As traditional VCs are extinct, new “augmented” VCs (the term coined by A. Retterath ) make increasing demands on startups. It concerns business model validation and startup teams’ role. Traditionally, VCs used to treat startup founders like children in a kindergarten, biding them with legal constraints instead of leveraging their qualities. This alienating position is being criticized today as noneffective.  Over the past decade, market penetration with unfinished products was proclaimed. The feasibility of an early market entry with a hastily made MVP is also subject to reasonable criticism.  The higher the fidelity of MVP, the better, and an experiment has a higher fidelity level. This is why the PROFIT.enterprises platform offers an experimental approach within a special framework to validate startups’ business models.
Experimenting with MVPs vs Business Planning
The validation process of startup business models ensures both parties, investors and founders that the startup direction is correct, despite inevitable uncertainty and a lack of knowledge about alternatives and consequences.  Startups’ business models embrace three factors: desirability, feasibility, and viability.
Desirability is related to the market and customers. Viability signifies sufficient revenues and reasonable costs to make profits. Feasibility denotes business activities, key resources, and partners. 
Until recently, a business plan was considered a solution to all three business model factors. It was assumed that the market was determined and a business model was somehow validated. These assumptions do not correspond to reality, and a “classical” business plan as a forecasting tool does not meet the requirements of the new epoch. So, the idea of replacement of planning with experimentation was born, to ensure investors that business models are viable while meeting the feasibility and desirability requirements.
It was implied that a Proof of Concept (POC) and then a minimal viable product (MVP) allows us to validate or invalidate a business model. An MVP is a simplified product that has some key features of a final product. The main goal of creating MVP is testing a product. While a prototype is just a demonstration of whether a product is technically feasible, MVP is intended to obtain customers’ feedback to learn about the business model. Despite the name, MVP is not necessarily a viable product that solves customer problems; even it is not necessarily a product (see Table 1).
Table 1 The Comparative Analysis of MVPs
Table 1 shows that the higher the level of functionality (fidelity), the more likely it is that the MVP will reflect reality adequately. Unfortunately, even high-fidelity MVPs like the “Concierge” have deviations called false positive and negative results. The former represents situations when a positively confirmed hypothesis will be invalid in reality. The latter indicates that a hypothesis has been disconfirmed, in reality will be valid. False positive cases occur when startups recruit enthusiasts that do not represent mass customers. False-negative results are possible with poorly functioning MVPs. 
Data-driven Startups for Augmented Investors
The “Augmented VCs” employ a hybrid approach, focusing at the same time on data and human factors. New VCs realize lead or co-lead strategy, building a deeper relationship with startup founders to win competitive deals.  The question is which startups they choose.
Technological startups can be roughly divided into three types:
- Startups that exploit hype topics (blockchain, AI/ML, IoT, etc.). They have no clear path to technology commercialisation, and their business models are poorly described (let’s recollect infamous ICOs).
- Startups that offer a new solution to some obvious problem with massive demand (education, wellness, food, etc.). The simplicity of such solutions is deceptive, and in practice, some solution has no economic sense (remember WeWork?).
- Startups that develop business models that capture values, leverages profits, and make technological solutions viable. They experiment a lot on an intuitive level due to a lack of tools and methods. This type is worth investing in.
Many people hold misconceptions regarding the inherent viability of novel business models (SAAS, multisided platforms, etc.), whereas new methods and frameworks are required to validate these models. The experimental validation process in an ever-evolving business environment requires quality data and algorithmic procedures that convert input data into meaningful outputs in a well-defined processing chain. The process starts from the POC stage to the MVP stage, often from low-fidelity MVP to high-fidelity one (see Fig.1).
The data-driven and algorithm-enforced approach allows startups to validate new business models before their performance, decreasing risks of failure and allowing a chance of success via:
- Collecting data, unifying its configuration, providing user access, and information processing
- Using scenario modelling and algorithms through an iterative process of testing and self-learning in
a continuous cycle.
- Actively involving users in the validation process, making it quicker and cheaper.
Balancing Alienation and Leveraging Teams Potential
Alienation when founders take investments, losing the ability to solely determine the destiny of their startups is inevitable. Traditional VCs used alienation to the fullest through liquidation preference, control provisions, and board positions. Augmented VC know that too much control generates false comfort and negative consequences for investors. They believe that alienation has to be reasonably minimal. 
The chosen level of alienation depends heavily on the team’s qualities. Startup team members have different skills and abilities to develop and perform efficient business models. In this way, investors’ understanding of team qualities leads to the right perception that business models are sound and could be effectively executed. The problem is how to recognise and quantify these qualities.
In estimating a startup team, the four groups of factors have to be taken into consideration:
- A strategic picture of startup development – vision, mission, and team strategic positioning that forms an enterprise’s style.
- People and their roles in the startup, including team members, key persons to hire, and contractors who are ready to outsource some functions.
- Economic measures that make the team a well-coordinated and profit-oriented mechanism.
- Team dynamics, including shared leadership, constructive conflicts, and psychological safety (see Fig. 2).
The PROFIT algorithm uses representative patterns embracing key factors of the team’s comprehensive estimation from the investors’ perspective (see Table 2).
The Validator: An Experimental Framework
The PROFIT.enterprises platform offers the Validator, employing the data-driven technique, predictive analytics algorithms, and a bit of the founder’s creativity. As a result, founders validate their business models to pave their realistic paths to profitability, and investors can see trustworthy validation.
The practical PROFIT tools include:
- Business canvas and templates to pre-process input data and stimulate insights.
- Algorithms to automate calculations and use standardised methods to produce output data.
- Tests to check preliminary founders’ hypotheses and solutions.
- Case studies to see examples of real business implementation for better understanding.
- AI/ML tools to collect and process unstructured data from alternative sources.
The Validator has a modular structure, including a dozen of units (see Fig. 3):
Module 1 Problem-Solution Fit
Unit 1 Enterprise Overview & Business Opportunities
Unit 2 Problem & Solution
Module 2 Product-Market Fit
Unit 3 Market & Target Customers
Unit 4 Competition & Unique Value Proposition
Unit 5 Technology & Product
Unit 6 Team & Management
Unit 7 Go-to-Market Strategy & Entry Timing
Unit 8 Minimal Viable Product & Traction
Module 3 Business Model Fit
Unit 9 Intangible-intensive Business Model
Unit 10 Financial Projections
Unit 11 Intangible Valuation
Unit 12 Investor’s Protection and Exit
As the Chinese proverb says, “When the winds of change blow, some people build walls and others build windmills.” So, investors who are trying to protect themselves from the current wind of changes with alienating measures risk missing the boat. At the same time, augmented investors leverage startup founders to validate profitable business models (windmills) together.
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PROFIT.enterprises is a technology platform that is focused on initial sourcing and screening for investments in early-stage startups. The platform makes startups fit for funding and shows investors attractive business opportunities by discovering and appropriating intangible assets. As a data intelligence partner, PROFIT provides investors with trustworthy information about startups’ prospective profitability and risks, making the deal flow efficient, quick, and convenient.