Business Ideas VI: Run My House

Idea: Run My House – manage all your household services from a single app

MVP: Running your own house sucks – even if you outsource tasks like yard service, gutter cleaning, pest control, and cleaning, it’s still a challenge to deal with numerous service providers by inefficient means like phone calls. What if you had an app that enabled you to simply check off the service subscriptions you desire, and to take pictures to show problems needing resolution? Even when homeowners work with their existing service providers, there are major communication inefficiencies – not to mention the difficulty in acquiring good providers in the first place!

The difficulty with an MVP in the home services market is chiefly a business problem – a substantial percentage of home service work is performed in the informal economy, and as a result it’s highly fragmented. As a result this business is best attacked in a single test market to start, as providers need to be secured across all major services in order for homeowners to realize value in the service.

Market: The combined household market for home cleaning, yard service, pest control, gutter cleaning, and related scheduled services exceeds $100 billion per year, and including non-scheduled maintenance the total market may exceed $500 Billion annually. This market is currently incredibly fragmented, in no small part because there are limited economies of scale in providing most of these services.

Unfortunately for the consumer, this leads to a terrible experience. If Run My House can capture a 10% fee for delivering volume to providers, while keeping the cost to consumers static, it should be possible to capture meaningful market share. With a total addressable market greater than $10B, there is true unicorn scale possible in this market.

Idea Score (0-10 scale): 7.5 points

Feasibility of MVP / Market Entry: 0.5

Building an MVP for RunMyHouse could be daunting, given the number of service providers that must be secured before the service becomes compelling. This sort of “full-stack” startup, providing a complete service rather than just software, has larger potential but also substantially greater risk and capital requirements. Typically it makes sense to attack individual metro areas individually, starting with a beta market and working through challenges there first.

A simpler alternate MVP might simply help homeowners organize communication with existing vendors – perhaps by providing the software for free, with vendors selling their services via the app. This Zenefits-style approach (the give-away-the-software part, not the HR disaster) could enable rapid expansion at lower cost.

Revenue Market Size: 4 (out of 4)

As noted above, the total market opportunity in the residential space is several hundred billion per year – a 10% take rate implies a true addressable market size of 20B+. Numerous public players in the home services and home sales space (ANGI, Z) point to the possibility of a unicorn valuation for a successful player.

Difficulty, Barriers to Entry, and Competition  (out of 2): 1

A large scale b2c rollout of this sort would likely require substantial funding. HomeJoy was a substantial failure in this space, showing that giving away services at negative margins can take even well funded startups down. Handy, its largest competitor, has since worked hard to get to profitability, underscoring the risks of the home services market.

Taking a software-only approach could lower the risk of rollout, but substantial marketing spending would still be required to get customers and providers onboard.

Riding Hype or a Trend? 2

Bringing fragmented, illiquid, hundred-billion dollar markets online has been one of the key success stories of the last 20 years of the internet. Home services has been among the final frontiers because of its deep fragmentation, but Uber and ride-sharing proved that change will come to even the most glacial industries.

Business Ideas V: BestUse

Idea: BestUse – analyze real estate through the lens of local zoning and code to determine best use, and identify underused properties

MVP: The process of identifying promising opportunities in real estate is largely a manual one today. Real estate investors and agents scour listings and property records to determine where opportunities to convert an old office into multifamily housing might exist, for example. BestUse would automate this process by using machine learning to compare zoning laws and potential uses to identify underutilized properties. A minimum viable product would involve targeting a particular metro area to analyze local zoning and building rules there in detail.

Market: BestUse has a likely path to market very similar to HiddenLevers (my current concern). The advantage of selling high value software in a niche market is that initial clients can be acquired very quickly after alpha release – the moment HiddenLevers portfolio stress testing worked in a minimal way for a professional audience, client acquisition amongst investment advisors began. With BestUse, real estate investors might quickly embrace a technology that enables them to identify “diamonds-in-the-rough”, properties currently languishing in a sub-optimal use.

The downside with this approach – the total addressable market tends to be limited: if real estate investors are willing to spend four to five figures per year for this capability, the total addressable market might be in the billion dollar range – enough to build a viable business, but not enough for a highly scalable growth path.

Idea Score (0-10 scale, up to 2 points per question): 6 points

Feasibility of MVP / Market Entry: 2

An MVP for BestUse would require a non-trivial initial effort to acquire needed real estate data and to plug in the appropriate analytics on local real estate codes. Actual market entry would likely follow a pattern similar to that for other niche analytics products – get in the hands of paying beta customers and iterate. This is a proven model with much lower risk than launching b2c oriented products.

Revenue Market Size or Eyeballs: 1

If the market is confined to analytics tools used by the commercial real estate industry, then the total addressable market is likely to be subscale (no unicorns here). A high margin $10M revenue business is possible, but getting past this to the next level is a key concern. Since the same sort of analytics is used in commercial real estate appraisal (an $8 billion market), adding this and related capabilities might push the scale a bit – but it’s not clear how to get to a $10B addressable market.

In a Growing Market? 0.5

The real industry is very mature, with growth rates unlikely to exceed the overall economy.

Difficulty, Barriers to Entry, and Competition 1.5

BestUse requires a combination of knowledge of real estate investing with technical modeling capabilities, providing a modest barrier to entry. The need to analyze zoning rules further raises the bar here.

Startups have started to appear in this space – Skyline is using similar analytics to partner invest in properties, an approach which might lead to greater overall market potential. Bowery Valuation is focused on automating real estate appraisals, a naturally related market.

Riding Hype or a Trend? 1

Applying advanced analytics and machine learning to any niche generates interest at the moment – but this is not a particularly innovative or new use case.

Business Ideas IV: Follow My Diet

Idea: Follow My Diet – Help users follow their diet’s guidelines when eating out

MVP: Eating within a diet’s guidelines is challenging for most, and is further complicated when eating out at restaurants. FMD solves this problem by detecting when a user is in a recognized restaurant, and showing only those menu options that meet their diet’s rules (the app would also show how to custom order at restaurants to stay within the diet). At launch the top 100 restaurant chains in America would be supported, representing the majority of all American restaurants – crowdsourcing additional restaurants and menu items should enable coverage to expand quickly from there. FMD will also enable the tracking and optimization of a user’s diet over the course of time – fall off track and the app will let you know what sorts of food choices would put you back on track for the rest of the day or week.

Market:

Roughly 15% of all Americans (45m people) are trying to follow a particular diet at any given time, with total spending in the diet and weight-loss industry exceeding
$33B last year. FMD could market the app toward existing diet providers in the space, as a management tool for their clients. FMD could also find a market in the management
of diabetes and other diseases where diet is an integral part of managing a long-term chronic disease.

With a large potential user b2c user base, freemium or advertising-based business models might make the most sense for FMD – but the possibility of a disease-management oriented approach remains open as well.

 

Idea Score (0-10 scale, up to 2 points per question): 4.5 points

(Overall this idea scored relatively poorly – I think pivoting it toward the health management space, perhaps diabetes or other food-related disease management, could strengthen the business case substantially)

Feasibility of MVP / Market Entry: 1

A substantial amount of restaurant menu data needs to be gathered and maintained in order to enable the app to function – but most of this is readily available and can be parsed online. With major restaurant chains commanding a huge market share in the restaurant industry, it should be possible to gather this data pre-launch in order to enable a functional product at launch.

Revenue Market Size or Eyeballs: 1

While the market size is large (as discussed above), advertising-supported products need to gain substantial scale in order to support a meaningful revenue stream. Since FMD is initially focused on helping dieters eat out, restaurants may be interested in sponsoring the app in order to drive traffic.

In a Growing Market? 1

The market for weight-loss and diet solutions is well established, and on the whole can no longer grow faster than single-digit growth rates. But the market for apps
that help manage diet-related diseases continues to grow rapidly, providing a strong potential growth niche.

Difficult, Barriers to Entry, and Competition 0.5

Numerous competitors exist in the diet app space, and apps even exist to find healthy restaurant options – but none attempt to analyze the mass market restaurant space. This is the opportunity for FMD – fast casual and similar restaurants can be hard to navigate for dieters, but it seems that there are no apps that attempt to solve this menu navigation process in a comprehensive way.

Riding Hype or a Trend? 1

Digital health apps, fitness trackers, and similar are a fast growing space. While diet-related apps operate at the edge of this space, the relationship may provide some halo for a business like FMD.

Business Ideas III: HalfTimer

Idea: HalfTimer – Link employed developers with spare capacity to half-time positions

The economy is going full steam. The number of job openings is at an all time high [1]. Technology positions are particularly in demand, with hundreds of thousands of developer positions unfilled nationwide.

MVP: Halftimer.com places developers interested in long-term part time employment with companies looking for experienced development talent. We have found that experienced developers are willing to lower their hourly rates by up to 40% in order to secure a long term contract that is in addition to their full time job. This differential enables savings for companies that work with HalfTimer – a substantial competitive advantage in the staffing business. The initial MVP need not involve more than outreach to employers and developers via LinkedIn, to staff the first several candidates and prove the model.

Market: 5M full time technology professionals

Halftimer.com builds on a concept successfully used by my other ventures to tap an underutilized resource: experienced, full-time employed developers. Many developers, particularly at large corporations, are not fully utilized whether in terms of mental capacity or even time (this documentary details the situation at length). There are almost 5m individuals employed in development-related positions in the US today – if even 10% have excess capacity, this represents a pool of 500,000 potential resources.

Scoring (0-10 scale, up to 2 points per question): 6 points

1. Feasibility of MVP / Market Entry: 1.5 points

The HalfTimer concept exploits an inefficiency: most employers historically won’t buy limited hours for professional work. On the developer side, developers looking for additional freelance work find it difficult to consistently find small projects that fit around their day jobs. HalfTimer attempts to solve this problem, and market entry is straightforward as this is just a new spin on existing staffing concepts.

2. Revenue Market Size or Eyeballs: 1.5 points

500,000 potential HalfTimers, with net revenue per resource at $15,000 = $7.5B/yr total addressable market. Put another way, staffing ~100 HalfTimers would generate 1.5M in net revenue (against roughly $6.5M in gross revenue), enough to run a profitable small startup. The crucial question: cost of acquisition of both employers and employees.

3. In a Growing Market? 2 points

The technology employment market continues rapid growth, and the core constraint remains supply – which is precisely the problem HalfTimer seems to resolve.

4. Difficulty, Barriers to Entry, and Competition: 1 point

Many existing players in the staffing space could potentially attack this idea, and technically there are no real barriers to entry. Gigster, Gun.io, TopTal, and FlexTeam are startups attempting to ease companies’ ability to find freelance talent – these are similar but not identical to the HalfTimer concept (startup competition bolsters the strength of the idea, as it confirms an idea is worth exploring).

5. Riding Hype or a Trend? 0 points

The gig economy has grown substantially, and HalfTimer represents an evolution halfway between freelance and traditional full time employment. But it’s not clear that concepts in this space have much mind-share at the moment.

[1] The JOLTS survey shows the number of openings to be at an all time high, even when compared to 2000 and 2007 on a relative basis.

Note: I changed the first scoring question to address feasibility rather than whether the idea is “transformative”, which seems to be an imprecise concept at best.

Business Ideas II: HelpGen

Idea: HelpGen – Auto-Generate Help for Apps that doesn’t Suck

Let’s face it – online help sucks. Online knowledge bases usually suck too. This is a problem I’ve faced both with my own companies, but also in using a wide array of web and mobile applications. Why does help always suck? Because it’s an afterthought, usually written by a harried human who has 5 other jobs and for whom this is not job 1. Taking the opposite approach, while the machines are getting smarter, they are not yet able to write context-sensitive help that could actually tell you how to use a product. Or could they…?

MVP (Minimum Viable Product):

Use machine learning combined with assisted workflows to help organizations create instant help and knowledge base materials for their products. HelpGen would first traverse the site or mobile app in question, making note of terminology used, available buttons, and inputs, and other interactions possible on each part of the application. HelpGen would then guide the user to write content for each term, control, and step on each screen of the application – using machine learning to suggest content whenever possible. By monitoring the application for updates, HelpGen would also flag changes requiring help content update. By removing the friction from keeping help up-to-date, HelpGen could dramatically lower the complexity and cost of maintaining quality context-sensitive help.

Market: 5M+ applications deployed globally

The number of web and mobile applications has exploded over time, so app stores alone (this doesn’t count millions of web applications) count roughly 5M applications globally. The top 10% of all applications might see enough use to require ongoing maintenance – but that is still likely around 1 million applications globally. SMB SAAS solutions in related spaces like support / chat / feedback capture provide both a likely business model and comparison on market size. Various reviews of applications in the help/support space seem to indicate that $50-100/month per customer is rapidly attainable, with broader feature sets required to expand beyond that. This implies a total addressable market size in the $5B range, which is quite reasonable as a starting point.

 

Scoring (0-10 scale, up to 2 points per question): 7 points

1. Is it Transformative? 1 point

The HelpGen concept would be truly transformative if AI or ML could be used to automate the entire process – but as with most use cases, that’s not possible just yet. This is more likely to be a guided process for some time, which is still far better than the status quo.

2. Revenue Market Size or Eyeballs: 1.5 points

As discussed above, comparisons with other leaders in the customer support space indicate a thriving and rapidly growing market. I consider a $10B addressable market to be the gold standard, and this idea isn’t far off that.

3. In a Growing Market? 1.5 points

This market is growing as rapidly as cloud and mobile apps themselves, since all require some form of help and support.

4. Difficulty, Barriers to Entry, and Competition: 1 point

Many existing players in the help/support space may choose to attack this idea if it begins to gain traction. This is also not a winner-take-all market, providing some room for a well-built product to grow without being sidelined immediately.

5. Riding Hype or a Trend? 2 points

AI and machine-learning are arguably THE trends of the moment, and while arguably overhyped, HelpGen could definitely ride this marketing wave.

Business Ideas I: Juggler, Never Let A Message Drop

Over the years I have kept a running spreadsheet of business ideas – my current business, HiddenLevers, was once a denizen of the same spreadsheet. But ideas have expiration dates [1], and my idea list has grown while my available time has shrunk. Over the next few months I will be sharing my ideas – I’d love to hear feedback and to inspire others to take the next step or gain inspiration. To provide structure, for each idea I’ll share my thoughts on what I thought the MVP might be, and a scoring of the idea using my own 10 point scale. Here goes!

Idea: Juggler – Never Let a Message Drop

Juggler would watch your firm’s emails, LinkedIn, and other messaging platforms to ensure that every inbound request is tracked and gets a response. The challenge today is that inbound business communication arrives across channels, and often comes in to many different personnel at your firm. Using AI, Juggler would determine which messages actually require response, and monitor these across all firm users, alerting managing when prospects and clients are awaiting response.

There are a ton of AI-based email solutions and also support email solutions from firms like Zendesk – but none of these seem to focus on this specific use case – firm-wide monitoring and taking a global look at all communications to a particular client.

MVP:

The MVP is simple – do the machine learning work to simply determine whether a particular email requires response. Emails asking questions clearly come to mind – but taking a true machine-learning approach, can we approach 99% accuracy here? This can then be married to a simple UI showing individuals (not messages) requiring attention – this sort of dashboard data could ideally then be integrated into Salesforce or other CRM platforms.

Scoring (0-10 scale): 6 points

1. Is it Transformative? 1 point

This is a fairly standard use of machine learning in 2018 – but the accuracy level required to make this viable is not. Also, many businesses still don’t take real advantage of CRM systems, and this idea automates some of the key value concepts from CRM for a small business (ie don’t let any leads slip through the cracks – I’m looking at you, contractors).

2. Revenue Market Size or Eyeballs: 1 point

This is a broad market – virtually every business could use this capability, so volume pricing of even a few dollars a month in a SaaS solution could scale quickly. Presuming that this capability is worth $5/user/month – the US market alone is greater than a billion per year.

3. In a Growing Market? 1 point

While email utilization is stable, multi-channel communication is growing – think chat, social media, VOIP (phone) – in theory the same approach could be applied to all of these.

4. Difficulty, Barriers to Entry, and Competition: 1 point

It may prove difficult to achieve the level of accuracy with machine-learning to inspire user confidence. If businesses suspect that even a few important messages might be slipping through, they will lose confidence and not use the product. Ideally the system ought to learn based on each user and firm’s data – posing a bit more complexity.

5. Riding Hype or a Trend? 2 points

AI and machine-learning are arguably THE trend of the moment, and while arguable overhyped – the Juggler idea definitely is riding this trend.

 

[1] James Watt’s steam engine was an excellent invention, and applying it to pumping water out of mines an excellent business idea – for the 1770s. The concept of hailing a car via smartphone was likewise a great idea – in 2009. It’s also possible to be too early – Yahoo Briefcase shutdown the same year DropBox was founded (although the latter was also a vastly superior implementation).

 

P.S. Investors out there, feel free to reach out if any ideas in this series are of interest – while my core business continues to grow rapidly, I’m open to discussions on how to seed fund and launch against many of these ideas.

The Great GOP Stimulus

The 2018 Trump stimulus exceeds the Obama-era stimulus package in size – will it pay off at the top of the economic cycle?

In 2010, when Barack Obama pushed for a stimulus package to help boost the American economy, it was decided by many in the GOP as wasteful spending. While there are more productive (infrastructure) and less productive (tax rebates) ways to stimulate the economy, any form of spending (or tax cut) is a form of economic stimulus – this is a point agreed by both economists and businessmen like Warren Buffet. In fact, any form of budget deficit is a form of stimulus, as the government borrows (or prints) money that it doesn’t have to spend it into the economy.

The past year has seen the GOP enact not one but two stimulus measures – first a budget which ended Obama-era budget caps and boosted spending by roughly $150B per year, and second the tax cut which reduces taxes by another $150B per year. Taken together these measures are adding roughly $300B per year in stimulus to the US economy, potentially adding 1.5% to GDP for each of the next few years. Adding this stimulus to a core GDP growth rate of 2-2.5% might thus make 4% possible in the near term, with the bill due much later. The total federal (non-central bank) stimulus under President Trump’s first will hit at least $1.2 Trillion, exceeding President Obama’s 2010 stimulus package by $350 Billion [1], but this time at the top of the economic cycle!

What does this tell us? A few key takeaways emerge:
  • While most economists agree that it’s better to do fiscal stimulus when the economy is at or near recession, democracies don’t work this way, and there’s little correlation between economic need and actual governance.
  • When either party has complete control of government, they take the opportunity to spend on favored initiatives – in Trump’s case the DoD received most of the benefit, while in Obama’s case a variety of energy efficiency, infrastructure, and other initiatives were funded.
  • Budget deficits haven’t been a major issue over the last decade, but the tax cuts in particular will layer on top of Social Security and healthcare spending trends to drive debt-to-gdp well past 100% [2].
  • The best stabilizers in the US economy (unemployment insurance) are effectively automated – extending this sort of stabilizer to infrastructure spending (spending more on transportation funding etc as unemployment rises) would not just help buffer downturns – it would also get taxpayers a better deal.

Time will tell whether the GOP’s late-cycle spending will extend the business cycle substantially, but in the long run US policy will improve if more of these decisions are put on auto-pilot, removing the uncertainty of the political winds and the desire to spend at the least opportune times.

 

[1] The Obama administration stimulus plan cost around $850B in the end, including only the 2010 Stimulus measure and its implementation. Extension of Bush-era tax cuts and similar are not counted here, as these were extensions of existing measures, rather than new tax cuts or new spending as in the Trump administration’s recent moves.

[2] Many charts and news reports on the debt refer only to the publicly-held portion of the US debt, but when debts to the Social Security trust fund are included as in this data from the Federal Reserve, the US debt-to-gdp ratio already exceeds 100%.