artificial intelligence

9月 012021

Analytics and Artificial Intelligence (AI) are changing the way we interact with the world around us – increasing productivity and improving the way we make decisions. SAS and Microsoft are partnering to inspire greater trust and confidence in every decision by driving innovation and delivering proven AI in the cloud.

In this demo, see how intelligent decisioning and machine learning from SAS and Microsoft help Contoso Bank – a fictitious banking customer – simplify and reduce risk in its home loan portfolio.

Let’s get started.

Part 1: Data and Discovery

Organizations can run faster and smarter by enabling employees to uncover insights. See how SAS and Microsoft help Contoso Bank gain new insight into its portfolio by bringing together data management, analytics and AI capabilities with seamless integration into the Azure data estate.

Key Product Features:
• Use built-in Power BI tools like smart narratives and sentiment analysis to quickly analyze structured and unstructured data.
• Connect your SAS Viya and Microsoft Azure environments with single sign-on via Azure Active Directory.
• Catalog your datasets across SAS and Microsoft in SAS Information Catalog for a holistic view of your data environment.
• Integrate data from Azure Synapse Analytics and other Azure data sources into a combined dataset in SAS Data Studio.
• No-code intelligence features in SAS Visual Analytics explain analytic outputs in natural language.

Part 2: Model and Deploy

AI has the potential to transform organizations. See how SAS and Microsoft enable Contoso Bank to quickly build and operationalize predictive models by bringing together SAS Viya advanced analytics and AI capabilities with Azure Machine Learning.

Key Product Features:
• Bring models from SAS Visual Analytics into SAS Model Studio as a candidate for production use.
• Create automatically generated pipelines in SAS Model Studio to select the best features for modeling.
• Register models built in open-source Jupyter notebooks within Azure Machine Learning into SAS Model Manager.
• Publish models from SAS Model Manager in Azure Machine Learning to be deployed in the Microsoft ecosystem.
• Schedule SAS model manager to monitor model drift in the SAS or Microsoft ecosystem to identify the right time to retrain models.

Part 3: Automate and Monitor

Building a data-driven organization means increasing productivity with the necessary insights and tools. See how SAS and Microsoft can help Contoso Bank rapidly operationalize the analytics and AI capabilities of SAS Viya through Power Apps and Power Automate to help employees make better decisions.

Key Product Features:
• Build decision flows in SAS Intelligent Decisioning to make calculated decisions at speed.
• Use AI Builder in Power Platform to extract and process information in Power Platform.
• Access SAS Intelligent Decisioning’s decision access engine in low-code applications by using Power Apps to ingest data and receive decisioning outputs.
• Connect to SAS Intelligent Decisioning from Power Apps and Power Automate with the SAS Decisioning connector.
• Embed Power Apps in Microsoft Teams or access via a mobile friendly web app.

To learn more about how SAS Viya integrates with Microsoft, check out our white paper SAS and Microsoft: Shaping the future of AI and analytics in the cloud.

Transforming Your Business With SAS® Viya® on Microsoft Azure was published on SAS Users.

4月 062021

Almost a year ago, in the depths of despair after the first 100 days of the COVID-19 pandemic, I wrote a post on how government agencies could consider their options using a framework called Respond, Recover and Reimagine. Now, with greater vaccine availability and mass vaccination by governments – in [...]

Does the public sector need decisioning? was published on SAS Voices by Lee Ann Dietz

1月 162020

Using Customer Lifetime Value in your business decision making is often important and crucial for success. Businesses that are customer-centric often spend thousands of dollars acquiring new customers, “on-boarding” new customers, and retaining those customers. If your business margins are thin, then it can often be months or quarters before you start to turn a profit on a particular customer. Additionally, some business models will segment the worth of their customers into categories that will often give different levels of service to the more “higher worth” customers. The metric most often used for that is called Customer Lifetime Value (CLV). CLV is simply a balance sheet look at the total cost spent versus the total revenue earned over a customer’s projected tenure or “life.”

In this blog, we will focus on how a business analyst can build a functional analytical dashboard for a fictional company that is seeing its revenue, margins, and a customer’s lifetime value decrease and what steps they can take to correct that.

We will cover 3 main areas of interest:

  1. First, screenshots of SAS Visual Analytic reports, using Customer Lifetime Value and how you can replicate them.
  2. Next, we will look at the modeling that we did in the report, with explanations on how we got used the results in subsequent modeling.
  3. Lastly, we talk about one example of how we scored and deployed the model, and how you can do the same.

Throughout this blog, I will also highlight areas where SAS augments our software with artificial intelligence to improve your experience.

1. State of the company

First, we will look at the state of the company using the dashboard and take note of any problems.

Our dashboard shows the revenue of our company over the last two years as well as a forecast for the next 6 months. We see that revenue has been on the decline in recent years and churns have been erratically climbing higher.

Our total annual revenue was 112M last year with just over 5,000 customers churning.

So far this year, our revenue is tracking low and sits at only 88M, but the bad news is that we have already tripled last year's churn total.

If these trends continue, we stand to lose a third of our revenue!

2. The problems

Now, let’s investigate as to where the problems are and what can be done about them.

If we look at our current metrics, we can see some interesting points worth investigating.

The butterfly chart on the right shows movement between customer loyalty tiers within each region of the country with the number of upgrades (on the right) and downgrades (on the left).

The vector plots show us information over multiple dimensions. These show us the difference between two values and the direction it is heading. For example, on the left, we see that Revenue is pointed downward while churns (x axis) are increasing.

The vector plot on the right shows us the change in margin from year to year as well as the customer lifetime value.

What’s interesting here is that there are two arrows that are pointing up, indicating a rise in customer lifetime value. Indeed, if we were to click on the map, we would see that these two regions are the same two that have a net increase in Loyalty Tier.

This leads me to believe that a customer’s tier is predictive of margin. Let’s investigate it further.

3. Automated Analysis

We will use the Automated Analysis feature within Visual Analytics to quickly give us the drivers of CLV.

This screenshot shows an analysis that SAS Visual Analytics(VA) performed for me automatically. I simply told VA which variable I was interested in analyzing and within a matter of seconds, it ran a series of decision trees to produce this summary. This is an example of how SAS is incorporating AI into our software to improve your experience.

Here we can see that loyalty tier is indeed the most important factor in determining projected annual margin (or CLV).

4. Influential driver

Once identified, the important driver will be explored across other dimensions to assess how influential this driver might be.

A cursory exploration of Loyalty Tier indicates that yes, loyalty tier, particularly Tier 5, has a major influence on revenue, order count, repeat orders, and margin.

5. CLV comparison models

We will create two competing models for CLV and compare them.

Here on our modeling page are two models that I’ve created to predict CLV. The first one is a Linear Regression and the second is a Gradient Boosting model. I've used Model Comparison to tell me that the Linear Regression model delivers a more accurate prediction and so I use the output of that model as input into a recommendation engine.

6. Recommendation engine

Based on our model learnings and the output of the model, we are going to build a recommendation engine to help us with determine what to do with each customer.

Represented here, I built a recommendation engine model using the Factorization Machine algorithm.

Once we implement our model, customers are categorized more appropriately and we can see that it has had an impact on revenue and the number of accounts is back on the rise!


Even though Customer Lifetime Value has been around for years, it is still a valuable metric to utilize in modeling and recommendation engines as we have seen. We used it our automated analysis, discovered that it had an impact on revenue, we modeled future values of CLV and then incorporated those results into a recommendation engine that recommended new loyalty tiers for our customers. As a result, we saw positive changes in overall company revenue and churn.

To learn more, please check out these resources:

How to utilize Customer Lifetime Value with SAS Visual Analytics was published on SAS Users.

10月 302019

I suffer from arthritis. You can tell just by watching me walk: Depending on the day, I have a slight limp, which varies in severity based on a number of factors such as the time of day and recent physical activity. Years of treatment for my condition have shown me [...]

I applied AI to my arthritis assessment. Here’s what happened. was published on SAS Voices by Mark Wolff

9月 092019

Editor's Note: This article was translated and edited by SAS USA and was originally written by Makoto Unemi. The original text is here.

SAS previously provided SAS Scripting Wrapper for Analytics Transfer (SWAT), a package for using SAS Viya functions from various general-purpose programming languages ​​such as Python.

In addition to SWAT, SAS launched Deep Learning Python (DLPy), a higher-level API package for Python, making it possible to use SAS Viya functions more efficiently from Python. In this article I outline more about what DLPy is and how it's implementation.

About DLPy

DLPy is a high-level package for the Python API created for deep learning and image action set after Viya3.3. DLPy provides an API similar to Keras to improve the efficiency of deep learning and image processing coding. With just a little rewriting of the existing Keras code, it is possible to execute the processing on SAS Viya.

For example, below is an example of a Convolutional Neural Network (CNN) layer definition; you can see that it is very similar to Keras.

The layers supported by DLPy are: InputLayer, Conv2d, Pooling, Dense, Recurrent, BN, Res, Proj, and OutputLayer. The following is an example of learning.

DLPy functions

Introducing DLPy's functions (partial excerpts), taking as an example the learning of multiple dolphins and giraffe images using CNN and applying test images to the model.

Implementation of major deep learning networks

DLPy offers the following pre-built deep learning models: VGG11/13/16/19, ResNet34/50/101/152, wide_resnet, and dense_net.

The following models also offer pre-trained weights using ImageNet data (these weights can be used for unique tasks by transfer learning): VGG16, VGG19, ResNet50, ResNet101, and ResNet152. The following is an example of transferring ResNet50 pre-trained weights.

CNN judgment basis information

Using the heat_map_analysis() method, you can output a colorful heat map and check where you focused on the image.

In addition, the get_feature_maps() method is used to get the feature map of each layer of CNN, and feature_maps.display() method is used to specify and display the obtained feature map layer and check can also do.

The following is the output result of layer 1 feature map.

The following is the output result of layer 18 feature map.

Deep learning & image processing related task support function

resize() method: Resize image data

as_patches() method: Image data expansion (generates a patch from the original image)

two_way_split() method: Data split (learning, testing)

plot_network() method: draws the structure of the defined deep learning layer (network) as a graphical diagram

plot_training_history() method: Iterative learning history display

predict() method: Display prediction (scoring) results

plot_predict_res() method: Display classification results

And of course, you can use DLPy to get data from a SAS Viya in-memory session, pass it to your local client, and convert it to common data formats like numpy arrays and Pandas DataFrames. The converted data can be smoothly supplied to models of other open source packages such as scikit-learn.

Regarding image classification using DLPy, videos are also available in the Deep Learning with Python (DLPy) Demo Series section of the DLPy product page.

SAS Viya: Package for Python API for deep learning and image processing: DLPy was published on SAS Users.

9月 032019

The startup ecosystem is dynamic and the flow of venture capital into tech is at an all-time high. Billions of dollars are invested in tech startups every year. Many tech startups market themselves as ‘powered by AI’ and pitch investors with buzzword laden phrases such as, ‘we leverage state of [...]

7 ways SAS empowers startups with artificial intelligence and machine learning was published on SAS Voices by Avinash Sooriyarachchi

4月 302019

Artificial intelligence is the attention-grabbing, overhyped, shiny object that every organization is searching to make use of. Yes, it is overhyped, but it’s also very real and very powerful. “We do not want to add to the hype. We do not want to add to the confusion. We want to [...]

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4月 122019

At the risk of oversimplifying, I think of artificial intelligence as what becomes possible after you’ve fully embraced analytics and you’re starting to get bolder about how to use it. Your models are getting better, your predictions are more accurate, your results are stronger and over all, confidence grows in [...]

6 thinks you didn't know about AI was published on SAS Voices by John Balla

4月 122019

At the risk of oversimplifying, I think of artificial intelligence as what becomes possible after you’ve fully embraced analytics and you’re starting to get bolder about how to use it. Your models are getting better, your predictions are more accurate, your results are stronger and over all, confidence grows in [...]

6 thinks you didn't know about AI was published on SAS Voices by John Balla