Tag machine learning

Soul in the Machine – Can we avoid the Google Duplex outrage in Conversational AI development?

Jason St-Cyr Leave a comment

This is Part 5 in the Soul in the Machine series.

In the Soul in the Machine series, I will be delving into our collective responsibility to ensure that computing systems are treating users fairly and responsibly. Specifically, I will be raising the ethical questions around current trends such as data privacy regulations and machine learning capabilities.

Right now, in Part 5, we are going to look at some reactions to conversational AI technologies, Responsible AI, and the line between CAN and SHOULD.

Should we build this?

In a previous article, I highlighted the need for teams to stop and ask if they SHOULD be doing something. Let us take an example from Google Duplex.

In this demo from Google, the caller was the AI, but honestly, it could easily have been the restaurant answering with an AI. There is another example of Google Duplex where the assistant books a hairdresser appointment, and others where it can handle interruptions and resume the conversation.

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Preparing for AI-driven customer experiences

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Digital marketers today are caught in the middle of a huge digital transition.

Behind them lie the old, slow, and manual methods of creating content, connecting with customers, tracking engagement, and measuring the results.

Ahead of them is the promise of the brave new intelligence-enabled world, teeming with possibilities—task automation, massive-scale analytics, and real-time, data-driven decision making powered by Artificial Intelligence, Machine Learning, and Deep Learning solutions.

For the most part, these promises have been made in good faith. Connecting clean, structured, and tagged data with powerful machine learning algorithms is an exciting prospect brimming with interesting use cases like:

  • Automated content tagging
  • Self-assembling web pages
  • Dynamic audience discovery
  • Predictive scenarios and best next steps

However, for many, this bold new future of seemingly magical efficiencies might be further away than it seems. The industry is currently packed with pundits and vendors getting excited about what might be possible with AI without thinking about the steps needed to get there. While the benefits of becoming an AI-enabled marketer are clear, the journey a marketer and their team needs to take is still hazy.

Read more on sitecore.com

Soul in the Machine – Combatting Unconscious Bias

Jason St-Cyr Leave a comment

This is Part 4 in the Soul in the Machine series.

In the Soul in the Machine series, I will be delving into our collective responsibility to ensure that computing systems are treating users fairly and responsibly. Specifically, I will be raising the ethical questions around current trends such as data privacy regulations and machine learning capabilities.

Right now, in Part 4, we are going to look at building a diverse team capable of implementing ethical AI and ML solutions. How do we ensure that the culture of integrity we are working so hard to support leads to fair results in our machine learning? How can we be conscious about battling unconscious bias in the solutions we develop?

Two heads are better than one

Building up the culture of integrity in an organization allows us to start asking questions about what we’re doing, and how we’re doing it, but a critical step is building a team that is capable of seeing WHEN these questions need to be asked.

Continue reading “Soul in the Machine – Combatting Unconscious Bias” →

Soul in the Machine – Top AI/ML Orgs and Unconscious Bias

Jason St-Cyr 1 Comment

This is Part 3 in the Soul in the Machine series.

In the Soul in the Machine series, I will be delving into our collective responsibility to ensure that computing systems are treating users fairly and responsibly. Specifically, I will be raising the ethical questions around current trends such as data privacy regulations and machine learning capabilities.

Right now, in Part 3, we are going to quickly look at the top organizations that are investing in the development of Artificial Intelligence, Machine Learning, and Deep Learning.

Continue reading “Soul in the Machine – Top AI/ML Orgs and Unconscious Bias” →

Continuous Improvement for Customer Engagement

Jason St-Cyr Leave a comment

Sitecore’s Customer Experience Maturity Model has been around for a while and it is still as relevant today as it was when it was first released. The model helps a team to understand where they are right now and where they could possibly be with customer engagement.

However, it isn’t easy to keep improving. I’ve written a lot in the past about using baby steps to continuously improve your software delivery. The same is true for marketing efforts. As teams we often focus on big-bang improvements: a new redesign, content re-work, big integrations to our back-end systems to better leverage our digital assets. All of this is awesome work to get us started.

Unfortunately, it often stops there. We lose the momentum to keep iterating and improving on how we engage with our customers. We need to have a plan that will allow for small incremental changes to our content and marketing automation. This ensures that we can give customers a better experience and ultimately help them with their problems.

How do we increment?

  1. Analytics. First off, we need to be tracking data. Whatever your analytics platform, make sure you know what’s happening on your site. Also, if you are using Sitecore xDB, make sure you have your xDB tracking enabled. You’ll need that data in the xDB for later steps.
  2. Content Tagging. Learning about visits is great, but we need to know more about what type of content engage people. Start by adding a few tags so you can get a baseline to learn about user behaviour.
  3. A/B and Multivariate testing. Once we have learned a little about the types of content our visitors view, we can start focusing on which variations of content are working for our visitors. This testing will greatly inform further improvements we make to the site. Keep it small, just a few tests so you can manage it.
  4. Personalization. Add a few rules-based personalizations into your website based on what you are seeing from analytics and results of testing. Again, focus on some big win personalizations: a hero banner, or a call-to-action button. Sitecore can even suggest personalizations based on analysis of your tests.
  5. Engagement Levels and Campaigns. At this point, we should be learning enough about our customers to start determining their engagement and moving them through a campaign. Our personalizations should start becoming less rules-based and more about where they are in our engagement level. Leverage your calls-to-action!
  6. Omni-channel. Start reaching out to your customers in different ways. Add a mobile app. Tap into your onsite kiosk. Develop email campaigns. Build a game app to complement your messaging. Keep making iterative improvements to add more sources to feed information back to your central xDB data. How can we reach the customer in a new way?
  7. Machine learning. At a certain point in the continuous improvement cycle, the data is just getting too big to handle by hand. Automation is required to process the information and look for trends. Microsoft Cognitive Services is one way to start adding some intelligence to the mix! You can read more on machine learning Sitecore’s “The Mind in the Machine” series.
  8. Learn. At this point, we probably need to take a moment to learn about what worked well and what didn’t. Time to improve our tests and personalization with the new information we have!

Want to see the original Customer Experience Maturity Model information? Check out the full PDF doc here:

Sitecore Customer Experience Maturity Model