Report by: Samuel Doyle (11975385) Samuel.r.doyle@gmail.com
University of Amsterdam, MBA Fulltime 2017-2018 Supervisor: Dr. Martijn Rademakers
Date of submission: Aug. 15th, 2018
Case Company of RLY Technologies
Optimal Business Model Design
for Internet-Native Start-ups
For Internet-Native Start-ups
Contents
1.
Abstract
... 32.
Introduction
... 43.
Literature Review/Underlying Theory
... 54.
The Frameworks/Tools + Application
... 85.
Managerial Recommendations/Implications
... 266.
Limitations and Conclusion
... 357.
Appendix
... 371. Abstract
Start-ups are becoming an increasingly important source for overall economic growth, but the concept of business model generation in the start-up context has not been explored in depth until relatively recently. This paper sets out to answer the underlying research question of: How
does one design an optimal business model for an internet-native startup business? In order to
do so, this paper will drive a dual-faceted strategy that is part theoretical, and part practical. On the theoretical side, the paper seeks to be novel in its marriage of generally later stage business model innovation frameworks, such as Kim & Mauborgne’s (2004) Blue Ocean Strategy, with that of early stage business model generation frameworks, such as Ries’s (2011) ‘Lean Start-up’. Before delving into the frameworks, the paper will survey pertinent academic literature on the core topics of business models, value propositions, and platform monetization strategies. Following from the literature review and frameworks, the paper will synthesize and build upon the commonalities between the articles to create an actionable theoretical framework for startups looking to craft and refine their value propositions in a quest to design the optimal business model.
The practical part of this paper will be a detailed exploration of the case study of the start-up social network platform RLY Technologies. RLY is an entrepreneurial application-based startup that is trying to become the dominant Generation-Z social media platform. They are going through the arduous process of business model generation & refinement and are currently experiencing difficulties in articulating their core value proposition and revenue model to potential investors. Given the pressing need to craft a compelling value proposition in an industry awash with well-funded and dominant incumbent competitors, RLY provides an especially interesting case for both testing this paper’s custom theoretical framework as well as answering the overarching research question on optimal business model design for start-ups. Drawing from my experiences working at RLY in tandem with the classroom learnings from my UvA MBA, this paper concludes by providing actionable recommendations that can be implemented to help RLY setup processes to continuously improve and refine their business model.
2. Introduction
In today’s world, Mobile Applications (Apps) have become fundamentally interwoven into the fabric of our daily lives. For many, including this paper’s author, it’s almost unthinkable to go an entire day without opening the Spotify app (music), WhatsApp (communication), or Instagram (entertainment). With an ever-widening breadth of services and utility offered by apps, it’s no wonder to see global app downloads topping 175 billion and consumer spending ballooning to $86 billion, with AppAnnie pointing to continued >20% growth for the global application market after 20171. Yet despite these rising tides of increasing smartphone penetration and increasing advertising & consumer spending on mobile, there are clear winners and losers emerging within this mix. It is often the app’s business model that ends up being the deciding factor. Without a well-developed business model, businesses will tend to fail to deliver or to capture value from their operations. This is particularly true of Internet and mobile application businesses, where the creation of revenue streams can be perplexing due to customer expectations that many services should be free (Teece 2010).
The focus of this report is on business model generation for mobile-first start-up businesses, with special thought given on how to build a revenue model and the corresponding monetization strategy. This paper centers around the case of RLY Technologies (Appendix A), a female-founded iOS platform startup business, that is trying to create a wholly new social media platform focused on authentic and moving content (videos/GIFs/iPhone Live Photos). Their aim is to understand how to craft a business model that can ultimately monetize its rapidly increasing user base in non-privacy intrusive ways. Given the recent public outcry2 surrounding privacy concerns on social networks, RLY is looking to avoid building their business model based on monetization strategies that rely on unscrupulous data collection and privacy-intrusive advertising. With RLY’s situation kept in mind, the key question this paper sets out to answer is: How does one design an optimal business model for an internet-native startup business?
In the next few sections, I will review the meaning and importance of business models, how to define one’s value proposition in a startup and internet/mobile context, and how a mobile-first business should go about choosing their revenue model and monetization strategies. The
1
https://techcrunch.com/2018/01/17/global-app-downloads-topped-175-billion-in-2017-revenue-surpassed-86-billion/
first part of this paper surveys the major literature surrounding the topic. Specifically, it delves deep into understanding what constitutes a business model and the role they have on ultimate business success, with specific attention paid to the discussion of value propositions and value capture. Building on that discussion, the paper looks at business model designs within multi-sided platforms. Thereafter, Osterwalder (2010)’s business model framework will be introduced to sketch the basic elements that comprise a business model. This framework section will weigh the discussion of revenue model and value proposition more heavily than other elements. I then will build on the business model framework with that of Ries’s (2011) Lean Start-up to create a more detailed framework for the process of business model generation at a startup. Given both Ries and Osterwalder’s emphasis on understanding a business’s value proposition, this paper will also utilize Kim’s (2004) Blue Ocean Strategy Framework to more fully flesh out the process of crafting a differentiated value proposition. This custom framework will be further augmented by Eisenmann’s (2006) and Parker’s (2016) strategies for multi-sided platforms to ultimately set the stage for a more focused discussion for RLY where I will outline some key insights and actions that can be implemented to build an authentic business model that can become a source of competitive advantage in of itself. Lastly, this paper will end with a review of the limitations of this custom framework and a brief conclusion.
3. Literature Review/Underlying Theory
Within this section, I aim to describe the meaning and importance of business models as relevant to the underlying research question on optimum business model generation for an internet-native business. To begin, with an understanding of what exactly constitutes a business model is required as this initial literature survey helps to bring clarity to an otherwise loosely-defined concept that is very fundamental to business success. From the review of the pertinent literature on business model definition, the importance of value propositions and value capture becomes clear. I then explore how value propositions can be defined and validated through grounded real-world continuous learning. Finally, I conclude this section by surveying how traditional management theory has evolved in the context of internet-native and platform-based business models.
While the phrase “business model” has become popularized in management jargon and seen in pop culture on shows such as HBO’s “Silicon Valley”, there is still some academic disagreement on the scope of what constitutes a business model. Shafer et. All (2005) cross-sectional literature analysis found a whopping 42 different components present in academic definitions of what is a business model. Compare that to other side of the spectrum, Christensen’s (2008) oft -cited work found business model to be simply be the combination of 4 key elements of Customer Value Proposition, Profit Formula, Key Processes, and Key Resources. Magretta (2002), see’s business model’s as parts of a broader narrative and defines them as “stories that explain how enterprises work”.
There also exists the question of how a business model is created, with Casedeus et. All (2010) seeing it as the byproduct of a firm realized strategy and Shafer (2005) seeing it similarly as the sum of the strategic choices that have been made thus far. Some academics, such as Porter (2001), have even referred to the phrase as simply part of the “Internet’s destructive lexicon”. Within all these competing and diverse definitions, there emerges a general theme around the importance of value, namely how does one create value, how does one deliver value, and how does an enterprise capture that value. For simplicity’s sake and to more closely align with the case company, this paper will use Tecee’s definition of a business model as the baseline for further analysis, which he sees as “how the enterprise creates and delivers value
to customers, and then converts payments received to profits” (2010).
B. Importance of the Value Proposition
Important to Tecee’s definition, is the discussion of creating value for customers, which represents an integral part of pretty much all the literature surveyed on business model definitions. In fact, Seddon et. All (2004), sees the business model simply as simply the outlining of the essential details of a firm’s value proposition for its various stakeholders. Value proposition is defined simply by Franca (2017) as the combination of products and services that create value for an enterprise’s customer. Franca’s simplistic definition opens the broader question of what it is exactly that creates value for the end customer, and Franca offers aspects such as newness, cost reduction, convenience, usability, performance, and ‘getting the job done’ as various avenues for value proposition creation.
Building on the ‘getting the job done’ aspect of a value proposition, I find Christensen’s (2008) layman’s explanation of this element of a business model to be very helpful in conceptualizing an otherwise rather amorphous and broadly-defined concept. He views value propositions as the ‘job to be done’ that solves a big problem or fulfils an important need for the target audience. Within that view, Christensen postulates that the single most important attribute of a customer value proposition will be its precision: how neatly and completely it solves the target customer job to be done (and nothing else). While Christensen’s ‘jobs to be done’ framework is a useful lens to view the concept of value proposition in, this framework requires management to have a deep understanding of the customer’s pain points, which is a tall order for a start-up application-based business that has had limited face-to-face interaction with their end customer. The question of how an enterprise validates its customer problem and proposed value proposition for will be addressed in more detail in the frameworks section. This paper will look to utilize Ries’ (2011) method of validated learning as the core mechanism for gaining a deeper understanding of the customer problem and ones proposed solution.
In addition to the importance of understanding the customer, the management literature surveyed also stressed the importance about having a unique value proposition. Uniqueness, or ‘inimitability’ as Barney (1991) named it, is widely viewed by management literature as one of the key building blocks of a competitive advantage. Porter (2001) sees the uniqueness of value propositions as the result of forced trade-offs between quality and cost, whereas other academics such as Kim & Mauborgne (2014) see uniqueness as not bounded by trade-offs but rather the product of innovation on multiple aspects at once. In the coming framework section, this paper will introduce the Blue Ocean Strategy from Kim & Mauborgne (2014) as part the broader discussion on the concept of value proposition innovation in the context of continuous business model generation and improvement.
C. Internet-Native and Platform-Based Business Model
Much of the literature surveyed thus far has been grounded in the traditional economic theory based on 20th century business models. The discussions of customer problems, value proposition and value capture get blurred when shifting focus to internet-native businesses such as RLY. The increasing proliferation of the internet has turned many longstanding business models on their head. Tecee (2010) asked fundamental questions about how businesses can continue deliver value to their customer, and how they can possibly capture value from new
information services that customers increasingly expect to receive without charge. Furthermore, building on Tecee’s work, Wirtz (2010) pushes for a deeper understanding of internet-native business. His resultant framework describes and breaks out four basic types of prototypical Internet business models: Content, Commerce, Context and Connection, with which one can begin to classify insurgent internet business models such as that of RLY. Parker (2016) dives into a specific breed of internet-enabled business models, the multi-sided platform. Within this mode of analysis, the topic of network effects becomes exceptionally important as they turn traditional economic thinking on their head. Parker sees that multiple sides’ participation in successful models generates cross-group network effects, or in other words, that each side benefits not only from its own supply-side increasing but also from that of the other side of the platform. These cross-group network effects work to create a virtuous cycle where more users on the platform add incremental value to all participants on the platform. It is then no surprise that Casedeus et. All (2010) see that successful platform-based business models work to generate a virtuous cycles of feedback loops that strengthen components of the model with each iteration. This paper will go into more detail in the framework section on how RLY and other platform-based businesses can look to harness network effects to drive an ever-improving value proposition for all their potential platform constituents.
4. The Frameworks
A. Business Model Canvas Framework
To begin the discussion of the major tools and frameworks surrounding optimum business model generation, it is the papers intention to lay the groundwork with a detailed analysis of what exactly constitutes a business model. This paper uses Osterwalder (2011)’s basic definition of the business model as “the rationale of how an organization creates, delivers, and captures value”, with the operative word being the value that is created, delivered, and captured by a start-up. Osterwalder’s Business Model Canvas (Appendix B) provides a visual representation of the 9 basic building blocks that form the system of how an enterprise looks to make money. His nine key elements cover the four-broad reaching categories of customers, offer, infrastructure, and financials. To explain his business model components, I will use the explanatory case of a successful mobile-first platform: Snapchat.
First and foremost, in Osterwalder’s Canvas, sits the Customer Segments, which make up the heart of the model. Without a customer, there is no real reason to exist as a business. One of the first decisions that needs to be made by a start-up in defining their model is to decide which customer segment they are going to serve and those that they are going ignore. In the case of Snapchat, due its status as a multi-sided platform, there are multiple groups of customers they must address in their business model definition. Snapchat requires both a large active user base as well as advertisers and content creators. On one side, it needs advertisers to fund the platforms operations, while on the other, the advertisers require a certain number of eyeballs to make it worth their while to advertise on the platform. Snapchat decided early on to specifically target the younger internet-native generation as their core customer base, which ultimately led to their advertising client selection being heavily overweight to consumer facing product companies and brands that were trying to engage with a younger audience.
The second building block is that of the Value Proposition, which is the solution to a customer problem or pain point. Importantly, Osterwalder makes the distinction that it’s not one single attribute but rather the aggregation of benefits that are being offered to customers. It is also important that the value proposition resonates with one’s proposed customer segment otherwise there will be a mismatch within the proposed business model. In the case of Snapchat, they helped their users communicate with each other in a manner that better reflects who they are. Their main tool they used to communicate their value proposition was their distinctive private message service that auto-deleted messages which empowered users with to engage with each other with a level of authenticity and playfulness that could not be found in other social networks at the time. Given the relative importance of value proposition to optimal business model selection, this paper will go more into depth in the following sections on the topic.
Thirdly, a start-up must consider their Channel choice when building a business model. Channels are the key the customer touchpoints that work to shape customer experiences and ultimate perceptions of the brand. Channels are all about delivering the value proposition to the right customer segment. For Snapchat, their early focus was on viral user acquisition channels. Any messaging app is naturally more fun when your friends are on it, and Snapchat worked to both minimize friction for inviting friends and to encourage users to invite as many of their friends as possible. Later in the company’s history, they began to try and broaden their
consumer touchpoints using physical channels such as the distribution of Snapchat spectacles and through their own branded content creation delivered via the app.
Fourth, comes the topic of Customer Relationship, which is about choosing the type of relationship it wants to build with their target customers. There exists a relationship spectrum of how personal or automated one wants the relationship to be. Importantly, this relationship choice is not static and should evolve over time as the business matures. For Snapchat, what started as a small-scale support network with live support operators quickly turned into a highly automated support system with only minimal company/customer interaction on most kind of service requests. This decision was mainly due to the increased scope of the customer segment and the resulting movement towards a more of a mass-market type of relationship.
Next, comes a discussion of the Revenue Stream, which is where a start-up needs to understand how they will generate cash from their customers. Much analytical thought is required here as the company must ascertain what value their customers segments are willing to pay and in what manner are they most willing to pay. Monetization strategies have long been one of the hardest things to figure out for otherwise highly successful app and internet-based companies, which can spell trouble when investor funding finally dries up because the business can’t self-fund its own operations. Given the importance of this topic, a more detailed examination of platform monetization strategies will come later in the paper. For Snapchat, this is still a major point of contention, as they have chosen to utilize an advertising-based revenue model, where they collect fees from those advertisers that wish to market to their users. The heavy-handedness of the advertiser intrusion into the in-app experience has arguably eroded the value proposition to users somewhat and illustrates the careful balance that must be walked when deciding on a revenue model.
Sixth on Osterwalder’s list is the topic of Key Resources, which are the key assets that enable a business model to function. They can take the form of tangible, financial, intellectual, or human capital. Depending on the other elements of the business model canvas, one type of resource may be most fruitful to focus on. In the case of Snapchat, it is not really their financial or physical resources that matter in the business model, but rather it is their intangible resources of brand, key partnerships, and strong app-developer human capital that make up the backbone of this value element. Following from key resources comes the seventh element of Key Activities, which comprise the most important processes and operations a company must
undertake to be successful. Just as some resources are more important for reaching markets, maintaining customer relationships, or earning revenues, the same can be said of defining one’s key activities. Often there is little valued added or strategic significance from retaining a process in-house, and management time and effort can be freed up through out-sourcing the process to a more specialized provider who has the economies of scale to deliver it more cheaply as a service than possible in-house. For Snapchat, the key activities engaged are generally focused around driving user growth and keeping those users engaged, as user growth is the fuel that their business model runs on.
The eighth block of the Business Model Canvas is the Key Partnership block. This describes the network of partners and suppliers that form the broader environment where the company operates. Osterwalder’s model distinguishes four key buckets of potential partnerships, namely strategic alliances, strategic partnerships (between competitors), joint ventures to develop new businesses, and buyer-supplier relationship. For Snapchat, they choose to deepen their relationship with existing advertising partner NBC Universal, through the creation of jointly run studio3. What was once a simple buyer-seller relationship, has evolved into that of a co-operative joint venture that is attempting to re-write the way that scripted content is delivered on mobile screens, potentially creating a win-win for both partners.
Cost Structure makes up the 9th and final element of the Business Model Canvas. While seemingly a simple function of the other 8 elements, cost structure decisions can make or break a start-up, especially in the early days. Broadly speaking there are two main buckets of cost structures, either a cost-driven model or a value-driven model and choosing one often must align with the other elements or will spell financial doom. Snapchat, with its large base of VC investors and easy access to equity funding, has seen itself tend towards a much more value-driven model when it comes to a discussion of cost centres. Snapchat views user growth as the ultimate driver of its business model, so within that lens it’s only natural to see them make large cost outlays on things surrounding content, user acquisition, and user retention. Cost structure normally becomes an element of higher importance later in a company’s lifecycle or if there is an internal liquidity crunch.
So, with the 9 key elements examined through the lens of Snapchat, it’s very important to emphasize that there is a certain level of cohesion that must be achieved between these building blocks. They should not be viewed as discrete building blocks but rather part of an inter-connected web of value creation, capture, and delivery. In the Snapchat example, it’s easy to see how cohesive the customer segments, value proposition, channels, and key activities all so closely intertwined to create what has been one of the most highly successful viral-growth based mobile applications of all time. The close-knit and deliberate interlinking between those elements enable Snapchat to build a better mousetrap. But if they are not vigilant, there are ominous dark clouds on the horizon in their business model elements of revenue and cost structure. The push for monetization has already begun to affect user growth and engagement, which has been accompanied by a rising costs in customer retention and decreasing efficacy of marketing spend. Ignoring those two elements could be the beginning of the end for what has been incredibly successful growth-oriented business model.
In addition to exploring Snapchat as a case study, it’s also important to ask the question of “are all aspects of a business model are equally important?” Ladd’s (2016) studies with early stage entrepreneurs, found that his start-up teams, who focused their efforts on target customer segments and value proposition, performed nearly twice as well as teams that did not spend a commensurate amount of time on those elements. This would suggest that RLY or any other aspiring platform spend its efforts on really understanding the customers pain and their proposed value proposition before delving into defining the rest of its business model.
B. Lean Start Up
While Osterwalder’s framework generally works to shed light on the topic of business model definition, a deep dive into Ries (2011) “Lean Startup” yields a more in-depth discussion of how one goes about getting to their desired business model. At the core of the Lean Startup model sits the central idea that a manager, instead of spending lots of time planning around assumptions, can work to drive constant adjustments through steering process called “build-measure-learn”. His framework encourages start-ups to use their amount learned as the measuring stick for progress in early days. But this learning needs to be validated and demonstrated empirically before a team can hope to discover valuable truths about their startups business prospects. Through empirical testing, startups can track and measure any positive improvements in their core metrics and eliminate any management efforts that are not
directed (and measured) at learning more about what customers really want. It is important to caveat that all validated learning should be backed up by measurable input collected from real customers.
Over multiple feedback loop cycles, it becomes clear what the customer values and the startup can work to better tailor their business operations to formulate a value proposition that resonates with the customer. To help speed up these cycles, Ries advocates for the use of Minimum Viable Product (MVP) to ensure short time between cycles with minimal efforts. MVP’s within this framework are designed not just to answer product design or technical questions, but mainly to test fundamental hypotheses about the business model. Using this framework, Ries sees MVP’s and validated learning as not only a more accurate approach, but also a faster one when compared to market forecasting or other classical business planning techniques.
While this paper agrees that speed is an undeniably important concept for start-ups to consider in their learning decisions, I would argue that there are significant risks associated with such a reckless focus on feedback loop iteration. Inherent in the definition of MVP are the terms “minimum viable”, which leads developers to generally skimp on architecture. From a Lean perspective, this makes sense as if one doesn’t have the time to build a full product, then one also doesn’t have the time to invest in architecture. But decisions about architecture can be especially important in determining the long-run success or failure of a business. This paper takes the case study of Evernote to illustrate the point. In its infancy, Evernote had many note-taking app-based competitors, some of which had all the latest UX/UI bells and whistles that were the result of faster time-to-market and build-measure-learn feedback loops. Evernote, instead of focusing on incremental customer improvements, chose to take a whole product architecture focus where they scrutinized their architecture with the goal of scaling into multiple products. Ultimately this deliberate focus resulted in a more vibrant ecosystem that enabled independent software vendors to build on top of their platform and thus unlocking positive network effects and is an effective example of Sheehan’s (2009) “Network Services Logic” for creating incremental value.
While the Lean Startup framework’s focus on speed may have some negative side-effects such as employee burnout, hasty rejections of good ideas, and the limited thought available for product architecture and longer-term business strategy, the framework still serves as an
excellent roadmap for a prospective startup to gain insight into refining their business model and ultimate value proposition. Importantly, this emphasis on speed enables startups to rapidly test their key assumptions for the business. Ries (2011) divides assumptions into two key buckets, “The value hypothesis” and “The growth hypothesis”. Utilizing this framework, a startup would want to both focus on testing whether its service or product delivers real value to customers while using it and focus on testing how new customers will discover their products.
In the case of RLY, the testing growth and value hypotheses unveils the key variables that ultimately control growth. On the value hypothesis, RLY should test the leap of faith value assumption that “social media super users have the free time to use another social network”. This assumption is key because social media super users are the engine of growth for content creation, which is the main reason people engage with the platform. Using the Lean Startup framework of validated learning would require empirical data to validate this assumption. In this example, we would argue RLY should look at user engagement metrics (average time of sessions, # of content created, app opens, # of friend invited, etc.…) for heavy users of Snapchat and Instagram, which will help to validate the key assumption that there is room for another social network in the lives of social medias heaviest users. To test a key growth hypothesis, such as “influencer marketing is the fastest way to attract new users”, RLY would then follow a similar procedure of examining its user acquisition process through validated learning. The empirical data collected in this case would then be on things such as signs by attributed channel, cost per acquisition, and the number of friends invited by those users brought in through influencer marketing. And it is through these repeated build-measure-learn cycles that RLY, or any startup in general, can uncover what customers want and how to craft the best possible engine of growth.
It is important to note that when validated learning points out that startup is headed for a dead-end, that the Lean framework does not advocate for completely abandoning the project. Rather it is about repurposing the learnings that have been done and finding a more positive direction to continue with. Ries (2011) names this repurposing as a “Pivot strategy” where he argues for structured course corrections that position the company to test new fundamental hypotheses about their product or strategy. He provides a catalog of potential pivot strategies that a startup could seamlessly utilize to shift direction mid-course. RLY, with its roots in Virtual Reality and now an aspiring social media network, has already undergone its own major
pivot. The scope of RLY’s past pivot is well beyond what Ries (2011) would advocate for given the lack of opportunity to utilize the learning of a B2B virtual reality services business in their now more B2C platform-centric model. Ries (2011) would instead argue that pivot requires one foot rooted in the organizations past learnings, while seeking even greater validated learning in a fundamentally new direction. That said, Ries would cheer RLY’s timing of the pivot decision as the framework advocates for pivoting sooner rather than later and tends to define a startups true runway as simply the number of pivots they have left in the tank. Utilizing Ries’s typology of pivot strategies on a go forward basis, would potentially involve RLY engaging in strategies such as a customer segment pivot (currently focused on female teenager market) or potentially an engine of growth pivot (currently focused on organic/viral user acquisition). But within all potential pivot options, it is important to remember to continue the process of validated learning about what customers want.
While this paper asserts that validated learning is the bedrock for business model creation and refinement, in the lens of a consumer-centric app business it also important that a startup produces high-quality experiences for customers as a primary goal. Ladd (2016) sees that too many build-measure-learn loops can result in diminishing or even negative return from customer interaction. He sees this coming from an erosion of confidence, on both sides, where too much feedback from customers leads management to so frequently change their ideas that they become disheartened and legacy consumers are not sure what the company stands for. This, like the Evernote case, would argue for a more deliberate and thoughtful pace of build-measure-learn feedback loops to craft those higher-quality consumer experiences. That said, I would argue that if one doesn’t know who the customer is and what they value, then one does not know what quality is, and the best way to gain true customer understanding is through repeated build-measure-learn cycles, albeit done in a more deliberate and thoughtful manner.
C. Blue Ocean Strategy & Value Proposition Innovation
One of the key linkages between both the Business Model Canvas and the Lean Start Up frameworks is the concept of a differentiated value proposition. While Ries’s (2011) work sees a tailored value proposition as the result of repeated “Build, Measure, Learn” cycles, and Osterwalder (2011) sees it in simple terms as simply the bundle of attributes in products or services that create some value for a customer, both stress the critical importance of the concept as a key to business success. Further building upon their frameworks with the goal of choosing
the optimal business model in mind, requires a deeper understanding of what exactly constitutes a value proposition, before understanding how a startup can design and ultimately innovate one. Traditional strategists such as Porter (2001) sees the uniqueness of value propositions as the result of forced trade-offs in the product and value chain. While there is extensive management literature on managing the strategic tension from trade-offs in value proposition design, it is this paper view that an aspiring social network or mobile application not look to bound itself to the classical view on value proposition design. Instead, this paper will introduce the multi-faceted Blue Ocean Strategy from Kim & Mauborgne (2014) as guidelines for value proposition innovation.
While the term “Blue Ocean” may adorn the headlines of Kim & Mauborgne’s (2014) oft-cited work, the framework actually begins with a required understanding of their so called “Red Oceans”. The authors characterize Red Oceans as maturing industries where the market has gotten incrementally crowded, and potential profits and growth have been reduced by an influx of entrants. The naming of those industries as “Red Oceans” comes from the resultant commoditization of that industry’s product and the cutthroat competition that then turns the ocean bloody. Within this Red Ocean context, management teams utilizing a structuralist or trade-off-based framework only can see two potential options for survival, namely a low-cost positioning or a high-quality differentiation strategy. Kim & Mauborgne stand opposed to that view as they write that “In contrast [to value-cost trade-off models], those that seek to create blue oceans pursue differentiation and low cost simultaneously” (Kim & Maurborgne 2004, p13). Blue Oceans in their eyes are the uncharted waters where a company can capture new demand that is free from traditional competition. This enviable position affords those companies in the Blue Ocean the prospects for profits and growth, but it also begs the question of how a startup can create or enter its own Blue Ocean, which this paper will touch on later.
While admittedly Kim & Maurborgne’s work is generally thought to be utilized in the context of managerial advice for those companies in maturing or dying industries, these “Blue vs Red Ocean” viewpoints in the context of an aspiring social media network paints an interesting picture. With 2.2 billion monthly active users4, and its suite of other highly used connectivity apps, Facebook is in a position of immense power when it comes to the broader social networking industry. Given its massive user base, Facebook captures the lion’s share of
the global marketing budget spent on social networks, which leads to a highly competitive red ocean for those left that stand to do battle in a market with such high-power concentration in a competitor. The cut-throatedness of this Red Ocean can be illustrated through the Snapchat Stories versus Instagram Stories analogy. Stories, which are public exhibitions of temporary content, were first a differentiated piece of product innovation wielded by Snapchat, and they were often credited with Snapchats share gains in the social media sphere. But Facebook quickly imitated the same product innovation, rolling out their own Instagram Stories which led to decreases in Snapchat’s Stories engagement of ~40 percent since the release of Instagram stories. Top creators on Snapchat used to get around 330,000 views per day until June 2016, whereas they now only get around 205,000 to 250,000 views per day5. Facebook using simple imitation was quickly able to turn what had been a leading point of differentiation for Snapchat into a highly contested red ocean. And if Snapchat, with its multi-billion-dollar market cap and high-profile board struggles in an increasingly red-ocean, how can an aspiring social network think to compete in such a red ocean? Through that lens, it becomes increasingly obvious that if a social media startup wishes to succeed in crafting a unique and innovative value proposition, they will need to consider a Blue Ocean strategy.
One basic framework put forward by Kim & Maurborgne (2004) for managers was their “Four Actions Framework” (Figure 1), which they intended to break the structuralist view of simple trade-offs between differentiation and low cost, and ultimately work to create a new perception of value. Utilizing this framework, enables a management team to break from the orthodoxy and identify attributes that could be eliminated, reduced, raised, or created. But just identifying the attributes that should be altered is not enough to create a unique value proposition. There is also the question of how one charts a path to its own Blue Ocean following the identification of the key attributes it wishes to change.
5
Figure 1: Four Action Framework
Blue Oceans, according to Kim & Maurborgne (2004) come about in one of two major ways, either through the founding a completely new industry or when a company alters the boundaries of an existing industry. This paper will largely examine the second route given its pertinence to the overarching research question. Within the lens of the second route, Kim & Maurborgne postulate that there are 6 generic ways to redefining market boundaries, which they named their “Six Paths Framework”. Namely they see that firms can: 1) Look across alternative industries, 2) Look across strategic groups within industries, 3) Look across the chain of buyers, 4) Look across complementary service offerings, 5) Look across emotional or functional appeal to buyers, 6) Look across time. Within these generic strategies it’s important to keep the focus on the fact that a startup needs to look systematically outside of its current red ocean that it intends to do business in. When utilized properly, their Six Paths framework can delivery key insight into how to possibly reconstruct market dynamics in their favor.
Building on Kim & Maurborgne’s framework for discovering Blue Oceans, Sheehan (2009) adds 3 more pathways, namely “Network Services Logic, Industrial Efficiency Logic, and Knowledge Intensive Logic”. Amongst Sheehan’s 3 incremental value creation logics, where managers could combine innovative bundles of ‘standard’ attributes to design wholly new offerings, we found the Network Services Logic most compelling for an app-based business such as RLY. Sheehan’s Network Service Logic at its core is about the creation of incremental value through connections with other constituents in the network. Take the example of Amazon, where they encourage buyers to connect to with both brands and other buyers through their reviews platform. An increase in the number of positive reviews gives consumers a better gauge of the quality of a vendor and they are more apt to purchase as such.
On the other hand, vendors benefit from honest consumer feedback and from a virtuous positive feedback loop in demand if their reviews are positive and large in numbers. Taking a systems-view as such and working to create network effects can help a start-up to find their own Blue Ocean. Sheehan (2009) recommends that to utilize this value creation logic efficiently, that start-ups should look to ensure after-sale services are available, other entities are encouraged to participate in the network, as well as work to create virtual communities within all sides of a platform model.
D. Monetization Strategies for Platforms
Monetizing a social network or other platform-based business provides for a unique managerial challenge. Academics and strategist tend to agree that the inherent value of any platform is derived largely due to the network effects it creates (Parker 2016; Eisenmann 2006). As already touched upon in the value proposition section, network efforts are what drives the positive feedback loops that accelerate user growth with minimal additional effort required by the platform operator. But despite being powerful engines of growth, network effects are relatively fragile and can easily be destroyed by premature or reckless monetization plans. As in the case of Myspace, Guardian (2015) writes that last nail in the coffin for the company was when chief Rupert Murdoch pushed them to make one billion dollars in revenue a year when they only doing 1/10th that amount, which led to ill-advised monetization strategies that destroyed the user experience. Parker (2016) agrees as he sees that any charge levied on users makes them less likely to use the platform, which erodes the positive network effects that built the platform in the first place. This tension and tight-rope walk between growth and profitability makes monetization a key concept that must be considered in every platform or business model design decision, regardless of where the company sits in its lifecycle of development.
Parker’s (2016) framework for platform monetization provides a strong base to understand how an aspiring platform business can wade into this issue and navigate the strategic cross-currents. Firstly, along the lines of this paper’s early outline of value proposition, he proposes that a platform needs to identify the excess value it creates for their participants. According to Parker (2016), value creation can be characterized in 4 large categories: 1) Value provided to consumers through access to content created on the platform; 2) Value provided to third-parties through providing access to a community; 3) Value provided to both consumers & producers
through providing tools that facilitate connection; 4) Value provided to both consumers & producers through curation to ensure higher quality of interactions. It is these papers view that these excess value creation buckets must align with the core customer value proposition as detailed earlier.
To bring real world examples into the discussion of this framework, one can simply look to Facebook, which I like to fondly think of as the ‘mother of all’ platform businesses. In the case of Parkers first value creation bucket, Facebook clearly provides a tremendous amount of value to the consumer through its news feed and profile system, but despite the incredible amount of utility those functions bring to an individual user, Facebook smartly does not attempt to directly monetize that by charging for access to the content for a consumer. In terms of the second value creation bucket, Facebook provides an amazingly detailed picture of its members and boasts the ability to create custom niche segments for its third-party providers. For the third bucket, Facebook works to remove the distance between producers and consumers by allowing big brands to create pages and profiles to engage with consumers. And lastly, Facebook works significantly to curate its platform, censoring that content which is hurtful or intentionally deceitful, which makes it easy for genuine interactions to take place. While Facebook serves as a great example to explain these concepts, it is also important to note that Facebook is a relative unicorn in that it generates significant value across all four of Parker’s (2016) value creation buckets, whereas most aspiring apps or social networks would only likely self-identify their excess value creation as falling into one or two of the buckets.
Following from an understanding of where excess value can be created, comes the discussion of how a start-up platform can look to monetize that platform-created value. A smart monetization strategy starts with looking at all four buckets of value in their entirety and figuring out which one can be most easily be re-captured by the platform without damaging their precious positive feedback loops of viral growth and platform-participant engagement. As in the case of Myspace, it’s almost always a mistake to try everything at once when it comes to monetization or do overly simplistic things like charging all participants for access to the platform. To begin with, Parker’s (2016) framework sees four overarching revenue models for an aspiring platform. Firstly, one can simply charge a transaction fee. Second, one can charge for access. Third, one can charge for improved or enhanced access. Lastly, a platform can charge for enhanced curation. When examining those broad models, it’s very important for a
start-up to keep in mind the ultimate goal of generating profits without reducing the platforms virtuous positive feedback loops.
To better understand how these broad-based monetization strategies work, it’s helpful to relate them to real life examples. A simple transaction fee model could look like eBay’s basic charges for listing an item for sale and then a simple percentage of the final sale value as commission. It is this papers belief that eBay’s initial listing fee actually goes against traditional monetization logic (which they get away with due to their size and reputation), as when one considers levying a transaction fee, they should wait until a transaction has closed (i.e. value has been created by those participating in the transaction), as an upfront listing fee may work to discourage users from engaging in the listing in the first place, which limits growth and achieving positive network effects.
In terms of a monetization model based on charging for access, Brazil’s Roomgo (EasyQuarto) provides an interesting example. The roommate finding service charges for those who wish to gain access to the platform to see available rooms in large cities like Rio de Janeiro6. The service does not charge those that post room listings, as the platform made a strategic choice to subsidize the supply side of their two-sided marketplace, but only those seeking accommodation. While this model works well in supply-constrained or high demand areas, their monetization strategy choice makes expanding coverage to cities characterized by an over-supply of rooms difficult as in those cases the scarcity in the marketplace sits on the demand side (and charging for access would only increase the demand shortage by deterring new signups). Eisenmann (2006) on the other hand argues that one should subsidize the network’s more price sensitive side and to charge more to those that are more responsive to the other platform participants growth. Either way, it’s important that platforms consider both supply/demand scarcity and the amount of platform-value created when making any decision regarding charging for access.
Parker’s (2016) third avenue for monetization involving charging for enhanced access is best illustrated by dating apps. This monetization strategy also works to minimize frictions around a negate impact to user growth as it allows for viral growth to drive increased sign-ups with no initial fee for access. Dating apps such as Happn or Tinder take care to preserve their
positive network effects as they make sure that all parties can participate and operate at a relatively high level. Their monetization strategy comes from the demand from some users for incremental services and utility offered to their users. This freemium and enhanced-access paradigm is widely used by many aspiring platform businesses, but when choosing this model one needs to stay vigilant that they aren’t seen as throttling user access through the division between services offered on one side of a pay-wall or the other.
The fourth platform monetization strategy involves charging for enhanced curation can be examined through the lens of Angie’s List. When platforms get to a certain scale, there comes a time when the quality of offerings begin to slip or when consumers struggle to parse through the sheer number of offerings. While that can spell untimely doom for a platform, it also provides an avenue for monetization if a platform can provide the tools to cut through the noise. In the case of Angie’s List, they recognized that customers were willing to pay for higher quality reviews. While other review aggregators allowed for all submissions, Angie’s list saw a monetization opportunity in prioritizing quality of content over the quantity. Funnily enough, Angie’s list originally charged an access fee to consumers wishing to view their database of higher-quality user reviews (Monetization Model #2) before introducing free and premium access7 (Monetization Model #3) which shows that a platform business operator needs to be flexible as they balance the tight-rope act between monetization and platform growth.
While Parker’s (2016) revenue models appear relatively straightforward and simplistic, this paper preaches caution and to tread lightly when making the transition out of a strictly user-growth oriented phase. A platform needs to be especially careful to avoid charging for services that they have been providing for free, as this can create resentment in its existing user base who have grown accustomed to these services being delivered for free (Tecee 2010). Additionally, some newer monetization techniques such native advertising (paid content that resembles unpaid) run the risk of turning users off to the platform as Wojdynski (2016) sees that when users find out native ads are “sponsored” or “advertising that they generally then lead to more negative evaluations of the poster. For example, much of Facebooks value proposition is based on how relevant it’s feed is for its users, and lots of advertisements could ultimately erode the underlying value proposition for the end users.
5. Continuous Platform Value Proposition Refinement Framework
With the relevant literature and academic frameworks now surveyed, it is important to perform a synthesis of the pertinent materials with the goal of the research question of ‘what is
the optimum business model design for RLY and other internet-native platform start-ups’ in
mind. While the authors of the aforementioned frameworks may attack the same problems from different angles and have varied preferences in terms of methods of execution, one theme whose importance is universally shared, is the concept of the value proposition. To address the research question, I initially wanted to first wade into a discussion of revenue models, but it became abundantly clear that without a requisite understanding of one’s underlying value proposition, that such discussions were misguided as they missed the elemental core of all business models. Ladd’s (2016) study again reminds us that business success is often a function of how carefully management pays attention to crafting its value proposition. To that end, I propose below a custom framework for how to identify, craft, and build a differentiated value proposition that represents the cornerstone of any business model formation plan. To do this in the context of an internet-native start-up, I will use the building blocks of Osterwalder’s (2011) business model canvas as the base, with Ries’s (2011) validated learning approach as the preferred method of execution for crafting the value proposition in the real world. Parker’s (2016) framework augments this custom methodology through his “excess value” creation typology, which I then look to improve via Kim & Mauborgne’s (2014) “Four Action Framework”. To summarize all these competing frameworks and deliver the best possible recommendations for an internet native platform business, I present this custom framework below (Figure 2):
Figure 2: Continuous Platform Value Proposition Refinement Framework
Within Figure 2, its important to first observe the mechanism by which the entire apparatus is run. This model uses Ries’ (2011) emphasis on validated learning as the core engine for value proposition and business model design. Why? Because quite simply the “build-measure-learn” loop cycle is the best way to continuously refine the value proposition of a company. I would stress that this custom framework is not something to be done once by a start-up but rather should be used as a framework for continuous learning to work to craft and refine the core of one’s business model. This model should be viewed in a dynamic sense, with the goal being incremental improvements and learnings with each iteration through the cycle. It is also important to remember that validated learning is grounded in real data and real customer feedback, and not only just a product of internal whiteboard sessions. This framework emphasizes the use of real world customer data as the guiding inputs for navigating these continuous cycles of validated learning.
With the motor of the engine being validated learning and the fuel being real world customer feedback, it’s time to talk about the other apparatuses needed to turn this framework into a fully functioning value proposition machine. To begin with, a deep and true
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Identify Customer
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- Ostewalder’s (2011) customer segments; know your customer
- Identify customer problem - Solve job to be done (Christensen 2016) - Identify potential value creation buckets on all sides of platform
- Choose value creation methodology/(ies) - Network Effects
- Kim & Mauborgne (2014) Four Actions Framework - Sustainable differentiation - Inimitability
understanding of the end customer is required. A start-up should first identify their core customer segment and map out their wants, needs, and desires through things such as customer empathy maps, personas, and survey work. Osterwalder’s (2011) canvas demonstrated that it’s important to be able to identify your core customer segment as one’s product offering may satisfy different needs for different sets of customers, and it becomes important to understand how to understand how customers intend to use your offering. Christensen (2016) characterizes this phenomenon as customers having “jobs to be done”, and they hire your product to fulfil a specific job to be done. While Christensen is all for customer empathy and understanding, he goes so far as to caution that a lot of customer research methods employed by today’s firms are taking them in the wrong direction as they fundamentally forget about what job the customer is hiring the product for. My custom framework argues to keep this “jobs to be done” principle in mind as a start-up looks to undergo the process of customer (and their problems) identification.
After successfully identifying customer segments, this framework then directs management’s attention to identifying the interplay between the various sides of the platform business. Should stakeholders have been properly categorized by management, it will become clear the competing needs of the various players on the platform. While it goes without saying that every need of every partner will not be met, the trick is finding symbiotic wants/needs/’jobs to be done’ between the platform parties that can be aligned through use in the platform. Using Parker’s (2016) typology, this framework sees value creation characterized across 4 general buckets: 1) Grant access to content created on the platform; 2) Give access to a specific community; 3) Facilitate connection; 4) Value provided through curation. With these typologies in mind, a start-up can look to find intersecting needs between its prospective platform participants and work to create a lasting value proposition that not only meshes with both parties’ desires but also creates excess value that can be captured by the platform and monetized later.
With a value proposition identified and a value capture methodology selected, the framework turns its attention to making one’s value proposition differentiated with the ultimate goal of building a competitive advantage. Part and parcel of this discussion of differentiation, is that of inimitability, which Barney (1991) saw as one of the four key ingredients sustained competitive advantage. Crafting a compelling value proposition is a difficult feat in of itself but crafting a unique and inimitable one is the true way to sustained business success if you
buy into the traditional competitive advantage logic of Barney and Porter. To create differentiation, this framework utilizes Kim & Mauborgne’s (2014) “Four Action Framework” & “6 Pathways” to help think about supercharging their underlying value proposition and distancing itself from the competition. This part of the framework is especially important to consider in the case of RLY given the strength of the incumbent firm’s (Instagram/Facebook) competitive position and their ability to quickly imitate and integrate ancillary product features.
Taken all together, these elements of the custom framework provide a solid initial roadmap for a start-up to engage in beginning to craft and refine their value proposition. I propose this framework as a way for a start-up to get the ball rolling but would advise that this framework be used in a dynamic and working context that requires customer feedback as inputs to further refine and develop one’s value proposition. It’s also important to again notice Ries’s (2011) concept of a pivot in respect to this custom framework. Should a cycle of iteration lead to a dead-end in terms of product acceptance, management should waste no time in pivoting their value proposition to more offer that more closely matches with their customer’s desires and jobs to be done. And it is through this use of continued validated learning that a start-up will be able to ultimately craft their most compelling value proposition that can become the bedrock for their sustainable competitive advantage. In the following section, I will utilize this custom framework in the lens of the RLY and their quest to design their optimum business model and will deliver recommendations to help generate an optimal business model.
6. Managerial Recommendations/Implications
RLY clearly is taking on a big challenge in trying to upend the status quo in the social media given the massive balance sheets and user bases of the incumbent players in the space. What should their value proposition be? How can they provide a compelling and differentiated experience to users? How will they be able to make money off their platform given the pushback on traditional privacy-intrusive advertising-based revenue models? These are questions I will attempt to address as I look to answer the overarching research question on business model selection for a startup platform business. I will first review the current definition of RLY’s business model in the lens of Osterwalder’s (2011) business model canvas. With the starting point mapped, I will then spell out actionable recommendations for the implementation of this paper’s Continuous Platform Value Proposition Refinement Framework. Following from that I will survey potential long-term monetization paths that mesh
with the rest of the proposed business model design. I will then conclude this section with a list of potential challenges and a timeline for implementation of the proposed recommendations.
A. RLY: Initial Business Model Canvas
RLY has both the blessing and the curse of being a rather early stage company, with only 5 full-time employees; while they have the flexibility to quickly adopt customer preferences and engage in build-measure-learn loops, they also lack the financial and human capital resources that larger firms can take for granted. Part and parcel of that lack of human capital is the limited thought that has been paid to business model selection given the bandwidth capacity limits of the existing team. While the team has a strong background in marketing and engineering, there was no single person responsible for designing & driving strategy and tackling bigger picture items like business model definition and value proposition refinement. Of course, much of this work is accomplished via RLY’s terrific product management team and ongoing customer validation work, but there was still a lack of a coherent and consistent understanding of what constitutes RLY’s business model. To best formulate recommendations, it is first to quickly clarify the current understanding of RLY’s business model according to the management as according to Osterwalder’s (2011) business model canvas (Table 1).
Canvas Element Definition to Date
Customer Segments Social Media Users in Generation Z (Age 13-24) in US; Mass market appeal; iPhone owners
Value Proposition Undefined; Newness and novelty aspects of having only looping/moving content in a platform format
Channels Mobile application-based delivery model; Only available in the United States iOS App Store; Marketing largely done through Apple Search Ads and Instagram
Customer Relationships High-touch model with active team curation of content on the platform; questions on scalability of level of dedicated personal attention required for curation
Revenue Streams None
Key Resources Team’s human capital; Two Sigma Ventures backing; intellectual property; RLY’s quirky brand
Key Activities Problem solving; product development; Platform activities such as user discovery and content curation
Key Partnerships None
Cost Structure Value driven cost model; High fixed cost business with negligible variable costs as business scales;
Table 1: Current Definition of RLY Business Model using Osterwalder’s Canvas
Quickly scanning this initial mapping of the business model canvas yields a couple of interesting insights. First, given this paper and the aforementioned authors’ focus on the importance of designing a compelling value proposition, there is a relative lack of clarity around what constitutes the value proposition of RLY. Secondly, there has been limited thought given to the non-end user side of the platform business. While clearly one side of a platform needs to be built to attract the other, thinking about the any future evolution of a multi-sided platform requires a deeper understanding of both sides of the platform. And lastly, there has been no work done to date on potential revenue streams, which ultimately will be required if the company wants to self-fund its growth and operations. The recommendations as spelled out in the remainder of this section hope to address some of these key gaps in the current business model definition through actionable and implementable recommendation.
B. Identify Job to Be Done
The first step suggested by this paper’s Continuous Platform Value Proposition Refinement Framework was to identify your customer and to understand their wants/desires/jobs to be done. This paper commends much of the work done so far as RLY has already engaged in significant work to gain a deeper understanding of their proposed customer base and has worked closely with their target market to co-develop their product with participation from end-users in all steps of the creation process. Additionally, RLY has made strides in terms of customer identification from a demographics perspective as they have narrowed their target customer segment (albeit still a relatively mass-market audience) to a specific age and geographic and have worked on building their product set around the wishes and desires of that specific audience. While all these customer identification efforts are in-line with Osterwalder’s (2011) business model canvas, there still is the matter of understanding the job to be done that those customers are hiring RLY to do.
To the end of better understanding how and why RLY users use the platform, we commissioned a user survey to explore several key questions that were integral to business model definition and selection. While the highlights of the survey are viewable in the Appendix, the chief reason that users opened RLY daily was essentially look at other people’s content (Appendix C). This suggests that users are using the service as a means of content discovery. This is a meaningful revelation, as understanding the core job that users are hiring RLY to do, helps the RLY team to understand where it should be investing the bulk of its time innovating. It also distinguishes who is the competition as RLY is not going after the same target market as social messaging services like Snapchat or WhatsApp, rather discovery platforms like Instagram or Pinterest. Going one step further, it is also important to understand they “why” of why a customer is using the app in a specific way. To explore that, I rely on Voss’s (2014) use of laddering and a means-ends-chain to arrive at the terminal values associated with social media use. He found that the most significant attributes are chat/pictures/mobile application, were very closely linked to terminal values such as fun/well-being/hedonism (Figure 3). While the research was done largely on the millennial population, it does still hold some explanatory value for why RLY users use the app; chiefly of which being the terminal value of just having fun.
The sum of these two key revelations – that people are hiring RLY to discover new content and that people have a terminal goal of fun – drives this paper’s first recommendation. I recommend that RLY should reformat its product roadmap to focus on features that are focused on fun and content discovery. This could take the form of improved categories, the introduction of searchable hashtags, and the creation of sub-communities based on interests. Additionally, holding content creation contests would be a quick way to boost fun, engagement, and user interaction in a manner consistent with the underlying job that users are hiring RLY to do.
C. Identify Value Creation Bucket
With a strong understanding of the customer job to be done, comes the framework’s recommended next step of identifying opportunities for value creation that could be captured by the platform. This paper’s framework utilizes Parker’s (2016) typology, where he broke out four buckets of excess value creation opportunities. It is this paper’s view that RLY is in a unique position to be able to pursue all 4 of the possible paths here, which is in-line with Kim & Mauborgne’s (2014) view on Blue Oceans and breaking from the traditional trade-off mindset that dominates traditional management thinking. Table 2 demonstrates the scope of opportunities for RLY in table format.
Value Creation Bucket RLY’s Opportunity
Give third parties access to a specific community
Work to develop active sub-communities based on specific interests; Example of partnering with services like TripAdvisor’s to have a book now feature under pictures of specific locations in the travel oriented interested groups
Grant access to content created on platform Become a portal of discovery for people seeking to view and enjoy all types of moving content (videos/gifs/live photos); Network effects create excess value as the quantity of user-created & RLY specific content grows
Facilitate connection Launch micro-influencer platform where influential users can connect with brands who wish to connect with their audiences in
