The next healthcare revolution will have AI at its center

The global pandemic has heightened our understanding and sense of importance of our own health and the fragility of healthcare systems around the world. We’ve all come to realize how archaic many of our health processes are, and that, if we really want to, we can move at lightning speed. This is already leading to a massive acceleration in both the investment and application of artificial intelligence in the health and medical ecosystems.

Modern medicine in the 20th century benefited from unprec­edented scientific breakthroughs, resulting in improvements in every as­pect of healthcare. As a result, human life expectancy increased from 31 years in 1900 to 72 years in 2017. Today, I believe we are on the cusp of another healthcare revolution — one driven by artificial intelligence (AI). Advances in AI will usher in the era of modern medicine in truth.

Over the coming decades, we can expect medical diagnosis to evolve from an AI tool that provides analysis of options to an AI assistant that recommends treatments.

Digitization enables powerful AI

The healthcare sector is seeing massive digitization of everything from patient records and radiology data to wearable computing and multiomics. This will redefine healthcare as a data-driven industry, and when that happens, it will leverage the power of AI — its ability to continuously improve with more data.

When there is enough data, AI can do a much more accurate job of diagnosis and treatment than human doctors by absorbing and checking billions of cases and outcomes. AI can take into account everyone’s data to personalize treatment accordingly, or keep up with a massive number of new drugs, treatments and studies. Doing all of this well is beyond human capabilities.

AI-powered diagnosis

I anticipate diagnostic AI will surpass all but the best doctors in the next 20 years. Studies have shown that AI trained on sizable data can outperform physicians in several areas of medical diagnosis regarding brain tumors, eye disease, breast cancer, skin cancer and lung cancer. Further trials are needed, but as these technologies are deployed and more data is gathered, the AI stands to outclass doctors.

We will eventually see diagnostic AI for general practitioners, one disease at a time, to gradually cover all diagnoses. Over time, AI may become capable of acting as your general practitioner or family doctor.

What we can learn from edtech startups’ expansion efforts in Europe

It’s a story common to all sectors today: investors only want to see ‘uppy-righty’ charts in a pitch. However, edtech growth in the past 18 months has ramped up to such an extent that companies need to be presenting 3x+ growth in annual recurring revenue to even get noticed by their favored funds.

Some companies are able to blast this out of the park — like GoStudent, Ornikar and YouSchool — but others, arguably less suited to the conditions presented by the pandemic, have found it more difficult to present this kind of growth.

One of the most common themes Brighteye sees in young companies is an emphasis on international expansion for growth. To get some additional insight into this trend, we surveyed edtech firms on their expansion plans, priorities and pitfalls. We received 57 responses and supplemented it with interviews of leading companies and investors. Europe is home 49 of the surveyed companies, six are based in the U.S., and three in Asia.

Going international later in the journey or when more funding is available, possibly due to a VC round, seems to make facets of expansion more feasible. Higher budgets also enable entry to several markets nearly simultaneously.

The survey revealed a roughly even split of target customers across companies, institutions and consumers, as well as a good spread of home markets. The largest contingents were from the U.K. and France, with 13 and nine respondents respectively, followed by the U.S. with seven, Norway with five, and Spain, Finland, and Switzerland with four each. About 40% of these firms were yet to foray beyond their home country and the rest had gone international.

International expansion is an interesting and nuanced part of the growth path of an edtech firm. Unlike their neighbors in fintech, it’s assumed that edtech companies need to expand to a number of big markets in order to reach a scale that makes them attractive to VCs. This is less true than it was in early 2020, as digital education and work is now so commonplace that it’s possible to build a billion-dollar edtech in a single, larger European market.

But naturally, nearly every ambitious edtech founder realizes they need to expand overseas to grow at a pace that is attractive to investors. They have good reason to believe that, too: The complexities of selling to schools and universities, for example, are widely documented, so it might seem logical to take your chances and build market share internationally. It follows that some view expansion as a way of diversifying risk — e.g. we are growing nicely in market X, but what if the opportunity in Y is larger and our business begins to decline for some reason in market X?

International expansion sounds good, but what does it mean? We asked a number of organizations this question as part of the survey analysis. The responses were quite broad, and their breadth to an extent reflected their target customer groups and how those customers are reached. If the product is web-based and accessible anywhere, then it’s relatively easy for a company with a good product to reach customers in a large number of markets (50+). The firm can then build teams and wider infrastructure around that traction.

Demand Curve: How to get social proof that grows your startup

When people are uncertain, they look to others for behavioral guidance. This is called social proof, which is a physiological effect that influences your decisions every day, whether you know it or not.

At Demand Curve and through our agency Bell Curve, we’ve helped over 1,000 startups improve their ability to convert cold traffic into repeat customers. We’ve found that effectively using social proof can lead to up to 400% improvement in conversion.

This post shares exactly how to collect and use social proof to help grow your SaaS, e-commerce, or B2B startup.

Surprisingly, we’ve actually seen negative reviews help improve conversion rates. Why? Because they help set customer expectations.

How businesses use social proof

Have you ever stopped to check out a restaurant because it had a large line of people out front? That wasn’t by chance.

It’s common for restaurants to limit the size of their reception area. This forces people to wait outside, and the line signals to people walking past that the restaurant is so good it’s worth waiting for.

But for Internet-based businesses, social proof looks a bit different. Instead of people lining up outside your storefront, you’re going to need to create social proof that resonates with your target customers — they’ll be looking for different clues to signal whether doing business with your company is “normal” or “acceptable” behavior.

Social proof for B2B

People love to compare themselves to others, and this is especially true when it comes to the customers of B2B businesses. If your competitor is able to get a contract with a company that you’ve been nurturing for months, you’d be upset (and want to know how they did it).

Therefore, B2B social proof is most effective when you display the logos of companies you do business with. This signals to people checking out your website that other businesses trust you to deliver on your offer. The more noteworthy or respected the logos on your site, the stronger the influence will be.

Social proof for SaaS

Depending on the type of SaaS product or service you’re selling, you’ll either be selling to an individual or to a business. The strategy remains the same, but the channels will vary slightly.

The most effective way to generate social proof for SaaS products is through positive reviews from trusted sources. For consumer SaaS, that will be through influential bloggers and YouTubers speaking highly of your product. For B2B SaaS, it will be through positive ratings on review sites like G2 or Capterra. Proudly display these testimonials on your site.

Social proof for e-commerce brands

E-commerce brands will typically sell directly to an individual through ads, but because anyone can purchase an ad, you’re going to need to signal trust in other ways. The most common way we see e-commerce brands building social proof is by nurturing an organic social media following on Instagram or TikTok.

This signals to new customers that you’ve gotten the seal of approval from others like them. Having an audience also allows you to showcase user-generated content from your existing customers.

How to collect social proof

There are five avenues startups can tap to collect social proof:

  1. Product reviews
  2. Testimonials
  3. Public relations and earned media
  4. Influencers
  5. Social media and community

Here are a few tactics we’ve used to help startups build social proof.

4 ways to leverage ROAS to triple lead generation

Businesses that don’t invest in their future may not have a future to look forward to.

Whether you’re investing in your human resources or in critical tech, some outlay in the short term is always needed for long-term success. That’s true when it comes to marketing as well — you can’t market your product or service without investing in advertising. But if that investment isn’t turning into leads and conversions, you’re in trouble.

A “good” ROAS score is different for each company and campaign. If your figure isn’t where you’d like it to be, you can leverage ROAS data to create targeted campaigns and personalized experiences.

It’s vital to identify and apply the most suitable metrics based on business goals, and there’s no one best practice or one-size-fits-all method.

However, smart use of the return on advertising spend (ROAS) data can triple lead generation, as I discovered when I joined Brightpearl to restructure the marketing campaigns. Let’s take a look at some of the ways Brightpearl used ROAS to improve campaigns and increase lead generation. The key is to work out what represents a healthy ROAS for your business so that you can optimize accordingly.

Use the right return metric

It is paramount to choose the right return metric to calculate your ROAS. This will depend partly on your sales cycle.

Brightpearl has a lengthy sales cycle. On average it’s two to three months, and sometimes up to six months, meaning we don’t have tons of data on a monthly basis if we want to use new customer’s revenue data as the return metric. A company with a shorter sales cycle could use revenue, but that doesn’t help us to optimize our campaigns.

We chose to use the sales accepted opportunity (SAO) value instead. It usually takes us about a month to measure, so we can get more ROAS data at the same time. It’s the last sales stage before a win, and it’s more in line with our company goal (to grow our recurring annual revenue), but takes less time to gather the data.

By the SAO stage, we know which leads are good quality­ — they have the budget, are a good fit, and our software can meet their requirements. We can use them to measure our campaign performance.

When you choose a return metric, you need to make sure it matches your company goal without taking ages to get the data. It also has to be measurable at the campaign level, because the aim of using ROAS or other metrics is to optimize your campaigns.

Accept that less is more

I’ve noticed that many companies harbor a fear of missing out on opportunities, which leads them to advertise on all available channels instead of concentrating resources on the most profitable areas.

Prospects usually do their research on multiple channels, so you might try to cover all the possible touch points. In theory, this could generate more leads, but only if you had an unlimited marketing budget and human resources.

For the love of the loot: Blockchain, the metaverse and gaming’s blind spot

The speed at which gaming has proliferated is matched only by the pace of new buzzwords inundating the ecosystem. Marketers and decision makers, already suffering from FOMO about opportunities within gaming, have latched onto buzzy trends like the applications of blockchain in gaming and the “metaverse” in an effort to get ahead of the trend rather than constantly play catch-up.

The allure is obvious, as the relationship between the blockchain, metaverse, and gaming makes sense. Gaming has always been on the forefront of digital ownership (one can credit gaming platform Steam for normalizing the concept for games, and arguably other media such as movies), and most agreed upon visions of the metaverse rely upon virtual environments common in games with decentralized digital ownership.

Whatever your opinion of either, I believe they both have an interrelated future in gaming. However, the success or relevance of either of these buzzy topics is dependent upon a crucial step that is being skipped at this point.

Let’s start with the example of blockchain and, more specifically, NFTs. Collecting items of varying rarities and often random distribution form some of the core “loops” in many games (i.e. kill monster, get better weapon, kill tougher monster, get even better weapon, etc.), and collecting “skins” (e.g. different outfits/permutation of game character) is one of the most embraced paradigms of micro-transactions in games.

The way NFTs are currently being discussed in relation to gaming are very much in danger of falling into this very trap: Killing the core gameplay loop via a financial fast track.

Now, NFTs are positioned to be a natural fit with various rare items having permanent, trackable, and open value. Recent releases such as “Loot (for Adventurers)” have introduced a novel approach wherein the NFTs are simply descriptions of fantasy-inspired gear and offered in a way that other creators can use them as tools to build worlds around. It’s not hard to imagine a game built around NFT items, à la Loot.

But that’s been done before… kind of. Developers of games with a “loot loop” like the one described above have long had a problem with “farmers”, who acquire game currencies and items to sell to players for real money, against the terms of service of the game. The solution was to implement in-game “auction houses” where players could instead use real money to purchase items from one another.

Unfortunately, this had an unwanted side-effect. As noted by renowned game psychologist Jamie Madigan, our brains are evolved to pay special attention to rewards that are both unexpected and beneficial. When much of the joy in some games comes from an unexpected or randomized reward, being able to easily acquire a known reward with real money robbed the game of what made it fun.

The way NFTs are currently being discussed in relation to gaming are very much in danger of falling into this very trap: Killing the core gameplay loop via a financial fast track. The most extreme examples of this phenomena commit the biggest cardinal sin in gaming — a game that is “pay to win,” where a player with a big bankroll can acquire a material advantage in a competitive game.

Blockchain games such as Axie Infinity have rapidly increased enthusiasm around the concept of “play to earn,” where players can potentially earn money by selling tokenized resources or characters earned within a blockchain game environment. If this sounds like a scenario that can come dangerously close to “pay to win,” that’s because it is.

What is less clear is whether it matters in this context. Does anyone care enough about the core game itself rather than the potential market value of NFTs or earning potential through playing? More fundamentally, if real-world earnings are the point, is it truly a game or just a gamified micro-economy, where “farming” as described above is not an illicit activity, but rather the core game mechanic?

The technology culture around blockchain has elevated solving for very hard problems that very few people care about. The solution (like many problems in tech) involves reevaluation from a more humanist approach. In the case of gaming, there are some fundamental gameplay and game psychology issues to be tackled before these technologies can gain mainstream traction.

We can turn to the metaverse for a related example. Even if you aren’t particularly interested in gaming, you’ve almost certainly heard of the concept after Mark Zuckerberg staked the future of Facebook upon it. For all the excitement, the fundamental issue is that it simply doesn’t exist, and the closest analogs are massive digital game spaces (such as Fortnite) or sandboxes (such as Roblox). Yet, many brands and marketers who haven’t really done the work to understand gaming are trying to fast-track to an opportunity that isn’t likely to materialize for a long time.

Gaming can be seen as the training wheels for the metaverse — the ways we communicate within, navigate, and think about virtual spaces are all based upon mechanics and systems with foundations in gaming. I’d go so far as to predict the first adopters of any “metaverse” will indeed be gamers who have honed these skills and find themselves comfortable within virtual environments.

By now, you might be seeing a pattern: We’re far more interested in the “future” applications of gaming without having much of a perspective on the “now” of gaming. Game scholarship has proliferated since the early aughts due to a recognition of how games were influencing thought in fields ranging from sociology to medicine, and yet the business world hasn’t paid it much attention until recently.

The result is that marketers and decision makers are doing what they do best (chasing the next big thing) without the usual history of why said thing should be big, or what to do with it when they get there. The growth of gaming has yielded an immense opportunity, but the sophistication of the conversations around these possibilities remains stunted, due in part to our misdirected attention.

There is no “pay to win” fast track out of this blind spot. We have to put in the work to win.

Crypto’s networked collaboration will drive Web 3.0

Web 1.0 was the static web, and Web 2.0 is the social web, but Web 3.0 will be the decentralized web. It will move us from a world in which communities contribute but don’t own or profit, to one where they can through collaboration.

By breaking away from traditional business models centered around benefiting large corporations, Web3 brings the possibility of community-centered economies of scale. This collaborative spirit and its associated incentive mechanisms are attracting some of the most talented and ambitious developers today, unlocking projects that were previously not possible.

Web3 might not be the final answer, but it’s the current iteration, and innovation isn’t always obvious in the beginning.

Web3, as Ki Chong Tran once said, is “The next major iteration of the internet, which promises to wrest control from the centralized corporations that today dominate the web.” Web3-enabled collaboration is made possible by decentralized networks that no single entity controls.

In closed-source business models, users trust a business to manage funds and execute services. With open-source projects, users trust the technology to perform these tasks. In Web2, the bigger network wins. In Web3, whoever builds the biggest network together wins.

In a decentralized world, not only is participation open to all, the incentive structure is designed so that greater the number of participants, the more everybody succeeds.

Learning from Linux

Linux, which is behind a majority of Web2’s websites, changed the paradigm for how the internet was developed and provides a clear example of how collaborative processes can drive the future of technology. Linux wasn’t developed by an incumbent tech giant, but by a group of volunteer programmers who used networked collaboration, which is when people freely share information without central control.

In The Cathedral & The Bazaar, author Eric S. Raymond shares his observations of the Linux kernel development process and his experiences managing open source projects. Raymond depicts a time when the popular mindset was to develop complex operating systems carefully coordinated by a small, exclusionary group of people — “cathedrals,” which are corporations and financial institutions.

Linux evolved in a completely different way. Raymond explains, “Quality was maintained not by rigid standards or autocracy, but by the naively simple strategy of releasing every week and getting feedback from hundreds of users within days, creating a sort of Darwinian selection on the mutations introduced by developers. To the amazement of almost everyone, this worked quite well.” This Linux development model, or “bazaar” model as Raymond puts it, assumes that “bugs are generally shallow phenomena” when exposed to an army of hackers without significant coordination.

The responsibilities of AI-first investors

Investors in AI-first technology companies serving the defense industry, such as Palantir, Primer and Anduril, are doing well. Anduril, for one, reached a valuation of over $4 billion in less than four years. Many other companies that build general-purpose, AI-first technologies — such as image labeling — receive large (undisclosed) portions of their revenue from the defense industry.

Investors in AI-first technology companies that aren’t even intended to serve the defense industry often find that these firms eventually (and sometimes inadvertently) help other powerful institutions, such as police forces, municipal agencies and media companies, prosecute their duties.

Most do a lot of good work, such as DataRobot helping agencies understand the spread of COVID, HASH running simulations of vaccine distribution or Lilt making school communications available to immigrant parents in a U.S. school district.

The first step in taking responsibility is knowing what on earth is going on. It’s easy for startup investors to shrug off the need to know what’s going on inside AI-based models.

However, there are also some less positive examples — technology made by Israeli cyber-intelligence firm NSO was used to hack 37 smartphones belonging to journalists, human-rights activists, business executives and the fiancée of murdered Saudi journalist Jamal Khashoggi, according to a report by The Washington Post and 16 media partners. The report claims the phones were on a list of over 50,000 numbers based in countries that surveil their citizens and are known to have hired the services of the Israeli firm.

Investors in these companies may now be asked challenging questions by other founders, limited partners and governments about whether the technology is too powerful, enables too much or is applied too broadly. These are questions of degree, but are sometimes not even asked upon making an investment.

I’ve had the privilege of talking to a lot of people with lots of perspectives — CEOs of big companies, founders of (currently!) small companies and politicians — since publishing “The AI-First Company” and investing in such firms for the better part of a decade. I’ve been getting one important question over and over again: How do investors ensure that the startups in which they invest responsibly apply AI?

Let’s be frank: It’s easy for startup investors to hand-wave away such an important question by saying something like, “It’s so hard to tell when we invest.” Startups are nascent forms of something to come. However, AI-first startups are working with something powerful from day one: Tools that allow leverage far beyond our physical, intellectual and temporal reach.

AI not only gives people the ability to put their hands around heavier objects (robots) or get their heads around more data (analytics), it also gives them the ability to bend their minds around time (predictions). When people can make predictions and learn as they play out, they can learn fast. When people can learn fast, they can act fast.

Like any tool, one can use these tools for good or for bad. You can use a rock to build a house or you can throw it at someone. You can use gunpowder for beautiful fireworks or firing bullets.

Substantially similar, AI-based computer vision models can be used to figure out the moves of a dance group or a terrorist group. AI-powered drones can aim a camera at us while going off ski jumps, but they can also aim a gun at us.

This article covers the basics, metrics and politics of responsibly investing in AI-first companies.

The basics

Investors in and board members of AI-first companies must take at least partial responsibility for the decisions of the companies in which they invest.

Investors influence founders, whether they intend to or not. Founders constantly ask investors about what products to build, which customers to approach and which deals to execute. They do this to learn and improve their chances of winning. They also do this, in part, to keep investors engaged and informed because they may be a valuable source of capital.

5 things you need to win your first customer

A startup is a beautiful thing. It’s the tangible outcome of an idea birthed in a garage or on the back of a napkin. But ask any founder what really proves their startup has taken off, and they will almost instantly say it’s when they win their first customer.

That’s easier said than done, though, because winning that first customer will take a lot more than an Ivy-educated founder and/or a celebrity investor pool.

To begin with, you’ll have to craft a strong ideal customer profile to know your customer’s pain points, while developing a competitive SWOT analysis to scope out alternatives your customers can go to.

Your target customer will pick a solution that will help them achieve their goals. In other words, your goals should align with your customer’s goals.

You’ll also need to create a shortlist of influencers who have your customer’s trust, identify their decision-makers who make the call to buy (or not), and create a mapped list of goals that align your customer’s goals to yours.

Understanding and executing on these things can guarantee you that first customer win, provided you do them well and with sincerity. Your investors will also see the fruits of your labor and be comforted knowing their dollars are at good work.

Let’s see how:

1. Craft the ideal customer profile (ICP)

The ICP is a great framework for figuring out who your target customer is, how big they are, where they operate, and why they exist. As you write up your ICP, you will soon see the pain points you assumed about them start to become more real.

To create an ICP, you will need to have a strong articulation of the problem you are trying to solve and the customers that experience this problem the most. This will be your baseline hypothesis. Then, as you develop your ICP, keep testing your baseline hypothesis to weed out inaccurate assumptions.

Getting crystal clear here will set you up with the proper launchpad. No shortcuts.

Here’s how to get started:

  1. Develop an ICP (Ideal Customer Profile) framework.
  2. Identify three target customers that fit your defined ICP.
  3. Write a problem statement for each identified target customer.
  4. Prioritize the problem statement that resonates with your product the most.
  5. Lock on the target customer of the prioritized problem statement.

Practice use case:

You are the co-founder at an upcoming SaaS startup focused on simplifying the shopping experience in car showrooms so buyers enjoy the process. What would your ICP look like?

2. Develop the SWOT

The SWOT framework cannot be overrated. This is a great structure to articulate who your competitors are and how you show up against them. Note that your competitors can be direct or indirect (as an alternative), and it’s important to categorize these buckets correctly.

Creative ad tech is on the cusp of a revolution, and VCs should take note

2021 has been a good year to be an ad tech investor. Valuations are surging, Wall Street is happy and exits are frequent and satisfying. It’s the perfect time to double down and invest in an area that has been largely ignored but is poised for major upside in the next few years: Digital creative ad technology.

Think about it. When was the last time we saw a major ad tech funding round that was directed at the actual ads themselves — the messages people actually see everyday? I’d argue that now is the perfect time.

The adtech startups that can figure out how to adapt ads that can interact with the remote control, a synced smartphone or voice commands — maybe even make them shoppable — can theoretically produce a game-changer.

Here are five reasons why VCs should consider ratcheting up their investment into ad tech startups building the next generation of creative tools:

Creative tech is far from being saturated

Consider how much has been spent over the 15 years on digital advertising mechanics such as targeting, serving, measuring and verification. Not to mention the trillions that have gone toward helping brands keep track of customer data and interactions — the marketing clouds, DMPs and CDPs.

Yet you can count the number of creative-centric ad tech companies on one hand. This means there is a lot of room for innovation and early leaders. VideoAmp, which helps brands make ads for various social platforms, pulled in $75 million earlier this year. Given how fast platforms like TikTok and Snap are growing, it won’t be the last.

Digital ad targeting is being squeezed

Ads need to do more work today. Between regulation, cookies going away and Apple locking down data collection, we’ve seen a renewed interest in contextual advertising, including funding for the likes of GumGum, as well as identity resolution firms like InfoSum.

But the digital ad ecosystem can’t get by only using broader data-crunching techniques to replace “retargeting.” The medium is practically crying out for a creative revival that can only be sparked by scalable tech. The recent funding for creative testing startup Marpipe is a start, but more focus is needed on actual tech-driven ideation and automation.

3 keys to pricing early-stage SaaS products

I’ve met hundreds of founders over the years, and most, particularly early-stage founders, share one common go-to-market gripe: Pricing.

For enterprise software, traditional pricing methods like per-seat models are often easier to figure out for products that are hyper-specific, especially those used by people in essentially the same way, such as Zoom or Slack. However, it’s a different ball game for startups that offer services or products that are more complex.

Most startups struggle with a per-seat model because their products, unlike Zoom and Slack, are used in a litany of ways. Salesforce, for example, employs regular seat licenses and admin licenses — customers can opt for lower pricing for solutions that have low-usage parts — while other products are priced based on negotiation as part of annual renewals.

You may have a strong champion in a CIO you’re selling to or a very friendly person handling procurement, but it won’t matter if the pricing can’t be easily explained and understood. Complicated or unclear pricing adds more friction.

Early pricing discussions should center around the buyer’s perspective and the value the product creates for them. It’s important for founders to think about the output and the outcome, and a number they can reasonably defend to customers moving forward. Of course, self-evaluation is hard, especially when you’re asking someone else to pay you for something you’ve created.

This process will take time, so here are three tips to smoothen the ride.

Pricing is a journey

Pricing is not a fixed exercise. The enterprise software business involves a lot of intangible aspects, and a software product’s perceived value, quality, and user experience can be highly variable.

The pricing journey is long and, despite what some founders might think, jumping head-first into customer acquisition isn’t the first stop. Instead, step one is making sure you have a fully fledged product.

If you’re a late-seed or Series A company, you’re focused on landing those first 10-20 customers and racking up some wins to showcase in your investor and board deck. But when you grow your organization to the point where the CEO isn’t the only person selling, you’ll want to have your go-to-market position figured out.

Many startups fall into the trap of thinking: “We need to figure out what pricing looks like, so let’s ask 50 hypothetical customers how much they would pay for a solution like ours.” I don’t agree with this approach, because the product hasn’t been finalized yet. You haven’t figured out product-market fit or product messaging and you want to spend a lot of time and energy on pricing? Sure, revenue is important, but you should focus on finding the path to accruing revenue versus finding a strict pricing model.