MVP

A Proven Methodology to Maximize Return on Risk

When Steve Blank uses the term ‘customer discovery’, or Eric Ries says ‘lean’, and we say ‘syncdev’ for synchronous customer and product development, we all mean much the same thing. But whatever you call it, putting the right form of customer engagement well in front of ‘design freeze’ is a breakthrough in the history of business processes for product development. Most important, without it you can’t, unless by chance, go to market with the Minimum Viable Product^TM (MVP^TM).

Since our first use of the term in 2001, the MVP concept has spread quickly and has become part of technology-business lexicon. It’s referenced in popular books The Start Owner’s Manual, The Lean Startup , Business Model Canvas, and Wikipedia. It’s even the subject of an HBO entrepreneur miniseries.

However, the technical definition of MVP has evaded many of its proponents. We define MVP as that unique product that maximizes return on risk for both the vendor and the customer.

Benefits

The MVP solves a variety of problems, especially on a product’s first release. Products without required features fail at sunrise but products with too many features cut return and increase risk for both vendor and customer. More, the return-on-risk ratio drops exponentially with an abundance of features. The permutations and combinations of use cases that must be designed for, tested, and fixed increase development staff, development complexity, quality assurance cost, cost of sales, and time to market for the vendor. It increases time to adoption, training, customer dissatisfaction, and level of service required for the customer.

Too large or too small a product are big problems. The MVP is the difficult-to-determine sweet spot between them. Teams flounder tactically in trying to determine the MVP.

SyncDev fixes these problems for early-generation and mature, bloated products by taking teams through a proven, methodical, intensely interpersonal, customer-facing, tools-based, customer experience that results in the MVP. To boot, the engagement process repairs relationships and builds a virtual backlog of customer purchase interest that accelerates revenue growth once the MVP ships.

History

Professor William Sharp of Stanford University won the Nobel Prize in Economic Sciences in 1990 for his contribution to the capital asset pricing model. The Sharpe Ratio (Return on Investment vs. Risk) became a major Wall Street brand and his basis for founding Financial Engines, Inc. [FNGN] in 1996. Sharpe’s team adopted SyncDev to develop their customer pipeline, product, and business model.

Over several years, SyncDev, Inc. came to see the Sharpe Ratio as a missing link for defining the right business strategy and especially the product design and spec, a decision that’s expensive and risky to change late.

What Is an MVP?

Simply speaking, the MVP is the sweet spot in the upper left quadrant of ROI on the vertical axis and risk, which correlates directly to effort and time to market, on the horizontal axis.

But the MVP is more than its definition; it’s more than a series of procedural steps. Throughout its nearly 15 years of popularization a vital part of what made its initial use successful has been overlooked. MVP is a mindset of the management and development-team. It says, think big for the long term but small for the short term. Think big enough that the first product is a sound launching pad for it and its next generation and the roadmap that follows, but not so small that you leave room for a competitor to get the jump on you.

Surprisingly, a team can be its own worst enemy when it comes to the MVP. Their imagination, pride, professionalism, brainstorming process, etc. heaps one idea on top of another without thinking through the consequences of investment and risk in pursuing them. Examples of products that put whole companies at risk are Symphony from Lotus, the source of the first major spreadsheet success. Lotus tried to integrate what is today Excel, PowerPoint and Word into a single product. It failed and dramatically devalued the company.

Another example is the Apple Newton. It was the iPhone and somewhat less but long before the required technologies were available. It nearly sunk Apple until the time at which Steve Jobs came back and rescued the company with the Mac.

Wide Applicability of Minimum Viable…

The notion of MVP doesn’t stop at product. Ask, ‘What is minimum viable…” anything and you’ll discover that it applies to most business-model decisions, not just product: market segments, customers, services, channels, promotion, and more. For example, Minimum Viable Product starts with Minimum Viable Market, i.e., a team might ask themselves, “What market segments and customers in them should we target who have representative yet modest requirements who are easily accessible through our existing channels?”

Answering that question puts a team well on it’s way to an MVP.

Walking the Talk of MVP

A new product often starts with a problem to be solved or an invention, accidental or otherwise. The next step is hypothesizing a product and market. While teams invariably seek to understand their market to some degree, many jump that step and go straight to execution, developing the product. As their imaginations work overtime, new features creep in, a risky methodology.

A hypothesis is the starting point but determining with confidence the likely min-max of the MVP requires the scientific method, testing the MVP with customers in the minimum viable market. SyncDev solves that for both consumer and business products with the SyncDev Customer Engagement Model, an at-your-side SyncDev Player-Coach, and on-line SyncDev Playbook, the three ‘tools’ that are the secret sauce of SyncDev. See Solutions.

For traditional business and consumer products these tools enable teams to ably face and interact with customers to determine their MVP. But still, it is a messy, difficult process. For web-based businesses the job may be easier. AB testing may substantially automate if-then product testing with users, but this is only useful if the demographics of the testers is known to the team. Customers, the ones who pay such as advertisers, are likely to require direct engagement to optimize the business model.

Facebook and Google have such demographic tools but other teams could be critically hampered by their absence. They may have to engage directly with customer and users in its place to learn who wants what and to apply that to the larger market. Furthermore, AB testing alone is still unlikely to reveal the explicit why? behind a failed test and the what? of the user’s preferred solution. That powerful, often qualitative, data is also likely to require direct engagement with users.