Did Joe Rogan Get Ripped Off?
Supercast on how they feel Spotify tricked him into giving up billions.
Oil companies are famous for approaching hapless farmers and buying drilling rights for their properties for next to nothing.
The farmers don’t realize they are sitting on tens of millions of dollars of oil, so they accept a $50,000 one-time payment. It feels like a huge win… Until they realize that they just lost out on tens of millions of dollars of oil riches. That’s Joe Rogan right now.
In September, I wrote a post about how Howard Stern is getting ripped off by Sirius. I made the case that Howard Stern is making $90 million a year when he could be making 2-3x by cutting out the middle-man and doing a subscription podcast.
Daniel Ek, the CEO of Spotify, who just closed an exclusive deal with Rogan to move his show (audio and video) to the Spotify platform.
If the numbers are to be believed, it’s a steal of a deal for Spotify: for $100-$200mm they secured the largest podcast audience in the world. I’m not exaggerating. Spotify’s market cap jumped by $3 billion in the 24h after the news of this deal broke. The market saw what Rogan missed: Spotify took his oil.
To the untrained eye, this looks pretty good for Rogan. His listeners can still access the podcast for free—as long as they use Spotify. Spotify premium subscribers get the podcast without ads, but free users will have to listen to ads (presumably sold by Spotify).
In my last post I estimated Rogan was making around $64MM/year. In contrast, the Spotify deal gets him 2-4x what he was making before, and as a bonus he doesn’t have to worry about the business side.
Not bad, right? WRONG.
Here’s why this is a bad deal for Rogan:
1. He lost control over his audience.
The magic of podcasting is that it’s free, open, and decentralized. Like email, when you have an audience (subscribers), you can reach them directly at any time, without any middle-man or algorithm (Facebook, Google, etc) getting in the way.
You own the relationship, and this is profoundly valuable. You can’t be screwed over by an aggregator getting in your way. When you’re a podcaster, subscribers are your currency. They are what make your podcast valuable.
By doing this deal, Rogan gives up control over his subscriber relationship. Any new audience he builds from here on out, he loses. His existing podcast feed will likely die as most people eventually unsubscribe due to inactivity.
If he goes back to being independent and ditches Spotify in 3 years, he has lost all of his new subscribers during that time, and some of his original subscribers as well. It’s like Disney licensing their Disney+ content to Netflix. It might net a big one-time payout, but it completely erodes the business value that would otherwise accrue to them.
And make no mistake: Joe Rogan is a business. As I said in my last post, were he to build the level of advertising and subscription revenue I think he is capable of, his corporation would easily be valued at over $1,000,000,000…
2. Spotify gets Rogan’s recurring revenue
Like the farmer, Rogan didn’t realize he was sitting on oil. He didn’t value it. To him, the only way to make money from his farm was by harvesting vegetables (advertising). It’s back-breaking labor in the sun (doing ad sales is a pain in the ass).
What he didn’t realize was that just under his feet, was a pool of oil so vast that it would immediately catapult him to billionaire status with minimal effort and build tens or maybe hundreds of millions of dollars of recurring revenue.
Let’s make something clear: Joe Rogan is the new Howard Stern. His audience is 10-12x larger than Howard Stern’s, and Sirius makes an estimated $290 million in revenue from selling subscriptions to Stern.
When Sirius did the deal with Stern, they took an all or nothing approach: You can only listen to Stern if you subscribe to Sirius.
Full stop. Zero access.
This wouldn’t make sense for Rogan. It’s too extreme and it would piss off his fans. Instead, a hybrid approach would make the most sense:
Rogan could have kept selling ads in addition to offering an ad-free stream as well as bonus episodes/extended content/video stream for paying subscribers only.
At just a 5% conversion rate, this is worth over $33mm [1] in annual recurring revenue. That might not sound like a ton compared to a deal valued at $100mm+/year, but when you add it to his advertising revenue, it gets him close to $100mm, fully independent of Spotify. Most importantly Rogan would have been building value and recurring revenue in his own business.
As I pointed out in my last post, if Rogan had added subscription, he would have owned a company that looked like the world’s best SaaS business. Crazy recurring revenue, with low churn and insanely high margins, growing at 25-50% per year as podcasting’s audience grows over time.
This revenue is INSANELY valuable and should be built by him, not Spotify. Not everyone has their head in the sand… While deals like this get a lot of press, successful podcasters who understand the value in owning their relationship with their audience are making moves in the space.
Case in point: earlier this month Ben Thompson (Stratechery) and John Gruber (Daring Fireball) launched Dithering, a subscription-only podcast. Ben Thompson underscores the value of staying independent:
“Owning my own destiny as a publisher means avoiding Aggregators and connecting directly with customers.”
Rogan’s friend Sam Harris, who has sworn off advertising and focused on subscription since he started in 2014, just made a much more aggressive push into subscription. In 2019, he started cutting off most episodes halfway through and only letting paying subscribers hear full episodes.
In 2017, Observer reported that Harris was getting about 1 million downloads per episode, so let’s estimate that his 2017 audience was around 1 million listeners. If we do some rough napkin math and assume that his show grew along with global podcast listeners (which grew 54% since 2017), that would mean that he currently has about 1.54 million monthly listeners.
Applying a 5% conversion rate with a price of $5/month to this works out to about $4.6 million in annual recurring revenue. Applying a 20% conversion rate—which I would expect him to get given his more aggressive push to subscription—works out to $18.4 million in annual recurring revenue.
With an estimated 12 million monthly listeners, Rogan’s recurring revenue potential is at least 8-12x what Sam Harris has been able to achieve.
3. He is Massively Shrinking his Audience and Impact
Restricting his listeners to people who use Spotify will dramatically lower his addressable audience, and I have no doubt that Spotify will eventually gate his content to paid Spotify subscribers in some way (extra episodes, video, ad-free, etc).
Make no mistake: Spotify’s endgame is to add Spotify subscribers. That’s their north star.
For context, Howard Stern—who just before his Sirius deal was one of the most widely listened to radio personalities in the world—now has an audience of less than 1 million per episode. When I tell most people my age (early 30’s) that I love Howard Stern, I get a blank stare.
Nobody knows who he is. Stern has lost his impact on culture in exchange for a big upfront payment.
Let’s be real: Stern and Rogan are already super rich. The difference between $50mm/year of profit and $100mm means zero to their day-to-day lifestyle. What I imagine does matter to them is the size of their audience and their impact, and both made choices that will limit that forever.
I hope that the reported numbers are low, because I don’t think even $100mm/year provides Rogan with enough upside to accommodate the trade offs:
- Losing his relationship with his subscribers
- Building someone else’s business/recurring revenue instead of his own
- A smaller audience and less impact
- He’s going to be just fine either way. I’m not shedding any tears for Joe, but as a business person I can’t help but shake my head at the lost potential.
The world’s largest podcasters are sitting on oil. There’s a reason Spotify is writing these seemingly insane checks: they’ve done the geological surveys. They are trying to cut deals with as many hapless farmers as they can before they all catch on. Spotify will spend hundreds of millions to reap billions.
Most people will look at Rogan and think he’s a genius living the dream. I think he’s a farmer who just got taken by Daniel Ek.
This articles first appeared here.
The Importance of Consistency in Social Media Marketing
Consistency is key in social media marketing.
For your audience to recognize your brand, you must be consistent. Being consistent in your brand allows you to grow in audience engagement and reach. From the tone of voice used in messages to the aesthetics of your profiles, you need to be recognizable to gain traction among your intended audience.
Consistent Voice
To be consistent in the tone used in social media messages, you must first understand who it is that you are speaking to when sending those messages. For example, if you’re speaking to millennials, you wouldn’t use the same vernacular as you would if you were speaking to those in the baby boomer generation.
Age, geographical location, gender, interests, aspirations–each of these segment groups of people, and knowing which group you’re speaking to allows you to choose a tone of voice that your audience will understand.
Consistency in the way you communicate online can allow people to recognize your voice in the same way you recognize your friend’s voice when they say “hello” over the phone. Brand recognition is needed to build a loyal audience and following on social media, so be true to your brand. You know who you are, what you do, and why it matters to people. It’s a balancing act. Aim to communicate in a way that stays true to your brand while also resonating with your audience. Keeping your mission, vision, and values in mind when selecting your voice and the content of your posts can help create the balance and consistency needed to market well on social media.
Consistent Content
Don’t be scattered in what you share via social media. Show what you know and share what your audience will find relevant to their lives. The goal is to be relevant and authentic.
According to Facebook Blueprint Creative Best Practices, “You need to create content that does one of the following: makes me cry, makes me laugh or surprises/provokes me.” You want to elicit a response from your audience, to naturally draw them in and get them to engage with the content you create and share.
When sharing others’ content on social media, consider the source before retweeting or hitting ‘share’ on Facebook. Your posts should be accurate and ethical in content, meaning you need to make sure news being shared by you comes from an accurate and ethical source. Double check that a piece of news is accurate by cross-checking the article or post on other online sources. If other trustworthy sources have confirmed that piece of news to be true, it’s probably safe to share. If your shared content is consistently trustworthy, then, you are too. Think of this extra step in verifying information as a way to safeguard your brand and reputation.
Consistent Posting
Find a balance when posting content. Make sure that you aren’t posting too much of a certain type of content and not enough of another. For example, posting videos about home maintenance tips twice a day, every day can cause an imbalance with your other posts, like house sales or information about your business that goes out only once a week.
Once you find a good balance in the amount you post for different kinds of content, be consistent when sharing that content. Having a posting schedule ensures that consistency in when you post and what you share. Schedule out each day’s posts for each social media channel and make sure that you stick with it.
Post on social media every day. Organic reach, which is the number of people you reach without paying to boost an advertisement or post, can reach only a certain amount of people. Each time you post is another opportunity to reach your audience. Posting each day strategically and consistently maximizes your organic reach.
Consistent Aesthetics
Keep your posts to an appropriate length. Posts that are too long can be burdensome and aesthetically unappealing. The same goes for the use of excessive hashtags in posts.
Hashtags are a great way to jump into the conversation surrounding a trending topic but too many can be cluttered and ineffective. A good rule of thumb is for the number of words in your caption to be longer than the number of hashtags used in the same post.
Your profile needs to have visual appeal. People are visual creatures and are naturally attracted to aesthetically appealing visuals. Make sure that your profile follows a theme. Have a color scheme for photos and use the same filter each time when editing pictures on Instagram. This creates consistency and coordination in your images and posts, which has a greater impact on consumers. Southwest Airlines uses these techniques on its social media. Its Instagram, in particular, maximizes its reach by having a cohesive, visually appealing theme that consistently reinforces its brand, which every business should aim to do when using social media to market to consumers.
This article first appeared here.
Some “what if” questions you should be asking right now
Shifting the need of our agendas from the reactive “to-do” agendas that are set to deal with the urgencies of a particular week to the proactive “What if?” agendas that can help us reimagine the future for our ourselves, our customers and our organizations.
Before we get to some questions you can build your “What if?” agendas around, I want to talk briefly about the benefits of having these kinds of conversations in the first place. I can think of at least three:
Challenging assumptions – It’s interesting how, even in the midst of a global health and economic crisis, it’s hard to let go of our long-held assumptions about how the world works and will work. Taking time to get your team engaged in some challenging “What if?” questions can challenge those assumptions and perhaps point out some blind spots that need to be addressed as you shape your “new normal.”
Preparation – It’s hard to prepare for something you haven’t lived through yet, but considering the range of possible responses to what the future may present can help you be more effective when it arrives. About nine years ago, I had the opportunity to spend the weekend on a Coast Guard cutter off the Florida Straits. As I wrote about back then, my biggest and most valuable takeaway from that trip was being able to see how much time and effort the leadership and crew spent in preparing for things that could happen. When one of those big events actually did happen, the crew handled it flawlessly because they had spent several hours preparing for the possibility. Getting your team engaged in “What if?” conversations and preparation could do the same for you.
Innovation – The late, great Harvard Business School professor Clayton Christensen was famous for coming up with and exploring the idea of disruptive innovation – the impact a small upstart company can have on an industry when it disrupts the competitive landscape by doing something radically new that works. Right now, we’re all in the place of dealing with a disruptive innovator called Mother Nature. “What if?” conversations can help us come up with new and innovative approaches to dealing with something we’ve never dealt with before.
So, with those benefits stated, here are some “What If?” questions along with some follow-ups you could pose for yourself and your team to work through:
- What if we have to remain socially distant for another couple of years, how would we do business? What else could we do to flex?
- What changes would we have to make or could we make to sustain and grow our business in a socially distant operating environment?
- What if we stopped doing 50% of the things that we’ve always done? What would they be? Why would we pick them? What would we do instead of those things that seem like a better use of time, attention and resources?
- What if we were designing our organization from scratch today?
- What would we change? What do we know about the current environment that leads to those conclusions? What trends do we already see that, if they continue, would have a big impact on the way we design for the future?
- What if we came out of this phase better and stronger than we were? What would have made that possible? If we assume our industry is going to still exist in the new normal, what changes will the winners have made to be the winners?
- What if we want to be one of the winners? What will we need to do to be one? Who will we need to reach and serve? What will they want in the future?
These questions and others like them are best considered in dedicated conversations. I’ve been facilitating some of those for clients over the past couple of weeks and have been struck by how the answers to one set of questions can shape and influence the answers to other sets of questions. It’s really a process of unlocking assumptions to the point where creative thinking about different possible scenarios can truly begin.
This article first appeared here.
Alphabet’s Next Billion-Dollar Business: 10 Industries To Watch
Alphabet is using its dominance in the search and advertising spaces — and its massive size — to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting.
With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet’s drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats — search and advertising — and its massive scale to find its next billion-dollar businesses.
To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet’s efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet’s effort to build smart cities.
Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It’s also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries — from electronics to healthcare to transportation to banking — and what else might be on the horizon.
1. Consumer electronics
Artificial intelligence could be the key to Alphabet owning the space.
Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile.
Today, however, Alphabet’s consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line — including the Pixel smartphone, the Chromebook, and the Google Home — but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).
Google hasn’t demonstrated a strong ability to compete with Apple on hardware — but in Alphabet’s vision of the next generation of consumer electronics, AI will be the critical differentiator, not hardware. Google’s reach through consumer apps like Maps and Assistant, plus the extensive adoption of Android, means that Alphabet can ultimately have a disruptive impact across software and hardware, from TV to voice assistants to watches to automakers.
Though the company has seen several of its products struggle or even fail due to tepid public reception or internal management difficulties — Google Glass, for example — its AI product launches, like Google Assistant, have shown more promise.
Android is the foundation of Google’s work in electronics. It has been the best-selling mobile OS every year since 2011, and today has about 85% market share in the smartphone market, with iOS accounting for most of the remaining 15%.
In 2014, Google launched its plan to expand the Android operating system to a range of other devices, which included a wearables project called Android Wear and Android TV.
In 2017, the tech giant released a selection of new “smart” home devices that are not reliant on the Android ecosystem: the Google Home, new Chromecast devices, a new VR headset, and a new Chromebook laptop with built-in Google Assistant.
Since its launch, Google Assistant has been rated far more capable and useful than its main competitors, Microsoft’s Cortana and Apple’s Siri. Driven by Google’s internal deep learning-focused Tensor Processing Unit chips (discussed later in Next-gen computing), Google Assistant is available on all Android and Google Home devices and runs on a reported 1B devices. Android itself is running on more than 2.5B devices today, while Android Wear is available on smartwatches from companies like Michael Kors, LG, and more.
Now automakers are coming to Google for help building out software for their in-car infotainment systems. Volvo, General Motors, Fiat Chrysler, and Nissan will all be working with the company to integrate Google Assistant, Google Maps, and Google Play Store into upcoming makes and models.
This approach is Google’s trademark play in consumer electronics: not building its own branded products, but building software that powers compelling consumer electronics products. Consumer demand puts pressure on watchmakers, automakers, and other companies to integrate tools like Google Assistant and Google Maps into their products. Even Microsoft is now releasing an Android phone.
With Android’s flexibility and reach, it can extend its software to a wide range of devices from different manufacturers.
In this sense, Google’s disruptive potential may have less to do with knocking out Huawei, Samsung, and other traditional electronics manufacturers and more to do with owning the software stack that makes those electronics work.
2. Healthcare
Alphabet is focusing on partnerships and leveraging machine learning to tackle a wide range of healthcare issues.
One of the industries that Alphabet is most engaged in today — and where it has the highest likelihood of having substantive effects on the world — is healthcare.
Alphabet is learning from the mistakes it made trying to go it alone in the healthcare space. Today, Alphabet is partnering with major institutions, focusing more on realistic applications rather than moonshots, and working on a wide range of solutions to different health problems. As shown below, work on these disease spans organizations under the Alphabet structure.
It might seem like a distraction for a company like Alphabet to invest so much in a field like healthcare — at least compared to autonomous cars, where it has a clear technological and engineering head start. But healthcare presents numerous $100B+ market opportunities, including pharmaceuticals, cancer research, and diabetes treatment — and Alphabet is betting that its superior machine learning capabilities and culture of innovation can allow it to find at least a few winners.
Google’s earliest attempt to disrupt healthcare was hamstrung by process issues and difficulties integrating with legacy tech. Google Health, founded in 2006, was a personal health record (PHR) service, which aimed to connect doctors, patients, and pharmacies. The main problem it faced was that it couldn’t pull in external data from a wide variety of third-party providers, which made it hard for most patients to use, and the service was discontinued in 2012.
Today, Alphabet’s focus is on forming valuable partnerships and applying its strengths — machine learning, artificial intelligence, robotics, and data — to a range of problems.
For example, Alphabet has run several AI-focused healthcare projects through its DeepMind subsidiary since 2016, including tests to better diagnose breast cancer and eye disease. Meanwhile, Calico is focusing on how AI can help extend the human lifespan and slow down the aging process.
Through Verily, Alphabet is working on applying technology to a broad array of life sciences issues, with separate projects dedicated to studying diabetes, cancer, wearables, robotic surgery, population health, and pharmaceuticals. Alphabet is working in partnership with existing institutions in these fields around the world, resulting in numerous clinical trials, patents, scientific papers, product launches, and community health initiatives. This suggests that Alphabet takes healthcare seriously as a pillar of its future portfolio.
Diabetes
So far, diabetes has been one of the most productive fields of inquiry for Verily, generating a series of pilots and relationships that have resulted in several clinical trials of its technology.
Verily partnered with Nikon subsidiary Optos to work on better detection methods for early diabetic eye disease in 2016. In 2019, the companies announced that the first clinical trials of their machine learning algorithm for detecting diabetic retinopathy were live in Aravind Eye Hospital in Madurai, India.
With this technology, a technician takes a photo of the patient’s eyes and uploads it to the Verily algorithm, which immediately screens for diabetic retinopathy and diabetic macular edema. The technician can then refer the patient to an eye care physician if necessary. On December 18, Verily announced a similar partnership with Thailand’s Rajavithi Hospital.
In 2016, Verily formed a new company, Onduo, with the drug manufacturer Sanofi. Though Sanofi restructured its role in the joint venture in 2019 amid a change in corporate strategy, it is staying on as a financial backer. Onduo’s offerings include a mobile app that analyzes glucose data from a cellular-connected blood glucose meter and photos of patients’ meals to provide nutritional guidance and insights.
The Onduo for Diabetes app includes access to past glucose readings and data, as well as the ability to reach out to a personalized care team for more information. In 2018, the company announced a plan to develop a new type of “all-in-one” insulin patch pump that’s simpler to use for patients.
In 2019, Onduo partnered with Orpyx Medical Technologies to make the SurroSense Rx system available to certain members of the Onduo diabetes management program. The system consists of a thin wearable that goes inside a patient’s shoes and alerts them when hazardous levels of pressure have been reached, helping to protect them from foot ulcers and issues that can lead to limb loss.
All of these efforts could be extremely valuable for Verily and for Alphabet as a whole. The global market for diabetes devices is projected to be worth $38B by the year 2026, propelled mostly by increasing incidence of the disease. That said, Alphabet isn’t the only company working on diabetes. Amazon, for example, is selling blood sugar monitoring devices directly to consumers and using Alexa to make it easier for diabetic patients to understand their current health. Meanwhile, Apple is more focused on creating integrations between existing diabetes devices and the Apple Watch and iPhone.
Robotic Surgery
Verily has identified another big opportunity in robotic surgery, a discipline similar to telemedicine designed to make surgery more accessible to parts of the world lacking in physicians and doctors.
Working with Johnson & Johnson, Verily launched Verb Surgical, a company with the aim of “changing the future of surgery to enable better patient care,” in 2015.
Verily’s robotic surgery patents show that the company has made significant progress in developing tools for doctors to conduct tests remotely. One patent describes an abdominojugular reflex test, which Verily claims can be performed more accurately using a pressure cuff and camera than by an in-person physician.
At the end of 2019, Johnson & Johnson announced that it would be acquiring the remaining stake in Verb Surgical, bringing the team and technology onboard as it continues working on the mass rollout of surgical robotic techniques.
Community Health
While much of Alphabet’s work in healthcare has focused on using AI and other advanced technologies to investigate specific diseases and improve treatment delivery, Verily and Sidewalk Labs have run projects exploring more local initiatives aimed at improving the health of entire communities.
In February 2019, Verily launched the OneFifteen project, a not-for-profit treatment center dedicated to studying opioid addiction and helping treat addicts in Dayton, Ohio.
The project also aims to generate data on which environments are best for treating addiction, what kinds of additional care are useful (including “recovery housing and vocational training”), and what types of in-person treatment are the most effective at helping addicts stay clean.
The Sidewalk Labs-incubated Cityblock Health started as a research project focused on the effects of urban living environments on health, then pivoted to setting up in-person “health hubs” in areas with high percentages of Medicaid and Medicare customers.
Today, Cityblock Health assists low-income patients with accessing care, and helps care providers better serve them. The company focuses on high-risk beneficiaries because this relatively small group of benefits recipients drives “the majority of the healthcare costs for Medicare and Medicaid” in the United States. In April 2019, Cityblock raised a $65M Series B funding round from Redpoint Ventures and 8VC, among others.
But Alphabet’s efforts to help broader populations of patients have not been uniformly well-received. For example, in November 2019, the Wall Street Journal reported that the medical records of millions of American patients had been made available to Google without their knowledge or the knowledge of their doctors. The records were released as part of a collaboration — code-named Project Nightingale — between Google Cloud and Ascension, a healthcare system made up of 2,600 medical facilities across 21 states.
Revelations about the depth and detail of the data that was given to Google — including patient names, dates of birth, hospitalization records, immunizations, and more — triggered the beginning of a federal Health and Human Services inquiry. It also prompted a Google whistleblower to write an editorial on their concerns about the project’s HIPAA compliance, which was published in The Guardian.
Cancer
Alphabet’s DeepMind and Verily have both run projects over the last several years dedicated to identifying and treating various types of cancers.
DeepMind announced in 2017 that it had successfully trained a set of algorithms to analyze images of breast tissue and identify tumors with about 92% accuracy.
In 2019, the Google Health team published a paper in Nature demonstrating the technology’s ability to spot breast cancer on a larger, more conclusive dataset — namely, mammograms from 91,000 women in the UK and the US.
During this study, DeepMind’s software reduced the incidence of false positives — an endemic problem in traditional mammogram screenings — by 5.7% among US participants. It also reduced the incidence of false negatives by 9.4% among US participants.
In July 2019, both Verily and GV took part in the $160M Series B round for Freenome, a San Francisco-based startup working on a early cancer detection screening system that uses machine learning to find markers of cancer in the blood before conventional tests can detect them.
Wearables
Verily is also exploring applications of wearables in the healthcare space. Its Study Watch, a biometric data-gathering wearable, received its first round of FDA approval in January 2019.
The device — specifically its electrocardiogram (EKG) feature — has been used in studies run by Verily and its partners since April 2017, and is now available more widely on a prescription basis.
About 10 months after the Study Watch was approved by the FDA, Google annouced its $2.1B acquisition of Fitbit. This gives Alphabet a consumer launchpad for its health monitoring technology and a potential vantage point from which to take on its main competitor in the wearables space: the Apple Watch.
Verily has made progress, but still lags behind Apple, which has been focused on incorporating healthcare features in the Apple Watch for years. In early 2020, Verily received additional FDA clearance for its “Study Watch With Irregular Pulse Monitor,” a version of its previous Study Watch that gives users the ability to monitor for arrhythmia. This feature made waves when it was first introduced into the Apple Watch in late 2018. Patents suggest Apple is working on even more advanced health features for the Watch, including noninvasive glucose monitoring, but it remains to be seen what Alphabet will work on next.
Pharmaceuticals
With its legacy systems, high margins, and middle men, the pharmaceutical industry presents numerous $100B+ opportunities for Alphabet.
Verily’s Project Baseline started in 2017 as an attempt to change the way that the $50B clinical trial space works.
Traditionally, a clinical trial requires a control group and an experimental group. The idea behind Project Baseline is to use the Verily Study Watch to crowdsource health data from 10,000 volunteer patients on a daily basis for five years, thus creating a kind of “universal control” that would eliminate the need for future control group testing.
Creating a universal control and monitoring people’s health on a daily basis, rather than just during a clinical study, means running trials can be more convenient for patients and analyzing the results can be easier for companies.
Four of the largest pharmaceutical companies in the world — Novartis, Otsuka, Sanofi, and Pfizer — joined the Project Baseline initiative in May 2019, with the aim of using its data to run more efficient clinical studies in areas like cancer and mental health.
While studies have been run on similar or larger scales in the past, no study has ever looked into this volume of people at this level of detail — including sleep, emotional health, heart rate, the genome, blood, tears, urine, and more — according to Stanford cancer researcher Sam Gambhir.
AI
While most of Alphabet’s public healthcare work has come through its two main healthcare-focused subsidiaries Calico and Verily, Alphabet AI subsidiary DeepMind also has healthcare as one of its primary fields of interest.
In 2019, DeepMind announced that its machine vision tool for diagnosing eye disease, during trials at Moorefield’s Eye Hospital in the UK, was able to correctly diagnose ailments like diabetic retinopathy as well as trained medical experts.
Alphabet’s advantage over other AI startups and corporates working on disrupting healthcare is not just rooted in technology — it is also rooted in scale and resources. For the trials at Moorefield’s Eye Hospital, a team of trained ophthalmologists and optometrists had to review more than 14,000 eye scans, with a senior medical expert on staff to mediate discrepancies.
In July 2019, the DeepMind Health team released a paper about the performance of a deep learning model developed to help predict the likelihood of a patient being diagnosed with an acute kidney injury.
In addition to Alphabet’s other work with Sanofi on diabetes and pharmaceuticals, the two companies are also working on a new AI-focused partnership as of 2019. The main goal of the partnership is to use AI to better understand specific “key” diseases, reveal the best treatment methods for patients, increase the personalization of healthcare treatment, and “better forecast sales and inform marketing and supply chain efforts.”
Life Extension
Calico, which is run by ex-Genentech CEO Arthur Levinson and focuses mostly on age-related diseases, has been using AI to analyze health data with the goal of tracking and extending healthy human lifespans since 2013.
A benefit of operating inside the Alphabet umbrella is that companies like Calico gain access to more sophisticated technology, making more effective laboratory techniques possible.
In one experimental example, Calico was able to use machine vision software to vastly expedite its study of yeast cell aging — important in understanding what causes yeast cells to degrade over time, and how that aging process works.
Having access to cutting-edge AI tools built by companies like DeepMind represents one of Alphabet’s biggest advantages over competitors like Amazon when it comes to building radical new solutions to healthcare problems.
Calico has worked closely on the biology of aging and anti-age-related therapies with the Broad Institute of MIT & Harvard since 2015, and with the pharmaceutical company AbbVie since 2014.
3. Next-gen computing
Alphabet is prioritizing quantum computing technology.
Google was one of the first massively successful tech companies built entirely on software, but that hasn’t stopped Alphabet from pursuing hardware interests aligned with its long-term ambitions.
Alphabet’s primary interest in this space revolves around the unique hardware demands of emerging technologies like artificial intelligence and quantum computing. Alphabet isn’t interested in competing with today’s OEMs to own the hardware market — it’s interested in building the hardware platform for these future technologies.
The company is looking to put its machine learning and engineering expertise to work building the next generation of high-performance computer chips and revolutionizing computing itself with quantum computing developments.
The main issue Alphabet is looking to address is that efficiently solving a certain class of problems with machine learning requires processing power that isn’t available through modern chips.
Since dedicated ML chips don’t need to run traditional software programs, designing one means starting more or less from a blank slate — so the playing field is relatively level.
While there are some physical limitations to contend with in developing chips for AI, Alphabet’s focus on quantum computing could help overcome them.
AI
When it comes to artificial intelligence, Alphabet is working on solving a two-pronged problem. AI applications require hardware with extremely high computation capacity, but they also need to optimize for efficient energy consumption.
In 2012, the image recognition system AlexNet, used by OpenAI, performed about one thousand trillion separate calculations every second. In 2016, Google’s DeepMind built a game-playing neural net called AlphaZero, which was able to become a grandmaster at the game Go within hours of simulated training. AlphaZero consumed about 300,000x the computing power of AlexNet.
Power cycle consumption with deep learning computations has, on average, doubled every 3.5 months for the last 7 years — and that trend has shown no signs of slowing.
New chips, on the other hand, are increasing at a pace of only about 3% every year, and even top-of-the-line hardware is inadequate for the needs of the most complex AI work today.
To address its own AI hardware needs, Google built a custom ASIC chip that it released in 2016 called the Tensor Processing Unit (TPU). The chip is specially designed to operate using neural net workloads, with its parallel computing capabilities today powering several of Google’s most critical applications: Translate, Assistant, AlphaGo, and Search.
Google’s Tensor Processing Units naturally integrate with Google’s widely-used (the fifth most popular open-source project overall on GitHub) machine learning framework TensorFlow. Engineers using TensorFlow within their own organizations can use TPUs to achieve much faster processing times on their own machine learning projects.
TensorFlow Lite, a new mobile version of TensorFlow, makes some of that same computing power available to developers working on mobile and other leaner projects, making it possible for smartphone-focused developers and others to bring technologies like rapid image classification and smart reply options to their applications.
Quantum Computing
While Google began working on TPUs largely to get around the diminishing returns of computing power associated with traditional chips, TPUs are running into issues scaling their processing power for the most complex deep learning applications.
One of Alphabet’s bets for the long-term future of AI involves developing an entirely new form of computing: quantum computing.
Google’s work on the research behind quantum computing dates back more than a decade, but today, Alphabet is focused on using the technology to break through the computing bottlenecks that are holding AI research back today.
Quantum computing is another space where Alphabet’s head start has allowed it to build a competitive advantage. Partnering with some of the most advanced organizations in the space, including D-Wave and NASA, Google has now claimed quantum supremacy, meaning it has reportedly achieved a crucial milestone in quantum computing ahead of all of its competition.
Google first started working on quantum computing back in 2006. In 2013, Google became the second firm to purchase a quantum computer from D-Wave, a Canada-based company that has been a major player in quantum hardware for decades.
A year later, Google hired a team of quantum computing experts to start their own lab at Google and focus on quantum computing research full-time.
This team built Google’s Quantum Artificial Intelligence Lab, which grew to become a collaboration between Google, NASA, and the Universities Space Research Association.
In September 2019, Martinis and his team announced the development of a new microchip called Sycamore. They estimated the 3 minute and 20 second long sample calculation run using this quantum chip would take somewhere around 10,000 years if you had 100,000 conventional computers.
The main area where Google’s work in quantum computing promises to be useful is artificial intelligence, where it’s technology promises to help address the need for increasingly high computation capacity. It will take time before Google is using quantum computing to better screen for disease or make advancements in materials science, but the advancements the company has made so far suggest this kind of breakthrough could occur in the future.
4. Transportation
Machine learning and AI are fueling Waymo’s dominance in self-driving tech, though commercialization is likely still far off.
Today, more than 40 automakers and tech heavyweights are working on building autonomous car technology. Progress towards working prototypes on the road has been slow, due to disasters like the fatal crash involving a self-driving Uber vehicle in 2018, and the inherent technical difficulty of building a self-driving car that can navigate complex environments safely.
Alphabet’s Waymo has advanced ahead of autonomous driving projects at Apple, Amazon, and Microsoft by a significant margin, and is broadly considered to be the industry leader in autonomous driving technology.
Alphabet has leveraged its expertise in machine learning and AI hardware, as well as its massive scale and war chest, to fuel Waymo’s progress.
The company’s cars have logged the most miles, both in the physical world and in simulations, and Waymo is currently operating the world’s only operational self-driving ride-hailing business. In 2019, the company started selling one of its 3D lidar models to third parties. In a statement, Waymo explained the move by citing its desire to scale up its production of sensors.
Waymo’s dominance on the technological front, however, has been undercut by the company’s more recent sluggish progress and failure to meet self-imposed deadlines on the commercialization of that technology.
Alphabet, however, in addition to building a ride-hailing business around Waymo, is also looking into applying the technology to the freight industry. The company is also expanding into other parts of the mobility space, including mapping, and through an investment in Lime’s electric scooter business, micromobility.
Waymo first showed off its self-driving hardware in February 2017, and began public demonstrations of its software installed in Chrysler Pacifica minivans a few months later. Not long afterward, Waymo partnered with ride-hailing company Lyft.
In October 2018, the company announced that Waymo cars had surpassed 10M real-life miles driven on the road. In the summer of 2019, the company announced that Waymo had surpassed 10B simulated miles — a critical component of the way Waymo cars are trained to understand obstacles and navigate the world correctly.
Amid speculation that Waymo’s partnership with Lyft would lead to a Google-supported ride-hailing service, Waymo launched its own commercial self-driving ride-hailing service in 2018. Today, that program — Waymo One — has more than 1,000 users in the Phoenix area. Among the volunteers using the service today, prices are comparable to regular rides from Uber or Lyft — about 60 cents a mile.
In July 2019, Waymo received permission to begin transporting human passengers in self-driving cars in California as well.
Waymo is unique among the technology companies and corporates working on autonomous driving technology mainly due to this public-facing orientation: its goal is not to produce its own autonomous vehicles, but to partner with an existing automaker or automakers to sell a safer, more efficient, and potentially cheaper ride-hailing service to the general public.
The potential prize here is massive. Despite the difficulties involved in building and bringing autonomous vehicles to the road, Goldman Sachs estimates that the popularization of autonomous car technology will take the ride-hailing industry from $5B to $235B by the year 2030.
However, a truly autonomous car experience for all is still on the distant horizon for Waymo. California still requires the company to place a safety driver in the driver’s seat when testing Waymo vehicles. No driver is now necessary for tests in Arizona, but as Waymo itself has acknowledged, Arizona represents many of the ideal testing conditions for the company’s autonomous vehicles which are unlikely to be found elsewhere around the world.
Reports from early trials of the technology have not been uniformly positive, with complaints emerging about how Waymo cars dealt with certain kinds of complex road situations, leading ultimately to Waymo electing to put safety drivers back into vehicles that had gone driverless. Waymo vehicles have also been involved in “dozens” of crashes according to the company’s own reporting, though no significant injuries have been reported.
Despite these issues, Waymo’s cars have the lowest instances of disengagement — when the algorithm running the car gives control back to the human monitor — per miles driven among all the different autonomous vehicle companies testing in California. In 2018, Waymo’s human safety drivers in California trials were only required to take back control of a vehicle once in every 11,000 miles. As far as accidents, Waymo was also superior, with just three collisions over more than 350,000 miles driven (GM had 22 over 132,000 miles).
But while the company’s progress has eclipsed that of its main competitors in the autonomous driving space, its own stated timelines have proven impossible to meet. In 2012, Google co-founder Sergey Brin predicted consumers would be riding in self-driving vehicles within five years.
In 2018, Waymo CEO John Krafcik admitted to the attendants of a National Governors Association fireside chat that the “time period will be longer than you think” for autonomous vehicles to be on US roads.
Autonomous driving, however, isn’t the only mobility solution that Alphabet has placed bets on. Both Google Ventures and Alphabet took part in scooter-sharing unicorn Lime’s $335M Series C round in July 2018, broadly anticipating a world where cars are less important as a form of personal transportation.
In August 2019, Alphabet announced a new integration with Lime that would enable users of Google Maps to see nearby available Lime scooters in more than 100 cities where the on-demand scooters are available. For more than 50% of all smartphone users in the US, Google Maps is the default navigation tool.
Alphabet’s integration with Lime is a useful example of how that popularity could give Alphabet a viable means towards owning the entire distribution layer of the mobility space. Google already dominates navigation in general. Today, Google Maps helps identify locations, give turn-by-turn directions, and give context to different destinations (like restaurant reviews and open hours).
If Maps can extend to actually helping move people from A to B, Alphabet can be the mediator between users, ride-hailing, and other mobility services.
5. Energy
Alphabet subsidiaries are looking to renewables to make the conglomerate more energy efficient and extend solutions to the public.
Alphabet is currently working on solving its intractable energy consumption challenges — largely driven by Google’s data centers — and then taking what it learns to offer household solutions that could disrupt the renewable energy industry in multiple ways.
Alphabet includes a few energy-focused companies under its umbrella, including Dandelion which offers geothermal energy services. For a while, Alphabet was also developing a new type of kite-based wind energy under its subsidiary Makani, but it pulled support for the business earlier this year.
Elsewhere, DeepMind is applying machine learning to the problem, looking for ways to use AI to bring energy usage and costs down.
Alphabet’s preoccupation with energy stems partly from its own energy consumption. Google said that its worldwide operations consumed a total of 5.7 terawatt hours of electricity in 2015: about as much as all of San Francisco county in the same year, according to the California Energy Commission. By 2018, Google’s energy consumption had nearly doubled to more than 10 terawatt hours, according to Statista.
For years, Google has also been one of the biggest consumers of renewable energy in the world. In 2016, no company in North America purchased more renewable energy, according to Bloomberg New Energy Finance. Finding cheaper, more efficient sources of renewable energy represents one of the best ways for Google to lock in energy prices for the foreseeable future and protect its business from shocks, like rises in the price of oil. That’s why the company is also focused on building out its own intelligent, renewable energy infrastructure. Using machine learning, Alphabet can consume electricity more efficiently. In 2016, the DeepMind team announced they had reduced the electricity bill for cooling Google’s data centers by 40%. Today, the program it built to monitor energy usage and make recommendations — which humans at Google were ultimately responsible for implementing — runs autonomously, with data center operators only supervising the recommendations that the AI-driven system implements.
Alphabet has also invested in offering energy from renewable sources like geothermal and wind. With companies Dandelion Energy, Alphabet is bringing some of that research to the public.
Dandelion Energy, for example, promises homeowners a cheaper home energy solution through the use of geothermal energy. The company raised a $16M Series A led by GV and Comcast Ventures in 2019, and raised a further $12M earlier this year to bring its total disclosed funding to $35M.
Geothermal energy, which extracts heat from the ground, can be both cheaper and cleaner than oil and gas. Additionally, Dandelion’s system can cost about half as much as traditional geothermal installations (which can cost up to $40,000) — driven partly by using more standardized units and a less expensive drilling technique for laying the associated infrastructure in the ground.
6. Smart cities
Sidewalk Labs aims to achieve dominance in the space by offering a comprehensive smart city package.
Alphabet’s smart city startup, Sidewalk Labs, began in 2015 with the mandate to come up with new kinds of technologies to improve urban life. In the 5 years since, Sidewalk Labs has become a kind of one-stop shop vendor for smart city technology.
Sidewalk Labs is positioning itself to be the dominant smart city vendor in two big ways: by leveraging Alphabet’s resources to make it financially feasible for towns and cities to work with the company, and by using other Alphabet companies to make its smart city offering more comprehensive.
Other Alphabet companies contributing to Sidewalk Labs’ work include Waymo (autonomous cars) and DeepMind (machine learning), as well as smaller internal startups and spin-offs including Cityblock (personalized health), Coord (mobility), Intersection (Wi-Fi), and Replica (urban planning).
While various startups offer some of these services, Sidewalk Labs can offer cities the full range. In June 2019, the company released a plan showing how it would put those services to work on its first full-scale project along a stretch of abandoned industrial space on Toronto’s Lake Ontario shoreline.
Sidewalk Labs plans to invest in a series of new mixed-use buildings, a renewable energy grid, and underground delivery tunnels, along with an extension to help bring the area onto the city’s light rail infrastructure.
A planned renewable grid will use energy recycled from buildings, sewers, lakes, and the earth to provide heating and cooling to the rest of the neighborhood.
The Quayside development will have public Wi-Fi, as well as a variety of sensors collecting information about traffic patterns in the area, energy consumption, and building activity.
On the business side, Sidewalk has announced that it will bear a “disproportionate share of the cost of upfront innovation” on the Quayside development, in exchange for later compensation if and when the system meets certain performance requirements.
With Alphabet’s ability to share in the risk of getting the smart city up and running, as well as its ability to be a “one-stop shop” for smart city technology, Alphabet and Sidewalk Labs have built a powerful set of competitive advantages in the smart city space. What that dominance means depends largely on how the Quayside experiment goes and how willing other cities and towns prove to be when it comes to giving a company like Alphabet a key role in urban development.
7. Travel
Google is leveraging its search capabilities in an attempt to disrupt online travel agencies.
Some of the most disruptive technology companies of the early 2000s were the online travel agencies (OTAs). Companies like Expedia and Priceline allowed people to bypass traditional travel agents, searching for and booking their own flights, hotels, cruises and rental cars more conveniently and more cheaply than before.
The supremacy of OTAs is largely built on the ability to search for your own travel accommodations; today, that’s being challenged by the company that pioneered internet search.
Alphabet is angling to disrupt OTAs by building a better search experience, asserting its dominance of the search space in general, and getting its results in front of consumers before companies like Expedia and Priceline (now Bookings Holdings) can.
In 2011, Google completed its acquisition of ITA, the developer of the Matrix search engine and used the acquisition to power its Google Flight Search. The Matrix search engine, built by scientists at MIT in the 1990s, was the first commercial software that allowed travel agencies and others to find and compare low airline fares for a given origin and destination.
Over the years, Google’s flight search competed with traditional OTAs’ search engines with various new features, including the ability to automatically identify a consumer’s home airport and customizable search options like looking for flights within a certain duration range. More recently, Google launched a feature that aims to better integrate browsing for flights and hotels. By saving your destination and date information from your flight search, Google can autofill that information into your hotel search, saving time and creating a more personalized experience for the user.
Google doesn’t yet sell its own flights and hotels — it aggregates listings from all the major OTAs and presents them on its search engine results pages and on its Google Travel tool pages.
The major OTAs spend significant sums every year marketing and running search ads through Google, which means that Google must toe a cautious line to avoid upsetting its partners.
But through its dominance in search, Google has a powerful vantage point from which to attack the highly valuable online travel bookings market — with its data superiority, it is aiming to deliver a product that is more personalized and relevant than that of the major online travel agencies (OTAs).
Google can use its massive amount of contextual data and infrastructure to deliver more personalized search results to potential travelers, but also to deliver lower and more predictable prices.
In 2019, in what may have been Google’s most aggressive move against the OTAs yet, the company launched a program that offered certain locked-in prices on holiday travel to Google Flights users. Using historical data on what specific flight routes cost, Google offered consumers the ability to get a refund if the prices on the pre-booked flights decreased after they booked them.
This move was also an indication of another big advantage that Google might have in the travel market: the ability to more accurately predict demand. Being able to project which airline routes and what destinations will be busy, and to what degree, could be a hugely valuable service for Google to offer to hotels, airlines, rental car services, cruise lines, and the rest of the travel industry.
In 2019, Google launched another feature that could divert consumers from OTAs like Expedia and Booking: smart hotel search.
Users can input their travel dates and see a list of all the available hotels that have capacity for that period, including highlighted “great deals” (rooms that are normally more expensive for that historical date). Each hotel’s page shows the different classes of room available, reviews sourced from Google users, other things to do in the area, and booking options.
Lastly, when booking a hotel room, the transaction takes place on Google itself, rather than forcing customers out to other booking sites to check out.
Google’s search platform also gives it an opportunity to push its airline and hotel results in front of users even sooner in the funnel. Currently, when it detects a query regarding a specific flight route, Google places a personalized Flights search box inside the search results page.
Google could one day sell flights, hotels, and other kinds of trips and accommodations to the millions of people who use Google every day right through its search results — and there’s evidence to suggest that many consumers would be on board.
Around 60% of US travelers use OTAs to plan trips, according to a 2018 MMGY study on travel habits. However, the study also found that Google ranked No.2 next to Expedia for “one-stop shops” that prospective travelers consider online. Google also ranked as the most popular method overall for researching flights and prices before traveling.
8. Gaming
Alphabet wants to change the way people game with Stadia, but the platform hasn’t lived up to its hype thus far.
From bets on VR and streaming to building its own video game console, few tech companies are as intent on bending the future of entertainment towards its own vision as Alphabet.
With Stadia, the company is planning one of its biggest entertainment projects yet: leveraging Google’s infrastructural might and its acquisition of YouTube to reinvent the way people game.
Alphabet has been seriously investing in gaming for a while. In 2015, it launched YouTube Gaming, a version of YouTube (and a Twitch competitor) specifically catered to video game content creators and their viewers.
YouTube currently has hundreds of thousands quarterly active gaming streamers. Over the 12 months leading up to September 2018, more than 50B hours of content in total were filmed. Those numbers make YouTube the second-most popular game streaming service behind Twitch.
Alphabet has invested in or acquired a host of gaming companies, including:
- Owlchemy Labs
- Agawi
- Niantic
- Green Throttle Games
- Beyond Games
- Bionic Panda
Of the FAMGA companies (Facebook, Amazon, Microsoft, Google, and Apple), only Microsoft has invested in or acquired a comparable number of gaming startups, with Amazon, Facebook, and Apple investing in far fewer. With its newest and biggest gaming project, Stadia, Google aims to leverage its scale, machine learning expertise, and technological infrastructure to build a new kind of console-less gaming experience.
Users pay for a subscription to Stadia’s online gaming platform and can play using a special Stadia controller that can link up with devices like smartphones or computers. Stadia’s model doesn’t require customers to buy a dedicated games console or physical games for their home.
Perhaps most importantly, all of Stadia’s processing takes place on Google’s cloud infrastructure — meaning, in theory, that powerful remotely-situated computers can beam high-quality graphics and CPU-intensive gaming experiences to users in their homes. However, cloud-based gaming has historically suffered from issues with delays in responding to gamers’ actions. Stadia aims to leverage Google’s extensive infrastructure to eliminate detectable latency and make cloud-based gaming as seamless as gaming at home. Google is also planning a free version of its Stadia platform, which it hopes will attract a wider audience.
One of Stadia’s features that could be compelling for many in the gaming community is the touted ability to play live with popular video game streamers. This type of feature could help drive growth for Stadia as the game streaming and e-sports spaces continue to grow.
However, Stadia’s efforts thus far have generally not lived up to the hype. The platform has experienced issues with pre-ordering, lags in gameplay, and lackluster reviews for its games library. Whether Stadia succeeds in changing the business model behind gaming will ultimately rely on whether it can deliver on its game-changing features before the platform expires from lack of interest.
9. Media
YouTube TV is battling with Amazon Prime and Hulu to win over cord-cutters.
Since Google acquired YouTube in 2006, the streaming site has become the most popular destination for online video. Around 5B videos are reportedly watched on the platform every day.
With its live TV and DVR service YouTube TV, Alphabet is trying to leverage YouTube’s dominance in general streaming media and the power of its brand to compete with traditional broadcast TV.
YouTube TV offers live TV from 70+ channels for $50 a month. The average cost of an extended cable TV service is over $70 per month, according to the FCC. YouTube TV’s model also offers unlimited DVR with no fees, as well as not requiring a fee for renting a box or for canceling.
Another key part of YouTube TV’s value proposition is the platform’s ability to use past viewing data to offer personalized recommendations about what to watch next.
For advertisers, YouTube TV could potentially offer a place to reach consumers in a more targeted way than traditional broadcast television. For example, the personalization data that YouTube collects could be used to give advertisers a more complex audience model and help them better target their ads.
One big problem YouTube has faced is concern over its reluctance to censor allegedly damaging or harmful content uploaded to the website. Alphabet’s Jigsaw, an internal unit working on identifying emerging societal threats on the internet, is trying to solve that problem.
One output of the company’s efforts is the Perspective API, which was developed by Jigsaw and Google’s Counter Abuse Technology team. The software uses machine learning to identify instances of abuse online, gives those instances a “score” depending on their severity, and makes that information accessible to human content moderators to help them make a decision about what action to take.
Since being made available in 2017, organizations like The New York Times, Wikipedia, and Reddit have tested Jigsaw’s ability to combat toxic comments sections.
Despite these issues, YouTube does have one big advantage over other television streaming services like Amazon Prime Video and Hulu: it has better penetration into regular consumers’ everyday lives.
Over 20% of video streaming during Q4 2019 took place on YouTube, making YouTube second only to Netflix (31%) according to Nielsen data. The report found YouTube to be far ahead of other streaming rivals like Hulu (12%) and Amazon (8%).
As the competition heats up between these four streaming services and emerging rival options from companies like Comcast, YouTube’s ability to stay top-of-mind could be vital as the company looks to expand its media ambitions.
10. Banking
Google is working to become the gatekeeper between its users and bank services.
Despite Google’s recent announcement that it would be launching checking accounts in 2020, its goal in banking appears to be less about becoming a bank and more about acting as the mediator between its millions of users and the services offered by traditional banks.
By becoming the conduit through which banks can offer services, Google stands ready to collect a vast amount of valuable data on what services are useful and why. It could then use that data to aggregate demand, similar to the way it approaches hotels and travel searching.
Google’s biggest fintech product so far has been Google Pay — formerly Android Pay — which merged with Google Wallet in January 2018. Google Pay was active in 28 countries by the end of 2018, and processed 1B transactions in its first year. In India, the product has 45M+ monthly active users.
India has been a particularly rich region for Google’s fintech ambitions, with the company launching the mobile payments platform Tez (later rebranded under Google Pay) in the country in September 2017.
Now, in 2020, the company aims to make its biggest foray into banking yet. Google says it plans to begin offering consumer checking accounts in 2020 in partnership with Citi and Stanford Federal Credit Union.
The checking accounts — under a project codenamed “Cache” — will be run by Citi and branded with the bank’s imagery. In keeping with Google’s desire to mediate the relationship with the end-user, users will ultimately access their accounts using Google Pay.
Offering checking accounts could be a way for Google to help its Pay service compete with mobile payment services from companies like Samsung and Apple, which have seen higher usage so far. It could also help Google collect valuable data on payment patterns and other types of consumer financial information, possibly helping it market its advertising services.
The move is not surprising from Citi’s point of view either, considering the partnerships that it has formed with companies like Paytm (India) and Grab (Southeast Asia).
For Citi, Google presents an opportunity to increase customer acquisition by working with one of the main platforms that consumers use to access the internet.
What’s next for Alphabet
Google’s search and advertising businesses have laid the foundation for Alphabet to pursue a broad range of projects, experiment with moonshots, and strategize how it might disrupt staid industries like healthcare, banking, and transportation.
However, this report is by no means representative of everything going on inside Alphabet today. The company has plenty of other projects at various stages that could one day prove disruptive.
For example, Project Loon is Alphabet’s play at expanding high-speed internet to remote and rural areas around the world. At the same time, the company’s umbrella includes a cybersecurity spinoff called Chronicle and a renewables project called Malta, among many others.
One of the most promising avenues of future disruption for Alphabet may be retail. The company partnered with a collection of major retailers in 2017, allowing consumers to use a Google Express front-end to shop from stores like Costco, Walmart, and Target.
Shopping Actions, which launched in 2018, bridged the gap between the mobile, desktop, and voice versions of the company’s shopping platform, which the company says led to as much as 30% higher cart totals in early tests.
Today, Google’s shopping service still can’t beat Amazon on logistics. But the success of one of Alphabet’s other in-progress experiments — the autonomous delivery drone service Wing — could one day be the last-mile solution that connects Google’s online storefront directly to customers and reconfigures the entire retail landscape in the process.
This article first appeared here.
Leadership by fools
If you’re willing for a time to be thought a fool, you’ll end up with the last laugh.
After a brief run of fewer than three years, Brandless has bitten the dust. The SoftBank-backed startup was conceived on the proposition that it could make consumer staples more affordable by selling them online and unbranded, streamlining the supply chain and avoiding the expensive “brand tax” consumer products manufacturers spend to build marketplace trust.
To paraphrase Mark Twain, this is not the first time reports of the death of branding have been greatly exaggerated.
Brandless attributed its failure to aggressive competition and the crowded e-commerce market, only underscoring why it was a flawed concept that fooled investors to the tune of more than $100 million.
When I first heard about the concept — building a brand on the idea that people don’t like brands — it seemed odd to me, and doing so under the brand name Brandless made it even more ironic. I predicted that it wouldn’t be long for this world.
It’s easy, of course, to Monday-morning quarterback ideas that fail. Lest I be tempted to glory in my prescience, I confess that in 1999 I took Amazon to task for trying to extend its brand beyond books. Brandless didn’t make sense and I saw it; Amazon did, and I missed it. (Oh, if I had only invested back then.)
There is, however, a larger point that both examples underscore: If you’re going to do something groundbreaking, you must be willing to at first be thought a fool.
It may only be for 10 minutes, until you can explain the rationale behind your crazy idea to your team. It may be for 10 months, until you can get an minimum viable product into the marketplace and demonstrate traction. It may be for 10 years (the jury was out on Amazon for a long time). Or it may be a lifetime or more before people recognize you were correct. At least we’re not burning heretics at the stake anymore.
Copernicus. Kepler. Galileo. Newton. Luther. Wilberforce. Einstein. All were willing to be considered fools in their pursuit of truth. Light bulbs. Bicycles. Airplanes. Personal computers. All ridiculed, at first. Absolut Vodka. Southwest Airlines. PayPal. Instagram. Amazon. All were supposed to fail.
Few people took the ideas of Sam Walton, Herb Kelleher or Fred Smith seriously when they first shared them with the world. But the world is better off because they were willing to be thought fools. It takes guts to lead.
That doesn’t mean there aren’t a lot of foolish ideas in business. They no doubt outnumber the good ones (mine certainly have). It’s a rare talent to be able to discern between inspiration and impossibility. Most of us are at best hit and miss at it, because no one can see around corners.
And it’s not just knowing what’s possible that’s a challenge; it’s when as well. “Power Branding” Principle #74 says, “Pedal too far ahead and your team may give up the chase.” Sometimes ideas arise before the world is ready for them.
Many people thought President John F. Kennedy foolish when he said we were going to send a man to the moon within the decade, but he timed it just right. By contrast, JC Penney wasn’t ready for the transformation former CEO Ron Johnson tried to ordain, which I have long thought was smart but poorly timed.
So how do you know? How can you determine whether an idea under consideration is foolish or farsighted? There’s no way to tell for sure, but here’s one test: If you’re trying to pull the wool over people’s eyes to gain power, influence or wealth, you’re likely to eventually be exposed as a fool.
But if you’re convinced that the math works, the tech can be developed or marketplace acceptance will come, your foolishness may one day be credited as vision. You just have to be honest with yourself and realistic about the possibilities.
Ignore universal truths about human nature, the limits of science or the length of your financial runway, and your idea will end up in the dustbin. That’s what happened to Brandless. But know something others don’t about the frontiers of knowledge, the possibilities of computer processing or an untapped human need, and you may just be ahead of the game.
If you’re willing for a time to be thought a fool, you’ll end up with the last laugh.
No one wants to be a scoffed at, but if you’re going to lead you have to risk it, because one thing is always foolish: Assuming that the status quo will continue as it has. Change is continuous and accelerating. Business models will be usurped. New ideas must be tried.
Ironically enough, having the courage to be occasionally thought a fool may be the best way to avoid becoming one.
This article first appeared here.
Why it will be so hard to return to ‘normal’
Amid crisis and disruption, we crave the calm of normality. But can we ever really define what “normal” is?
I am writing this in my home office, wearing my bathrobe. I am currently placed under a stay-at-home order, which requires me to stay in my house unless I need to travel for very specific reasons, like shopping or health needs. It also means I no longer have to keep to office dress codes. Besides my husband and neighbour, I haven’t spent physical time with anyone in more than a month. I speak with my parents over video chat, and call other family members over Facebook Messenger. I stay abreast of friends’ lives thanks to their many regular updates on social media. I do most of my shopping online. I spend a fraction of my day outside.
How abnormal! And yet even before Covid-19 hit, I often sat writing in my home office, staying connected with my family and friends via various technologies, shopping online. The stay-at-home order may be new, but I can’t pretend that social distancing is unprecedented. Our technologies and social media have been distancing us from each other for years.
Of course, I am one of the lucky ones. Around us, local economies are faltering. Healthcare systems are strained. People continue to unexpectedly lose their loved ones, and regret that they couldn’t be with them in their final moments.
This has led many of us to wonder about normality: when will things “return to normal,” and what will a “new normal” look like? As one article discussing the disruptions Covid-19 has brought to Life As We Know It puts it, “It’s tempting to wonder when things will return to normal, but the fact is that they won’t – not the old normal anyway. But we can achieve a new kind of normality, even if this brave new world differs in fundamental ways.”
By this standard, the old normal is the one in which our healthcare systems and governments are not prepared to deal with things like Covid-19; the new normal, in contrast, is mostly like the old normal, except in this one we are prepared for global pandemics.
The new normal, in other words, changes what was wrong but keeps what was right with the old normal. But if the old normal was wrong, then why did we call it normal? Similarly, if the new normal is different from the old one, how can we pretend we’re still dealing with “normal”?
What does “normal” really mean, anyway? The word “normal” appears straightforward enough. But like many of our words, as soon as we begin thinking about it, it starts to fall apart at the seams.
Take, for instance, the first entry in Merriam-Webster’s dictionary definition of normal: “conforming to a type, standard, or regular pattern”, as in “He had a normal childhood”. In the same vein, the entry continues, the word means “according with, constituting, or not deviating from a norm, rule, or principle.”
n a fascinating Philosophy Talk podcast, philosopher Charles Scott notes that the word normal possesses a certain kind of authority or “power to divide and distinguish things”. The word sneakily passes from description to prescription. We start with a widely observable fact (most people are heterosexual) and quickly construct a hierarchy with our observable fact placed at the very top (heterosexuality is the best/most natural orientation to have). The fact with which we started our process of categorisation becomes the standard or norm, and everything that diverges from that norm is not just different but abnormal and therefore less than normal.
But as Scott asks, why do we judge normal to be better than abnormal? Being overweight is fairly normal in the United States – many doctors, however, seem to encourage their patients to be abnormal in this regard. What he is getting at is that our concept of normal pulls double duty; it tells us that what is, ought to be.
Random acts of kindness, even when they are in short supply, might be seen as normal in an aspirational sense.
As sociologist Allan Horowitz points out, the dilemma that “normality” forces upon us is that “in most cases no formal rules or standards indicate what conditions are normal”. In the absence of such rules, those who wish to identify normality will normally turn to one of three different definitions. The first is the statistical view, “where ‘the normal’ is whatever trait most people in a group display”. Normal is what is typical, what most people do – which means it is impossible for any individual to be normal.
Most people have two legs and the ability to breathe, and possess desires for sociality so these conditions are seen as normal. The trouble with seeing normal in this way is that it may trick us into accepting statistically widespread phenomena as good. A majority of Nazi Germany’s citizens, Horowitz notes, supported policies of racism and genocide in the 1930s and 1940s. Was Nazism, then, a “normal” philosophy for humans to hold?
The second way of defining “normal”, says Horowitz, is as some sort of ideal, which comes through in the word’s etymology. In Latin, norma referred to a carpenter’s square, which assisted tradesmen in establishing a perfect right angle. The norm provided a concrete standard that, if followed, allowed the user to reproduce a specific pattern. Normal-as-ideal, then, might be in harmony with normal-as-ubiquitous, but it might be quite different. So, for instance, Nazism may have been widespread in Germany, but it was not normal because it did not live up to the ideal society we wish to achieve. On the other hand, random acts of kindness, even when they are in short supply, might be seen as normal in an aspirational sense: we want compassion to be a guiding norm in our societies.
The third definition looks to evolutionary science and defines normality “in terms of how humans are biologically designed by natural selection to function”. What is normal for a human being, then, are all those behaviours which makes it fit to thrive in its particular niche. The capacity to feel shame when betraying a loved one is normal in this scheme, as is the desire for one’s offspring to survive.
These three definitions of normality – (1) statistical, (2) aspirational, (3) functional – often end up sliding into each other during everyday conversation. This collapse is evident in many of our discussions about what “the new normal” will look like once Covid-19 is under control. The new normal will mean that most of us will go back to most of what we were doing before the pandemic struck (1), but that our societies will make changes for the better (2), which will end up being good for the survival of our communities (3).
So we kind of want to go back to where we were, but we also kind of don’t. We want things to be the same, but we also want them to be different. We want to return to normal but we know deep down that our journey won’t be a return so much as a departure.
The question, then, is why would you use the word “normal” at all?
The definition of “normal” might be hard to pin down, but its function is pretty clear: normal is safe. It’s familiar. In the aftermath of the devastation of World War One, Warren Harding’s presidential campaign promise was simple: “America’s present need is not heroics, but healing; not nostrums, but normalcy.” Harding knew Americans wanted to get back to life as they knew it before war disrupted the flows and rhythms of their daily lives. He understood that in the face of fear, people long to go back to a time before the fear set in. His rhetoric connected with the public, which voted him into the White House on 2 November 1920.
Eventually nostalgia became a longing for a different time; more specifically, for a time that never existed.
Harding and his supporters were, we might say, nostalgic for the normal. Just like we are.
Nostalgia comes from two Greek words: nostos, meaning homecoming, and algia, meaning longing. To be nostalgic is to long for home. Swiss doctor Johannes Hofer first coined the term in his dissertation in 1688 “to define the sad mood originating from the desire to return to one’s native land”. Hofer believed his patients’ malady was that they longed for their homes. Nostalgia was originally a longing for a different place. Eventually it became a longing for a different time; more specifically, for a time that never existed. Nostalgia, writes Svetlana Boym, “is a romance with one’s own fantasy”.
In Longing For Paradise, Jungian analyst Mario Jacoby explores the human propensity to idolise a past normality which never existed:
“We project backward into the Golden Twenties, the Belle Epoch in Paris, the time of the Wandervogel, the medieval city, Classical antiquity, or life ‘before the Fall’. The world of wholeness exists mostly in retrospect, as a compensation for the threatened, fragmented world in which we live now.”
When it comes to defining normality, many people assume we start with an idea of what is normal and then, only as an afterthought, define what is abnormal. What if the exact opposite is the case? Maybe we start with something that feels off, something that causes us to experience a great deal of anxiety, and then we imagine a carefree time before these feelings set in. We don’t begin with normality and then categorise those instances where it is transgressed. We begin with all of those things that we instinctively feel are “abnormal” and then try to find comfort by erecting a norm that resolves our anxieties. We then locate this norm “in the past”, which gives us the benefit of claiming the norm as our own. This, after all, may seem easier to attain than one that requires all the hard work of creation. It is not something we need to build from scratch; all that is necessary is that we return home to it.
In a few months, my life will “return to normal”. I’ll sit at home writing essays in my lavender robe, staying in touch with family members via video chatting, and creating excuses for not working out as much as I’d like to.
For others, it will be a longer road. Some local businesses will reopen; others will shutter. Some people will never come back from the ICU. Some people will continue to struggle to fill their food pantries or pay their rent.
Some politicians will make renewed pledges about access to public healthcare. They will remind us to remain vigilant in the aftermath of a pandemic. Some people will agree with our politicians; some will despise them and take to social media to mock them.
The more things change, the more they stay the same…
We will all continue to face daunting challenges for which we are not prepared. Scientists and medical providers will try and outsmart these challenges; they will succeed in some ways, but the challenges will keep coming. Modern medicine, as advanced as it is, is still, in the grand scheme of things, relatively young.
That we will continue on, that we will, has always been the norm not only of humanity, but of all life.
In the past 500 million years, our planet has witnessed five mass extinctions. Many scientists believe we are currently living through a sixth. At some point in the future, our species will no longer be considered the pinnacle of evolution, human beings having been surpassed by other forms of life.
And yet despite the enormous challenges we face on individual, local and global levels, we will remind ourselves and each other that we will get back to normal.
Perhaps if there is something to hold onto in all of this, it is not our definition of normality but our insistence on saying “we will”. We’re not sure what exactly the future will look like – which is why we prefer to discuss it in the familiar terms of the good ol’ days – but we know that it’s coming to greet us.
That we will continue on, that we will, has always been the norm not only of humanity, but of all life, as French philosopher Henri Bergson pondered in the early 20th Century. Bergson used the term élan vital to describe the mysterious impulse toward an open future that seems to animate all life. In fact, this impulse is what life is. Life, says Bergson, “since its origins, has been the continuation of one and the same impetus which separates itself into diverging lines of evolution”.
Whatever it is, however we name it, it seems to always be our normal: we will.
This article first appeared here.
Driving Subscription Sign-Ups with Paid Content Distribution Sequencing
Content is like a master key to success.
As a storyteller, there are countless ways to leverage your content and achieve meaningful business results. Whether you’re looking to increase paid subscriptions, drive affiliate revenue, or acquire new, high-value email subscribers.
What’s more, when you combine the value and versatility of your content with Facebook’s ability to reach large audiences at scale, you can create something extraordinarily powerful for driving conversions: a content-based funnel.
That’s right; direct response campaigns are not your only option. In this blog post, we’ll teach you how to use a content-based funnel and paid distribution on Facebook to convert casual readers to loyal members, and loyal members to paying subscribers.
In order to recognize the value that content-based funnels offer, it’s important to first understand the strategy behind them. Content-based funnels employ a “soft sell” approach that’s designed to gradually guide users toward conversion. Unlike direct response strategies, which aim to immediately convert a user, a content-based funnel engages readers with content in order to move them along multiple stages leading up to a final conversion event. This process of using content to methodically move users down a marketing funnel is a strategy called sequencing.
There are many types of content-based funnels. They can be short and require minimal effort from the user, like a funnel for acquiring newsletter subscribers. They can also be long and require users to reach a high level of engagement, like the journey of nurturing new readers into becoming paying subscribers.
For simplicity’s sake, let’s first look at a generic, four-step content-based funnel through the lens of a publisher:
1. New Users
At the top of the funnel are new users who have just clicked on a Facebook post to view an article.
2. Engaged Users
New users move down the funnel to become engaged users after consuming content – an article, slideshow, video, etc.
3. Qualified Users
Once they’ve taken an action to qualify themselves as good candidates for conversion, engaged users become part of the qualified audience.
4. Converted Users
Finally, some members of the qualified audience complete a final conversion action (purchase, sign up, etc.) and become converted users, reaching the bottom of the funnel.
Your final funnel will ultimately depend on a variety of factors. To illustrate this approach, we’ve chosen a five-step funnel that converts a new user to a paying subscriber.
The Benefits of Sequencing
While direct response marketing has a long history of efficacy, there are a few noteworthy drawbacks, including high cost-per-acquisition (CPA) prices and difficulty scaling campaigns. Luckily, many of these shortcomings can be overcome by implementing a content-based funnel and sequencing approach.
Overcoming High CPA Costs
Anyone who has managed direct response campaigns will tell you that they are not cheap. It makes sense, though: for nearly all direct response campaigns, users are asked to take some sort of action – and to do it in that exact moment. Naturally, this translates into low click-through rates in an exceedingly crowded space, driving up CPC and CPA prices.
With a content-based funnel, however, the roles are reversed. The publisher isn’t asking something from the user, but instead is offering something of value to the consumer: content. In providing value for “free,” the intention is still to convert users, but to do so by showing meaningful value over multiple touch points.
Overcoming Challenges Scaling
Direct response campaigns can be difficult to cost-effectively scale, especially if readers are unfamiliar with a brand. It’s not hard to imagine that people would be open to reading a helpful article from an outlet that they’ve never heard of. It requires very little effort and readers walk away having gained valuable information. Getting people to make a purchase from an unfamiliar brand? Not nearly as easy. Even if people are familiar with the brand, those people exist in finite numbers, and eventually even successful direct response campaigns will suffer diminishing returns.
Overcoming The “Stickiness” Problem
Since direct response methods all but eliminate any consideration phase that users go through before converting, they tend to be “bouncy,” meaning they have low engagement and high drop-off rates. By using a content-based approach, there’s a much higher chance that the user will engage with the content (they self-select, after all), and perhaps go on to consume additional content during the same session, moving them further down the funnel and increasing opportunities for conversion.
Content Selection and Sequencing
Unsurprisingly, content selection is key to the success of a content-based funnel. That said, it takes time to figure out what content is going to work best. Due to the fact that testing a content-based funnel can be expensive, especially if there’s an acquisition event involved, we recommend considering the following questions before spending a single cent on paid distribution.
1. What evergreen content do you have that has performed well historically?
Choosing evergreen content allows your content-based funnel to stay evergreen.
2. What kind of content are you best known for?
What content is interesting to readers? Where do users click first on your site? Where do they spend the most time? Are loyal users or subscribers over indexing on a certain type of article or video? You can leverage the content most interesting to the users who are currently converting in order to find other qualified audiences.
3. What stories do people read right before converting?
Often, this is in line with the content you’re best known for, but can vary vastly from publisher to publisher. For some, it will be a great sports section. For others, it’ll be intriguing opinion pieces. This content will be a crucial tool for lower-funnel targeting.
With your answers to these questions in mind, the next step is to actually select the content that you’ll use for your funnel. In the early stages of the funnel, you want to find the right content to bring new audiences into the site, and then keep them coming back.
After bringing the right users in for the first time, you can bring them back easily with more “clicky” content at a very low price. As you move further down in the funnel, you have fewer, more engaged users. You can use your best converting content and a higher bid in order to bring them back to the site and convert them.
This article first appeared here.
Improve strategic alignment through better metrics
Strategic alignment seems to be a friction point that has proven difficult to smooth.
I have been helping a client for some time now on strategic planning and culture transformation. With a solid strategic plan and plan administration process in place and a roadmap for cultural change, I am proud to say that they’ve come a long way on both fronts. However, their journey is far from over.
Complicated by a re-organization that moved the firm from a function-centered reporting structure to a market-centered one, the newly realigned leadership team finds itself struggling to drive the new organization to flawless execution. With one foot in the old paradigm and one in the new, staff are stressed to understand how to best operate within the new organizational construct.
In an effort to provide a path forward, we’ve decided to focus on metrics. Because people pay attention to what they’re measured by, the best way to get a behavior change is to measure to the new behavior intended to be instituted.
Top-Down Strategic Alignment Approach
A way to drive change and ensure alignment is to start at the top, or strategic layer comprised of your top leaders, of the pyramid and decide on the two or three key things needed to manage the business. Once determined, the middle-tier metrics, what we’ll call tactical comprised of your middle management, should be defined by cascading and interpreting the top-level measurements. Similarly, the lower-tier metrics, which we’ll refer to as operational comprised of managers and supervisors, should be defined by cascading and interpreting the middle-tier measurements.
When done this way, the resulting set of metrics will serve to improve strategic alignment.
Of course, there is three main specifics to account for as you craft an aligned set of metrics, including:
1. Time Perspective
It important to note that the time horizon associated with an effective metric shifts, as you move up and down the pyramid. For instance, a top-level metric may be tracking performance towards a goal that sits 3 or 4 years out. Metrics related to business retention, net new sales and total revenue growth are ones you would typically find at the top of the organization. The measurement would tracking against performance this year on the achievement of these goals.
However, mid-tier metrics would be tracked for a goal to be achieved this year. For example, a metric for the middle tier might be on-time delivery to customers with the associated lower tier metric being just-in-time parts ordering.
So, the strategic metric of customer retention aligns with the tactical metric of on-time delivery which aligns with the operational metric of on just-in-time parts ordering.
2. Data Collection
The way that metrics are collected and tracked is also critical to proper strategic alignment. It’s best to collect data as work is performed, not count things after the fact. If metrics are tracked in real-time, you improve your chances of addressing developing issues before they become damaging conditions later on.
Take just-in-time parts ordering metric, for instance, if the data is reported and collected on a monthly basis, you may not know for nearly a month that critical parts have been out-of-stock and have been delaying production (thereby affecting on-time delivery, as well). It would be weeks later that you’d know that you needed to find other suppliers to fill the gap.
Thus, data must be collected in a timely fashion to create the best opportunity to maintain strategic alignment.
3. Data Assessment
The assessment of the metric data collected is critical to ensuring strategic alignment. Mistakes can happen when data is misinterpreted. Indeed, if your assessment indicates everything is hunky-dory and, in fact, it’s not, you will have missed the opportunity to take timely corrective action in addressing an emerging situation and give it additional time to fester into a big problem down-the-road.
To extend further our just-in-time parts ordering metric example, let’s assume parts ordering and delivery was being reported on a daily basis and you failed to recognize that parts delivery slippage was occurring, your business would still be adversely affected by production delays.
Leaders must understand metric data and efficiently evaluate it up and down the pyramid lest metric collection becomes irrelevant.
To sum, people pay attention to what they’re measured by, so metrics (if done right) can be the enabler needed to help management and staff, alike, to adjust their behaviors so that they can strategically aligning their efforts for the good of the business.
This article first appeared here.
Stop Rushing In With Advice
Why your words of wisdom probably aren’t worth very much.
There’s a time and place for advice. But when giving it is your default response to colleagues and friends who face difficult situations (and for most of us, that’s the case), it becomes a problem.
It’s an insidious habit — one you’ve been encouraged to adopt all your life. From your early days in school, through exams in college, and into your career, it’s always been about having the answer. And biology is colluding with societal influence. When you give advice, your brain gets a dose of feel-good chemicals. You feel smart and accomplished, poised and helpful. The buzz is intoxicating. No wonder you’re giving advice all the time.
But most of it is not useful or effective. Here’s why.
1. You’re solving the wrong challenge.
More often than not, you’re offering solutions (brilliant or not) to the wrong problem. You’ve been suckered into believing that the first challenge mentioned is the real issue. It rarely is. But because we’re all twitchy-keen to help and “add value,” you jump in and solve the first thing that shows up.
2. You’re proposing a mediocre solution.
Let’s say you sidestepped that first mistake and took a little time to identify the real problem. Unfortunately, you’re still likely to make suggestions that are not as good as you think they are. There are reasons for that. To start with, you don’t have the full picture. You have a few facts, a delightful collection of baggage, a robust serving of opinion, and an ocean of assumption. Your brain is designed to find patterns and make connections that reassure you that you know what’s going on. (Chances are, you don’t.) Add to that your own self-serving bias, which is what behavioral scientists call it when you’re over-inclined to believe your ideas are excellent, and the nuggets of gold keep coming.
3. You’re displaying poor leadership.
Even if you avoid the first two mistakes, you’ll reach a crossroads: Do you supply an answer that’s fast and right? Or give someone else who’s less experienced or less senior the room to figure things out? Down one path: speed and a confirmation of your status within the group. Down the other: an act of empowerment — and with it, an increase in confidence, competence, and autonomy.
Sadly, most of us choose the first path. We’ve been conditioned to do so. But problem-solving becomes much more interesting and effective when we stay curious and know when to step out of the way.
This article first appeared here.
Being a Rainmaker During COVID-19
Refocus your efforts on being a great rainmaker and build your base, not just in customers, but also your talent, your product, marketing and sales, finance, legal and human resources, say Peng T Ong, Managing Partner, Monk’s Hill Ventures.
Over the last few weeks, we have seen—and will continue to see—survival of the fittest. At Monk’s Hill Ventures, we’ve started bucketing companies into three categories.
The first is the ice bucket—any business that should essentially be put on ice during coronavirus. The chances of revenue for some businesses are almost zero this time compared with previous crises given worldwide downturns. Many of these businesses are related to travel or events.
The second category is pivot businesses, i.e. businesses where current customers are no longer buying and the business either needs to pivot to a new customer base or a new product altogether. Sometimes this could be just focusing more on a certain segment of existing prospects and customers (this is easier), sometimes it is creating a new channel (this is much harder during the crisis).
And finally, the third category is the winners—businesses that are thriving in this climate such as healthcare tech, collaborative tech, online grocery shopping and e-commerce.
One thing that is now becoming very clear, particularly for founders in bucket two and three, is that it is now time to strap on your boots and become a great rainmaker. The reason is this—any sales engine you build today may cease to be viable in the next 3-12 months. What is true today might not be true in 12 months. Refocus your efforts on being a great rainmaker and build your base, not just in customers, but also your talent, your product, marketing and sales, finance, legal and human resources.
A simple, but strategic question, these founders need to ask themselves is what happens when life returns to normal and all of this is over? How do you build good habits when the world comes back to normalcy?
For companies in bucket three who just addressed the onslaught of demand or MAU, here are a few considerations:
On runway: Runway is survival. That’s why bucket 1 companies should go on ice. We have seen too many startups (even in normal times) that have to shut down because they made the most rosey or hopeful assumptions. Now is not the time to plan on the most optimistic scenarios. When you don’t know what to expect, runway is the single most important determinant of survival. Cut your burn so you can survive longer.
On talent: Don’t be cheap. Find the best people for your team now, whether it is another rainmaker, a CFO or a CMO. This is also your opportunity to see who among your team is cool under fire, who you would want with you in the thick of battle. Keep these folks with you the rest of your career.
On the business model: Besides being a great rainmaker selling and fulfilling heavy demand from customers, ascertain what you can do to ensure the world continues to stay more in your favor post-pandemic. Are there any adjustments that need to be made in terms of cost structure, loyalty point system or membership model?
On customer retention: How do you continue to build and keep people using your product when things go back to normal? What additional products can you offer? How can you continue to be ‘addictive’ to your customers who will be going out more and may not be engaging as much on your collaborative tool or edtech platform? Do you need to create new digital content and marketing strategy?
While things are good now, take a step back, and think about what the new normal will be and your business’ place in this new reality. If anything, coronavirus has put an accelerant on digitalization and adoption of many new things for consumers including online shopping, remote working, online education and productivity tools.
The shift of consumer behavior in Southeast Asia will also enable the growth of other verticals including logistics and fintech. Ironically, we also see consumers returning to similar behavior we’ve seen in previous recessions or downturns, which is a shift towards more budget-friendly spending. Another outcome of all this is greater demand for more cost-efficient and better solutions from consumers, pushing founders to reiterate and refine business ideas, models and solutions.
This article first appeared here.