5 best practices for implementing voice marketing in 2018

Hey Alexa, play some music.
Ok, Google, turn on the lights.

Five years ago, these commands would have made no sense. But for the past two and a half years, voice-enabled speakers have steadily gained traction, introducing the world to voice-activated technologies. As we approach 2018, there’s no sign of slowing down the smart speaker revolution. Continue reading “5 best practices for implementing voice marketing in 2018”

AI might just live up to all the hype one day

If AI isn’t the most hyped technology of the 21st century, it’s certainly right up there with earlier manias for mobile, virtual reality, the internet of things, and big data. Companies large and small feel pressure to claim they use AI in some key way to drive their business. But does AI deserve this level of hype? On one end of the spectrum are the doomsayers (including heavyweights like Stephen Hawking and Elon Musk) who see the technology posing an existential threat to the future of humanity. In contrast, there are those who see AI as the breakthrough that could solve many of the world’s most intractable problems. Visionary Ray Kurzweil believes AI will soon enhance virtually everyone’s mental capabilities. Musk’s own startup, Neuralink, is reportedly developing a brain-to-machine interface that could improve memory or allow for more direct interfacing with computing devices. Continue reading “AI might just live up to all the hype one day”

Can Facebook Messenger help you build a deeper relationship with your audience?

Maybe, but it’s a lot of work. Here’s what Annenberg Media tried.

Chatbots seem to be popping up everywhere. You can find them on chat apps, on your SMS platform or in apps of their own. If you’ve used Siri or Amazon’s Alexa, you’ve used a chatbot.

Many news organization use chatbots to deliver the news, but few use it to have a conversation. The options for what a user can do beyond asking for the organizations’ top stories tends to be fairly limited.

Continue reading “Can Facebook Messenger help you build a deeper relationship with your audience?”

SEO is not enough in the age of voice

With so many technological innovations now transforming our lives, it should be noted that the ideas for these innovations have existed for decades in science fiction novels and television. The capacity to talk to a computer (and have it talk back) was a staple of Gene Roddenberry’s, Star Trek, where the Starfleet computer was voiced by Roddenberry’s wife, Majel. The 1970 movie, Colossus: The Forbin Project, featured a supercomputer that was intended to prevent war and proclaimed itself “the voice of World Control.” And before Google’s self-driving cars, the 1980s brought us KITT, an advanced artificially intelligent, self-aware, and nearly indestructible car from the TV show, Knight Rider. Continue reading “SEO is not enough in the age of voice”

Why the best approach to voice marketing might be nothing at all


Seemingly overnight, capable voice recognition joined forces with artificial intelligence and machine learning to push voice-enabled experiences to the forefront of business thinking. But before brands leap into the fray, they need to recognize where and when to invest in the new technology. For some, that means investing elsewhere, for now. Continue reading “Why the best approach to voice marketing might be nothing at all”

Social bots are ruining the internet for the rest of us


We’ve all seen the stories and allegations of Russian bots manipulating the 2016 U.S. presidential election and, most recently, hijacking the FCC debate on net neutrality. Yet far from such high stakes arenas, there’s good reason to believe these automated pests are also contaminating data used by firms and governments to understand who we (the humans) are, as well as what we like and need with regard to a broad range of things.

Let me explain. Continue reading “Social bots are ruining the internet for the rest of us”

Introducing the Pinterest chat extension and bot for Messenger

Hayder Casey, Pinterest engineering manager, Growth

More than 200 million people use Pinterest every month to find ideas to try, from recipes to gifts to decor for the home. Family and friends are often a big part of these plans, and that’s why nearly 1M Pins are shared to Facebook Messenger each week. Today we’re making it easier for Pinners to collaborate with others on Messenger by launching a new chat extension and bot.

Continue reading “Introducing the Pinterest chat extension and bot for Messenger”

3 sci-fi movies that teach you to love your new AI overlords

If you’ve seen The Matrix, Terminator, or WarGames, you’ve probably already planned out your bunker for the post-AI apocalypse. Technology is continually advancing: Television is moving to the internet, the internet is reaching the remotest areas of the world, and the globe may soon be covered in actual robots.

The eventual takeover of AI has been prophesized by hundreds of books, films, and TV shows, but a few brave directors see a brighter future for humans and robots. These three films show a positive side to the rise of AI, and I’m 90 percent sure they weren’t produced by robots. (Spoilers ahead!) Continue reading “3 sci-fi movies that teach you to love your new AI overlords”

Amazon Alexa skills to accept payments

Developers and businesses making skills for Amazon’s Alexa will soon be able to accept Amazon Pay and make purchases directly within voice apps from the Alexa Skills Store. The news was announced today during the Alexa State of the Union at AWS re:Invent in Las Vegas. Other Alexa news shared today includes plans to bring Alexa to Australia and New Zealand in early 2018 and adding $100 million to the Alexa Fund for international investment. Continue reading “Amazon Alexa skills to accept payments”

Tech giants are using open source frameworks to dominate the AI community


Tech giants such as Google and Baidu spent from $20 billion to $30 billion on AI last year, according to the recent McKinsey Global Institute Study. Out of this wealth, 90 percent fueled R&D and deployment, and 10 percent went toward AI acquisitions.

Research plays a crucial role in the AI movement, and tech giants have to do everything in their power to seem viable to the AI community. AI is mostly based on research advances and state-of-the-art technology, which is advancing very quickly. Therefore, there is no business need to make closed infrastructure solutions, because within a few months everything will be totally different.

In such a situation, the only winning strategy for tech giants is to offer open source solutions to attract members of the AI community and eventually become part of the AI community themselves. This is a relatively new model in the tech industry. Continue reading “Tech giants are using open source frameworks to dominate the AI community”

AI could help solve the world’s healthcare problems at scale


In a world with limited doctors, emerging diseases and superbugs, and sharply rising healthcare costs, how can we successfully tackle healthcare problems at scale?
This is just one of the critical challenges India’s explosive startup community hopes to solve by implementing AI in new and innovative ways to serve the needs of 1.324 billion citizens. This is a feat that carries huge implications for the US and other healthcare ecosystems around the globe.

To understand how dire the situation is, it’s worth considering India’s health paradox. The country’s deep demographic dividend — which occurs when the majority of a country’s population consists of working-age individuals — is driving rapid and unprecedented growth, but it is also a ticking time bomb. With an average age of 27, India has one of the youngest and most educated populations in the world. Since 1991, this phenomenon has fueled approximately 7% annual growth, produced new goods and services, and reduced dependents in the economy.

But in order to keep reaping the benefits of this dividend, India’s young population needs to have access to quality nutrition and healthcare. In addition, as the dividend declines (as we are witnessing in China), the country will need new infrastructure in place to care for its aging population. And unfortunately, the infrastructure that is necessary doesn’t exist today.

The doctor-to-patient ratio in India is one of the worst in the world, with just 0.2 doctors for every 1,000 Indians (for comparison, there are 1.1 doctors for every 1,000 Americans in the US). Modern medical facilities — and as a result, doctors — are heavily concentrated in urban areas. In addition to heart disease, cancer, and is so bad, for instance, that it was deemed equivalent to smoking 44 cigarettes per day.

The fundamental reason behind India’s healthcare issues is resource scarcity. India needs more medical facilities and more medical expertise, and both of these require time and billions of dollars to develop. But such resources are not easily obtainable, so we must consider other ways to dramatically increase access to existing resources in an effective and inexpensive way.

This is where AI has the potential to reshape India’s healthcare problem. Manu Rekhi, Managing Director of Inventus, says, “Indian AI platform companies are building upon two decades of India’s IT industry expertise. They are supercharging how software and human intelligence can partner to create new human-in-the-loop AI systems for global markets as well as the bottom of the pyramid.”

Indeed, a number of Indian startups have implemented deep AI expertise to move the needle on specific health conditions and disease. In some cases, these companies offer technology and distribution opportunities, which attract Fortune 500 giants to partner with them both for the India market and globally.

One such company is Tricog Health, a startup handpicked by GE’s healthcare accelerator program for its cloud-based cardiac diagnosis platform. Coronary heart disease is increasingly prevalent in India, having escalated from causing 26% of adult deaths in 2003 to 32% in 2013. Tricog increases access to cardiac care across 340 cities in 23 states, including in some of the most remote locations in India. The company’s platform collects physiological data and ECGs from medical devices in the field then uses specialized AI to process the data in real-time and provide a diagnosis to the cardiologist.The cardiologist then reviews and recommends next steps to the GP or nurse in the field instantaneously using the Tricog mobile app. By using Tricog’s AI engine, a few specialists can diagnose over 20,000 patients.

Another startup, Bengaluru-based Aindra Systems, is using AI to tackle cervical cancer, which is the second most common cancer in Indian women between the ages of 15 and 60. In fact, India represents a whopping one-third of the total global incidences of cervical cancer. Aindra’s solution can detect cervical cancer in its early stages and measurably increase the odds of survival. The company increases the productivity of pathologists who screen cervical cancer samples, who otherwise typically need to manually examine each sample and flag cases with a high cancer probability to an oncologist for further review.

Adarsh Natarajan, Founder and CEO of Aindra Systems, says “Our vision is to implement mass cervical cancer screening using AI, and help the 330 million Indian women in the at-risk age bracket. With early detection, up to 90% of deaths can be avoided with appropriate treatment. Aindra’s computational pathology platform includes an affordable and portable, ‘point-of-care’ cervical cancer screening device to automate deep learning analysis and bring down the screening time significantly to help detect cancer at an early stage.”

The AI boom in healthcare is just starting, and the up-and-coming list of players is endless. Niramai is working on early detection of breast cancer. Ten3T is providing remote health monitoring services via AI to detect anomalies and alert the doctor. HealthifyMe, a Bangalore startup, is working on lifestyle diseases like obesity, hypertension, and diabetes. With its AI-enabled nutrition coach, Ria, HealthifyMe brings the best of elite nutrition expertise with AI in the loop.

And of course, global corporate leaders like Google bring their capabilities to India as well. Google recently partnered with Aravind Eye Hospitals to use image recognition algorithms to detect early signs of diabetic retinopathy, an eye disease that can cause blindness in diabetics if not treated early. Aravind Eye Hospitals is the largest eye care group in the world, having treated 32 million patients and performed 4 million surgeries. They have provided 128,000 retinal images to Google that have been invaluable for the application of AI to detect diabetic retinopathy in 415 million at-risk diabetic patients worldwide.

With a bevy of solutions on the rise, India is poised to leapfrog some of the key barriers of conventional healthcare, which of course has profound implications for healthcare delivery in other countries, including the US. With rising costs and unfavorable government policies, an increasing number of people are priced out of access. The burden on emergency rooms across the country is increasing as more people are unable to afford preventative care at primary care centers. AI-assisted technologies could reduce the costs in the US using the same mechanism — affordably scaling access to millions of people.

These startup-driven innovations and global platforms are just the tip of the iceberg. AI can ultimately become a force multiplier in bringing preventative healthcare facilities to anyone and everyone, rather than just urban or affluent communities. As you’ll often hear AI experts say, “more data beats better algorithms.” In other words, simpler algorithms only need a larger training dataset to generate accurate, valuable predictions for both payers and providers. With 1.3 billion citizens, India has the potential to provide the vast amounts of data needed to improve the accuracy and precision of our algorithms and empower both startups and large companies to help solve healthcare problems around the world.

Pranav Deshpande works with Startup Bridge at Stanford, an annual conference held in December at Stanford where top technology innovators from India and Silicon Valley build strategic partnerships to innovate for the world.  

7 metrics for monitoring your chatbot’s performance

Researchers estimate we will speak to chatbots more than we speak to our spouses by 2020. Obviously, companies that implement chatbots are doing something right. However, businesses still have a hard time determining whether or not their bots are up to snuff. While there are plenty of effective chatbots on the market, there are also many that don’t quite meet consumers’ needs. So how do you measure the success of your chatbot?

This is the dilemma facing an increasing number of companies that use chatbots as part of their customer experience. 80 percent of businesses want to implement a chatbot by 2020, but many still face the challenge of gauging the efficacy of the technology.

Google’s Chatbot Analytics platform recently opened up to all, but it is still necessary for businesses to develop and understand their own chatbot success metrics to effectively use the platform.

The process of defining the best KPIs for your company’s bot will depend on your business goals and the functions you want your bot to perform.

Here are seven metrics of success you can use to identify opportunities for improvement in your company’s chatbot.

Revenue growth

The first question any prospective investor wants to know about a company is whether or not it makes money. Therefore, the best indicator of a chatbot’s value is its financial benefit.

There are many ways to evaluate a bot’s impact on revenue – the best one for your bot will depend on its purpose. Another interesting wrinkle is that your chatbot can have a knock-on effect on a number of areas.

For example, you can measure a customer service bot’s profitability growth by the amount of money it saves the company compared to maintaining a customer service team 24/7. But you will want to take the bot’s impact on customer service into account. If self-service rates are higher and clients are more satisfied, that will result in repeat customers and higher online sales, thus impacting top-line revenue growth.

Self-service rate

Nirvana comes for businesses the moment a user gets exactly what they want from the chatbot without any human input.

If your chatbot’s goal is to change a user’s password, you would measure success by the percentage of user interactions that end with this as a result.

The self-service rate closely correlates the cost savings aspect of revenue growth – in other words, how much money did your chatbot save?

Satisfaction rate

What better way to find out exactly how well your chatbot is doing than to ask the very people who use it?

Your chatbot can help you determine this metric by asking the key question for the Net Promoter Score – “On a scale of 1-10 how likely is it that you would recommend our chatbot to a friend/colleague?” As a lead indicator of growth, the NPS provides a crucial foundation for understanding your chatbot’s customer experience performance.

Activation rate

At this point, it’s worth reflecting on AARRR and its importance in measuring the success of your business.

The activation rate in the context of a chatbot refers to when a user responds to its initial message with a question or an answer which is relevant to your business goals.

For example, a chatbot designed to provide you with weather updates would receive an activation rate when you enter your location – thus allowing the bot to provide you with the information.

How can this KPI help? If for some reason people were not responding when the weather chatbot first reached out to them, the botmaster would be able to tinker with it to enable a more satisfactory outcome.

Confusion triggers

Unfortunately, even bots with the most robust natural language processing are unable to understand everything a user says.

These errors are a useful indicator for measuring whether or not you need to improve your chatbot’s matching.

Bear in mind there are three different triggers, each of which necessitates its own type of response.

There is first the simple confusion from the bot if it cannot understand a comment. A basic “Sorry, I didn’t understand that. Can you ask again in a different way?” response would suffice.

Second is if the user sends a number of messages which are outside the remit of your chatbot. After a couple of attempts, it would be worth programming your bot to relay a message that reminds the user of its exact purpose.

The final trigger is if the bot forces a user to speak to a customer service agent after the interaction. Each of these will tell you something different about how your chat agent is performing.

Retention rate

Once again referring to AARRR, the retention rate represents the percentage of users who return to the chatbot over a specified period of time.

This timespan would vary between the bots depending on their purposes. For example, a fitness chatbot would require daily interaction and would benefit from analyzing its 1-day retention.

Artificial intelligence/machine learning rate

How strong is the AI in your chatbot? You can use the percentage of user questions that are correctly understood to measure this.

Which leads us the million, if not billion dollar question — can my chatbot learn independently?

Chatbots with machine learning can measure progress by comparing the improvement in self-service rate over a period of time without human intervention.

An agent with robust machine learning will be able to continually run its own gap analysis to highlight potential areas of improvement.

The demand for chatbots among Millennials is clear. Consumers are asking for simple and effective customer service, but not every chatbot is capable of delivering on this promise without a few tweaks. In a market that is becoming increasingly crowded, these KPIs can help you keep your chatbot one step ahead of the pack.

Jordi Torras is CEO and founder of Inbenta, an artificial intelligence technology company.

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