analytics

6月 142016
 

“Good afternoon, Mr. Yakamoto. How did you like that three-pack of tank tops you bought last time you were in?” Washington D.C. Year 2054. Chief of PreCrime John Anderton is running from the law for a crime he has not committed yet. After a risky eye transplant in order to […]

Location analytics and the 'Minority Report' approach was published on SAS Voices.

6月 142016
 

I recently read an article that claims 35% of men, and 40% of women in the US are now obese. Yikes! I wondered when this happened, and whether it had been a gradual or sudden change. If I only had some graphs ... which the article didn't provide. I did some […]

The post The big fat truth about the US weight problem appeared first on SAS Learning Post.

6月 132016
 

When I travel for work, I want to get the most out of my trip. So while I was in Las Vegas in April for SAS Global Forum, I used my free time to check out what’s in store for the Analytics Experience at the Bellagio, Sept. 12-14. Take a […]

The post Analytics Experience – Maximize your conference trip appeared first on SAS Learning Post.

6月 092016
 

With recent advances in quadcopters, or drones, they have become pretty capable and fun flying machines. And just about anybody can afford the entry-level models. They've recently become prevalent enough that the government has started coming up with rules, and the Federal Aviation Administration (FAA) started requiring owners to register drones that […]

The post These aren't the drones you're looking for - but those are! appeared first on SAS Learning Post.

6月 072016
 

The UK’s National Health Service (NHS) Confederation has done lots of great research on how to enhance decision making so that every decision delivers greater value to patients (in terms of clinical outcome) and to health care organisations (in terms of operational effectiveness). In its most recent report on this […]

Why evidence, not culture, is the key to driving value-based healthcare was published on SAS Voices.

6月 072016
 

To some people, electricity is like air: There for the taking. For others, circumventing paying a utility bill is a just cause, sticking it to “Big Energy” for their perceived transgressions against their customers. In either case, not paying for energy is considered fraud and a crime. In some states […]

Fraud detection is like crime fighting, only geekier was published on SAS Voices.

6月 062016
 

There's no doubt that artificial intelligence (AI) is here and is rapidly gaining the attention of brands large and small. As I talk to customers and prospects, they are interested in understanding how AI and its subcomponents (cognitive computing, machine learning, or even deep learning) are being woven into various departments (marketing, sales, service and support) at organizations across industries.

Here are some examples of cognitive computing and machine learning today at organizations, and how these capabilities will enhance customer experience in the future.

I think it's important to start with a few foundational facts:

  • AI as a practice is not new – John McCarthy and others started their research into this area back in the 1950s.
  • AI and its subcomponents are rooted in predictive analytics (neural networks, data mining, natural language processing, etc., all have their beginnings here).
  • Automation and the use of supervised and unsupervised algorithms are crucial to machine learning and cognitive computing use cases.
  • Deep learning uses the concept of teaching and training to accomplish more advanced automation tasks. It’s important to note that deep learning is not as prevalent from a customer experience perspective as machine learning and cognitive computing. Let's take a look at what AI means for brands as the customer experience becomes the primary differentiator for marketing organizations.

algorithms

A cognitive computing use case

Cognitive computing enables software to engaging in human-like interactions. Cognitive computing uses analytical processes (voice to text, natural language processing and text and sentiment analysis) to determine answers to questions.

For example, a SAS customer uses automation to provide a quicker response to service requests that come in to the brand's contact center. It can send an automated reply to service inquires, direct the customer to appropriate departments, and send customer responses back to the channel – all using SAS solutions. These capabilities reduces the number of replies that require human intervention and improves service response times. This same use case can be applied across industries such as retail, telecom, financial services and utilities. The end result? A happier customer and an improved customer experience.

cognitive computing

Analytics: the core of machine learning

Machine learning uses software that can scan data to identify patterns and predict future results with minimal human intervention.

Analytics play an important role. Model retraining, the use of historical data and environmental conditions all serve as inputs into the supervised and unsupervised algorithms that machine learning uses. For example, some of our large telecom and financial services providers use data, customer journey maps and past patterns to be able to serve timely and relevant offers during customer interactions.

Many of our customers can do in less than one second, and are providing response and replies that are relevant and individualized. Another great example of machine learning is the development work that SAS is doing currently with regard to its marketing software.

Our customer intelligence solutions use embedded machine learning processes to make setting up activities and completing tasks in the software easier for analysts and marketers alike. For instance, the software will automatically choose the optimal customer segment and creative combinations for a campaign. It will also recommend the best time to follow up with a customer or segment and on the customer’s preferred devices. Machine learning also gives marketers the ability to understand how to use and modify digital assets for the most reach and optimal conversions.

The newest addition to artificial intelligence

Deep learning, a newer concept that relies on deep neural networks – is certainly something that is coming to the marketing and service realms. Many companies have started looking at how we teach and train software to accomplish complex activities – drive cars, play chess, make art (the list goes on). As for marketing, I believe we will see deep learning being used to run marketing programs, initiate customer service interactions or map customer journeys in detail.

These are just a few examples of how we are seeing AI improve the customer experience. You and I, as digitally empowered consumers, will certainly benefit from man and machine working together to automate the interactions that we have with brands on a daily basis. I urge you to keep an eye out for how brands big and small are automating the interactions they have with you – I think you will be pleasantly surprised with the outcome.

tags: artificial intelligence, cognitive computing, customer analytics, deep learning, marketing automation, marketing software, predictive analytics, Predictive Marketing, SAS Customer Intelligence 360

How artificial intelligence will enhance customer experiences was published on Customer Intelligence.