Tuesday, July 11, 2017

Building a "Job Finder" skill for my Amazon Alexa Device

Growth


According to a recent NPR and Edison Research Smart Audio Report, 5% of the 1,620 surveyed respondents owned an Alexa device (Echo or Echo Dot) and 2% owned a Google Home device. Even more revealing was the fact that 42% of Smart Speaker owners said that "their Smart Speakers are essential to their everyday lives". By any stretch Smart Speakers have reached a significant level of product/market fit and in a little over 2 years Amazon Alexa devices are leading the pack and growing at a rapid pace:

Skill


I decided to build my first Alexa skill to better understand the underlying Alexa Skills Kit that powers all Amazon Echo devices to give users this new intuitive voice interface. I was particularly interested in voice-user interface (VUI) design and how easy it would be to go from idea to building and then deploying a new skill. For the actual implementation I decided to use the new Flask-Ask framework by John Wheeler to build the skill along with using Zappa to deploy my Python code into AWS Lambda

I settled on a simple idea for a "job finder" skill that allows users to answer 2 simple questions that Alexa asks them about which city and job role they were interested in. The skill would then return the most relevant job listings that had been posted in the last 24 hours according to the answers given. I imagined users waking up in the morning with their coffee in hand and asking their Alexa "job finder" skill to get the latest updates for their ongoing job search. If the skill responded with some particularly timely and relevant jobs, the user could then decide to immediately open their laptop and apply for those jobs or, if not, the user could simply continue to enjoy their coffee and resume the job search later on in the day.

Implementation


I spent a few evenings coding up the "job finder" skill's python backend and configuring the interaction model (the intent schema and sample utterances) to get it working locally. I ended up with a single job_finder.py file for the bulk of my application logic which is only 116 lines of code and a template.yaml file that contains a number of the templated launch, answer and re-prompt responses which are required for the skill. All-in-all the learning curve was reasonable and most of the learning came from understanding the new type of interaction model set out by the Alexa Skills Kit.

Demo


As of writing this post the "job finder" skill is being reviewed by Amazon's Alexa's team and therefore is unavailable at present to enable on your own Amazon Echo device (the skill should be certified in a week or less). In the meantime I've recorded a demo using Amazon Alexa Testing Tool called Echosim.io:


Learnings


  • I've capped the number of jobs Alexa responds with to a maximum of 5. I think this is a manageable amount but given that Alexa might misunderstand the job role given by the user, it could be frustrating to have to listen to all 5 jobs if they are based on an incorrect interpretation. Therefore I would potentially improve the skill's VUI to either have Alexa repeat the job role specified by the user before all 5 jobs are listed out OR I would pause Alexa after the first job listed and ask the user a yes/no question on whether Alexa should continue or not.
  • I've purposefully kept each job listing very brief so it's easy for users to consume while listening but it might be useful for users to get more detailed information. I could improve the skill's VUI by asking a follow-up question at the end of listing the 5 jobs to know whether or not the user wanted more details about any or all of the jobs listed.
  • I'm using the AMAZON.ProfessionalType intent slot and this is currently only supported in the US. Therefore when the skill is certified by the Amazon team it will only be available to US based Alexa devices. I've asked about UK support for the AMAZON.ProfessionalType intent slots but have not yet received information about when UK support for it will be available. 
  • Analytics are very important for knowing what is and is not working from a user's perspective. I often use tools like Google Analytics for websites and Mixpanel/Amplitude for mobile apps which have been very insightful to better understand user behaviour. For my Alexa skill I'd like to add in something like Dashbot analytics to get a window into what users are and are not doing with the skill.
  • Most importantly I would like to find ways to improve the VUI for subsequent invocations of the "job finder" skill. If a user has already performed a prior job search then I'd like to be able to have search saved against their user profile and then have the skill look it up and ask the user whether or not it should be re-used. This will reduce the number of questions Alexa asks the user upon each new "job finder" skill invocation. Such functionality can be accomplished by identifying users by their UserId obtained from the session and then querying a database for prior jobs searches to re-use.

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