you created.
5. Choose Import data.

The data import process time depends on the size of the file. In our case, it should be around 10 seconds. When it’s complete, we can see the dataset is in Ready status.

6. To preview the first 100 rows of the dataset, choose the options menu (three dots) and choose Preview.

7. Choose Models in the navigation pane, then choose the churn model you created as part of the prerequisites.

8. On the Predict tab, choose Select dataset.

9. Select the churn-no-labels.csv dataset, then choose Generate predictions.

Inference time depends on model complexity and dataset size; in our case, it takes around 10 seconds. When the job is finished, it changes its status to Ready and we can download the results.

10. Choose the options menu (three dots), Download, and Download all values.

Optionally, we can take a quick look at the results choosing Preview. The first two columns are predictions from the model.

We have successfully used our model to predict churn risk for our current customer population. Now we’re ready to visualize business metrics based on our predictions.

Import data to QuickSight

As we discussed previously, business analysts require predictions to be visualized together with business metrics in order to make data-driven business decisions. To do that, we use QuickSight, which provides easy-to-understand insights and gives decision-makers the opportunity to explore and interpret information in an interactive visual environment. With QuickSight, we can build visualizations like graphs and charts in seconds with a simple drag-and-drop interface. In this post, we build several visualizations to better understand business risks and how we could manage them, such as where we should launch new marketing campaigns.

To get started, complete the following steps:

1. On the QuickSight console, choose Datasets in the navigation pane.
2. Choose New dataset.

QuickSight supports many data sources. In this post, we use a local file, the one we previously generated in Canvas, as our source data.

3. Choose Upload a file.

4. Choose the recently downloaded file with predictions.

QuickSight uploads and analyzes the file.

5. Check that everything is as expected in the preview, then choose Next.

6. Choose Visualize.

The data is now successfully imported and we’re ready to analyze it.

Create a dashboard with business metrics of churn predictions

It’s time to analyze our data and make a clear and easy-to-use dashboard that recaps all the information necessary for data-driven business decisions. This type of dashboard is an important tool in the arsenal of a business analysts.

The following is an example dashboard that can help identify and act on the risk of customer churn.

On this dashboard, we visualize several important business metrics:

• Customers likely to churn – The left donut chart represents the number and percent of users over 50% risk of churning. This chart helps us quickly understand the size of a potential problem.
• Potential revenue loss – The top middle donut chart represents the amount of revenue loss from users over 50% risk of churning. This chart helps us quickly understand the size of potential revenue loss from churn. The chart also shows that we could lose several above-average customers as a percent of potential revenue lost that’s bigger than the percent of users at risk of churning.
• Potential revenue loss by state – The top right horizontal bar chart represents the size of revenue lost versus revenue from customers not at risk of churning. This visual could help us understand which state is the most important for us from a marketing campaign perspective.
• Details about customers at risk of churning – The bottom left table contains details about all our customers. This table could be helpful if we want to quickly look at the details of several customers with and without churn risk.

Customers likely to churn

We start by building a chart with customers at risk of churning.

1. Under Fields list, choose the Churn? attribute.

QuickSight automatically builds a visualization.

Although the bar plot is a common visualization to understand data distribution, we prefer to use a donut chart. We can change this visual by changing its properties.

2. Choose the donut chart icon under Visual types.
3. Choose the current name (double-click) and change it to Customers likely to churn.

4. To customize other visual effects (remove legend, add values, change font size), choose the pencil icon and make your changes.

As shown in the following screenshot, we increased the area of the donut, as well as added some extra information in the labels.

Potential revenue loss

Another important metric to consider when calculating the business impact of customer churn is potential revenue loss. This is an important metric because it helps us understand the business impact from customers not at risk of churning. In the telecom industry, for example, we could have many inactive clients who have a high risk of churn and but zero revenue. This chart can help us understand if we’re in a such situation or not. To add this metric to our dashboard, we create a custom calculated field by providing the mathematical formula for computing potential revenue loss, then visualize it as another donut chart.

1. On the Add menu, choose Add calculated field.

2. Name the field Total charges.
3. Enter the formula {Day Charge}+{Eve Charge}+{Intl Charge}+{Night Charge}.
4. Choose Save.

5. On the Add menu, choose Add visual.

6. Under Visual types, choose the donut chart icon.
7. Under Fields list, drag Churn? to Group/Color.
8. Drag Total charges to Value.
9. On the Value menu, choose Show as and choose Currency.
10. Choose the pencil icon to customize other visual effects (remove legend, add values, change font size).

At this moment, our dashboard has two visualizations.

We can already observe that in total we could lose 18% (270) customers, which equals 24% ($6,280) in revenue. Let’s explore further by analyzing potential revenue loss at the state level.

Potential revenue loss by state

To visualize potential revenue loss by state, let’s add a horizontal bar graph.

1. On the Add menu, choose Add visual.

2. Under Visual types¸ choose the horizontal bar chart icon.
3. Under Fields list¸ drag Churn? to Group/Color.
4. Drag Total charges to Value.
5. On the Value menu, choose Show as and Currency.
6. Drag Stage to Y axis.
7. Choose the pencil icon to customize other visual effects (remove legend, add values, change font size).

8. We can also sort our new visual by choosing Total charges at the bottom and choosing Descending.

This visual could help us understand which state is the most important from a marketing campaign perspective. For example, in Hawaii, we could potentially lose half our revenue ($253,000) while in Washington, this value is less than 10% ($52,000). We can also see that in Arizona, we risk losing almost every customer.

Details about customers at risk of churning

Let’s build a table with details about customers at risk of churning.

1. On the Add menu, choose Add visual.

2. Under Visual types, choose the table icon.
3. Under Field lists, drag Phone, State, Int’l Plan, Vmail Plan, Churn?, and Account Length to Group by.
4. Drag probability to Value.
5. On the Value menu, choose Show as and Percent.

Customize your dashboard

QuickSight offers several options to customize your dashboard, such as the following.

1. To add a name, on the Add menu, choose Add title.

2. Enter a title (for this post, we rename our dashboard Churn analysis).

3. To resize your visuals, choose the bottom right corner of the chart and drag to the desired size.
4. To move a visual, choose the top center of the chart and drag it to a new location.
5. To change the theme, choose Themes in the navigation pane.
6. Choose your new theme (for example, Midnight), and choose Apply.

Publish your dashboard

A dashboard is a read-only snapshot of an analysis that you can share with other QuickSight users for reporting purposes. Your dashboard preserves the configuration of the analysis at the time you publish it, including such things as filtering, parameters, controls, and sort order. The data used for the analysis isn’t captured as part of the dashboard. When you view the dashboard, it reflects the current data in the datasets used by the analysis.

To publish your dashboard, complete the following steps:

1. On the Share menu, choose Publish dashboard.

2. Enter a name for your dashboard.
3. Choose Publish dashboard.

Congratulations, you have successfully created a churn analysis dashboard.

Update your dashboard with a new prediction

As the model evolves and we generate new data from the business, we might need to update this dashboard with new information. Complete the following steps:

1. Create a new file churn-no-labels-updated.csv by randomly selecting another 1,500 lines from the original dataset churn.csv and removing the Churn? column.

We use this new dataset to generate new predictions.

2. Repeat the steps from the Use the customer churn model section of this post to get predictions for the new dataset, and download the new file.
3. On the QuickSight console, choose Datasets in the navigation pane.
4. Choose the dataset we created.

5. Choose Edit dataset.

6. On the drop-down menu, choose Update file.

7. Choose Upload file.

8. Choose the recently downloaded file with the predictions.
9. Review the preview, then choose Confirm file update.

After the “File updated successfully” message appears, we can see that file name has also changed.

10. Choose Save & publish.

11. When the “Saved and published successfully” message apears, you can go back to the main menu by choosing the QuickSight logo in the left upper corner.

12. Choose Dashboards in the navigation pane and choose the dashboard we created before.

You should see your dashboard with the updated values.

We have just updated our QuickSight dashboard with the most recent predictions from Canvas.

Clean up

To avoid future charges, log out from Canvas.

Conclusion

In this post, we used an ML model from Canvas to predict customers at risk of churning and built a dashboard with insightful visualizations to help us make data-driven business decisions. We did so without writing a single line of code thanks to user-friendly interfaces and clear visualizations. This enables business analysts to be agile in building ML models, and perform analyses and extract insights in complete autonomy from data science teams.

To learn more about using Canvas, see Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas. For more information about creating ML models with a no-code solution, see Announcing Amazon SageMaker Canvas – a Visual, No Code Machine Learning Capability for Business Analysts. To learn more about the latest QuickSight features and best practices, see AWS Big Data Blog.

About the Author

Aleksandr Patrushev is AI/ML Specialist Solutions Architect at AWS, based in Luxembourg. He is passionate about the cloud and machine learning, and the way they could change the world. Outside work, he enjoys hiking, sports, and spending time with his family.

Davide Gallitelli is a Specialist Solutions Architect for AI/ML in the EMEA region. He is based in Brussels and works closely with customers throughout Benelux. He has been a developer since he was very young, starting to code at the age of 7. He started learning AI/ML at university, and has fallen in love with it since then.

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