Month: October 2017

How Digital Experience Management Differs from Content Management

Posted by on October 12, 2017

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When considering Digital Experience Management and Content Management, it’s best to have a concrete definition of both terms to fully understand how they differ.

What is digital experience?

Digital experience includes a number of things, including communications, processes and products from every digital aspect that engages an audience. This includes wearables, use of the web and mobile devices, beacons and recognition. The information gathered is analyzed to provide insight into customer relationships, identity, and intentions as they interact with businesses and organizations. This helps determine how companies deliver these digital experiences for their customers for future success.

What is content management?

Content management is also known as CM or CMS. It involves the collection, acquisition, editing, tracking, access and delivery of both structured and unstructured digital information. This content includes business records, financial data, customer service data, images, video, marketing information and other digital information.

With content management, you create and manage content, finding ways to generate awareness across multiple channels to reach more people. While having content is good, it’s more about offering the entire “experience” to the user that will give them more enhanced, enriched engagement. Content Management Systems have continuously evolved, integrating contextual digital experiences. This requires a comprehensive and effective strategy, the right tools, the right approach, and most of all, the right technology. An optimal digital experience embraces all these elements to provide personalized, responsive, and consistent experiences for every user you engage.

How is this done?

Digital experience (DX) management works in conjunction with content management, but is more comprehensive and fulfilling to the user. Think about the different outlets that engage customers – websites, social media, microsites, text messages, mobile apps and more. All these elements offer a complete digital experience. The processes and technology that provide these customized, consistent experiences is the management of it all.

One of the best ways they differ is that in digital experience management, the distribution channels all have objectives to follow and limitations. These help drive specific requirements for content and how to manage it. For instance, your tone and CTA will be different based on the digital platform you use. Additionally, when interacting with content, users want personalized experiences based on analytics you have determined appropriate for that channel. This allows them to seamlessly interact within that experience.

Web publishing used to be the first line of engagement, but not anymore There are too many channels users interact with that require ways in which publishers can gather feedback to quickly adjust their content. Without this management model in place, the system will not work.

Tools of the trade

There are a number of tools and systems to manage the digital experience. There are options for advanced analytics, to enhanced marketing tools that manage content based on channel. There’s also an emerging breed of Digital Experience Platforms (DXP), which provide businesses with an architecture for delivering consistent and connected customer experiences across channels, while gathering valuable insight and digitizing business operations.

When you have systems that work well together, being able to track successes becomes easier. When determining which tools will work best, you may want to start with product mapping. As a basis, the digital experience tool should include a combination of inbound marketing automation, analytics, and content management. Getting a system developed to meet all your needs is key.

As different avenues of engagement now drive the customer experience rather than the web, delivering a comprehensive and holistic experience is key. The digital experience is more complete, diverse and authentic – future thinking, while integrating content is how it should be done.

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.