Pennywise, but dollar foolish – What pitfalls can happen when trying to be “lean”

by Felipe Arias


The lean mentality has allowed startups to identify key issues and potential pitfalls at an early (and cheaper) stage in their lifecycle and has provided entrepreneurs with the data necessary to pursue ideas or pivot accordingly. Certainly, putting into place rigorous thinking that drives an entrepreneur to develop a vision, translate that vision into a set of testable hypotheses, identify what part of the vision is required to be tested through a minimum viable product, prioritize the tests, and then iterate through learning before spending on scaling and optimization makes intuitive sense. In practice, the drive to be “lean” can push entrepreneurs to miss opportunities, avoid key tests due to difficulty and cost, as well as misattribute the competitive advantage that will be developed by their business. Below, I include three pitfalls uncovered by those brave enough to go before us. In these pitfalls, entrepreneurs may feel that they are wisely conserving resources, but, in doing so, they are avoiding key hypotheses that need testing.

1. Not scaling sales force when appropriate. Sean Ellis introduces this pitfall in his blog post on “Bringing a Network Effect Business to Market,” wherein companies with network effects need to speed to market in order to grow the value of their network based product. David Skok hammers the point further in his series of posts on SaaS economics and building a successful sales force. However, we still see entrepreneurs who feel that they need to test one more hypothesis before scaling up. Not only do they miss the opportunity for addition revenue and margin, they may be missing out on testing other key hypotheses and gaining customer feedback.
2. Hero CEOs. Jeff Bussgang pointed out the potential to increase the efficiency frontier for an organization through applying the economic principle of comparative advantage. Sure, an aspiring CEO may be the absolute best person to gather every piece of data or even write every line of code, but leveraging the relative talents of others frees up the CEO to work on the things only he can do. One of our class guests was quick (and proud) to identify himself as a hero CEO. His talents are obvious. However, his “lean” drive to close every deal not only might have slowed down his company's growth, but also delayed him from developing a sustainable business model. As a company scales, a CEO cannot run every department, and a key part of launching a new venture is understanding when to leverage new resources.
3. Not leveraging available data and partners. The example of Mint using Yodlee vs. Cake Financial building a data resource themselves is certainly not the only one. In this example, Mint was able to move forward, test other hypotheses, and build a successful business. Cake was stuck plowing resources into developing a back end system while missing the opportunity to develop a product that truly inspired the market. In this pitfall, entrepreneurs may be trying to conserve cash, equity, or favors, but end up draining resources and missing the opportunity to build a sustainable competitive advantage. If a piece of your product is available on the market, it makes sense to not have to re-invent the wheel. In Cake’s position, they may have had to go back later to build the backend to get all the data they needed, but they could have pieced together an MVP to test quicker using the available data. Companies may feel that if I am eventually going to have to build it, I might as well avoid spending twice and start now, but, in reality, a more lean approach would be to test that hypothesis before building for scale.

Entrepreneurs in the field will certainly have more examples of when they tried to pursue a “lean” approach, but it ended up costing more in terms of time, cash, resources, and opportunities. For those looking to start ventures, it will be important to try to learn from their pitfalls. Ultimately, being lean is not about conserving resources as long as possible, it is about using them to test hypotheses as efficiently as possible.