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Leveraging xAPI to deliver Customer 360 Applications & Insights

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Two years back, we accidentally stumbled upon Experience API (xAPI), an e-learning specification developed by Advanced Distributed Learning (https://www.adlnet.gov/xapi/). The standard allows learning content and learning systems to speak to each other and track all types of learning experiences in a consistent way.

 

In a nutshell, every single learning experience is captured as a STATEMENT; a standardized JSON that explains “Who did What”. A simple example is below:

 

Let’s say Adam Gordon experienced “Augmented Reality”, then, that can be captured as below:

 

{

  "actor": {

    "name": “Adam Gordon“,

    "mbox": "mailto:adam@example.com"

  },

  "verb": {

    "id": "http://adlnet.gov/expapi/verbs/experienced",

    "display": { "en-US": "experienced" }

  },

  "object": {

    "id": "http://example.com/activities/AugmentedReality”,

    "definition": {

      "name": { "en-US": “Augmented Reality“ }

    }

  }

}

We have been using the xAPI in our learning modules and we are loving it. The flexibility of JSON and the standards-based framework is giving us the power to understand end users based on their usage patterns and deliver beautiful learning experiences. Coming from CX background, we started thinking what if we leverage the Statement concept to address challenges in the CX world. As most of you in the space are aware, a key ask from customer facing stakeholders is for the ability to have a 360 Degree View of the customer. However, the systems are often siloed and make the 360 degree view a distant dream. Of course, there can be a centralized data warehouse that houses all kinds of customer interactions; this is great but in reality, these are massive projects that take a significant amount of time and money! In an agile world, we want everything now.

 

So as a proof of concept, we started expanding statements to capture not just Learning Experiences but also Customer Interactions. The statements can capture a number of other attributes/parameters but consider the following statements below:

 

“Customer1” attended an “Event”. This can be expressed as:

 

{

  "actor": {

    "name": “Customer1“,

    "mbox": "mailto:customer1@example.com"

  },

  "verb": {

    "id": "http://adlnet.gov/expapi/verbs/experienced",

    "display": { "en-US": “attended” }

  },

  "object": {

    "id": "http://example.com/activities/event",

    "definition": {

      "name": { "en-US": “Event“ }

    }

  }

Similarly, each interaction can be captured easily in the form of simple statements. Interaction specific statements (e.g Customer placed an order,  Customer saw a rep, Customer called the Call Center etc) can be pushed into an LRS compliant Mongo Database using simple API and later be consumed by downstream applications. This will drastically simplify analytics model and provide a strong foundation to deliver remarkable enterprise applications for all customer facing stakeholders. Most importantly, statements are business friendly and have the power to put the customer at the center!

In a recent pilot, my team leveraged Amazon Lambda to monitor enterprise platforms, social media for Orders, Activities, twitter feeds and other possible interactions, then push them into a Google Firebase from where real-time apps can consume and display the latest and greatest information to the right users at the right time!

So far, our pilot has been going great because we are able to capture the gist of every interaction easily quickly with JSON driven statements. Only time will tell if the approach is right or wrong but here at Focal, we do not hesitate to make mistakes and learn from them. We also envision working with ADL to expand the vocabulary to leverage the same framework to include more nouns, verbs, and objects that can potentially fit into the learning eco-system as well. 

If you are interested in learning more about xAPI, refer to:

http://tincanapi.com/

https://www.adlnet.gov/xapi

 

 

 

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Tagged in: Agility amazon CRM xAPI

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