customer experience

10月 132020
 

SAS is on a marketing transformation mission. One that has targeting and personalization at the forefront. One that looks at customer journeys from a true omnichannel perspective – not through each of the channels in which we're executing. We’re bringing in brand, demand and engagement with the customer at the center [...]

3 ways SAS is modernizing its marketing technology – and the customer experience was published on SAS Voices by Michele Eggers

9月 042020
 

As SAS Viya adoption increases among customers, many discover that it fits perfectly alongside their existing SAS implementations, which can be integrated and kept running until major projects have been migrated over. Conversely, SAS Grid Manager has been deployed during the past years to countless production sites. Because SAS Viya provides distributed computing capabilities, customers wonder how it compares to SAS Grid Manager.

SAS® Grid Manager and SAS® Viya® implement distributed computing according to different computational patterns. They can complement each other in providing a highly available and scalable environment to process large volumes of data and produce rapid results. At a high level, the questions we get the most from SAS customers can be summarized in four categories:

  1. I have SAS Viya and SAS Grid Manager. How can I get the most value from using them together?
  2. I have SAS Viya. Can I get any additional benefits by also implementing SAS Grid Manager?
  3. I have SAS Grid Manager. Should I move to SAS Viya?
  4. I am starting a new project. Which platform should I use - SAS Viya or SAS Grid Manager?

To better understand how to get the most from both an architecture and an administration perspective, I answer these questions and more in my SGF 2020 paper SAS® Grid Manager and SAS® Viya®: A Strong Relationship, and its accompanying YouTube video:

I’ve also written a three-part series with more details:

SAS Grid Manager and SAS Viya: A Strong Relationship was published on SAS Users.

9月 042020
 

As SAS Viya adoption increases among customers, many discover that it fits perfectly alongside their existing SAS implementations, which can be integrated and kept running until major projects have been migrated over. Conversely, SAS Grid Manager has been deployed during the past years to countless production sites. Because SAS Viya provides distributed computing capabilities, customers wonder how it compares to SAS Grid Manager.

SAS® Grid Manager and SAS® Viya® implement distributed computing according to different computational patterns. They can complement each other in providing a highly available and scalable environment to process large volumes of data and produce rapid results. At a high level, the questions we get the most from SAS customers can be summarized in four categories:

  1. I have SAS Viya and SAS Grid Manager. How can I get the most value from using them together?
  2. I have SAS Viya. Can I get any additional benefits by also implementing SAS Grid Manager?
  3. I have SAS Grid Manager. Should I move to SAS Viya?
  4. I am starting a new project. Which platform should I use - SAS Viya or SAS Grid Manager?

To better understand how to get the most from both an architecture and an administration perspective, I answer these questions and more in my SGF 2020 paper SAS® Grid Manager and SAS® Viya®: A Strong Relationship, and its accompanying YouTube video:

I’ve also written a three-part series with more details:

SAS Grid Manager and SAS Viya: A Strong Relationship was published on SAS Users.

8月 182020
 

As the operations director of any organization’s contact center, you’ll want to ensure that your employees have the right tools in their hands to deliver superior customer service ultimately leading to happy customers and improved revenue for your company. In a previous series on How SAS Visual Analytics’ automated analysis takes customer care to the next level, we saw how automated analysis used machine learning and natural language generation (NLG) to determine which factors were most important in predicting if telecommunications customers would be likely to upgrade to a different mobile plan. We then used this information to create a list of customers our customer care workers could contact to promote our new products. But what about making on-the-fly decisions about what offer(s) best supports a given customer’s needs? To provide support for this type of analysis, SAS recently introduced the automated prediction feature within SAS Visual Analytics on SAS Viya.

What is automated prediction?

Automated prediction, in less than a minute, runs several analytic models (such as decision trees, gradient boosting, and logistic and linear regression) on a specific variable of your choice. Most of the remaining variables in your dataset are automatically analyzed as factors that might influence your specified variable. They are called underlying factors. SAS then chooses the one model (champion model) that most accurately predicts your target variable. The model prediction and the underlying factors are then displayed. You can adjust the values of the underlying factors to determine how the model prediction changes with each adjustment.

Let’s look at how automated prediction works.

Here we have the same customer table we were working within our previous blog posts. This table contains 121 columns (i.e. variables) containing usage and demographic information from a subset of customers who have contacted our customer care centers. One of these columns is a flag that indicates whether that customer upgraded their plan or not. We’ll use this as our target variable.

We’ll right-click on the Upgrade Flag variable and choose Predict on a new page in our report (Figure 1 below).

Figure 1: Selecting automated prediction.

With the automated prediction feature, SAS Visual Analytics created this easy-to-use form in less than a few minutes—by using advanced analytic models and machine learning in the background—that can help predict whether a customer is likely to upgrade (Figure 2 below). SAS analyzed all the other variables in the dataset to determine which ones were most likely to influence our Upgrade Flag. We can modify some of the values in the form and see how it affects the outcome. The factors are listed in order of their relative importance.

Figure 2: Automated Prediction results.

Under the prediction, SAS provides further details using natural language generation (NLG) (Figure 3 below). For example, here we see that the most prevalent value in our data was Did not upgrade with 87.87% of the records having that value. We can also see what type of model was chosen as the champion model; the model that best predicts whether a customer will upgrade. In our current example, the Gradient Boosting model provides the most accurate results.

Figure 3: Natural Language Generation (NLG) provides details about the prediction.

Now, let’s say that a customer calls in. We can fill in the values for each of these parameters listed that are specific to that customer. The analysis is automatically updated using the model previously generated and provides an updated prediction.

Here we see that this customer is likely to upgrade (Figure 4 below). Now we can discuss other mobile plans with this customer.

Figure 4: Updated prediction based on values entered for a specific customer.

What if we want to learn more about the model behind the prediction and what variables were deemed the most important? We can find that information when we maximize the object (Figure 5 below).

Figure 5: Maximize to see model details.

Now we can see at the bottom the steps that were taken to create the champion model (step 2) (Figure 6 below). SAS ran a decision tree, logistic regression, and a gradient boosting model and found that the gradient boosting model provided the greatest accuracy (91.67% accurate in predicting upgrade flag).

Figure 6: Prediction model description.

If we select the Relative Importance tab, we can see the relative importance of each of the underlying factors (Figure 7 below).

Figure 7: Relative importance of underlying factors.

Total Days Over Plan has the greatest influence on our Upgrade Flag variable. Days Suspended Last 6M is the next most important variable whose impact is 43.97% of Total Days Over Plan (Figure 7 above).

Automated Prediction is a fast and easy way to gain an understanding of how variables influence a target and to consider “what if,” by seeing how modifying those underlying factors affects an outcome. Multiple models were run on the data and a champion model was chosen for us using machine learning. We were able to see the accuracy of the champion model and the relative importance of each influencer. All this within a few minutes! This provides a great foundation for moving forward to more advanced modeling techniques. For those who wish to have more control over the models, SAS Visual Analytics also provides capabilities to build and modify other advanced analytical models such as gradient boosting, linear and logistic regression, and decision trees. Furthermore, as your company’s analytic maturing increases additional products can be easily added on to provide even more model choices (Forest, Neural network, Support vector machine, etc.) and capabilities. SAS’ platform and products support the whole analytical life cycle from data preparation all the way through model deployment, model performance management, and decision intelligence.

Take the next step in learning more about SAS Visual Analytics on SAS Viya by signing up for a free trial!

How SAS Visual Analytics' automated prediction takes customer care to the next level was published on SAS Users.

8月 182020
 

As the operations director of any organization’s contact center, you’ll want to ensure that your employees have the right tools in their hands to deliver superior customer service ultimately leading to happy customers and improved revenue for your company. In a previous series on How SAS Visual Analytics’ automated analysis takes customer care to the next level, we saw how automated analysis used machine learning and natural language generation (NLG) to determine which factors were most important in predicting if telecommunications customers would be likely to upgrade to a different mobile plan. We then used this information to create a list of customers our customer care workers could contact to promote our new products. But what about making on-the-fly decisions about what offer(s) best supports a given customer’s needs? To provide support for this type of analysis, SAS recently introduced the automated prediction feature within SAS Visual Analytics on SAS Viya.

What is automated prediction?

Automated prediction, in less than a minute, runs several analytic models (such as decision trees, gradient boosting, and logistic and linear regression) on a specific variable of your choice. Most of the remaining variables in your dataset are automatically analyzed as factors that might influence your specified variable. They are called underlying factors. SAS then chooses the one model (champion model) that most accurately predicts your target variable. The model prediction and the underlying factors are then displayed. You can adjust the values of the underlying factors to determine how the model prediction changes with each adjustment.

Let’s look at how automated prediction works.

Here we have the same customer table we were working within our previous blog posts. This table contains 121 columns (i.e. variables) containing usage and demographic information from a subset of customers who have contacted our customer care centers. One of these columns is a flag that indicates whether that customer upgraded their plan or not. We’ll use this as our target variable.

We’ll right-click on the Upgrade Flag variable and choose Predict on a new page in our report (Figure 1 below).

Figure 1: Selecting automated prediction.

With the automated prediction feature, SAS Visual Analytics created this easy-to-use form in less than a few minutes—by using advanced analytic models and machine learning in the background—that can help predict whether a customer is likely to upgrade (Figure 2 below). SAS analyzed all the other variables in the dataset to determine which ones were most likely to influence our Upgrade Flag. We can modify some of the values in the form and see how it affects the outcome. The factors are listed in order of their relative importance.

Figure 2: Automated Prediction results.

Under the prediction, SAS provides further details using natural language generation (NLG) (Figure 3 below). For example, here we see that the most prevalent value in our data was Did not upgrade with 87.87% of the records having that value. We can also see what type of model was chosen as the champion model; the model that best predicts whether a customer will upgrade. In our current example, the Gradient Boosting model provides the most accurate results.

Figure 3: Natural Language Generation (NLG) provides details about the prediction.

Now, let’s say that a customer calls in. We can fill in the values for each of these parameters listed that are specific to that customer. The analysis is automatically updated using the model previously generated and provides an updated prediction.

Here we see that this customer is likely to upgrade (Figure 4 below). Now we can discuss other mobile plans with this customer.

Figure 4: Updated prediction based on values entered for a specific customer.

What if we want to learn more about the model behind the prediction and what variables were deemed the most important? We can find that information when we maximize the object (Figure 5 below).

Figure 5: Maximize to see model details.

Now we can see at the bottom the steps that were taken to create the champion model (step 2) (Figure 6 below). SAS ran a decision tree, logistic regression, and a gradient boosting model and found that the gradient boosting model provided the greatest accuracy (91.67% accurate in predicting upgrade flag).

Figure 6: Prediction model description.

If we select the Relative Importance tab, we can see the relative importance of each of the underlying factors (Figure 7 below).

Figure 7: Relative importance of underlying factors.

Total Days Over Plan has the greatest influence on our Upgrade Flag variable. Days Suspended Last 6M is the next most important variable whose impact is 43.97% of Total Days Over Plan (Figure 7 above).

Automated Prediction is a fast and easy way to gain an understanding of how variables influence a target and to consider “what if,” by seeing how modifying those underlying factors affects an outcome. Multiple models were run on the data and a champion model was chosen for us using machine learning. We were able to see the accuracy of the champion model and the relative importance of each influencer. All this within a few minutes! This provides a great foundation for moving forward to more advanced modeling techniques. For those who wish to have more control over the models, SAS Visual Analytics also provides capabilities to build and modify other advanced analytical models such as gradient boosting, linear and logistic regression, and decision trees. Furthermore, as your company’s analytic maturing increases additional products can be easily added on to provide even more model choices (Forest, Neural network, Support vector machine, etc.) and capabilities. SAS’ platform and products support the whole analytical life cycle from data preparation all the way through model deployment, model performance management, and decision intelligence.

Take the next step in learning more about SAS Visual Analytics on SAS Viya by signing up for a free trial!

How SAS Visual Analytics' automated prediction takes customer care to the next level was published on SAS Users.

8月 182020
 

As the operations director of any organization’s contact center, you’ll want to ensure that your employees have the right tools in their hands to deliver superior customer service ultimately leading to happy customers and improved revenue for your company. In a previous series on How SAS Visual Analytics’ automated analysis takes customer care to the next level, we saw how automated analysis used machine learning and natural language generation (NLG) to determine which factors were most important in predicting if telecommunications customers would be likely to upgrade to a different mobile plan. We then used this information to create a list of customers our customer care workers could contact to promote our new products. But what about making on-the-fly decisions about what offer(s) best supports a given customer’s needs? To provide support for this type of analysis, SAS recently introduced the automated prediction feature within SAS Visual Analytics on SAS Viya.

What is automated prediction?

Automated prediction, in less than a minute, runs several analytic models (such as decision trees, gradient boosting, and logistic and linear regression) on a specific variable of your choice. Most of the remaining variables in your dataset are automatically analyzed as factors that might influence your specified variable. They are called underlying factors. SAS then chooses the one model (champion model) that most accurately predicts your target variable. The model prediction and the underlying factors are then displayed. You can adjust the values of the underlying factors to determine how the model prediction changes with each adjustment.

Let’s look at how automated prediction works.

Here we have the same customer table we were working within our previous blog posts. This table contains 121 columns (i.e. variables) containing usage and demographic information from a subset of customers who have contacted our customer care centers. One of these columns is a flag that indicates whether that customer upgraded their plan or not. We’ll use this as our target variable.

We’ll right-click on the Upgrade Flag variable and choose Predict on a new page in our report (Figure 1 below).

Figure 1: Selecting automated prediction.

With the automated prediction feature, SAS Visual Analytics created this easy-to-use form in less than a few minutes—by using advanced analytic models and machine learning in the background—that can help predict whether a customer is likely to upgrade (Figure 2 below). SAS analyzed all the other variables in the dataset to determine which ones were most likely to influence our Upgrade Flag. We can modify some of the values in the form and see how it affects the outcome. The factors are listed in order of their relative importance.

Figure 2: Automated Prediction results.

Under the prediction, SAS provides further details using natural language generation (NLG) (Figure 3 below). For example, here we see that the most prevalent value in our data was Did not upgrade with 87.87% of the records having that value. We can also see what type of model was chosen as the champion model; the model that best predicts whether a customer will upgrade. In our current example, the Gradient Boosting model provides the most accurate results.

Figure 3: Natural Language Generation (NLG) provides details about the prediction.

Now, let’s say that a customer calls in. We can fill in the values for each of these parameters listed that are specific to that customer. The analysis is automatically updated using the model previously generated and provides an updated prediction.

Here we see that this customer is likely to upgrade (Figure 4 below). Now we can discuss other mobile plans with this customer.

Figure 4: Updated prediction based on values entered for a specific customer.

What if we want to learn more about the model behind the prediction and what variables were deemed the most important? We can find that information when we maximize the object (Figure 5 below).

Figure 5: Maximize to see model details.

Now we can see at the bottom the steps that were taken to create the champion model (step 2) (Figure 6 below). SAS ran a decision tree, logistic regression, and a gradient boosting model and found that the gradient boosting model provided the greatest accuracy (91.67% accurate in predicting upgrade flag).

Figure 6: Prediction model description.

If we select the Relative Importance tab, we can see the relative importance of each of the underlying factors (Figure 7 below).

Figure 7: Relative importance of underlying factors.

Total Days Over Plan has the greatest influence on our Upgrade Flag variable. Days Suspended Last 6M is the next most important variable whose impact is 43.97% of Total Days Over Plan (Figure 7 above).

Automated Prediction is a fast and easy way to gain an understanding of how variables influence a target and to consider “what if,” by seeing how modifying those underlying factors affects an outcome. Multiple models were run on the data and a champion model was chosen for us using machine learning. We were able to see the accuracy of the champion model and the relative importance of each influencer. All this within a few minutes! This provides a great foundation for moving forward to more advanced modeling techniques. For those who wish to have more control over the models, SAS Visual Analytics also provides capabilities to build and modify other advanced analytical models such as gradient boosting, linear and logistic regression, and decision trees. Furthermore, as your company’s analytic maturing increases additional products can be easily added on to provide even more model choices (Forest, Neural network, Support vector machine, etc.) and capabilities. SAS’ platform and products support the whole analytical life cycle from data preparation all the way through model deployment, model performance management, and decision intelligence.

Take the next step in learning more about SAS Visual Analytics on SAS Viya by signing up for a free trial!

How SAS Visual Analytics' automated prediction takes customer care to the next level was published on SAS Users.

4月 022020
 

In February, SAS was recognized as a Leader in the 2020 Gartner Magic Quadrant for Data Science & Machine Learning Platforms report. SAS is the only vendor to be a leader in this report for all seven years of its existence. According to us, the topic of the research is [...]

SAS Customer Intelligence 360: Analyst viewpoints was published on Customer Intelligence Blog.

3月 252019
 

These days, an ever-increasing number of customer interactions are taking place over digital channels and every single digital interaction offers an incredible source of customer intelligence for organisations to tap into. With every visit, customers leave a valuable trail of digital breadcrumbs. These breadcrumbs give organisations the ability to follow [...]

5 ways your digital analytics strategy is hindering your customer experience was published on Customer Intelligence Blog.

2月 252019
 

In my previous blog post, I explored how reinforcement learning is taking the guesswork out of marketing to deliver great experiences. Let’s take a look at two additional areas where AI is transforming the customer experience: recommendations and natural language processing. Personalised recommendations get a boost from AI We’ve all [...]

Two more ways AI is transforming the customer experience was published on Customer Intelligence Blog.

2月 192019
 

Artificial Intelligence (AI) has caught everyone's attention in recent years, mainly because of its disrupting nature which gives it enormous potential with countless applications. Among the many possibilities that AI promises, customer experience (CX) is an area that offers immense opportunity for organisations to differentiate. Welcome to the experience economy [...]

Is artificial intelligence the future of customer experience? was published on Customer Intelligence Blog.