What Is Lean Startup? Here's What You Need to Know

You've probably heard a lot of buzz about "lean startups".  You may wonder if it's mere hype, applies just to tiny bootstrap ventures, or if adopting some lean startup methods might actually benefit your company.

One of the top problems companies face as they make product decisions
is that the process of learning the market, and learning what makes the product successful, is slow and unreliable. Sometimes they suffer analysis paralysis, swerve from one big deal to the next, allow conventional industry wisdom to stifle innovation, or squabble over uninformed personal opinions. In other cases, they make decisions quickly but fail to learn from their inevitable mistakes until after they've invested exorbitant amounts of time and money.

If you find that your organization is facing this problem, it's worthwhile to consider lean startup methods.

Just as scientists use the scientific method to learn how the universe works, your product team can apply lean startup methods to learn what business model for your product will work in the market. Lean startup practitioners:

1. Collect data. Practitioners observe and interview prospects to gain qualitative insights into their situations, challenges, and the ways they operate. They also examine quantitative data for further insights.
2. Form hypotheses. Based on their observations and insights, practitioners form and capture hypotheses about the business model for their products. These hypotheses include the problems to solve, the key elements of the solution, the unique value proposition, the buyer and user personas, key metrics or user activities, costs, and revenue streams. Hypotheses may also include measurable predictions of the impact of various marketing or sales tactics.
3. Conduct experiments. Recognizing that at least some of their initial business model hypotheses are likely to be wrong, practitioners design and run experiments to test the hypotheses. Often these experiments confront prospects with real-world choices (such as functional product) and measure how prospects behave when confronted with these choices.
4. Learn and adjust. Having conducted experiments to test hypotheses, practitioners analyze the results and adjust their hypotheses.

These activities often occur in parallel and not necessarily in this sequence. For example, entrepreneurs commonly think of product ideas prior to formally collecting market data.

Applying these methods in an iterative or continuous fashion enables product teams to confront product strategy risks and quickly and reliably learn markets with more targeted - and ultimately, less wasteful - business investments.

Lean startup practitioners essentially apply agile methods to the entire business for a product. Most software companies have adopted at least some agile development practices. But unlike companies that iterate merely on the development of the product, lean startups iterate on the requirements, the positioning, the target markets and personas, and sales and marketing tactics.  They monitor and optimize the sales and usage funnels. They emphasize the delivery of working product and prospect-touching experiments over exhaustive planning and documentation devoid of external feedback.

Summary

Learning markets reliably and expeditiously, and learning what will make products successful, is one of the primary challenges many companies face, whether those companies are startups or large, established corporations. By applying scientific practices and agile principles across the entire business for a product, lean startup is designed to address this challenge.

How does your company currently make product decisions?  How would you know it's time to add new approaches or practices to the mix? The next blog entry will explain how you can begin to put some lean startup methods into practice right away, once your team is ready for some change.

Thanks to Koen Bosma, Ash Maurya, and Emiliano Villareal for providing helpful feedback on this blog entry, and for their thought leadership on the topic of lean startup methods.

5 Ways Companies Make Product Decisions

In the last blog entry, we reviewed the four problems that companies face, or are trying to overcome, as they make product decisions.  Now we'll look at the ways that most companies make their product decisions.

Companies that develop, market, and sell products and solutions make strategic and ongoing tactical decisions.  They decide what features to include in their products, what messages they will use to communicate the value of their products, what marketing tactics they will use, what prospective customers they will target, and many day-to-day choices. Whether or not these decisions are deliberate or ad hoc, most companies use some combination of the following ways of making product decisions.

(A downloadable "map" that summarizes the product decision landscape is included at the end of this article.)

Customer Wants
Product decisions based on feature requests, focus groups, and what prospects and customers say they want.


Companies are selling products to make money by creating happy customers.  With the “customer wants” model of making product decisions, you reach out to prospective and existing customers, since they’re the ones who will ultimately be buying your product.  If you are able to deliver what prospects want, they are much more likely to buy your product.

To gain insight into what they want, companies listen to what prospects say during sales and customer support calls, tally up feature requests, monitor support and discussion forums, and conduct focus groups and surveys.  A conversation with a customer might include explicitly asking her what she thinks of a particular feature idea, or she might offer her own feature ideas.

Pros:

1. Incorporates direct feedback from prospects and customers rather than speculation from inside the company about what they may want.
2. Can lead to prospects becoming customers once you’ve implemented the requested features.

Cons:

1. Customers are experts on their own situations and challenges but don't know what they want, so you end up implementing features that don't provide value.
2. Research shows that customers' hypothetical predictions about what they would buy are not reliable.

Deal Driven
Product decisions driven by the next big deal in the sales pipeline.

The ultimate measure of a successful product is how much money it makes. At any particular time, sales may be working on a deal that could bring in a large amount of revenue for the company.  The prospect in such a deal often has particular needs that the product could address with some additional development.  In the deal-driven approach to product decisions, the needs of prospects in these major deals drive the product decisions and priorities.




Pros:

1. Increases the likelihood that revenue-producing deals will convert.
2. Ties product decisions and priorities to revenue potential.

Cons:

1. Leads to scattered, incoherent value propositions for the product.
2. Causes abrupt swings in product direction, eroding the morale of the product team.

Intuition
Product decisions based on common sense and what's cool.



Disruptive and innovative products often come from visionaries who incorporate cool technologies and have an intuitive sense for what consumers want.  Executives and members of the product team are themselves consumers and thus have their own personal opinions about the most effective ways to market and sell a product. Developers on top of the newest technologies see how they can apply the technologies to implement innovative product features.  Since everyone in the company is a potential user of the product, they all chime in on what the best design and user interface is.  In many organizations, these sorts of intuitions drive product decisions.

Pros:

1. Anticipates needs that prospects don't yet realize they have.
2. Leverages internal knowledge and avoids expensive market research.

Cons:

1. Effective marketing often defies common sense. Despite the fact that we're all consumers, most members of the product team probably haven't studied marketing principles.
2. Personal preferences and intuition often don't reflect those of the target market.

Industry Experience
Product decisions based on prior industry experience and accumulated wisdom.



Some companies rely on employees with prior experience in a domain or industry to guide product decisions.  Experience provides wisdom about a market and what works and doesn't work in an industry.  Based on industry background, such as knowledge of the competitive landscape, customer needs, and existing technologies and practices, members of the product team make judgments about what features to include in the product and how to market and sell it.



Pros:

1. Reduces or eliminates the learning curve for understanding the customers, technology, competition, and needs in an industry.
2. Brings industry connections and relationships that sales and development can leverage.

Cons:

1. May inhibit innovation and outside-the-box thinking. Most companies emphasizing industry experience in their hiring practices do not, as a general rule, innovate well.
2. Provides no guidance for tackling risks and unknowns outside the prior industry experience.

Left Brain
Product decisions based on analyses such as Kano and A/B testing and documented as detailed product specifications.



To take the intuition and guesswork out of making product decisions, team members with a left-brained bent employ a variety of rigorous approaches and analytical tools to determine and document product priorities and marketing tactics.

For example, a member of the team may maintain a spreadsheet with candidate market problems to solve, or with all the proposed enhancements to the product, and rate them on various criteria.  They base product decisions on the items with the highest ratings.  Some more sophisticated product managers analyze customer preferences using Kano analysis, rating features in terms of the extent to which they evoke surprise and delight, satisfaction, dissatisfaction, indifference, or an erosion of overall perceived value.

In some cases, business analysts, product managers, or product owners will then compose detailed product specifications.  Often, the individuals with analytical instincts will go far beyond writing epics and the basic user stories, and will delve into interaction design.

For determining the most effective marketing tactics, the team may use A/B tests and other data, seeing which ones work best in practice.

Pros:

1. Brings transparency and rigor into the process of making product decisions.
2. Distills disparate data into actionable information. 

Cons:

1. Can lead to products with incoherent and scattered value propositions.
2. Ignores timeless marketing principles.
3. Biased to product decisions with available data and to tactical alternatives that are easiest to measure.