Sarah Lisker is a Program Manager at the UCSF Center for Vulnerable Populations and a collaborator in The OpEd Project. She manages SOLVE Health Tech, which bridges private sector innovation with public health expertise to make digital health accessible.
When a digital health company announces a new app, everyone seems to think it’s going to improve health. Not me.
Where I work, in San Francisco’s public health system, in a hospital named after the founder of Facebook, digital solutions promising to improve health feel far away.
The patients and providers in our public delivery system are deeply familiar with the real-world barriers to leveraging technology to improve health. Our patients are low-income (nearly all of them receive public insurance) and diverse (more than 140 languages are spoken). Many of them manage multiple chronic conditions. The providers that care for them struggle with fragmented health records and outdated methods of communication, like faxes and pagers.
So when companies tell us they will cure diseases, drive down costs, and save lives with state-of-the-art technology, I am often hesitant.
If we’re designing health apps for those who already have access to healthcare, nutritious food, clean air to breathe, and stable housing, we’re missing the point.
It’s no surprise that health apps are incongruous with the needs of low-income, diverse, and vulnerable patients when these populations are unlikely to be a part of user testing. In addition, the science that technology developers draw from is generated by clinical trials conducted on participants who often do not reflect the diversity of the United States.
Over 80% of clinical trial participants are white, and many are young and male. Women, racial and ethnic minorities, as well as older adults must be included in clinical trials to ensure the results — drawn on not only for product development but also for clinical care and policy — are relevant for diverse populations.
Research conducted by my colleagues at the UCSF Center for Vulnerable Populations demonstrates that patients who are low-income are unable to access many digital health apps. One of our patients testing a popular depression-management app said, “I’d get really impatient with this” and expressed concern that “Somebody that’s not too educated would be like, ‘now, what do I do here?’” A caregiver testing a different app also voiced frustration, saying “Yeah, it’s an app that makes you feel like an idiot.” Yet, despite these barriers, the majority of our study participants (most of whom have smart phones) also express a high interest in using technology to manage their health.
While the private sector is great for innovation, it will fail to improve health in a meaningful way without real-world evidence generated in partnership with diverse patients. In addition, these for-profit companies face long odds to benefit their shareholders in a substantial way without learning how to reach the 75 million patients on Medicaid (including 1 in 3 Californians) who stand to benefit from digital health solutions.
There’s an answer, though, and it’s within reach. To truly improve health outcomes, digital health companies must partner with public health experts and patients to not only ground themselves in evidence-based research, but also build products that meet the needs of all patients.
Along with the compelling business potential of innovating for Medicaid, infrastructure to support this work is growing. For example, organizations like HealthTech4Medicaid are bending the arc of innovation towards the patients who need it most through advocacy and key partnerships with payers, policy makers, care providers, and technology developers.
To truly revolutionize health, let’s demand that technology creators and scalers include diverse end users early and often. Otherwise, the app “for that” will be for them, not for all of us.
Deborah Eisenberg is the founder of TechStarts PR, where she helps technology companies both big and small hone their message and reach their audience.
There comes a time for many startup companies where they either realize they need to do a nationwide roll-out, or they need to actively target buyers in the middle of the country. If you are a startup on either the east or the west coasts, it’s worth thinking about how this market might present its own set of unique challenges, and how you plan to overcome them.
There are a lot of misconceptions about what some people call “flyover country”, and as a San Francisco native who spent two decades in NY, DC, and Boston before moving to Pittsburgh, I can assure you they are almost all wrong. Without getting into specifics, the reality of “middle America” is that it’s the same as anywhere else.
Income, education, world view, and waistlines are all varied. It’s pretty accurate that San Francisco possesses a culture obsessed with fitness and entrepreneurship. But, California isn’t necessarily all like that, and if you think it is, I encourage you to go to Bakersfield, the Central Valley, or Eureka sometime.
In addition, just because the stereotypes are wrong doesn’t mean there’s nothing different about doing business here. As you think about how to conduct your rollout, here are some things you should consider:
As with any market, research is key since it informs every other aspect of the rollout. Start by looking into who your competition is.
Since there are fewer VC backed startups in middle America, and smaller companies tend to get less press, the research may be harder. However, there are some major universities that are actively putting money into their own Entrepreneurship programs and those spinoffs often do very well.
Sense and compute are the electronic eyes and ears that will be the ultimate power behind automating menial work and encouraging humans to cultivate their creativity.
These new capabilities for machines will depend on the best and brightest talent and investors who are building and financing companies aiming to deliver the AI chips destined to be the neurons and synapses of robotic brains.
Like any other herculean task, this one is expected to come with big rewards. And it will bring with it big promises, outrageous claims, and suspect results. Right now, it’s still the Wild West when it comes to measuring AI chips up against each other.
Remember laptop shopping before Apple made it easy? Cores, buses, gigabytes and GHz have given way to “Pro” and “Air.” Not so for AI chips.
Roboticists are struggling to make heads and tails out of the claims made by AI chip companies. Every passing day without autonomous cars puts more lives at risk of human drivers. Factories want humans to be more productive while out of harm’s way. Amazon wants to get as close as possible to Star Trek’s replicator by getting products to consumers faster.
A key component of that is the AI chips that will power them. A talented engineer making a bet on her career to build AI chips, an investor looking to underwrite the best AI chip company, and AV developers seeking the best AI chips, need objective measures to make important decisions that can have huge consequences.
A metric that gets thrown around frequently is TOPS, or trillions of operations per second, to measure performance. TOPS/W, or trillions of operations per second per Watt, is used to measure energy efficiency. These metrics are as ambiguous as they sound.
What are the operations being performed on? What’s an operation? Under what circumstances are these operations being performed? How does the timing by which you schedule these operations impact the function you are trying to perform? Is your chip equipped with the expensive memory it needs to maintain performance when running “real-world” models? Phrased differently, do these chips actually deliver these performance numbers in the intended application?
Image via Getty Images / antoniokhr
What’s an operation?
The core mathematical function performed in training and running neural networks is a convolution, which is simply a sum of multiplications. A multiplication itself is a bunch of summations (or accumulation), so are all the summations being lumped together as one “operation,” or does each summation count as an operation? This little detail can result in difference of 2x or more in a TOPS calculation. For the purpose of this discussion, we’ll use a complete multiply and accumulate (or MAC), as “two operations.”
What are the conditions?
Is this chip operating full-bore at close to a volt or is it sipping electrons at half a volt? Will there be sophisticated cooling or is it expected to bake in the sun? Running chips hot, and tricking electrons into them, slows them down. Conversely, operating at modest temperature while being generous with power, allows you to extract better performance out of a given design. Furthermore, does the energy measurement include loading up and preparing for an operation? As you will see below, overhead from “prep” can be as costly as performing the operation itself.
What’s the utilization?
Here is where it gets confusing. Just because a chip is rated at a certain number of TOPS, it doesn’t necessarily mean that when you give it a real-world problem, it can actually deliver the equivalent of the TOPS advertised. Why? It’s not just about TOPS. It has to do with fetching the weights, or values against which operations are performed, out of memory and setting up the system to perform the calculation. This is a function of what the chip is being used for. Usually, this “setup” takes more time than the process itself. The workaround is simple: fetch the weights and set up the system for a bunch of calculations, then do a bunch of calculations. Problem with that is that you’re sitting around while everything is being fetched, and then you’re going through the calculations.
Flex Logix (my firm Lux Capital is an investor) compares the Nvidia Tesla T4’s actual delivered TOPS performance vs. the 130 TOPS it advertises on its website. They use ResNet-50, a common framework used in computer vision: it requires 3.5 billion MACs (equivalent to two operations, per above explanation of a MAC) for a modest 224×224 pixel image. That’s 7 billion operations per image. The Tesla T4 is rated at 3,920 images/second, so multiply that by the required 7 billion operations per image, and you’re at 27,440 billion operations per second, or 27 TOPS, well shy of the advertised 130 TOPS.
Batching is a technique where data and weights are loaded into the processor for several computation cycles. This allows you to make the most of compute capacity, BUT at the expense of added cycles to load up the weights and perform the computations. Therefore if your hardware can do 100 TOPS, memory and throughput constraints can lead you to only getting a fraction of the nameplate TOPS performance.
Where did the TOPS go? Scheduling, also known as batching, of the setup and loading up the weights followed by the actual number crunching takes us down to a fraction of the speed the core can perform. Some chipmakers overcome this problem by putting a bunch of fast, expensive SRAM on chip, rather than slow, but cheap off-chip DRAM. But chips with a ton of SRAM, like those from Graphcore and Cerebras, are big and expensive, and more conducive to datacenters.
There are, however, interesting solutions that some chip companies are pursuing:
Traditional compilers translate instructions into machine code to run on a processor. With modern multi-core processors, multi-threading has become commonplace, but “scheduling” on a many-core processor is far simpler than the batching we describe above. Many AI chip companies are relying on generic compilers from Google and Facebook, which will result in many chips companies offering products that perform about the same in real-world conditions.
Chip companies that build proprietary, advanced compilers specific to their hardware, and offer powerful tools to developers for a variety of applications to make the most of their silicon and Watts will certainly have a distinct edge. Applications will range from driverless cars to factory inspection to manufacturing robotics to logistics automation to household robots to security cameras.
New compute paradigms:
Simply jamming a bunch of memory close to a bunch of compute results in big chips that sap up a bunch of power. Digital design is one of tradeoffs, so how can you have your lunch and eat it too? Get creative. Mythic (my firm Lux is an investor) is performing the multiply and accumulates inside of embedded flash memory using analog computation. This empowers them to get superior speed and energy performance on older technology nodes. Other companies are doing fancy analog and photonics to escape from the grips of Moore’s Law.
Ultimately, if you’re doing conventional digital design, you’re limited by a single physical constraint: the speed at which charge travels through a transistor at a given process node. Everything else is optimization for a given application. Want to be good at multiple applications? Think outside the VLSI box!
Two years ago, Lime was a great addition to guacamole, rather than a sidewalk. The market wasn’t sure about car sharing and whether it had long-term viability. Now, with the acquisition of Drivy, Getaround is the largest car-sharing platform with partnerships the likes of Uber and Toyota. Uber and Lyft were (and are) a phenomenon, but there were still pundits who weren’t sure if Uber would ever overcome the adversity of its culture.
At the same time, I wrote a series of four articles on the latest transport technologies, and the waves they would create with perspectives focused on the impact on retail, commercial real-estate, short-haul travel and hyperloop. Among those predictions was the impact hyperloop and autonomous vehicle technology would have on commuting, short-haul air travel and the retail industry.
Since then, these technologies have continued to develop and evolve, and it’s worthwhile to revisit assumptions and assertions. Some of the more optimistic expectations put upon them by their proponents have so far failed to be realized, and they are no closer to becoming a reality in our day-to-day lives.
This begs the question as to whether they will still become the industry disruptors many pundits, including me, suggested they would, or if expectations have become more tempered.
Both hyperloop and autonomous vehicle technology have had their ups and downs over the past two years, but they’re still set to change the way we (and the things we need) travel.
Delayed promotion to the back seat
When people think about transport innovation, we often think of self-driving cars or, maybe, flying cars.
Many believed that we’d be relegated (or promoted) to the back seat as soon as 2020. We would be sitting comfortably while fleets of autonomous cars chauffeured us along. Over the past two years the landscape has consolidated and the players are arguing what’s possible.
Driverless cars haven’t managed to achieve some of the targets that were being set for the technology two years ago. For instance, as we discussed, Tesla CEO Elon Musk claimed in 2015 that the company’s cars would be fully autonomous by 2017 — a prediction that, of course, didn’t and still hasn’t come to pass as of mid 2019. And in January this year, Nissan — one of the main proponents of autonomous vehicle technology — said “true autonomous cars will not happen within the next decade.”
But it would be overly pessimistic to suggest the technology isn’t coming at all. The progress has been incredible.
Disruptive leaps forward often result in a net gain in employment.
Ford CEO Jim Hackett said that “[w]e overestimated the arrival of autonomous vehicles,” at an April 2019 Detroit Economic Club event. Ford believes its fully driverless cars will be in commercial operation by 2021, and the technology has remained a major and consistent talking point in the media. At the annual WSJ conference, D.Live, Waymo CEO John Krafcik said that “autonomy will always have constraints,” to communicate his belief that fully autonomous Level 5 transport is not coming anytime soon.
Industry pundits like the Boston Consulting Group (BCG) would argue that Waymo is leading the pack on unlocking the promise of autonomous technology. Tesla’s founder and chief, Elon Musk, feels that Teslas will leapfrog Waymo with an upgrade in 2020 that will make more than a million cars fully autonomous. “By the middle of next year, we’ll have over a million Tesla cars on the road with full self-driving hardware, feature complete, at a reliability level that we would consider that no one needs to pay attention.” My excitement is tempered by the fact that Musk said before that Teslas would be fully autonomous by 2017. That said, I wouldn’t slight him for being audacious, as I do believe he was just being overly optimistic rather than scamming the market.
We shouldn’t forget everyone’s favorite punching bag, Uber, which entered the race in 2015 when they first partnered, then acquired, an entire Carnegie Mellon autonomy lab. Their foray into self-driving abruptly stopped after a tragic accident that killed a pedestrian in Arizona. At this point, it would seem more likely they are going to use the technology rather than develop it themselves.
Driverless cars will create more jobs than they will destroy
In my piece titled “Transport’s coming upheaval,” published in the original series on TechCrunch, I suggested that new modes of transport, such as autonomous vehicles and hyperloop, would end up creating more jobs than they would eliminate. They, coupled with improvements in remote work technologies, should contribute to lowering the cost of human capital by allowing them to comfortably move outside of urban centers to lower-cost housing.
Job loss has been one of the common themes in the discussion around the innovative transport technologies. Some reports have suggested that autonomous vehicle technology could destroy 300,000 jobs a year, and that hyperloop would have a devastating effect on the trucking industry. But as I previously posited, history shows us that, more often than not, disruptive leaps forward often result in a net gain in employment.
Take, for instance, the introduction of the personal computer in the 1970s. It initially destroyed 3.5 million jobs in total, including those in typewriter manufacturing, secretarial work and bookkeeping. But it went on to help create 19.3 million jobs, in the U.S. alone, across a wide range of industries and occupations, according to McKinsey estimates.
New transport innovations will have a similar effect, creating many new jobs. Even though driverless cars aren’t yet available for commercial purchase, there have been developments with the technology that give us a better idea as to how it will likely affect global workforces.
Rather than be a disaster for the world of work, autonomous vehicles and hyperloop could be a boon for employees.
As a whole host of companies, including Waymo, Tesla, Cruise and Ford, strive to make a breakthrough with autonomous vehicle technology, more workers are required to make the driverless car dream a reality. According to the online talent platform ZipRecruiter, the number of job listings related to driverless cars increased 27% year over year in January 2018, and the amount of job postings in the autonomous vehicle sector rose by 250% from the second quarter in 2017 to the second quarter in 2018 due to a hiring spree at the beginning of the year. Indeed, a report from Boston Consulting Group and Detroit Mobility Lab released in January estimated that self-driving and electric cars would create more than 100,000 jobs in the U.S. over the next decade.
In fact, the trucking industry seems ripe for change, and not just because of the benefits that autonomous vehicle technology would bring. There is a shortage of truck drivers in the U.S., according to CNBC. The unemployment rate fell to 3.9% percent in July of last year, meaning companies are struggling to recruit for a job that has long, demanding hours.
Drivers for both trucking and autonomous taxis won’t be irrelevant for some time. For trucking, there is a need for a human to secure the cargo and manage the many checkpoints. For taxis, if Waymo’s CEO is correct, there will still be routes where the driver may be needed, especially in high traffic cities with variability in routes, road quality, construction and traffic conditions.
As the new transport technologies are slowly introduced, they will indeed eliminate existing jobs after, first, making them much more enjoyable for the workers. But evidence suggests that those jobs will be replaced by new ones that require different experiences and levels of education. Rather than be a disaster for the world of work, autonomous vehicles and hyperloop could be a boon for employees everywhere.
What happened to hyperloop?
Two years ago, there was a ton of buzz around what Elon Musk once deemed a “fifth mode of transport.” Hyperloop — a form of terrestrial travel where pod-like vehicles travel in near-vacuum tubes at more than 700 mph — was set to be up-and-running by 2020, with plans to create routes between San Francisco and LA, and Washington and New York.
The impact of this, as I discussed in my original transport series, would be huge for commuting and real estate, and would be a devastating disruptor for short-haul air travel and some trucking routes. Even though hyperloop isn’t being talked about in the same way it was, the promising global projects are far from dead. There are still plenty of developments that suggest hyperloop could be a major form of transport in the future.
Virgin Hyperloop One is now testing empty pods along its 1,640-foot-long, 11-foot-high tube just north of Las Vegas; and in October last year, Hyperloop Transportation Technologies (HTT) unveiled its first full-scale capsules, which it believes will be passenger-ready by the end of 2019. However, many of the widely publicized Hyperloop routes — LA to San Francisco, and Washington to New York — have gone cold in recent years. As have plans to create a high-speed rail across California. In February, California Governor Gavin Newsom said that plans for the new track had been scaled back from the previous grand ambition to connect north to south, saying that, “The project, as currently planned, would cost too much and take too long.”
Efficiency isn’t the only factor that would put self-driving in good stead against airline competitors.
The financial problems the California high-speed rail track has come up against could be an ominous sign for hyperloop technology in the U.S. These types of transport systems are often vastly expensive (the California high-speed rail project was set to cost $68 billion, if completed), and there’s no guarantee they’ll return the investment. Taiwan’s high-speed rail, for instance, suffered heavy losses due to depreciation charges, interest burdens and lower-than-expected demand. And while Elon Musk claimed the LA to SF hyperloop track would cost as little as $6 billion, the SpaceX founder’s estimates have been largely rebuked, with some critics claiming the track would actually cost closer to $100 billion.
Hyperloop is becoming a commercial reality as soon as 2021, just not in the United States. HTT will be building a 10 km track to connect Abu Dhabi to Al Ain and Riyadh, Saudi Arabia. The hope is to be operational by the universal exposition, Expo 2020, on October 20th, 2020.
Clearly, hyperloop still has a lot of questions to answer if it is to fulfill the expectations placed on it, but leaving the technology by the wayside without further testing would be foolish when taking into consideration the environmental and commuting benefits hyperloop would bring. If the technology proves to be cost efficient and as effective as its proponents have previously claimed, it will still have a huge impact on how we and our cargo travel.
A new way to travel and commute
I continue to believe that self-driving technology will disrupt short-haul air travel in a massive way. Why would you go through the hassle of airport security when a terrestrial mode of transport could get you to your destination even quicker?
Efficiency isn’t the only factor that would put self-driving in good stead against airline competitors. Commuting would be easier, too. In all likelihood, traveling by car would be more comfortable and spacious than air travel, but it would also be more amenable to good Wi-Fi connection. In the two years since writing the original series on innovations in transport, in-flight Wi-Fi has improved, but it’s often costly and leaves much to be desired.
Autonomous vehicles will be the next step in brick-and-mortar retail innovation.
Volvo, for instance, released an autonomous car concept in September last year of an electric vehicle that can double up as a living room, bedroom and office. The car, named the 360c, benefits from a larger interior thanks to its lack of a bulky combustion engine and steering wheel. The 360c can be configured in four different ways, with spacious seating, a table and a fold-away bed.
This type of travel would revolutionize how we commute. Workers traveling long distances would surely choose to spend more time in a spacious, work-friendly driverless car than by air travel, if it meant they could comfortably work en route. And it’s a vision that automotive companies with an eye to autonomous vehicle technology are considering seriously.
As we’ve already seen, the claim that new transport innovations such as driverless cars and hyperloop will destroy more jobs than they’ll create is specious at best. But that doesn’t mean the technology won’t change certain roles in the sector.
As cars become more autonomous and the form-factors evolve, it will allow the drivers to provide more services to passengers.
This type of new mobile retail could go on to sell far more than just a few select products in an Uber, though, and it may have a knock-on effect on the retail industry as a whole — an assertion I made in the original series.
Two years ago, retail was suffering badly and, in large part, that trend continues as many fail to adapt. Today, it’s still in a state of flux, with constant disruptions threatening the future of brick-and-mortar stores. Those stores that are surviving the onslaught are adapting and improving with the latest technology. For instance, many companies, such as Ikea, are using augmented and virtual reality to make the shopping experience more immersive.
The reality is that scooters, e-bikes and other modalities will continue to infiltrate our cities.
Autonomous vehicles will be the next step in brick-and-mortar retail innovation. The technology could allow fleets of stores on wheels to come to consumers on demand straight to their location. When I made the claim two years ago, it may have seemed a bit far-fetched, but since then, plenty of businesses have started utilizing the concept.
Walmart, Ford and Postmates are reportedly collaborating on a pilot program in Miami where goods will be delivered to consumers’ doors in a driverless vehicle. They aren’t the only ones exploring how to use the technology in retail. In mid-2017, Swedish company Wheelys launched Moby Mart — a fully autonomous, staffless supermarket on wheels. The service currently operates in Shanghai, China, and is available 24/7.
Consumers have shown an increasing appetite for on-demand food delivery services since I wrote the original series. Uber Eats is only three years old, but it’s already valued at $20 billion; and one of its main rival, Postmates, made more than 35 million deliveries in 2018. As autonomous vehicle technology becomes more widely adopted, more businesses will see the advantage in using it to deliver efficient services to a growing customer base.
New kids on the block
E-bikes have been a steadily growing market since the end of the 20th century, but with the help of on-demand bike sharing they’ve exploded in major cities. Meanwhile, another form of transport left the playground and moved mainstream. Scooters have long been a staple, but since 2017, they’ve changed the landscape of short city commutes.
According to a report released by the National Association of City Transportation Officials, riders took nearly 39 million trips on shared electric scooters in 2018. For the first time they surpassed e-bikes by nearly 10%.
The biggest names behind the scooter boom in the U.S. are Lime, Bird and Scoot. Ironically, their scooters are powered by inventor Dean Kamen’s technology that was at the heart of the Segway. It only took nearly two decades for his future to be realized with a slight design change.
Although I’m not clear that the scooter rental companies are as big a financial opportunity as their investors are hoping, I do believe they aren’t going anywhere. The reality is that scooters, e-bikes and other modalities will continue to infiltrate our cities as urban planners move away from designs centered around automobiles.
The future of innovation in transport
With the setbacks and failed predictions that have been made of autonomous vehicles and hyperloop technology, it would be easy to be skeptical if they will come at all. But, as is often the case with innovation and change, adoption can be slow, and there are often unforeseeable delays. However, with so many startups and major global businesses — from Waymo to Virgin — betting heavily on the future of hyperloop and autonomous vehicles, it’s surely a question of when rather than if they come to pass.
As we’ve seen, these technologies have made huge strides in the two years since I wrote the original series, and the applications of them are starting to be realized. And those applications go far beyond faster, more convenient travel. As more businesses sit up and take notice of the potential driverless cars and hyperloop have to offer, they will continue to shape the future of transport, retail, work and much more.
Nora is an unconventional HR expert based in Toronto. After helping successful startups like FreshBooks and Wealthsimple grow, she founded Bright + Early, an HR consultancy focused on helping scaling companies build impactful people programs.
There is a special chaos that happens when a startup reaches 30 employees. People have a harder time tracking what’s going on, and it’s easy for some to feel left out or ignored.
Right when you want employees focusing on taking the company to the next level, they’re suddenly focused on their own futures. Insecurities and politics can abound, and the work can suffer.
How to stop the madness? In my experience, it all comes down to structure. It might seem early, or scary to a company used to succeeding on grit, but 30 is a key time to begin putting processes into place.
You’re no longer 10 people sitting around a table together, and communication can start to break down. Looking to large companies is no help either. It’s easy to get lost in a sea of frameworks, and you don’t want to overwhelm your team.
What steps can you take to keep things on track and scale effectively? How much is too much?
My company, Bright + Early, works with companies at exactly this stage, helping them grow up without losing the culture that makes them special. For a company just on the verge of scaling, here’s what I recommend.
Retailers and brands have both seen a tremendous shift in traditional retail dynamics, with merchants and marketplaces increasingly ceding control of the online and in-store shopping experience to the brands themselves. Democratizing access to data through new verticalized tools, however, represents a unique opportunity for retailers to leverage this trend by further transforming the retail dynamic and changing their role in the process.
Marketplaces and third-party sellers have always represented a kind of data “blind spot” for brands. Both provided little visibility on customers and even less control over customer experience or satisfaction.
Verticalized tools that provide new levels of data access are changing all that. For example, b8ta is offering a Retail-as-a-Service model and software platform to brands and retailers to better manage and analyze their in-store experience. Companies like Chatter Research are capturing real-time customer feedback that can be integrated side-by-side with POS data to further improve store performance. Solutions like these enable both parties to collaborate and give brands a unified omnichannel strategy. It also provides retailers with a unique opportunity to rethink their purpose and elevate their value proposition within the retail ecosystem, while also expanding margins and driving potential new revenue streams.
Brands already own the entire customer experience through their O&O stores and e-commerce sites. Amazon has also started providing access to more robust customer and sales information through their API. This has encouraged brands to build internal expertise while increasing their desire to have greater insight into — and control over — the sales process. The impetus now is on third-party retailers and marketplaces to provide similar (or better) opportunities and insight to match what O&O and e-commerce sites now provide.
The democratization of data access is a rare bit of good news.
Retailers are already shifting their focus to product discovery, search and transaction. They are more focused on ensuring a positive, in-store user experience — from processing a transaction (the global retail automation industry is expected to reach $21 billion by 2024) to finding and purchasing the product and accelerating conversions. These shifts, coupled with increased data visibility and analysis, fundamentally alter the value proposition for the retailer.
Platforms — like the above-mentioned b8ta and Chatter Research — allow retailers to capture data and provide it to brands so that they can ultimately be smarter about marketing and promoting through tracking customer visits, interactions and transactions. Soon, smart retailers will leverage this data access to an even greater degree, as brands increasingly rely on third-party retailers/marketplaces to grow their sales and market share. Retailers will sell it directly to brands using data marketplaces or use it to negotiate more favorable terms with product supply.
There are derivative benefits for retailers, as well. As more verticalized tools are deployed and adopted by both brands and retailers, they will continue to marry transactional data with user behavioral data while mapping consumer identification to brand marketing activity. Once the data is properly analyzed it will increase not only revenue per square foot but product margins in physical stores, as well, by helping retailers identify and recover lost sales. It also will lead to incremental investment by brands in shopper marketing, transforming advertising into selling.
The data holistically makes retailers stronger.
As merchants and third-party sellers struggle to reverse years of decline, the democratization of data access is a rare bit of good news. It changes the economics for all stakeholders involved, alters the roles of brands and merchants and creates new, much-needed monetization opportunities for retailers. Unlocking the value of data and empowering brands with it allows retailers to focus on where they can make the highest impact. While roles will change, data connectivity will ultimately strengthen partnerships and improve outcomes for all.
Revel Partners has published a white paper on retail, the brand-direct economy and the impact of data on retail efficacy and consumer satisfaction. To view it in its entirety click here.
Round sizes are up. Valuations are up. There are more investors than ever hunting unicorns around the globe. But for all the talk about the abundance of venture funding, there is a lot less being said about what it all means for entrepreneurs raising their early funding rounds.
Take for instance Seed-stage dilution. Since 2014, enterprise-focused tech companies have given up significantly more ownership during Seed rounds. What gives?
Scale is an investor in early-in-revenue enterprise technology companies, so we wanted to better understand how this trend in Seed-stage dilution impacts companies raising Series A and Series B rounds.
Using our Scale Studio dataset of performance metrics on nearly 800 cloud and SaaS companies as well as Pitchbook fundraising records covering B2B software startups, we started connecting the dots between trends in valuations, round sizes, and winner-take-all markets.
Bottom line for founders: Don’t let all the capital in venture mislead you. There’s an important connection between higher Seed-stage dilution and increased investor expectations during Series A and Series B rounds.
These days, successful startups are growing up faster than ever.
Kevin Krim is EDO‘s President & CEO. His 21-year career has spanned search, social and TV advertising across start-ups and major companies like Yahoo and NBCUniversal. Sebastian Chiu is EDO‘s Chief Data Scientist. He earned his undergraduate and post-graduate degrees from Harvard, working previously as a data scientist at Dropbox.
One of the most-discussed plot twists in recent advertising has been the pivot of Direct-to-Consumer (DTC) brands to linear TV. These data-driven, digital-first players are expanding well beyond Facebook and Instagram—and becoming serious players on the largest traditional medium in advertising.
A January 2019 Video Advertising Bureau study found that in 2018, 120 DTC brands collectively spent over $2 billion in TV ads—up from $1.1 B in 2016. 70 of those 2018 advertisers ran TV ads for the first time.
But while we know that they’re advertising on TV, what may be less discussed is whether they’re succeeding on television—and what strategies they use to achieve their success.
At EDO, we have a unique and differentiated ability to measure how DTC advertisers perform on TV by tracking incremental online searches above baseline in the minutes immediately following individual TV ad airings as viewers translate their interest in advertised brands and products directly into online engagement with them.
By measuring incremental search activity across 60 million national TV ad airings since 2015, we are able to effectively isolate the effects of TV ad placement and creative decisions that are most likely to cause online engagement.
We ran the numbers on DTCs as well as advertisers in various other categories to better understand how DTCs specifically are succeeding in TV ads—and what DTCs who are considering TV advertising can do to achieve success on TV.
The DTC revolution is a quintessential David and Goliath story. In vertical after vertical, small, digital-native upstarts are changing the game and overtaking major brands. Does that story play out on TV as well—or is TV advertising one area where DTC marketers have finally met their match?
To answer that question, EDO looked at how effectively TV ads elicited viewer activity since September 2018 across eight major industry categories including DTC. Guided by historical ad performance across billions of ads, we rated ad performance based on how closely the DTC ads came to meeting the benchmark volume of brand-related online activity in the minutes following each TV ad airing.
We index each industry accordingly—giving an index value of 100 to an ad that meets benchmark standards, and below-par ads getting a score under 100 while higher-scoring ads receive a score over 100. We chose to set our index baseline of 100 to the average Consumer Packaged Good (CPG) ad since it is such a large and broad ad category. Our results are as follows: