As an investor with Contrary Capital, free-lance advisor for Southeast startups, and leader of Emory's entrepreneurship organization, I frequently have conversations with founders who are looking to synthesize their "story" prior to raising seed funding. In this series, I attempt to ask the same questions of startups that have raised seed funding from eminent venture firms, and then back into what may have been their answer (albeit through the lens of my industry perspective).

Startup: Pinwheel

Backers: First Round Capital, Upfront Ventures

Raise: $7.0m (seed)

What is the simplest form of the value proposition?

"API-as-a-Service" platform connecting developers of financial technology across budgeting, lending, deposits, and risk use cases with consumers' payroll environment to enable disruptive applications.

Are they creating "space" or positioning against established players?

Pinwheel is aiming to unlock an entirely new avenue of data from a set of closed systems, making it a unique disruptor by almost any definition. While some its use cases have offline or legacy analogs (e.g., income verification), many, such as payroll-based contributions towards consumer loans, are simply not viable with currently available solutions. And similar to Plaid, there exists an extremely compelling first-mover advantage in this market, due to the complexity of integrating with a large, diverse set of payroll providers. If Pinwheel is scale these integrations quickly, it is unlikely customers will turn to a competing platform that is lagging behind. However, Plaid itself is uniquely positioned to enter this space with their own suite of offerings, posing a threat even if Pinwheel established a considerable lead.

What does "product-market fit" / "stickiness" look like?

Pinwheel's success in the near-term is dependent on its platform execution. Customers need to feel comfortable that Pinwheel's platform will scale with their products, support the leading payroll providers (e.g., ADP) as well as fringe players, and protect consumer data and adhere to stringent compliance standards. Most customers, especially large financial institutions, will test Pinwheel's platform with a limited reach product, such as home equity loans under $100,000 for prime borrowers, before employing it in their broader products or services, such as direct deposit switching. If Pinwheel can establish reliability during these test runs, I expect customers to aggressively expand their usage of Pinwheel's platform.

What would "success" mean in the 5-year horizon?

The metric of success for Pinwheel is unique to companies in this category: spark an ecosystem of applications that intrinsically rely on the platform to provide a new experience for consumers. The diversity of applications could be endless, but shifts within the "low-hanging" verticals would indicate that Pinwheel is successful in supporting this ecosystem:

• Paycheck-linked products: ‍Financial institutions should build products that collect payments directly from a borrower’s paycheck, resulting in fundamental adjustments to competitive rates and default risk models.
• Payroll visibility: Consumers have a vested interest in understanding every element of their paystub, e.g., how it fluctuate per pay period, how allowances change their withholding amounts, etc. Financial budgeting applications such as Mint or Personal Capital should use Pinwheel to centrally surface this data to improve differentiation and overall experience.
• Direct deposit switching: Direct deposit enrollment is a clear pathway for banks to increase average account balance and subsequently upsell additional products (e.g., brokerage). Therefore, they should be eager to utilize Pinwheel to replace the current solution (generating a PDF with account numbers and then requesting the consumer to contact his/her employer) with something that seamlessly connects accounts to payroll providers.
• Income verification: Credit agencies, loan servicers, and property managers alike would benefit from up-to-date pay stubs and tax documents to instantly and securely verify their customer’s income and employment. Pinwheel should be the go-to choice to anyone, from established players like LendingClub to upstarts like NYC-based SavvyScreen, building a consumer risk assessment stack.

What are the major forces (headwinds or tailwinds) that could impact success?

Due to the unvalidated nature of many downstream applications of Pinwheel's solution, the size and growth of this market is unclear, but the recent $3b acquisition of Plaid by Visa is indicative of the lucrative relationship Pinwheel can establish with developers reliant on its platform. However, a major risk exists from the supply side of Pinwheel's platform. It is clear that Pinwheel must first establish a "back-door" integration with a majority of payroll providers by authenticating with a consumer's credential and then scraping data off the relevant pages before engaging in official agreements with these providers. This poses a three-pronged issue: 1) collecting a large quantity and diversity of credentials from risk-averse consumers to build out these integrations, 2) updating these integrations regularly to support changes, and 3) adapting to the eventual financial and operational considerations posed by formal agreements. The payroll market is less fragmented than, for example, the banking market, giving more power to providers such as ADP in setting the terms of the agreement.

In the many commercial due diligences I have completed for SaaS acquisitions, I have seen that ~3x pipeline coverage is necessary to comfortably achieve growth targets. And while the methodology to qualify leads varies between companies, the necessary coverage ratio applies to a pipeline of qualified leads, meaning the quality feeding into the funnel is as important as the volume. Here are some strategies I look at when advising SaaS startups that are struggling to reach that ratio of quality leads:

• Improve target customer definition - While not a lead generating activity in itself, a major hurdle to lead generation is having detailed definitions and a prioritized list of target customer profiles. Levels of specificity that are often missing include: sub-sector, stage of growth, department and level (of the buyer), and any competitor interest. Once these profiles are established and then validated by internal data and customer conversations, all lead-gen activities (inside sales, paid search, email marketing, etc.) improve their value.
  
• Ensure the marketing funnel is not inverted - For high quality leads to reach the sales team, they need to engage with the brand in the right order. The highest volume of unique views should hit broad-based content, then hit targeted content, and finally arrive at product pages; it is often the case that this structure is inverted. A common mistake is with paid ads; they should largely be viewed as part of the middle to bottom-end of the marketing funnel, utilized to close "warm" leads that have already engaged through another channel. Fixing this could be as simple as refactoring CTAs across owned channels or reallocating the paid advertising budget towards the content fit for the audience.
  
• Further invest in content - Content is an extremely high-ROI activity, particularly when it leads to "earned" coverage, or non-paid content published on third-party channels, that generate backlinks to owned channels. Example areas of content for enterprise SaaS solution that could result in earned coverage include a proprietary survey covering target buyer (i.e., customer success) pain-points, explanation of why key activities provided by solution are a "need-to-have" over a "nice-to-have," or other data-driven reports. Press releases of new funding, features, or, most notably, joint partnerships between companies are also a way to generate earned coverage.
  
• Address bottom of funnel conversion - Regardless of whether the marketing funnel is operating correctly, bottom-of-funnel conversion is always a key factor in converting views into demos. Chatbots have become ubiquitous because they generally convert ~3x better than requesting emails, primarily due to their ability to pique a potential customer's interest with engaging content or the option to quickly schedule time with a sales rep. Another strategy I would look at is providing potential customers an offer right before conversion. These offers could be as simple as additional product information, detailed pricing sheets, or even limited time pricing offers (such as first 3 months free, 3 accounts free forever, etc.).
  
• Piggyback on partners - While early-stage SaaS startups may not yet be able to drive revenue for upstream partners (e.g., Salesforce, Segment, Zendesk, etc.), an interesting strategy may be to go after VARs, SIs, or other non-technology partners of these market leaders. It may be preliminary to set up an entire partner program, but hand-picking partners that sell to target verticals and already boast a "best-of-breed" position (i.e., pick and choose the best offerings rather than cross-selling for one company) could be a strong entry point. Additionally, ensuring a comprehensive presence on platform marketplaces (e.g., AppExchange) is a good way to catch a few interested eyeballs.
  
• Improve targeted SEO - Often newly launched SaaS startups have very few mentions in the top 20 search results for "[target vertical] software." While this broad search area may be tough to break into, targeted keywords like "customer success orchestration" or "best customer success software for Salesforce," for example, would help drive inbound interest from the right set of customers.

When advising both established SaaS leaders and emerging startups, I come across a few general considerations when determining how to price and package a workflow-driven SaaS solution. Setting the stage, the potential options usually align to three simple models:

• Account-based pricing: $ per account * # of active accounts
• Seat-based pricing: $ per seat * # of enrolled seats
• Volume-based pricing: $ per unit * # of units (often data related) consumed

The main difference between the account and seat-based models is that accounts can be potentially shared, while seats are tied to an individual user. Account-based pricing, therefore, can make it tougher to up-sell more accounts if a customer is utilizing the model to make the SaaS solution broadly available for light or infrequent users.

However, for both account and seat-based models, simple "t-shirt size" (small, medium, large) pricing packages can be used to cater types of accounts to individual customer segments, and therefore restrict potential account vs. usage discrepancies. Through a careful analysis of usage trends, certain features, such as reporting capabilities, could be withheld in lower-tier accounts or accounts could automatically scale up based on data volume and/or level of customization (e.g., number of custom events within a 360 dashboard). Another option is differentiation based on account type. Field team managers may need expanded functionality to orchestrate planning while executives would only require "viewer" access (a model championed by BI tools). The key is to have the packages follow the behaviors of a maturing customer, either in top-line growth or organizational structure, aligning their incentives to the provider's (i.e., only increase the burden on the customer when it is offset by their success). And without implementing these strategies under an account or seat-based model, renewal volume at an established customer is largely tied to the provider's ability to "scope-creep" into new uses cases for their solution, a difficult proposition for even the most beloved offerings.

It also can be valuable to explore a go-to-market strategy that involves a combination these models. Many SaaS solutions naturally lend themselves as data platforms in addition to an workflow or orchestration platform. Fully adopted users often originate mounds of propriety data, custom views, saved states, and account profiles, to name a few, making it reasonable to sell a "NewCo Cloud" offering that splits up the financial consideration between the data warehousing and user experience.

Finally, I have seen success when providers choose an entry price point based on the idea of value-based pricing, wherein the product generates ~10x ROI for the customer. Since this is understandably difficult to quantify for many solutions, I advise leaning on the data collected from initial customers around how much it would cost for customers to replicate the solution in-house or through a combination of other tools, how much time is currently spent recreating the same output, and what financial outcome have similar solutions driven. This generates visibility into levers the customer would use when evaluating the business case for for the solution and point me in the direction of right pricing. However, that being said, pricing conversations should not focus on the toolset's premium on the comparable cost basis, but rather the fact that the SaaS solution would provide a "premium" solution. On average, I have observed that enterprise technology customers, which are often the early-adopters for new SaaS solutions, are open to fast-tracking pilots in the $20-50 per account/seat per month range, based on a quick margin calculation on an ACV within the $25-50k range.

Since I am a sporadic gamer at best, I have been surprised to find myself developing into more than a casual consumer of esports over the past few years. Cognizant of the "n of 1" trap, I sought to validate my overwhelming intuition that esports is on the cusp of the mainstream. I have attempted here to compile a sampling of the core strengths of esports, especially those that make it more attractive than current entertainment models. Even with a few substantial obstacles (also highlighted below), I expect the exponential growth experienced by most major "IRL" sports this past century to be a plausible model for esports in the next one.

Sampling of core strengths

• Esports allows a linear progression from beginner to pro skill level, with very limited investment, which incentivizes more individuals to participate in the process.
• Creation of tiered leagues (premier, relegated, etc.) and tournaments very quickly allows deeper proliferation into communities (as seen by the English football model).
• Live broadcasting of esports has very little incremental cost, even when expanded to large-scale levels like LAN tournaments.
• The ability for competitors to compete remotely and audiences to watch from home reduces cost of content and protects against structural change like the kind we are currently experiencing with the onset of COVID-19. Case in point, according to a recent report by Verizon, video game usage in the U.S. has risen 75 percent during peak hours (weeknights and weekends).
• Although viewership has been heavily male dominated and there have been severe discrimination issues (such as Gamergate), no gender-based separation in competitive environments may, over time, attract a broader audience.
• End-to-end complete control of the experience allows pro leagues and teams to engage fans in-game with decals, in-game tokens, experiences, etc. It's challenging to visualize the scope of this feature, but a parallel would be every baseball field automatically generating a Yankees logo behind home plate 🤯.
• Tracking of major competitor statistics are fully automated, extremely accessible, and directly comparable to novice players. For example, a Super Smash player at home can compare their combo efficiency against a leading professional on sites like https://tracker.gg, a feature that both increases engagement and allows viewers to better "learn from the pros."
• Nation / country tournaments, which historically have posted the highest Nielsen ratings (e.g., World Cup, Summer Olympics), are simple to organize (virtually if needed) and potentially more inclusive than traditional sports due to reduced need for expensive training and development resources.
• Content can be higher energy and more consistent, especially considering that esports titles, in large part, have no interruptions through penalties, breaks, etc.

Immediate concerns

• Currently, every game developer is looking to get in on a piece of the action, resulting in a hodgepodge of titles. In the future, developers need to think thematically about their potential viewers, i.e., whether esports titles should fall into prevalent themes by interest area. Potential themes could include: strategy game-board (traditional example: American football, esport example: Dota), high-energy, simple to follow (traditional example: soccer, esport example: Rocket League), "in the action" (no traditional option, esport example: Gran Turismo), "head-to-head" (traditional example: boxing, esport example: Super Smash Bros), to name a few.
• Player and team turnover is high due to linear progression of talent (i.e., there is an efficient market of talent to replace underperforming players) and non-guaranteed nature of many esport salaries. There are examples of stars emerging over time (e.g., Faker in League of Legends), but only in situations with high longevity of the title and continued investment by the organization.
• Small system or network glitches cause resets or invalidate results, which likely has an effect on viewers' immediate interest and ongoing investment.
• Most games have not been optimized to allow for breaks (for rest, team coordination, commercials, etc.), likely reducing average watch time (only 2.2 hours per session according to SuperData Research).

Despite these obstacles, I expect esports not only to endure their current challenges, but also to grow substantially over the next several decades.

As I worked my way through various consulting teams and environments, I came across a few terms that weren't immediately clear, so I decided to document them. Some of these might be specific to my experience while others likely have proliferated across the consulting / business spheres, but maybe it can serve as a useful starting point for a new analyst / associate. Also, some of them are pretty hilarious 😆.

Probably just VP speak worldwide

• Blocking & tackling | Basic tasks that must be done to meet short-term goals, more of the mundane than the revolutionary
• Cover your ass (CYA) | Material of any type (footnote, extra email, backup slide) that supports, qualifies, and/or sometimes further obscures a statement
• Softness | The number is liberally calculated; headwinds should be expected that will drive the number down (in the case of revenue or profit) or up (in the case of cost)
• Horse trading | Bargaining in a "zero sum game" between various executives, particularly for investment funding or relief for potential overspending
• Swim lanes | Individual processes or workstreams; often used to instruct analysts to order activities sequentially and categorize them by eventual outcome

Likely featured in "House of Lies" or #consultingmemes

• Straw man / straw dog | A presentation with headers filled out and/or descriptions on each slide to mock-up the potential "story"
• Quarterback | Run the show, be the "point" person, but also in consulting ensure the deliverable is tailored for executive consumption
• Flash it | Present the slide or model on the projector, TV, or screenshare, prepare for rapid, real-time feedback and editing
• Run interference | Protect someone else, usually a partner or executive, from the criticism due for a particular activity, often for controversial initiatives like restructuring
• Boil the ocean | Try every different possibility, interview every stakeholder, stay up all night going through different options until one makes sense
• Bogey | An outlier (usually negative) that needs to be addressed
• Double bubble | Counting something twice, as to inflate both numbers; also commonly used to refer to increases in both stocks and bond markets
• Please fix | Either the entire slide needs to be rethought / restructured / reformatted or there is simply just a lack of footnotes or page numbers; it is really up to you to decipher the context

Maybe localized jargon from my firm/teams?

• Mickey Mouse | Make it less wordy, more conceptual, and clearly convey the key message
• Squishy | The number is imprecise, commonly due to either conscious executive manipulation (to show the impact or cost favorably) or expedited analysis (potentially due to necessity to announce a program to the Street)
• Round out the bullets | Similar goal as with Mickey Mouse, but focused specifically on the supporting text on the slide; also, there is a need for tone and syntax consistency that is implied
• Good guy vs. bad guy | Good = tailwind, bad = headwind
• Dice it 8 ways to Tuesday | Take the data, Pivot it (that one you should know) by all the appropriate dimensions, and highlight any clear trends or outliers
• Peanut butter | Spread the value amongst multiple line items or categories, as to avoid creating an additional line items or to meet reporting requirements

As with many technologies (see VR, 5G, etc.), natural language conversations were oversold well before the underlying technology could meet the goal of transforming user experiences. Companies branded relatively simple voice recognition and keyword mapping as "artificial intelligence," and users quickly became frustrated with dead-ends while interacting with chatbots or virtual assistants. Furthermore, on the supply side, companies had not meaningfully organized data to support indirect or "fuzzy" matching (e.g., there may not be a direct answer about GDP growth, but here are some points on inflation and unemployment that may help).

But in this next decade, I expect partnerships between a few major players (Google especially) and many targeted entrants (e.g., Drift, Expressive) to refine the NL formula and radically transform the way we interact with many applications. The most significant impact of conversational platforms will be the further democratization of technologies across a wide user base. Navigating through cluttered menus or disparate articles is not an experience that leads to quick adoption, clear value capture, and prolonged use. Developers have been aiming to alleviate this and make applications as self-service as possible, and the panacea may be "natural" conversation.

This will certainly upend business models that rely on antediluvian interactions (such as click-bait advertising), and unfortunately, there is significant risk that the major AI players will capture much of the value through hosting, data access, and IP licenses. But upstarts that deeply understand their customers' conversational workflows will have an opportunity to leapfrog slow-moving players in long-standing industries (e.g., BI tools) or create completely new solutions that fundamentally rely on this new form of interaction (e.g., Woebot). The one major headwind is the potential loss of jobs, but as with manufacturing, skills will migrate from providing the services to overseeing and improving them.

One of our core strategy offerings was helping CxOs wrap their heads around what required to transition towards "data-driven growth." As most of our clients fit into the category of "mature but agile" software leaders, they had arrived at the conclusions: 1) they are collecting valuable customer interaction data and 2) they require a dedicated enterprise data function to interpret this data and stay competitive. But where these leaders struggled was how to port the insights generated from their data science teams into actionable directions that could be utilized by their customer-facing teams (marketing, sales, success, support, etc.).

The inevitable first solution presented by the client was to simply expose their capable teams to the generated insights and then compare their relative effectiveness to status quo. However, we cautioned against jumping into this process without further analysis. While a simple solution seemed enticing as it would reduce lead times to potential growth, there are a plethora of considerations that, without being addressed, could impact the utility of these insights. Some of the more significant challenges include:

1. Team members may not have the necessary skills or resources to address the insights, rendering them useless to actually drive growth.
2. Team members may not have the required education in the parameters and constraints of the insights to appropriately extrapolate its coverage; this can especially come into play when insights are not updated on a daily cadence. Additionally, tangential to the issue of coverage is the possible false assumption that the insight establishes causation rather than correlation.
3. Even if the insight is deemed "significant," the predictive strength may be unclear to the teams utilizing it, potentially resulting in either over or under reliance / fitting of behavior to the insight.
4. A hierarchy may not be established between generated insights or between insights and other data elements, such as team member assessment or real-time customer response.

To rectify these issues, the safest option to develop a pilot system, where a clear experiment is defined, tested, and then critically evaluated before putting into practice. But before we recommend doing so, a clear set of principles must be established to help mitigate potential logistical questions, such as:

• How can we isolate the effect of targeted insights when functions are inherently interrelated (e.g., success <-> support)?
• Should we develop and track a new operational metric that is aligned to the direct effect of the insight, or should we judge all pilots by core metrics such as net retention rate?
• Have we established a clean baseline or control group to evaluate pilot results? Similarly, should we compare insight-driven actions to the status quo or the complete absence of any action?
• Should the completion of an insight-driven actions affect future insight generation (i.e., a feedback loop)?
• Should complexity or cost be a factor when evaluating pilot efficacy?
• And maybe most importantly, how do we protect our current operations during the process of testing new insights, especially when on large sample sizes are required to be significant?

A panacea for the gap between data-driven insights and customer actions remains elusive, but guiding our clients through these decision points have made the prospect of building a repeatable, reliable pilot system achievable.

As an investor with Contrary Capital, free-lance advisor for Southeast startups, and leader of Emory's entrepreneurship organization, I frequently have conversations with founders who are looking to synthesize their "story" prior to raising seed funding. In this series, I attempt to ask the same questions of startups that have raised seed funding from top-tier venture firms, and then back into what may have been their answer (albeit through the lens of my industry perspective).

Startup: Esusu

Backers: Acumen Fund Sinai Ventures, Kleiner Perkins, Katapult Accelerator, Plug and Play Tech Center, Global Good Fund, Temerity Capital Partners

Raise: $1.6m (seed)

What is the simplest form of the business proposition (the Lobby Pitch™)?

Platform enabling benefits of rotating credit and savings associations to groups of young, struggling savers, with additional benefit of direct-line reporting to credit agencies.

Are they creating "space" or positioning against established players?

Many new entrants are hoping to tackle the gap of credit and money management for those currently unable to participate in traditional institutions. Disregarding the many predatory offerings, many startups have sought alternative ways to boost credit scores (e.g., MoCaFi), and Esusu fits firmly into that landscape. However, its innovative approach, allowing friends and family to offer each other no-interest loans in a rotating format, and its deep integration into banking systems and credit agencies (namely Equifax) provide a level of separation, that, if it proves attractive to its core audience, could make it the preferred provider of this service.

What does "product-market fit" / "stickiness" look like?

Unlike other offerings, Esusu is not promising to "instantly" boost a customer's credit status through additional reporting or debt refinancing / consolidation. Therefore, the customers must find the Esusu's savings vehicle to be beneficial from both a pecuniary and behavioral perspective. Retained customers would need to invite their friends and family onto the platform and participate in multiple pay cycles for the concept to be fully realized, especially as the fee structure is based on pay cycles rather than percentage of transaction value.

What would "success" mean in the 5-year horizon?

Financial literacy is a huge goal for many fintech startups, but Esusu's unique approach requires specific customer education on the value of its financial service (rotating savings groups), a challenge not generally shared by, for example, a mobile-first brokerage. Success would mean establishing rotating savings groups with mainstream finance, and then expanding Esusu's offerings to either traditional banking (i.e., home, auto, other personal loans) with potentially alternative repayment options and/or other forms of P2P lending (similar to that of Lending Club) in an open marketplace.

What are some market forces (headwinds or tailwinds) that could impact that?

Consumer hesitation to adopt new financial practices may prove an insurmountable obstacle, preventing widespread adoption of Esusu's solution. However, more near-term is the risk of major credit agencies either minimizing the impact of completing pay cycles within a savings group on an individual's credit profile or changing their direct-line relationship with providers like Esusu. Nevertheless, there is an unfortunately trend towards financial instability in many countries, which may buoy previously underutilized solutions into mainstream adoption.

As an investor with Contrary Capital, free-lance advisor for Southeast startups, and leader of Emory's entrepreneurship organization, I frequently have conversations with founders who are looking to synthesize their "story" prior to raising seed funding. In this series, I attempt to ask the same questions of startups that have raised seed funding from top-tier venture firms, and then back into what may have been their answer (albeit through the lens of my industry perspective).

Startup: Prodly

Backers: Shasta Ventures, Norwest Venture Partners

Raise: $3.5m (seed)

What is the simplest form of the business proposition (the Lobby Pitch™)?

Management tools for enterprise applications / configurations within the enterprise environments (namely Salesforce) aimed at simplifying IT operations.

Are they creating "space" or positioning against established players?

Prodly fits in an unique space tangential to DevOps called AppOps, essentially the work an IT manager must be responsible regardless of whether the environment is hosted on IaaS or utilizing PaaS or even SaaS solutions. While their core offerings of build, test, release, and monitor are similar to that of any continuous integration or application orchestration platform, their focus is on reducing extremely long lead-times between business process requirement (BPR) definition and application deployment by targeting common IT environment challenges like data integrity, version control, and referenced data deployments. This narrowness of focus and aim to bring application management outside of a limited set of IT managers positions Prodly more so against the providers (Salesforce, Oracle, SAP) than their downstream tools.

What does "product-market fit" / "stickiness" look like?

As with any back-office enterprise tool, it is necessary to see Prodly be an integral part of an IT manager's workflow. The real test, however, will be the ability for an administrator outside of IT operations to use this Prodly's toolset independently. The painpoint is more-so around the bottleneck between the other functions and IT rather than the quality of the applications actually deployed. Frequent updates like package rationalization should feel effortless and large business events like acquisitions or business model changes should feel manageable.

What would "success" mean in the 5-year horizon?

Prodly is currently focused on Salesforce and specifically on the CPQ, billing, and field service environments. The goal would certainly be to expand to the rest of Salesforce's offerings and potentially even act as a unified platform across ERP, CRM, etc. application release and testing. Success could also mean creeping into the development space, rather than focusing on the test and release phases. Rather than simply supporting for "low-code" applications, Prodly could move into specific tools that make designing these applications more efficient and robust.

What are some market forces (headwinds or tailwinds) that could impact that?

Large enterprise players certainly have an incentive to fix the application management bottleneck to remain competitive and natively support vital toolsets their customers rely on. This could either result in Prodly being a prime acquisition target, or a situation where most of Prodly's advantages are adsorbed into the provider's platforms. Also, migrating, testing, and deploying these applications often falls upon professional service firms (e.g., Cognizant), which are currently in the process of developing similar tools that allow them to stay in the loop at large enterprises.

As an investor with Contrary Capital, free-lance advisor for Southeast startups, and leader of Emory's entrepreneurship organization, I frequently have conversations with founders who are looking to synthesize their "story" prior to raising seed funding. In this series, I attempt to ask the same questions of startups that have raised seed funding from top-tier venture firms, and then back into what may have been their answer (albeit through the lens of my industry perspective).

Startup: Public.com (formerly Matador)

Backers: Greycroft

Raise: $2.0m (seed)

What is the simplest form of the business proposition (the Lobby Pitch™)?

Mobile-first, no-commission brokerage focused on capturing first-time investors through a social stack, automatic high-interest cash management, and thematic-based guidance.

Are they creating "space" or positioning against established players?

Matador is entering a crowded, established space. The most direct peer is Robinhood, but with the recent trend of brokerages competing on commission and building out mobile-friendly toolsets, almost all major platforms (e.g., Schwab, E*Trade, etc.) can be seen as potential competitors. Matador's emphasis on platform accessibility and social accountability is aimed at novice or casual investors, but the core offering is still a online brokerage.  

What does "product-market fit" / "stickiness" look like?

A speciality brokerage like Matador would want to see a high percentage of trades being driven by Matador's unique feature set. Conversion from social conversations to active trades would be a key driver of engagement, as would usage of Matador's thematic lists (e.g., "meatless revolution") for targeted stock picks. Matador is making a bold bet by formalizing "investment clubs" and would need to prove a much larger set of investors are interested in participating.

What would "success" mean in the 5-year horizon?

Adoption of Matador's non-brokerage features will determine if the platform is a simply an option in the increasingly homogenous category fintech apps trying to be budgeting tools, online banks, and brokerages/advisors all at once or a clear market leader with the "secret sauce" to attract the large base of disinterested consumers. The similarities with Robinhood and Acorns are clearly apparent; it is unlikely all three will survive with the current level of differentiation.  

Heavy VC investment in Robinhood has shown that the cost of educating and successfully on-boarding "non-traditional" investors far exceeds the revenue that can be generated through returns on uninvested cash. Transitioning to premium offerings billed as a percentage of AUM or flat monthly charge will be vital to turn a large customer base into a profitable one.

What are some market forces (headwinds or tailwinds) that could impact that?

Consumer reticence towards utilizing multiple services to manage their financial health could severely impact Matador's ability to attract users. It certainly possible that consumers will "fall into" brokerage or advisory services from their banks or other essential financial services, rather than branch out on their own to find a better-suited solution. This may be, however, driven by a systemic lack of financial literacy, something Matador and its peer set are aiming to address.

Update Aug. 2019: Matador has changed their name to Public.com

When working with enterprise software companies, I have noticed that channel mix becomes a key pain-point when making the transition to a subscription selling model. Most of our clients organize their GTM operations, including marketing / demand generation, sales, and customer success, by customer size, segmenting enterprise, middle market, and SMBs / individuals.

Generally, they, in an effort to maximize customer lifetime value while minimizing customer acquisition cost, employ a direct GTM approach with enterprise customer, utilize their channel for middle market customers, and develop a low or no-touch model for SMBs / individuals. However, when pursuing a subscription transition, GTM leadership may need to redefine their GTM strategy across all of these three segments:

• In the enterprise segment, they must build an in-house customer success team or repurpose their professional services capabilities to convey to their largest customers they are invested in business case realization that would warrant an ongoing relationship. They also need to retrain and re-incentivize sales teams to “land small” with their core product and then “expand large” with upsells of additional seats / consumption, or cross-sell a related collection of products, which will dramatically increase the lifetime margin of the customer.
• In the middle market segment, I advise my clients to consolidate the channel from favoring the “long-tail” channel strategy to favoring a few large players can help maintain strong partner-led sales. These few large players have the financial stability to get past an initial drop in net new revenue due to lower upfront costs, have the leadership to develop a customer success team independent of service offerings, and can put forth a unique value proposition, e.g., product augmentation, managed service, “t-shirt” size product packaging, etc., that will sweeten the business case of the solution.
• And finally, they must develop as much collateral as possible to ensure efficient time to value for self-service SMB/individual customers. While some clients have developed detailed documentation and walkthroughs, investment in in-product collateral (like quick start projects) is a stronger determinant of success.

While this just scratches the surfaces on the breadth of changes required to strategy in a business model transformation, keeping these elements may help these more “legacy” players operate more like their subscription-first counterparts / competitors.

A common question we get asked from our emerging (< $1b revenue) SaaS clients is: when do I start focusing on the efficiency of my sales and marketing operations? Based on quantitative research as well as our anecdotal input from clients, the answer may be quite simple: never. This may seem counterintuitive, but a few, key observations suggest its validity:

• SaaS players must prove to their investors that their products are consistently catching on with new customer segments and/or use cases, as they must offset the market dynamics. The inherent deferred revenue model lowering the lifetime value of each customer, lower switching costs allowing customers to “try and then buy,” and the burden of displacing an existing service or an in-house solution are all risks of a SaaS product heavily concentrated in a few large customers or single market vertical. Acquiring new customers does pose a higher customer acquisition cost than upselling or cross-selling existing customers, but net new logos create more overall revenue opportunities and develop a more defensible customer base.
• Average customer acquisition costs between the faster SaaS growers and most profitable SaaS players is comparable (as per OpexEngine and KeyBanc SaaS surveys), showing the best companies are able to scale their operations linearly rather than inefficiently or non-strategically. Net dollar retention, the lifeblood of SaaS metrics, is surprisingly also comparable between the faster growing and most profitable companies.
• SaaS players are being measured by investors on their ability to generate growth. The “magic number,” coined by Lars Leckie of HWVP, is a common metric for investors, calculated as prior years’ sales and marketing expense over incremental recurring revenue in the current year. Fastest growing companies boast 1.25 figure and boast a 5-10x revenue multiple, while the most profitable hover around the average of 0.75 figure with a 2-5x revenue multiple.

So instead of reducing sales & marketing investment, I usually suggest exploring these questions instead:

• Should you refocus on acquisition of new customers vs. retention of customers likely to churn?
• Are you effectively upselling / cross-selling customers that have passed initial value creation?
• What are your worst customer / segments in regards to customer acquisition cost vs. lifetime value?
• Can you evolve your strategy into the next phase of your growth plan, such as entering new markets or pushing new products?

As an investor with Contrary Capital, free-lance advisor for Southeast startups, and leader of Emory's entrepreneurship organization, I frequently have conversations with founders who are looking to synthesize their "story" prior to raising seed funding. In this series, I attempt to ask the same questions of startups that have raised seed funding from top-tier venture firms, and then back into what may have been their answer (albeit through the lens of my industry perspective).

Startup: Kapwing

Backers: Kleiner Perkins, Shasta Ventures, Sinai Ventures, Village Global, Shrug Capital, ZhenFund

Raise: $1.7m (seed)

What is the simplest form of the business proposition (the Lobby Pitch™)?

Lightweight, browser-based content creation tool aimed at newly prevalent formats (such as memes or square Instagram videos), for lean design teams or individual creators.

Are they creating "space" or positioning against established players?

Kapwing is creating space for a new set of design tools aimed not at professional brands teams that have established workflows, design guidelines, asset management, etc. but at upstart brands or individuals that want to hit the ground running. In that way, they are positioned more against an iMovie (give-away tool, extremely limited functionality, but high usability) than a Final Cut Pro. They are also creating a consolidation / analytics tool for culturally relevant but often ephemeral media (gifs, memes, etc.), something that isn't currently being pursued by other players.

What does "product-market fit" / "stickiness" look like?

Kapwing is certainly going after creators focused on quantity over quality, so "product-market fit" would mean high engagement not only with editing but publishing / exporting on a frequent basis (likely weekly). In the same vein, the rate of completion from creating a new project to publishing would likely need to be high to warrant continued use of a targeted, nascent, feature-poor option. And like most SaaS solutions, conversion from free-tier, activated customers to paying, retained customers will point to whether customers feel the solution meets the need for their consistent creation pipeline vs. one-off video resizing or background removal.

What would "success" mean in the 5-year horizon?

Signing on a "long-tail" of SMBs or individual creators to paid accounts is key, but if Kapwing can tailor their content tools so effectively to new use cases that users clamor for verticalization into social media management (e.g., Buffer) and asset management (e.g. Creative Cloud), there is potential for a really defensible position.

What are some market forces (headwinds or tailwinds) that could impact that?

Entrants by direct competitors in the content creation space is certainly a risk, but the larger headwind may be from social platforms incorporating much of Kapwing's toolsets natively into their offerings. Facebook/Instagram, Twitter, etc. have created a host of extremely successful complementary businesses, but there seems to be an emerging trend from these platforms to better understand their users and recapture certain revenue or engagement models (e.g., Facebook Marketplace replacing Buy & Sell groups). These platforms also have access to a wealth of data that if utilized, would certain advantage them when designing targeted tools. Tailwinds are certainly the continuing democratization of content creation (influencers vs. traditional media) and the "create from anywhere" trend discouraging long lead-times between capture and publishing.

As an investor with Contrary Capital, free-lance advisor for Southeast startups, and leader of Emory's entrepreneurship organization, I frequently have conversations with founders who are looking to synthesize their "story" prior to raising seed funding. In this series, I attempt to ask the same questions of startups that have raised early stage funding from top-tier venture firms, and then back into what may have been their answers (albeit through the lens of my industry perspective).

Startup: Smartcar

Backers: New Enterprise Associates, Andreessen Horowitz

Raise: $10m (Series A)

What is the simplest form of the business proposition (the Lobby Pitch™)?

Provide "connected car" applications with real-time, vehicle-specific data elements, through standardized API-based middleware that does not require additional hardware.

Are they creating "space" or positioning against established players?

Smartcar solves two major problems:

• Real-time access to data such as fuel tank status or tire pressure has historically been limited, with only the largest freight and trucking companies with thousands of expensive assets choosing to invest in enabling technologies. By partnering with OEMs to extract raw, ECU data, more companies and industries can take advantage of these potential insights.
• Development of "connected car" applications has been limited by proliferation of bespoke hardware or deep partnerships with OEMs. Relieving third-party developers of this burden can usher in a new era of applications, similar to how Plaid influenced the fintech space.

In this way, Smartcar could be the enabler of new solutions, and therefore is positioned to be the preferred provider of this service. Early "connected car" entrants such as Automattic could make a play in the data-broker B2B space, but initially, Smartcar seems to be in a league of its own.

What does "product-market fit" / "stickiness" look like?

In its core verticals / use cases, Smartcar should expect to be the sole or preferred service provider for this data. Therefore, a high volume and percentage of requests should go through its API, indicating that developers are building their solutions through Smartcar versus around Smartcar. And similar to API-centric offerings like Twilio, Smartcar should be the defacto brand around automotive data.

What would "success" mean in the 5-year horizon?

Smartcar initially supports a limited number of OEMs, so success will center around building enough market presence to convince others to partner with Smartcar or at least support its platform. This would require heavy investment in customer success for leaders in core verticals (ridesharing, fleet management, insurance, etc.) to refine their use cases and bubble up Smartcar's data beyond analytics into continuous operations or even product features. The goal for Smartcar should be a "long-tail" of viable connected car solutions that are powered off Smartcar's data.  

What are some market forces (headwinds or tailwinds) that could impact that?

The largest players in Smartcar's largest market, ridesharing, are also deep-technology companies with significant market power. Smartcar should expect home-grown solutions at these companies, and, while unlikely, prepare for the possibility those solutions may hit the market. OEMs are also moving towards new ownership models (e.g., Audi's subscription offering), and while they are likely to initially out-source this layer of their stack, continued trends away from vehicle purchasing may encourage them to reconsolidate ownership of this data. Finally, autonomous vehicles will be built ground-up to enable easy data capture and transmission, so when they arrive and have mass market appeal, Smartcar may be cut out of the equation.

Coming out of high school, my vision of the collegiate experience was intimately tied to the idea of entrepreneurship. Even though it is drastically more commonplace now, the late 2000s felt inundated by stories of students formulating ideas and turning them into viable ventures within the sphere of the collegiate environment. It seemed as if the old rules of higher education as a pathway to career stability was making way for a new rules as the breeding ground for risk-takers.

When I arrived on campus at Emory University, the reality felt a little different. I entered at a time where entrepreneurship felt more like a buzz word than a concerted movement, but there was certainly an feeling that a shift was coming. At Emory, the difference between old and new felt more pronounced, as the few students in Emory key communities of business, medicine, and technology interested in entrepreneurship were demonstrably insulated from the broader community. Through involvement with various facets of the Emory startup community, I noticed a common thread of a few rather surprising traits between these future founders.

• Breaking out the gargantuan task of launching a company into manageable pieces might be key to building momentum for older, workforce-wizened founders, but student founders needed to be able to visualize their idea as intrinsically separate. Otherwise, it was common for the tasks that fell under the new venture to merge into ever-present list of items stemming from coursework and other extracurriculars, resulting in ineffectual prioritization favoring the non-entrepreneurial tasks that came with clear deadlines. Imperative to future founders was "protecting" the task of building as an isolated function, requiring an uninterrupted focus and heightened level of attention.
• As would be expected, confidence was key, as the road ahead meant approaching customers, investors, partners, etc. with a demeanor impervious to criticism or rejection. But students I met that had confidence in their ability gain quick proficiency in new skills or comfort amongst new communities felt increasingly rare. They felt the challenge posed by a transitioning to a new full-stack framework or adjusting their target market was within the natural course of their development rather than a disruption. And this type of confidence was often clear well before they embarked on an entrepreneurial venture; for example, a common thread was hearing them refer to difficult course as "intriguing" rather than "challenging" or "complex."
• And finally, there seemed to be an almost inverse correlation between risk tolerance towards new ventures and other "valuable" characteristics. Top performers in class, leaders of key organization, and well-connected students seemingly had the tools that translated well into key tenets of entrepreneurship, but didn't seem to have the incentive to pursue it. It was almost as if these students had pushed themselves into a "corner of success," while others felt comparatively free to make mistakes. This is likely true outside of the collegiate sphere, but it was further exacerbated by the "protector-ward" relationship between faculty and students. Professors encouraged students to thrive within known environments (e.g., academics), unintentionally disincentivizing students who achieve initial success there to change course.