Siri is a house­hold name despite not being a real person. It goes without saying that voice tech­nol­ogy is here to stay, but what does that mean for data analy­sis? This blog post will help your orga­ni­za­tion under­stand and imple­ment voice analyt­ics.

Voice technology: a short history

Voice inter­ac­tion with comput­ers has been around for a while, popu­lar­ized early on by Dragon Dicta­tion. However, it didn’t go main­stream until Apple Siri hit the market. And now-a-days, it’s stan­dard for smart phones to respond to commands in the blink of an eye.

Siri and its alter­na­tives certainly make intel­li­gent personal assis­tants. That being said, they are still discon­nected from a direct revenue stream. Enter Alexa Voice Services (Amazon). With Alexa, you can purchase anything avail­able on Amazon using just your voice and an Echo. “Alexa, add cookies to my shop­ping cart.” “Alexa, play the sound­track to Stranger Things.” ‘Alexa, do this and that.’ Your house can constantly be waiting for your next request!

Luckily, Alexa and other voice plat­forms are opening up to 3rd party devel­op­ers. As a result, your busi­ness could start engag­ing with your users/customers without them ever needing a phone or computer. The future is great!

But what does the future mean for analytics?

As digital marketers and analysts, data is our sword and shield. When mobile apps first came out, we didn’t know what to do, leaving us rather exposed. We had to adapt the solu­tions that we already knew (web analyt­ics) to this new world.

Now, we are in the same posi­tion again with voice analyt­ics. We are unsure how to adapt, and end up making best guesses on solu­tions to new prob­lems. If users don’t use phones or comput­ers to engage with your busi­ness, then tradi­tional measure­ments become useless. Track­ing site refer­rals, down­loads, and video plays? All irrel­e­vant. Perhaps the future is not so great.

The silver lining

This all may sound bleak, but voice analyt­ics aren’t as compli­cated as they sound. It helps to compare it to an inter­ac­tive voice response system (IVR). We are all famil­iar with ringing up a call center. You end up trapped in the night­mare of an IVR system, mashing the “0” button in hopes of getting to a human. But as annoy­ing as talking to a computer may be, the solu­tions imple­mented in an IVR are some­times useful. At minimum, they can read a caller’s account history and inter­pret the tone/temperament of the the caller.

The problem? These solu­tions just route you to the right person or area of the IVR. In other words, they can’t collect data in a mean­ing­ful way.

Now imagine that you could collect voice analyt­ics on each indi­vid­ual user (or in aggre­gate, for you privacy cautious folks). Think about the ques­tions you would need answered. What was the user trying to get help with? How did they ask their ques­tion? What response was most effec­tive in resolv­ing their inter­ac­tion?

Due to a matur­ing analyt­ics market, there are analyt­ics prod­ucts that can answer these ques­tions. Thanks to flex­i­ble offer­ings like Google Univer­sal Analyt­ics, Mixpanel, and other event driven analyt­ics solu­tions, you can analyze users’ voice queries within your analyt­ics soft­ware.
Speech Analytics

Voice analytics example implementation

Your devel­op­ers can include voice analyt­ics code like they normally would other event driven analyt­ics solu­tions. Even better, they can do it in a language they already use, such as Node.js.

We’ll use Alexa Skills as an example.

In the devel­op­ment envi­ron­ment, run the follow­ing:
Google Univer­sal Analyt­ics: (g)
npm install universal-analytics --save

Mixpanel: (m)
npm install mixpanel --save

Include and adjust accord­ingly in your appli­ca­tion index.js file:
(g) var ua = require('universal-analytics');
(g) var gUA = ua('UA-XXXX-XX'); // your Tracking-ID

(m) var Mixpanel = require('mixpanel');
(m) var mixpanel = Mixpanel.init('aaa111bbb222); // your Token

Track­ing Events
If the user was not under­stood:
(g) gUA.event("user error","misunderstood statement").send();
(m) mixpanel.track("user error",{result: "misunderstood statement"});

A success­ful query:
var utteranceData = ("intent: " + utteranceValue).toString();
(g) gUA.event("user query","successful query", {query: utteranceData});
(m) mixpanel.track("successful query", {query: utteranceData});

A failed query:
var utteranceData = ("intent: " + utteranceValue).toString();
(g) gUA.event("user query","failed query", utteranceData).send();
(m) mixpanel.track("failed query", {query: utteranceData});

Capture bugs in your Intent:
var veryBad = false;
(g) gUA.exception("out of memory", veryBad).send();
(g) gUA.event("intent error","out of memory", "fatal: " + veryBad).send()
(m) mixpanel.track("intent error", {error: "out of memory", fatal: veryBad});

User ends session/engagement:
(g) gUA.event("exist","session ended).send();
(m) mixpanel.track("session ended");

Of course, there are some pretty substan­tial differ­ences among voice plat­forms. Program­ming languages and user iden­ti­fi­ca­tion in partic­u­lar tend to vary. But it’s nothing some app-based modi­fi­ca­tions can’t fix.

Now that your devel­op­ment team is build­ing appli­ca­tions with voice inter­ac­tion, each of those inter­ac­tions can be sent to your analyt­ics solu­tion. You’ll be well on your way to under­stand­ing the user expe­ri­ence in this new tech­nol­ogy!

If you’d like to learn more about analyt­ics for non-web centric tech­nolo­gies like apps, mobile apps, voice tech­nolo­gies and more, get your free ticket to ObservePoint’s Analyt­ics Summit. I’ll be hosting a session called “Hey Siri, What are App Analyt­ics?”, where you’ll get a crash course in track­ing data for these emerg­ing tech­nolo­gies.