artificial intelligence

2月 252019
 

There will be more than 55 billion IoT devices by 2025 – more than four devices for every person on earth. That means that big data is only getting bigger. All of that high frequency, high velocity data from connecting the physical world to the digital world is available, and [...]

Unlocking the value of IoT data with the artificial intelligence of things was published on SAS Voices by Tim Clark

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.

1月 162019
 

The potential for artificial intelligence (AI) and the Internet of Things (IoT) to transform the way health care and therapies are delivered is tremendous. It’s not surprising that the health care and life sciences industries are being flooded with information about how these new technologies will change everything. While it’s [...]

How are AI and advanced analytics transforming health and life sciences? was published on SAS Voices by Cameron McLauchlin

1月 142019
 

In the second of three posts on using automated analysis with SAS Visual Analytics, we used the automated analysis object to get a better understanding of our variable of interest, X-Sell and Up-sell Flag, and how it is influenced by other variables in our dataset.

In this third and final post, you'll see how to filter the data even more to set up your customer care workers for success.

Remember how on the left-hand side of the analysis we had a list of subgroups with their probabilities? We can use those to filter our data or create additional subsets of data. Let’s create a calculated category from one of the subgroups and then use that to filter a list table of customers. If I right click on the 87% subgroup and select Derive subgroup item a new calculated category will appear in my Data pane.

Here is the new data item located in our data pane:

To see the filter for this data object we can right click on it and select edit.

We can now use this category as a filter. Here we have a basic customer table that does not have a filter applied:

If we apply the filter for customers who fall in the 87% subgroup and a filter for those customers who have not yet upgraded, we have a list of customers that are highly likely to upgrade.

We could give this list to our customer care centers and have them call these customers to see if they want to upgrade. Alternatively, the customer care center could use this filter to target customers for upgrades when they call in. So, if a customer calls into the center, the employee could see if that customer meets the criteria set out in the filter. If they do, they are highly likely to upgrade, and the employee should provide an offer to them.

How to match callers with sales channels

Let’s go back to our automated analysis and perform one more action. We’ll create a new object from the subgroup and assess the group by acquisition channel. This will help us determine which acquisition channel(s) the customers who are in our 87% subgroup purchased their plans from. Then we’ll know which sales teams we need to communicate to about our sales strategy.

To do this we’ll select our 87% group, right click and select New object from subgroup on new page, then Acquisition Channel.

Here we see the customers who are in or out of our subgroup by acquisition channel.

Because it is difficult to see the "in" group, we’ll remove those customers who are out of our subgroup by selecting out from the legend then right click and select New filter from selection, then Exclude selection.

Now we can see which acquisition channel the 87% subgroup purchased their current plan from and how many have already upgraded.

In less than a minute using SAS Visual Analytics' automated analysis we’ve gained business insights based on machine learning that would have taken hours to produce manually. Not only that, we’ve got easy-to-understand results that are built with natural language processing. We can now analyze all variables and remove any bias, ensuring we don’t miss key findings. Business users gain access to analytics without them having the expert skills needed to build models and interpret results. Automated analysis is a start and SAS is committed to investing time and resources into this new wave of BI. Look for more enhancements in future releases!

Miss the previous posts?

This is the third of a three-part series demonstrating automated analysis using SAS Visual Analytics on Viya. Part 1 describes a common visualization approach to handling customer data that leaves room for error and missed opportunities. Part 2 shows improvements through automated analysis.

Want to see automated analysis in action? Watch this video!

How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 3 was published on SAS Users.

1月 032019
 

You're the operations director for a major telco's contact center. Your customer-care workers enjoy solving problems. Turning irate callers into fans makes their day.

They also hate flying blind. They've been begging you for deeper insight into customer data to better serve their callers. They want to know which customers will likely accept offers and upgrades they're authorized to give. Their success = customer satisfaction = your company's success, right?

Automated analytics facilitate that level of insight, and this post introduces you to it. It will help you begin to think through what it looks like to equip your contact center workers to be heroes. Two subsequent posts will further demonstrate how SAS Visual Analytics leverages automated analytics.

What is automated analytics?

If you're already familiar with business intelligence tools, it's not a stretch to call automated analytics disruptive, significantly changing the way you see BI. In essence, automated analytics uses machine learning to find meaningful relationships between variables. It provides valuable insights in easy-to-understand text generated using natural language.

Automated analytics, which is expanding to include Artificial Intelligence, overcomes barriers to insight-driven business decisions by reducing:

  • Time to insights.
  • Bias in the analysis.
  • The need for more employee training.

An analyst-intensive approach to better insights

Now put your analyst hat on and imagine a day in the life of interpreting data visualizations. Pictured below is a report created to explore and visualize customers' interactions with a telecommunications company. It contains usage information from a subset of customers who have contacted customer care centers. Enhanced by adding cleansed demographics data, this report is being used to target customers for cross-sell or up-sell opportunities.

Note that the Private Label GM channel have the highest upgrade rate of 50%. This could mean that customers who purchased their plans through the Private Label GM channel were not well informed on their options and might have purchased a plan that did not fit their needs. We could investigate this further and see how we can assist our customers better when purchasing their plans through the Private label GM channel.

This report also shows us that the unknown handset type had the highest upgrade rate of all phone types. Unknown handset indicates that this customer brought their phone over from another company. So, this high upgrade rate is not surprising as a recent promotion targeted those users to switch their phones and upgrade.

The analysis showing our upgrade rate and total upgrades by plan type shows us that the Lotta Minutes Classic plan had the highest number of upgrades. This is not surprising as it also has the highest number of accounts. However, the Data Bytes Value plan had the highest upgrade rate but very few accounts. We could focus on the Unlimited SL plan customers and offer them upgrades, as they seem to be more likely to upgrade than customers on other plans and there are quite a few customers still on that plan.

From the analysis on the bottom we can see the correlation of other variables to our variable of interest, cross-sell and up-sell flag. We can see that Total Days Over Plan, Delinquent Indicator and Days Suspended Last 6M are weakly correlated to upgrades.

What’s interesting here is that data plan is not correlated to our variable of interest, Xsell and Upsell flag. This tells me that if we had started a campaign targeted on Unlimited SL customers, we probably wouldn’t have much success.

We might want to target customers based on total days over plan or delinquent indicator or days suspended in the last 6 months, but they were only weakly correlated.

While this correlation provided great insight and may have prevented us from going down the wrong path, I had to physically choose the variables I wanted to include. I used my own logic and chose variables I thought might influence our variable of interest, Xsell and Upsell Flag.

Risk of mistakes, missed opportunity

But there are many other variables in this dataset. What if one of the other variables that I hadn’t thought of was correlated? I would miss some key findings. Or what if multiple variables in combination better predict our variable of interest, Xsell and Upsell Flag?

To dive deeper we could add a decision tree, or other charts to try to determine where we should focus our future efforts. This would take some time to build and we’d need to interpret the results on our own. However, if we use automated analysis the application would:

  • Choose the most relevant categories and measures.
  • Perform the most appropriate analytics for our data.
  • Provide us with results that are easy-to-understand.

Upcoming: A closer look at automated analytics

In next week's post, you'll see what happens when we turn loose the power of automated analytics with the SAS Viya Platform and let SAS Visual Analytics analyze all the measures and categories.

What's your experience with automated analytics? Share in the comments.

How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 1 was published on SAS Users.

12月 072018
 

Once again, I have chosen to take a traditional Christmas song or carol and create a fun technology-related version of it to share with you. This is the fifth year and the eighth song, so I hope you enjoy your 2018 holiday song. Grandma got over run by a neural [...]

Grandma got over run by a neural network was published on SAS Voices by David Pope

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 052018
 

In part one of this blog posting series, we introduced machine learning models as a multifaceted and evolving topic. The complexity that gives extraordinary predictive abilities also makes these models challenging to understand. They generally don’t provide a clear explanation, and brands experimenting with machine learning are questioning whether they [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 2] was published on Customer Intelligence Blog.