Tag: artificial intelligence

Interactive Bots Leverage Machine Learning to Provide Progressively Better Digital Experiences

Posted by on January 22, 2018

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Artificial intelligence has been floating around as a topic since the 1950s, so why it is suddenly coming to prominence in the language of marketers? Massive companies such as Microsoft, IBM, Apple, Facebook and Google’s parent Alphabet are making significant investments in the field in the hopes of garnering the additional market share promised by more intelligent user interactions. While messenger bots are still in their infancy, marketers everywhere are starry-eyed with the potential of offering instant self-service to customers in a way that feels very customized — and might even result in larger purchases and more consistent interactions. Are these automated systems a hit or a miss in the eyes of consumers? That all may depend on how well systems are integrated and the bots are programmed.

Types of Artificial Intelligence

Many of us are familiar with AI from hearing about chess matches between human masters and computers, as computers attempted to anticipate our next action. After years stuck in labs at MIT and Stanford, the field of artificial intelligence began to branch to natural language, with computers attempting to recreate the way humans select language to be used in a more conversational tone.

Machine learning is a particular type of AI that involves providing a computer with a vast quantity of data, and asking for predictions based on new data. As computers continue to aggregate information, this process becomes much more instinctive for machines. Another type of artificial intelligence involves programming artificial neural networks, an advanced concept that requires multiple layers of features in order to make better predictions. Machine learning that goes to this level is considered deep learning and it can require a high level of resources to execute it effectively.

Data-based Learning

The timeline for useful AI has accelerated in recent years, with Google and others making leaps in the field by feeding millions of images into a complex neural network, initially programming it to recognize cats within an image. From this breakthrough, Google has been a continued leader in AI by leveraging the functionality to bring enhancements to everything from Gmail to Street View and Google Translate. Google’s research scientists help fan the flame of AI interest by regularly publishing papers on their learnings, which in turn encourages others to continue their work in the field.

Amazon is another top organization utilizing AI in both their distribution center and on their website for enhanced recommendations. Consumers may not realize it, but Amazon’s Alexa uses the data from the millions of daily interactions to continue learning and improving both speech and intuitive customer recommendations.

The Rise of the Chatbots

The focus on AI as a marketing tactic is relatively new, and the explosion of chatbots in the last several years bears out the value that organizations are seeing as customers begin to record positive interactions. Most companies are still in the trial and error stage, but others are leveraging technology that is more mature. For example, the Cosmopolitan of Las Vegas hotel now “employs” an AI named Rose who interacts with hundreds of customers on a daily basis via text message — even tossing in kiss emojis when the situation demands. This sassy robotic lady helps extend the brand with customers while quickly solving everyday challenges such as concierge and housekeeping duties.

These chatbots appeal to individuals who are already using Facebook messenger or other programs such as WhatsApp to chat with friends, as they more seamlessly integrate to the tasks that customers need. Facebook now boasts over 100,000 bots that are actively chatting with customers and the continued innovation helps drive market interest and adoption. While interesting for basic needs where the conversation is unlikely to branch, AI is still in its infancy and many organizations are simply in beta testing or playing with chatbots instead of relying on them to perform critical business functions.

Integrating Chatbots with Your DXP or CMS Platform

Chatbots are not only exceptionally cool, but they can also integrate with your Digital Experience Platform (DXP) or website Content Management System (CMS) to deliver the ultimate in personalized experiences. This is especially true of organizations with an eCommerce component, as businesses are seeing double-digit sales and conversion rate improvements from chatbots versus social ads, for instance.

It’s important that chatbots are not treated as a siloed part of your marketing strategy, but instead are fully integrated into the overall experience. Chatbots are another channel for the dissemination of information, and should be fully integrated just as your email marketing and SMS messaging channels are. This is where a thorough knowledge of structured content comes into play. Instead of creating a separate grouping of content for your chatbot, a skilled partner will help you understand how to leverage the content you’re already creating for this fascinating new distribution channel.

Better Experiences?

There may still be some question about whether or not the chatbots offer a truly improved customer experience as opposed to working with a human customer service representative, for example. While chatbots are still relatively limited, they are able to quickly offer status updates, provide balances, let you know of special offers, detail which newsletters you wish to sign up for and complete purchases. As app downloads continue to decline and mobile-first websites grow in prominence, bots are an opportunity to reach customers where they already are: Facebook Messenger with 1 billion users per month, SMS texts and programs such as What’sApp, Slack and Kik. Chatbots do provide the one thing that it can be difficult to deliver in human-to-human interactions: personalization at scale.

 

Machine Learning is State-of-the-Art AI, and It Can Help Enhance the Customer Experience

Posted by on October 05, 2017

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Is artificial intelligence the same as machine learning? Machine learning is really a subset of artificial intelligence, and a more precise way to view it is that it is state-of-the-art AI. Machine learning is a “current application of AI” and is centered around the notion that “we should…give machines access to data and let them learn for themselves.” There is no limit to that data (or Big Data).  The challenge is harnessing it for useful purposes.

In his Forbes Magazine piece, contributor Bernard Marr, describes AI as the “broader concept of machines being able to carry out tasks in a way we would consider ‘smart.’” So, AI is any technique that allows computers to imitate human intelligence through logic, “if-then” rules, decisions trees and its crucial component, machine learning.  Machine learning, as an application of AI, employs abstruse (i.e., difficult to understand) statistical techniques, which improve machine performance through exposure to Big Data.

AI has broad applications…

Companies around the world use AI in information technology, marketing, finance and accounting, and customer service. According to  a Harvard Business Review article, IT garners the lion’s share of popularity in AI activities, ranging in applications that detect and deflect security intrusions, to automating production management work. Beyond security and industry, AI has broad applications in improving customer experiences with automatic ticketing, voice- and face-activated chat bots, and much more.

Machine learning is data analysis on steroids…

AI’s subset, machine learning, automates its own model building. Programmers design and use algorithms that are iterative, in that the models learn by repeated exposure to data. As the models encounter new data, they adapt and learn from previous computations. The repeatable decisions and results are based on experience, and the learning grows exponentially.

The return of machine learning

Having experienced somewhat of a slump in popularity, AI and machine learning have, according to one software industry commentator, Jnan Dash, seen “a sudden rise” in their deployment. Dash points to an acceleration in AI/machine learning technology and a market value jump “from $8B this year to $47B by 2020.”

Machine learning, according to one Baidu scientist will be the “new electricity,” which will transform technology. In other words, AI and machine learning will be to our future economy what electricity was to 20th century industry.

The big players are pushing AI and machine learning. Apple, Google, IBM, Microsoft and social media giants Facebook and Twitter are accelerating promoting machine learning. One Google spokesman, for example, recognizes machine learning as “a core transformative way in which we are rethinking everything we are doing.”

How Machine learning has transformed General Electric…

A striking example of how AI and machine learning are transforming one of the oldest American industries, General Electric, is highlighted in this Forbes piece. Fueled by the power of Big Data, GE has leveraged AI and machine learning in a remarkable—and ongoing—migration from an industrial, consumer products, and financial services firm “to a ‘digital industrial’ company” focusing on the “Industrial Internet.” As a result, GE realized $7 billion in software sales in 2016.

GE cashed in on data analytics and AI “to make sense of the massive volumes of Big Data” captured by its own industrial devices.  Their insights on how the “Internet of Things” and machine connectivity were only the first steps in digital transformation led them to the realization that “making machines smarter required embedding AI into the machines and devices.”

After acquiring the necessary start-up expertise, GE figured out the best ways to collect all that data, analyze it, and generate the insights to make equipment run more efficiently. That, in turn, optimized every operation from supply chain to consumers.

5 ways machine learning can also enhance the customer experience…

Machine learning can integrate business data to achieve big savings and efficiency to enhance customer experiences, by:

  1. Reading a customer’s note and figure out whether the note is a complaint or a compliment
  2. Aggregating the customer’s personal and census information to predict buying preferences
  3. Evaluating a customer’s online shopping history or social media participation and place a new product offering in an email, webpage visit, or social media activity
  4. Intuitively segmenting customers through mass customer data gathering, grouping, and targeting ads for each customer segment
  5. Automating customer contact points with voice- or face-activated “chat bots”

How Rivet Logic can make you future-ready and customer friendly

Your business may be nowhere near the size of General Electric. You do, however, have a level playing field when it comes to leveraging Big Data and machine learning products to a winning strategy. What we do is help you plan that strategy by:

  • Aligning your business goals with technology—What are the sources of your own data and how can they harness the power of NoSQL databases, for example?
  • Designing your user experience—What do you need? A custom user interface, or a mobile app with intuitively simple user interfaces?

We can do that and much more. Contact us and we’ll help make your business future-ready to collect, harvest, and leverage all the great things you are doing now.