Are We Really Ready for Voice Assistant - Why User Engagement is Challenging for Voice Apps

Five Levels of Consumer Awareness

All the way back in 1966, Eugene Schwartz laid out the 5 phases a consumer goes through before they buy a product:

• Completely Unaware: The consumer doesn’t even know that they have a problem, though they have their own ideas and preferences
• Problem-Aware: The consumer is aware that they have some kind of problem, but they’re not aware there are solutions
• Solution-Aware: The consumer is aware that there are solutions to their problem out there, but they don’t know about yours
• Product-Aware: The consumer knows that you sell a product that matches their problem, but they’re not convinced that it will completely solve it or be right for them
• Most Aware: The consumer knows they have a problem, they know solutions exist, and they know your product is a solution—now, they just need to be “closed.”

Where is Voice Adoption on the Awareness Scale?

The answer is: all over the place!

To some degrees, all of us are somewhat familiar with brand names such as Siri, Alexa, Google Assistant or even Cortana. Does it mean, as long as the shiny little speakers make their way into people's living room, we can all go home? 

At the same time, millions of people are still risking their lives and others everyday - texting, typing and swiping on their phone behind a wheel when they should've relied on their voice as a much safer way to command their phone.

Why is User Engagement so Challenging for Voice Assistant?

Voice use cases are many but far in between. Alexa has 100+ skills, same as Google assistant as actions and Hound as domains, although all is not equal.

Adopting Eugene Schwartz's 5 levels of awareness:

• Completely Unaware: Over the holidays Google and Alexa have been educating users on holiday themed use cases such as Play New Year's Mad Libs. I played it and it was fun for the first time but not a use case that sees a typical users more often than once a year.
• Problem-Aware: During one of the in-house user studies, almost all the users acknowledge it is very dangerous to text and drive. 10-20% admitted they're guilty of charge. Are most users not aware that they can use their voice to check messages, reply and launch apps, or the products in market lack appeal one way or another?
• Solution-Aware: Many users know to use their voice on Google map and Waze to get to destinations. These voice add-on features aren't making or breaking the deals though.
• Product-Aware: Philips hue light bulbs are known for being one of the most popular personal wireless lighting solutions, working alongside google homes. The name brand comes to people's mind when you talk about voice controlled lights.
• Most Aware: Streaming music and control home appliances such as lighting on smart speakers. The most commonly known and adopted voice use cases include setting up alarm and inquire weather forecast. They exist in all the voice assistant products, therefore have become a hygiene factor instead of a differentiator.

Summary

In spite of the fact that Amazon & Google manage to convince a dozen million consumers on the initial investment for the hardware, all voice assistant providers still struggle on getting the usage up in volume as well as frequency.

Voice will eventually take over the current UI of type, tap & swipe, but so far, voice assistant providers continue to see screen-involvement & cross-devices to be the engagement key for voice service - counter intuitive I know. This shows us that it will take another few years to see higher adoption and deeper engagement in voice interface as the technology catches up for better UX and more reliable result or action taken upon command.

Read my related growth articles:

Test Improved Conversion over Control by 20%. Deploy? Think Twice.

i18n & l10n - Boost Your Addressable Market by 10X+ 

Why You Should Ignore Everything You Have Been Told About Growth Tactics

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The Rise of Voice and Collective AI

WHAT DO WE WANT?
Natural language processing!
WHEN DO WE WANT IT?
Sorry, when do we want what?

— Benedict Evans (@BenedictEvans) January 22, 2017

Ben Evans is a partner at the renown venture capital firm Andreessen Horowitz (also called a16z). He generously shares his industry insights with the public and has hundreds of thousands of followers on Twitter.

In Ben's recent post Voice and the Uncanny Valley of AI, he started with

Voice is a Big Deal in tech this year. 

The Rise of Voice

Ben attributed the rise of voice to four causes: 

1. The advancement in machine learning reduced the error rate in voice recognition and natural language processing.
2. Better hardware, lower cost.
3. If voice most existed as people's side projects in the past, the resource is abundant today. 
4. The desktop-to-mobile platform evolution has shown, the more engaging UX is, the more control companies have which own these platforms - Apple and Google had more control over the mobile user base than Microsoft ever had with the desktop.

The twitter feed from Ben at the top of the post was mocking at AI not being able to connect dots in a human way. In fact, the ability of today's virtual assistants at handling context in a conversion is continuously increasing. You can ask a series of questions such as:

"What are the 5 star rating restaurants nearby?" 

"How far is the first one?" 

"How much  does it cost for Uber to take me there?"

And product such as Hound handles it like a champion. You don't even need to get out of the app to call for a Uber ride of your choice.

The Era of Sharing Economy, Knowledge & Intelligence

Collective AI came into view as one of the key contributors to the increased AI performance. Collective intelligence is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. What happens when we apply this to building AI?

SoundHound is the leading innovator in voice-enabled AI and conversational intelligence technologies. SoundHound's vision of Collective AI is to provide the tools and platform for developers to build more intelligent solutions easily and rapidly. 

The era of sharing economy, knowledge and intelligence brings a voice-enabled AI platform and its technology to the ISV community - something once only possessed and controlled by name brands such as Google, Apple, Microsoft and Amazon. What a gift!


The closing thought - what's the face of your personal AI? 

For the record, my personal AI definitely has the face of Wall-E. ;o)

Read my related machine learning articles:
Want More Funding? Machine Learning!
How to Build a Recommendation Engine without Machine Learning Experts?

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Want More Funding? Machine Learning!

Crystal ball, what's on the investors' mind?

Parker Barrile posted on What VCs Talk About After Your Pitch yesterday and as suggested in the intriguing subject line, he has let all the entrepreneurs in on one of the most prized secrets in the startup world. 

All businesses chase after product market fit and in the same sense there's no intel more valuable than what investors are in fact looking for when evaluating an investment opportunity. In Parker's article, he pointed out two things to focus on not only in business plans but also in pitch decks:

1. The problem the business is solving
2. Building the moat

And Buffett agrees.

How to build a moat with machine learning

Machine learning can do the heavy lifting for you and turn your big data into a moat around your business castle. 

With machine learning, Netflix has come a long way from a DVD rental business to world's leading internet television network and from a content aggregator to an original content producer. Quite a moonshot accomplishment if you ask me! Youtube is catching up. With all the top influencers and the attached fan base in their pocket, they even have more interesting flavors added into the big data cookbook.

Machine learning can also turn the most bland business ideas into brilliant products that consumers can't live without. I've personally fallen in love with gmail's priority inbox feature. It takes into consideration several dimensions of data features:

1. Social features to examine the relationship between the sender and receiver based on open rate
2. Content & Thread feature to determine the recency and relevancy of email content to what receiver has acted on in the past
3. Label feature indicates preference explicitly expressed by the user

Last but not least, machine learning isn't all science and no art. Gmail team skillfully balances user need on minimizing the false negative rate - users will likely have vastly different levels of emotional response to an urgent email ending up in the junk box vs. a spam message occasionally slips through the priority inbox.

So, ready to adopt machine learning yet, my entrepreneur friends? Or shall I rephrase, ready for a big fat paycheck from your next investor? When you're face to face with investors, discover how to effectively engage an audience and work a room.

No Machine learning expert? No problem. Learn step by step on how to work around it.

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How to Build a Recommendation Engine without Machine Learning Experts?

Want to serve relevant content? YES! 

Have machine learning expert? NO.

Now what?

Before the machine learning era and personalized content, old timers pop the page with a list of most popular items of all time.

The common issues you'd notice with this approach:

1. Content Gets Old - Users finding same content every time they sign onto your service.
2. New Items Never Get Surfaced - Since the leader board may not normalize the popularity by time elapsed since publishing, great new contents have a hard time making it.
3. You Don't Know How to Pick your Battle - If you decide to let the new content take over, your collapsed conversion may give you a heart attack, especially if you have a less active user base.

How do you build a simple recommendation engine with the highest take rate and decent user experience? The answer lies in understanding your user behavior, in this particular case: Usage Pattern. 

Here is step by step to build a simple but high performing recommendation engine

1. Show Only Convertible Items - Remove items from the recommendation list if the user has already taken it in the past, or if a user has clicked through very recently and abandoned the flow thereafter.
2.  Segment your User Base - Analyze the distribution of user activity across a reasonable time period, say 4 weeks. Finding a cut off to group the users into high activity cohort and low activity cohort. For example people who are active once every other day will go to segment A and those who are active only once a week or longer go to segment B. There is no universal formula for that because it varies by the business you are in, use industry benchmark and your own data to make a decision.
3. Balance your Recommendations - Test and observe how segment B respond to best-selling catalog items. Since they don't log in as often, you make sure they see the most bullet proof offerings when they do. On the other hand, offer newer popular contents to users who frequent your shop in general as they have likely consumed fair share of the most popular content.

The above three steps to build an effective recommendation engine is for a catalog of moderate number of genres and/or relatively homogeneous user base. For users that may range from young teenagers to business users that could have zero overlap in their consumption, the above approach is not the best - well I highly doubt you want to serve them with one product anyway.

Follow me on twitter (@qindizhang) or add me to your Google+ circle, my friend! Let me know in the comment what other topics you want to read about!