Functional Thinking for Superior Value Propositions That are Highly Profitable

It seems self-evident that to win in the market, it is necessary to continuously offer value propositions that, in the eyes of the customers, are superior to the competition.

Leaders and innovators, therefore, benefit from refining their understanding of exactly how products and services generate customer value.

When presented with value propositions, customers make a means-ends assessment of its value. In technical terms, they are making a weighted multi-attribute judgment balancing the utility they get against what they need to give up to obtain it. What they need to give up can, in a broad sense, be referred to as costs - or use of customer resources. Resources can, for clarity, be divided into time, money, and effort.

Utility, in turn, only occurs when a customer's need is satisfied (Lindstedt and Burenius, 2003). Innovators sometimes view these needs as 'jobs to be done', in the form of desired results (‘gains’), or as 'problems to solve' (‘pains’) (Ulwick, 2005; Christensen et al., 2016). Note that to beat the competition intangible aspects, such as emotional and social needs, are just as important as more concrete benefits! After all, competition is settled when the customer semi-rationally make a purchase, nowhere else.

With this, we can express value as an equation (below), as discussed in an earlier post .

Using insights about customers and their needs, the innovator must create a solution (a product-service bundle) that generates value by satisfying those needs.

How, then, can innovators make good choices about technology and design during the product development process? How can value propositions be systematically developed to maximize the chance of a profitable business?

What the innovator requires is a way to link the attributes of the solutions to the needs of customers in a way so the value of different alternatives can be compared.

The widespread popular frameworks that simplify innovation are useful but won't help here. While they seem to bridge the gap between customer needs and solutions they actually don’t. How could they - often they don't even define what value is!?

So what is the alternative?

The direct connection to need satisfaction implies that precise value analysis is about understanding specifically what the value proposition does in regard to customer needs!

As not everything about a product or service is important to customers, we need an analytical construct that 1) isolates the need-relevant aspects of a product or service and 2) expresses these aspects in a clear and uniform way.

Luckily, a systematic way to do this already exists. It recognizes that customer value occurs only when a product-service solution is used, i.e. not when it is invented or produced (e.g., Lepak, Smith, and Taylor, 2007).

In other words, value comes from the outcomes of using a solution - to the extent these outcomes match customer needs. In Value Engineering, this is thought of as the solution's functional relevance for the customer.

Specifying value with functions

Functions are an analytically effective format for clearly and systematically specifying each user-relevant outcome of a system (Miles, 1961).

To work well, function statements need a standardized form (or syntax). For example:

“the car – transports – the family”

In this form, prescribed by Lindstedt and Burenius (2003), the function [transports] would be the Main Function of the car, or in other words the reason the family [Function Receiver] buys the car [Function Provider] at all.

According to the authors, Main Functions are accompanied by other functions that, in combination, achieve the end result. A video explaining this function syntax, function types, and linked function systems can be found here .

Note that some functions decrease value because they have negative consequences. These are Unwanted Functions.

Likewise, some functions are necessary for the system to work but do not directly affect customer needs. They are called Support Functions, and the problem with them is that they add to costs but not to value.

By separately stating all the outcomes from the solution in this manner, any value proposition can be compared to any other value proposition.

Simulating Choices and Assessing the Competition

Functions have immense analytical utility. Innovators can, for example, simulate product development choices by exploring how potential design or technology changes precisely impact both the value for customers and the costs for the business.

Simply start by listing the customer-relevant functions in question. Then add the performance that the alternatives promise for each of these functions. Any difference in functional performance can now be assessed in light of the costs each option would incur.

Innovators can also compare their innovation with competing value propositions in the same manner to verify if it has a?Value Delta?sufficient for making it likely that customers will buy it at a profitable price. All this can be done?before anything is produced!

For instance, the value of a car can be compared in detail to that of another car (rival alternative), a motorcycle (substitute alternative), a car-sharing service (product alternative = a new solution from another innovator), or whatever the customer uses today, such as walking (market alternative) (Kim and Mauborgne, 2004; Moore, 2002). Here is a video explaining how it works.

Once done, this functional comparison can be reused to demonstrate the innovation's superior value to customers in a clear and compelling manner. Such value-based selling is helpful for overcoming the customer-awareness disadvantage innovations have compared to established products when attempting to cross the 'chasm' (i.e. from early customers to the mainstream market segments) (Moore, 2002).

Why do we need this?

Popular innovation frameworks like Design Thinking and canvas models help us understand customers more effectively. Through appealing methods and simplification, they have also broadened participation in innovation, allowing more people to contribute valuable ideas. However, these frameworks often lack guidance on how to systematically apply customer insights for optimal product and technology choices. Instead they rely on refined versions of brainstorming and trial-and-error.

While approaches like Minimum Viable Products and Rapid Prototyping are useful, they fall short of supporting a comprehensive value analysis. Therefore, they have less utility when it comes to decision-making in the innovation process. They can't guarantee profitable or competitive value propositions or even ensure that the customer will be willing to purchase.

Moreover, their over-reliance on customer input typically results in great but overengineered products, generating poor business outcomes.

Advantages of functional thinking

With functions - especially if their performance can be quantified - it is possible to make very precise solution improvement choices, as explained above. But there are also other advantages.

First, the set of functions also gives innovators a systemic view of their creations. Here is how:

Inside a solution, both Function Providers and Function Receivers are subsystems that are, in turn, also made up of subsystems. This makes it easy to create a function map, with components as nodes connected by functions, that shows how value occurs!

Function maps illustrate the function structure of a product, like below, thereby providing a comprehensive visualization of exactly how it generates value (Lindsted and Burenius (2003); Yassine, 2018).

Secondly, the four function types can be directly applied to strengthen a value proposition (Lindstedt and Burenius, 2003). Specifically, leaders can increase value by changing the solution in four ways:

• Improving the performance in the Main Function(s),
• adding, or improving performance on, Additional Functions,
• removing or mitigating Unwanted Functions,
• and/or removing Support Functions (or reducing their cost).

Increased customer value translates directly to competitive strength and a chance for higher revenues!

Third, function specifications also provide a great starting point for systematic creativity.

Starting from a functionally specified product, new solutions can easily be ideated by adding, removing, or reconfiguring subsystems (Altshuller, 1988). TRIZ is a comprehensive methodology useful here.

Playing around with product-service attributes in this manner often reveals new ways of doing things that can be less costly, have equal or better performance, fit new market segments, or even meet other needs. This is, in essence, systematic inventive thinking (Goldenberg et al., 2003).

[In coming publications, I am writing a lot more about using the functional approach for novel ways of working with strategy and ecosystem analysis ]

Summary

Customers can be expected to choose among value propositions based on the value maximization principle. However, their preferences are formed through a weighted multi-attribute judgment. It is, therefore, critically important to work actively with both sides of the Value Equation. Unfortunately, many contemporary frameworks overlook the denominator, sometimes even disregarding the significance of price for customers (e.g., Osterwalder et al., 2014; Adner, 2021). This can lead to critical misjudgments and disasterous business decisions.

With a nuanced analysis of customer needs, functional outcomes, and the systemic structure of function dependencies in the solution, businesses can make precise decisions that will result in superior value propositions.

Functions thereby serve as a useful abstraction layer that bridges the technical aspects of a solution to the needs of target customers.

To summarize, working with functions has many advantages for innovation:

• Clarity and efficiency: Functions express outcomes in a formalized syntax (here as Function Providers connected to Function Receivers by Active Verbs). Being technology agnostic and free from fluffy customer opinions, these statements are very straightforward and easy to understand for everyone. This clarity of communication helps cross-functional teams solve problems and make decisions throughout the innovation process.
• Functional decomposition: As mentioned, solutions can be broken down into underlying components where both Function Providers and Receivers are seen as subsystems (Yassine, 2018). The hierarchical nature of how function statements link different system parts together makes it possible to work with functions at any level of analysis (Stone, Wood, and Crawford, 2000). Innovators can, therefore, go into any level of detail when analyzing a problem but still safely rely on a consistent pattern of reasoning and logic. It works all the way from molecules to business models and beyond .
• Decision support: Matching functional performance to customer needs specifies the value of different solution alternatives. Innovators can then add the costs of each alternative and thereby simulate choices for decision-making in the innovation process.
• Credible ex-ante assessment: Using such simulations, innovators can also verify if the value proposition is likely to reach customer adoption (Value Delta), competitive advantage and profitability, beforehand. This shortens the innovation process and dramatically increases the chances of success, compared to relying on methods like Design Thinking or Lean Startup.

Audax on innovation and value: https://audaxstrategy.com/advisory-areas/#innovation

Inspiration from:

Adner, R. (2012) The wide lens: A new strategy for innovation. London: Penguin Books.

Adner, R. (2021) Winning the right game: How to disrupt, defend, and deliver in a changing world. Cambridge, MA: The MIT Press.

Altshuller, G. (1988) Creativity as an exact science. Translated by Anthony Williams. New York: Gordon & Breach.

Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Know your customers’ jobs to be done. Harvard Business Review.

Goldenberg, J. et al. (2003) Finding your innovation sweet spot, Harvard Business Review.

Kim, W. C. and Mauborgne, R. (2004) Blue ocean strategy, Harvard Business School Press.

Lepak, D. P., Smith, K. G. and Taylor, M. S. (2007) Value creation and value capture: A multilevel perspective, Academy of Management Review.

Lindstedt, P. and Burenius, J. (2003) The value model: how to master product development and create unrivaled customer value, Nimba.

Miles, D. (1961) Techniques of Value Analysis and Engineering, McGraw-Hill.

Moore, G. A. (2002) Crossing the chasm. 3rd edition. New York: Collins Business Essentials.

Osterwalder, A. et al. (2014) Value proposition design: How to create products and services customers want. John Wiley & Sons.

Stone, R. B., Wood, K. L. and Crawford, R. H. (2000) A heuristic method for identifying modules for product architectures, Design Studies.

Ulwick, A. (2005) What customers want, McGraw-Hill Professional Publishing.

Yassine, A. (2018) ‘Managing the development of complex product systems - what managers can learn from the research’, IEEE Engineering Management Review.