When a customer comes across an IoT touch point, through which an organization’s content is accessed (or not-accessed) and when the necessary end-user applications are in place – notifications of each of the customer’s interactions with the organization’s content is collected as different types of content activity (or non-activity) events. Once a customer creates and signs onto an organization’s respective account at a particular IoT touch point, an organization can conceivably obtain and keep track of a 360° view of the customer’s experience, by collecting data such as:
+ Content activity statistics – such as content views, clicks, downloads, likes, & shares
+ Video activity – such as recordings of facial expressions, etc.
+ Audio activity – including voice commands, tone of voice, etc.
which can then be stored into an organization’s database, which will then allow machine learning to take place. How this occurs is best summarized by the following SAS Article excerpt:
“Today’s machine learning tasks are tackled in four primary ways through:
1. Machines that need to be taught by example before they can apply the resulting insight to similar tasks
2. Machines that can extrapolate from a general pattern and apply it to other data
3. Machines that can, unsupervised, study data to find patterns, getting better with experience (though never autonomous)
4. Machines that can work with and exploit a given set of rules to move towards a desired outcome”
As the article states, the data analysis performed by machine learning is a key building block of AI. The article makes a key observation around organizations being risk adverse to putting AI to work, centered around a concern for having the human expertise to manage AI adoption:
“Organizations may want to jump on the AI bandwagon because it’s such a hot topic, but they have to identify what they want to do with it,” advises Ainsworth. “By the same token, other people perhaps still have a negative perception of AI, from how it’s often portrayed, such that it can seem overwhelming. But in many cases they’re already leveraging forms of machine learning every day when they run a search on the internet, upload photos to social media or shop online with major retailers. Ultimately, it’s about using the right tool for the right job. AI requires a strategy with clearly defined tactical steps to successfully implement that larger plan. AI can provide valuable insights, but what you do with that information still requires human direction.”
In my next post, I will attempt to provide a little more familiarity with some AI concepts, that can help reduce the hesitancy towards adopting it in an effort to optimize a customer’s connected experience.
Brendan Hukill 