Posts

183. Ahmed Elsamadisi: The Stories Data Can Tell Us If We Ask The Right Questions

/0 Comments/in /by

How do companies make decisions? Data certainly don’t make decisions, nor do analytics, nor do the computers they run on. Human begins make decisions — the human factor is crucial. Subjectivism is paramount, even in the age of big data and A.I. The key still lies with the people who are interacting with the data to generate human insights.

Ahmed Elsamadisi is one of the leading data scientists in the world. He’s worked on self-driving cars and nuclear defense and some of the biggest business challenges on earth. He believes that it is the stories we tell from data that drive business success. We are privileged to interview him at Economics For Business podcast, and he gave us a lot of useful advice we can all use every day in managing our businesses.

Key Takeaways and Actionable Insights

The data community has made data and algorithmic analysis far too complex, to the point where it’s no longer useful for business.

The path-dependent route to today’s complex data tables was paved with lots and lots of columns and lots and lots of rows. These data tables are leftovers from the early days of computing SQL language was designed to manipulate these rows and columns. A.I. comes along and can analyze all the possible combinations of data cells. Business executives ask their data departments to generate a lot of these combinations to search for patterns. It often takes a long time, a lot of revisions, and generates no clear answers.

Another aspect of history is the use of dashboards. We tend to design dashboards rather than formulate good business questions. The metrics on dashboards are sometimes useful for operations but they’re often not at all useful for understanding the causal connections between data points. Consequently, different people can interpret them in different ways and there is no consensus as to what they mean and what to do about it.

The purpose of data analytics is to generate good decisions that lead to action.

The entrepreneurial method drives towards D and A: decisions and actions. Analytics should help to formulate the hypotheses on which to base decisions. The problem with complex dashboards and algorithmic pattern recognition is that they often don’t give clear direction on recommended action, especially when the interpretation varies depending on who is doing the interpreting.

Ahmed’s experience is that sharing a numerical dashboard with 10 executives is very likely to result in 10 different interpretations, and the resultant confusion and disagreement freezes action rather than accelerating it.

We need data to tell us stories that we can all rally around.

The most powerful tool for developing consensus around action is narrative — often called storytelling. While 10 dashboard interpretations might lead to 10 different action plans, a single well-told story can align everyone who hears it, understands it, and internalizes it. We heard about the power of narrative in episode #181 (Mises.org/E4B_181) in which Brian Rivera explained the role of storytelling and sensemaking in The Flow System of management, and in episode #152 (Mises.org/E4B_152) where Derek and Laura Cabrera explained the power of aligned mental models for driving business. Stories achieve alignment.

Ahmed Elsamadisi built his service, narrator.ai, to output data analytics in the form of a story. The complexity riddle is removed and replaced with a narrative that all executives, not just data scientists, can understand. Narrator.ai re-integrates data science with the all-important human element of understanding stories.

The way to get data to tell stories is with a conversation.

Ahmed says that the way we ask questions (data queries) is flawed. It’s quite a normal practice to set the A.I. to search the data tables to look for patterns to see if anything interesting emerges. This is what Ahmed calls “lazy hypothesis generation”, which is never going to yield useful actionable insights (yet many big analytics companies are taking in huge customer revenues for just this service). Clients may claim to be making data-driven decisions but that’s mis-characterizing this business behavior, typical though it may be.

Ahmed advises us to think more in terms of a conversation with data. To facilitate this, he has developed a universal data model with just three variables: an entity (such as a customer), an action, and time. Every business question is about a customer taking some action in some time period. The universal data model enables the conversation: what action did the customer take in what period of time, e.g., when did they open the email and what action did they take after opening it. This is not a database query, it’s a more thoughtful question about the customer experience and how to understand it.

Ahmed told us that training customers in this conversational mode of interaction with the universal data model results in a cultural shift in thinking. The conversation can go back and forth in several iterations until the understanding is fully honed. Clients hear the data talking to them through the stories that narrator.ai generates. The have deeper insights and a story to share to form a consensus around the action that the story suggests. Narrator.ai clients have used stories for everything from describing new product specs to updating board decks.

Great conversations with data are based on empathy and thinking about the customer experience.

At Economics For Business, we elevate customer empathy a the most important business skill, in the context of an understanding of customer value as subjective, a good feeling from an enjoyable or satisfying experience.

Ahmed advises us to think in this same way when formulating conversations with data to generate insights. If we think about the customer’s experience, desired and actual, and the actions they take before and after that experience, and the time context of the experience, we’ll do well in formulating good questions. The action component of the universal data model is central to the Austrian deductive method: knowing what people do can help us deduce motivation and expectation. Knowing what they did next can shed light on the ends they had in mind. Actions like opening e-mails or repeat buying are also revealing of intent and expectations. The more we converse with the data, the more insight we can gain.

Storytelling with data is another implementation of subjective quantification — with the benefit of enhanced intuition over time.

In episode #176 (Mises.org/E4B_176), Peter Lewin introduced us to the Austrian concept of subjective quantification — turning customers subjective valuations into numbers such as capital value on a balance sheet. We tested the subjective quantification term with Ahmed, and he endorsed it — with a major addition. It’s important to include the dimension of time. If, over time, we have better and better conversations with data and formulate better questions and hypotheses, we’ll get better and better at generating insights. Our intuition will improve. We’ll get a better “feel” for the data. Even our empathy can become more accurate.

Additional Resources

Narrator.ai and its excellent blog, Narrator.ai/blog

“Top Ten Signs You Have A Data Modeling Problem”: Mises.org/E4B_183_Blog

Ahmed Elsamadisi on LinkedIn: Mises.org/E4B_183_LinkedIn

182. Gordon Miller: What’s Your Absorptive Capacity for User-Generated Innovation?

/0 Comments/in /by

It’s often the case that lead users — the most sophisticated, committed, and energetic users — are an excellent source of innovation ideas. Those customers who are most engaged are thinking the most intensely and the most creatively about what they want from the usage experience. We came across a particularly instructive example: video game modders. Who are modders, what do they do, and what can we learn from them? Professor Gordon Miller has studied this important entrepreneurial phenomenon, and he joins Economics for Business to share his knowledge.

Key Takeaways and Actionable Insights.

Modding is user-generated value innovation.

Modding, from modifying, is the act of a changing a game, usually through computer programming, with software tools that are not part of the game. This can mean fixing bugs, modifying content to improve it, or adding content. But modding is not an activity taken on by those at game companies—developers release patches and downloadable content, not mods. Modding is instead done by players and fans of the game… Modding is more than adjusting the preferences or game settings, it is making changes that cannot be made through the game as it is.

Game producers and designers enable and encourage this user innovation.

Game producers have come to recognize that the creative ideas and initiatives of the modding community can contribute new value to their businesses and franchises. Games like Minecraft enable users to explore, within a predesigned GUI, a practically endless 3-dimensional world to build innovative structures and other things like functional computers and console emulators. Minecraft also makes available code and tools for modders to create mods that are essentially new games, or major innovations within the original game. The famous DOTA (“Defense Of The Agents”) game is entirely the product of the modding community, encouraged and enabled by the developer, Valve Software.

Modding is a practical application of the theory of absorptive capacity.

Absorptive capacity refers to the capability of a firm to recognize, collect, assimilate, process, transform and use external knowledge for competitive advantage in innovation, flexibility, and overall business performance. The external sources of knowledge are knowledge networks, either formal or informal or a combination of both. Formal networks might include suppliers and partners, university research departments and labs, and even industry share groups. It’s sometimes called open innovation — actively looking at and tapping into what other firms are doing.

Informal networks are those like the modder community — lead users, user groups, tinkerers, and so on. This is sometimes referred to as distributed innovation or user innovation — it’s not the producer originating the innovation, but an external informal source.

The challenge is to be able to generate awareness of these sources of knowledge, evaluate them, bring them inside to the company for evaluation and processing, and turn them into useful innovations or internal changes.

In highly dynamic industries, it is productive to tap into these knowledge networks.

Professor Miller refers to the external networks of knowledge, both formal and informal, as the wisdom of the crowd. If you are operating in an environment characterized by high dynamism and rapid change, the wisdom the of crowd is an important and often decisive resource.

  1. The wisdom of the crowd can contribute to innovation and business performance, especially in the form of idea diversity.
  2. Innovation performance improves through better firm capitalization of knowledge resources.
  3. The wisdom of the crowd offsets firm rigidity — making it more receptive to new ideas,
  4. Entrepreneurial judgment can increase innovation performance by increasing absorptive capacity.
  5. Innovation performance feeds back into absorptive capacity, creating an iterative self-improvement loop.

Professor Miller proposes three areas of business development by capitalizing on external user groups.

First, firms struggling to innovate due to internal rigidities may well benefit from developing communities — similar in concept to modding communities – connected to their own industries. By absorbing and incorporating the learning that occurs in such groups, they can take advantage of readily available innovative ideas for change.

Second, these communities may also provide a wellspring of talent for enhancing the firm’s absorptive capacity in useful ways. This is a pool of unique and entrepreneurial individuals with the potential to enhance the firm’s human capital and make the firm more explorative.

Third, even if the firm does not fully tap in to all the knowledge coming from the community, there is still the potential for new solutions to emerge that are stimulated by external ideas. There are always hobbyists and fans, and technology easily facilitates their interactions. Crowdsourced knowledge provides a uniquely useful tool for enhancing organizational innovation.

The wisdom of the crowd is a path to profit.

Modding as an art form allows players to express what they most want games to be. This becomes a useful indicator for determining the most profitable paths to pursue. Firms seeking to enhance their innovative capabilities and remain profitable must pay attention to external sources of learning, however informal.

Additional Resources

Download our free E4B PDF: “Assessing Your Firm’s Absorptive Capacity”: Mises.org/E4B_182_PDF

The Invisible Hand In Virtual Worlds: The Economic Order of Video Games by Matthew McCaffrey: Mises.org/E4B_182_Book

181. Brian Rivera on the Flow System

/0 Comments/in /by

The traditional approaches to the structure and management of firms are becoming barriers to customer value. The Austrian capital theory approach recognizes that all value in the corporation flows to it from the value experiences of customers. Therefore traditional organizational design — centralization, hierarchies, divisions, bureaucracy, command-and-control — insofar as they are poorly aligned with customer value actually detract from the value of the firm.

There are alternative approaches to business organization, several of which we have highlighted in Economics For Business. One well-articulated alternative is The Flow System (Mises.org/E4B_181_Book). We talk to one of the authors of the concept, Brian Rivera.

Key Takeaways and Actionable Insights

The first principle of all business organization is the delivery of customer value.

The superiority and broad applicability of the Austrian business model emanates from its value-dominant logic. The purpose of business is to facilitate a value experience on the part of the customer. Only value matters, and all else (resources employed, raw materials used, production costs, organization, supplier partnerships, etc.) follows. Austrian capital theory enables managers to identify value drivers (i.e. what resources, raw materials, production costs, organization, partnerships result in the most value for customers).

The focus of the Flow System is to deliver the best value to the customer through FLOW: the interconnection of complexity thinking, distributed leadership, and team science.

Flow is another term for entrepreneurial judgment.

In Brian Rivera’s book, The Flow System, flow is described as “a narrative of in-the-moment decision making of judgments”. It is entrepreneurial action and interaction with the environment, irrespective of structure. It’s goal-oriented adaptive and collaborative behavior of teams and firms.

The Austrian perceptions of the market as a flow, value as a flow and capital as a flow mean that the Austrian business model is perfectly consistent with The Flow System.

Mastering complexity thinking is fundamental to implementing the flow system.

Many business environments exhibit high variability and uncertainty. We’ve used the term VUCA to characterize them: volatile, uncertain, complex and ambiguous. All business managers and entrepreneurs can benefit from adopting a complexity world-view, and understanding business as a complex system.

Complex adaptive systems are open, continuously dynamic, evolving, learning, and responsive to external changes. They can oscillate between order and disorder, they’re non-linear and can’t be predicted or controlled.

Brian Rivera highlights a number of techniques to manage in such an environment, including:

Sensemaking: the development of narratives or storytelling to conceptualize the complex environment and develop an appropriate set of mental models. The question to ask is, “What’s the story?” — the story that can unite the firm and its partners around a shared understanding and shared purpose.

Weak signal detection: in complexity, signals are never clear; uncertainty is the norm and errors are always a possibility. Weak signal detection is simply intensifying the scnning of the environment for insights and noticing more, so that both threats and opportunities can be detected earlier to avoid surprise.

Action: the only source of real knowledge about the world is experience, and experience results from action. Therefore, The Flow System emphasizes action — the D and the A in the OODA loop.

The Flow System employs a new definition of leadership: distributed leadership.

Distributed leadership is described as leadership that extends horizontally, vertically and every place between. The tools of leadership are not structures (such as hierarchy and top-down management) but methods:

  • Psychological safety
  • Active listening
  • Intent
  • Shared mental models
  • Bias towards action
  • Collaboration
  • Mentoring.

Perhaps the most essential factor is psychological safety among team members. It’s a group property — a shared belief in which the team is safe from interpersonal risk taking. Individuals can speak up, take risks, and experiment without fear of criticism or reprisal so long as every action fits within the shared belief framework. There is no command structure, and teams are the building blocks of the organization.

There’s a new field of team science for collaborative functioning in the workplace.

Team science is multi-disciplinary. Teams are necessary for the development of solutions in many problem areas, and the research behind team science has been conducted in many fields (ecology, healthcare, organizational science, psychology and more).

A team is a collection of individuals with a shared goal, who interact and are interdependent in their tasks, who have different roles while sharing responsibility for outcomes, and constitute a social entity embedded in a larger system (a business unit or corporation) requiring them to manage relationships across organizational boundaries.

A major section of the book The Flow System is devoted to an overview of the current state of team science as it relates to business organizations, covering team size and composition, teamwork, team processes and team transitions, team culture, team effectiveness, and combining teams for multi-team scaling.

Here’s a sample concerning the functions of shared leadership in a team:

  • Compelling team purpose — exceeding individual goals.
  • Members work jointly to integrate their complementary talent and skills.
  • Outcomes are collective, joint efforts.
  • Members adapt their working approach to each other.
  • Mutual accountability plus individual accountability.

Core principles and attributes of The Flow System.

  1. Customer first
  2. Value is a flow
  3. Complexity thinking, distributed leadership and team science can facilitate the flow when they are interconnected and synchronized.

Additional Resources

E4B Knowledge Graphic — “The Flow System Guide” (PDF): Mises.org/E4B_181_PDF

theflowsystem.com

flowguides.org

The Flow System by by John Turner, Nigel Thurlow, and Brian Rivera: Mises.org/E4B_181_Book

Teams That Work: The Seven Drivers Of Tea Effectiveness by Scott Tannenbaum and Eduardo Salas: Mises.org/E4B_181_Book2

180. Raushan Gross On the Newly Emerging And Newly Enabling Institutions Of Entrepreneurship

/0 Comments/in /by

Entrepreneurship today is a movement, a welling-up of new economic creativity, combined with a great desire for economic freedom and the joys of self-reliance and discovery. The movement is newly empowered by enabling institutions that simply weren’t around a few years ago, including the internet and its digital economic platforms. Professor Raushan Gross is a great observer and great documenter of this entrepreneurial surge, and he joins the Economics For Business podcast to share some of his original and distinctive observations about the very human aspects of his new entrepreneurial studies.

Key Takeaways and Actionable Insights

Let’s not over-theorize and over-professionalize entrepreneurship: it’s people finding new ways to thrive by creatively serving other people.

There’s an explosion of university entrepreneurship programs, entrepreneurship research and entrepreneurship methodologies. There’s an attempt to professionalize entrepreneurship, to make it a product of business schools.

Raushan Gross sees things differently, through a humanist, subjective and ethical lens. He looks at the culture of entrepreneurship, the social movement of individuals making their way in life in a new manner, seeing new opportunities to make their lives better for themselves and their families by making life better for others.

There’s a newly emerging set of institutions and a new class of entrepreneur: the digitalpreneur.

Economists take an interest in how institutions shape behavior and economic activity. They see institutions as constraints. They sometimes call them “the rules of the game”. Professor Gross has a different take. The new institutions of entrepreneurship — the internet, digital platforms, e-commerce, digitization in general — are not constraining; rather, they are openings to a new space with new possibilities. This digital space is welcoming. There’s abundant knowledge to be shared. There are new ways to think about access to resources, about production and marketing and organization. There’s a new world of price signals, much more flexible and fast-changing, and the route to cash flow and profit is faster.

Professor Gross identifies digitalpreneurs as a new economic class: not higher or lower, not defined by their origins or background, free to move at any speed and to access any place in their relentless, unbounded pursuit of entrepreneurship.

Today’s entrepreneurs are rewriting economic history: from the invisible hand to the visible hand to the digital hand.

Adam Smith introduced the metaphor of the invisible hand — the concept that individual economic actors and firms entrepreneurially pursuing their own profit goals generate the economic system we call free market capitalism, with benefits for all of society. Friedrich Hayek expressed a similar idea as “spontaneous order”. The invisible hand guided the rapid growth in real standards of living of the industrial revolution.

Then the visible hand imposed itself: the concepts of management control, and of planning and centralization. Creativity, innovation, and rapid growth were suppressed, while bureaucracies expanded. We got “Bullshit Jobs”, in David Graeber’s locution, from which creativity and caring were expunged.

Professor Gross takes us beyond both the invisible hand and the visible hand to the digital hand, which gently guides digitalpreneurs to participate in or even create new markets. The digital hand is generative. It enables digitalpreneurs to operate their own digital platforms, to construct their own digital economy, to assemble their own economic knowledge and to find their own unique place in the knowledge economy. The digital hand opens up new pathways to economic freedom.

Digital entrepreneurship can be conducted at any scale, but watch out for the dead hand.

Where are the corporations in their embrace of digitalpreneurs? Certainly, there are the new digital corporations like Amazon and Google who seem willing to hire members of the new class and turn them loose in creative experimentation. But what about the old economy corporations who need to make the transition to the new world? Are they hiring entrepreneurs? Are they enabling entrepreneurs, freeing them from bureaucracy and from the command-and-control hierarchy? The evidence so far is that they are not.

How to integrate the entrepreneurial orientation into a corporate organization remains an unsolved mystery. How can the corporate advantages of reach and scale be leveraged to further realize the senses of purpose and meaning that drive entrepreneurship? How can corporations shift to the entrepreneurial culture?

They need to find ways to eliminate what Professor Gross calls the Dead Hand — bureaucracy, regulation, control, risk-aversion, centralization, procedures, and rules.

But corporate culture is not the only barrier to the realization of the entrepreneurial society. There are other cultural barriers to overcome.

Professor Deirdre McCloskey is famous for her analysis that the catalyst for what she calls The Great Enrichment — the 3000% increase in real standards of living in certain Western countries from 1800 to the present — was a change in how we talked about entrepreneurship. The perceptions and descriptions of the bourgeois life of commerce transitioned from scorn to admiration. Entrepreneurs came to be seen as bold and innovative, a force for good, providers of desirable services enhancing the quality of life.

Professor Gross sees a fresh need for such a change in language and cultural support for the new age of digital entrepreneurship. One example he gives is the language of venture failure. Initiatives that are concluded early or don’t hit some target or don’t attract sufficient buyers or don’t generate enough profit to be sustainable are deemed “failures”. This characterization tends to lead to erroneous conclusions about risk (as in risk of failure) and about the people who engaged in the initiatives (“failures” or, worse, “losers”).

There’s a much different and better way to frame the same data as learning, and augmenting the pool of knowledge. When we think of entrepreneurship as a flow, we can visualize how information flows from the past to the present, elevating the intelligence of every entrepreneur and every firm that’s operating today. Not only does knowledge flow, it compounds, so today’s entrepreneurs can be exponentially more informed than their predecessors.

The more we adopt this win-win cultural approach to cumulative entrepreneurial knowledge-building, as opposed to the win-lose language of failure and success, the closer we’ll come to the beneficent entrepreneurial society that Adam Smith imagined, before he was so rudely interrupted.

Additional Resources

Join Economics for Business today and receive a free copy of The Emerging Institutions of Entrepreneurship eBook by Raushan Gross: Mises.org/E4B_Join

179. Mark Packard On Entrepreneurial Valuation, Part 2: How Entrepreneurs Create Value

/0 Comments/in /by

There is an excellent, deeply researched, Austrian economics-founded theory of customer value: the value learning cycle, which we explored thoroughly in Episode #178 (Mises.org/E4B_178). How do entrepreneurs and executives apply that theory to create customers, delight them, and grow strong brands and businesses? That’s the subject of the second part of Mark Packard’s business handbook for value creation, Entrepreneurial Valuation: An Entrepreneur’s Guide To Getting Into The Minds Of Customers (Mises.org/E4B_179_Book).

Key Takeaways and Actionable Insights

Entrepreneurs can’t directly access the customer’s mental model, but they can apply empathy to run simulations.

Entrepreneurial empathy is the ability to see the world through the mental model of the customer. We all see the world through mental models rather than directly, and each of us has our own, unique mental model. But mental models can also be shared and aligned. A mental model is a way of thinking about real situations or about the real world. It’s quite possible to describe someone else’s mental model. We can first ask them questions (“How do you think about your current situation?” “What do you do when the car you drive gets to 50,000 miles on the odometer?”) and then run hypotheses or ideas through the model that emerges (“How do you like this?”, “How does this make you feel?”, “Would you buy this product?”)

Empathy is knowledge-based, and therefore can be practiced by any entrepreneur.

It’s not the case that some people are more capable of empathy than others. Since empathy is knowledge-based, it can be learned, developed, and trained. It’s a process of filling different buckets of knowledge about your customer. There’s factual knowledge about them, as well as factual knowledge about their consumption or usage (e.g., location, frequency, any reports, or ratings they’ve provided). And then there’s experiential knowledge — what an experience felt like to them.

Only the customer has this experiential knowledge, only they can feel it. But if the entrepreneur can understand the customer’s mental model, it’s possible to simulate what that experience might feel like — feel what they feel. It’s possible to get closer and closer by experiencing it yourself: eating the food you’re offering them or the beverage you’ve designed, using their mental model rather than your own. The customer’s experiential knowledge is tacit — it can’t be communicated directly — but entrepreneurs can get closer to it through simulation, and interpret it through empathic technique.

Be aware that there is always the risk of what Mark calls interpretive loss — we listen or observe but we don’t interpret the data properly or fully. Our downloadable pdf provides direction on where interpretive loss occurs and how to safeguard against it.

There are some techniques to reinforce the accuracy of empathic investigation.

  • Lead users: In every category, there are users who feel needs and experience unsatisfaction / dissatisfaction more intensely. Give investigative priority to them.
  • Contextual in-depth interviews: Communication can be more productive using specific techniques from our E4B tools library. The contextual in-depth interview technique is one of our useful tools.
  • Ethnographic deduction: Ethnography is the technique of observing users in action. It’s a better tool than a survey or questionnaire — what users do is more informative than what they say when answering surveys. Researchers deduce motivations from observation.
  • Behavioral data: Some data streams can be the equivalent of ethnography — observing users buying or searching as an indicator of their needs, preferences, and concerns.
  • Entrepreneurs can also learn from themselves: We are all both consumers and producers. In the categories that are most important to you, observe your own behavior as a user. Be aware of your concerns as a customer. Make your empathy channel customer-to-customer.

From value propositions to innovation.

Developing a value proposition is a problem-finding process. Designing an innovation is a problem-solution process.

Problem-finding is the development of knowledge of a problem to be solved from the customer’s perspective, using the experiential learning from the mental modeling exercise. A problem is not the same as a need — it’s a specific gap in the solution landscape of products and services from which the customer can choose, a gap that can be filled with a new solution yet to be identified but capable of identification.

Problem-solving is the application of resource knowledge and technical knowledge to identify a new solution. The entrepreneur must navigate multiple uncertainties to arrive at a solution — demand uncertainty (is there real demand?), technical uncertainty (will it work?), resource uncertainty (will I be able to gather the resources to get to a solution?), capability uncertainty (can I do this?), and competitive uncertainty (will someone else beat me to it?).

Mark’s book includes a multi-step process for problem-solution creativity. One of the most interesting is knowledge combining.

What’s a pancake boat? It’s a combination of two very basic words and ideas that represents the potential for something new. Perhaps a very flat-profile boat for floating under low bridges. Or a breakfast barge touring the harbor. The point is the combination. When entrepreneurs can combine technological knowledge with problem knowledge, it’s possible to invent a new solution without inventing a new technology.

Mark has two suggestions to help with knowledge combining. One is to become interested in technologies. If you are having a hard time devising a solution, it’s probably because you are not familiar enough with technologies that are already available to do so. Find tech websites that can keep you up-to-date on the latest discoveries and applications. The more you understand about the properties and capabilities of resources and technologies, the better you can leverage those properties and what they do.

The second suggestion is a specific method. List as many different resources, technologies, and skills that you know about — software skills, hardware skills, people skills, technologies you’ve worked with, processes you’ve worked with, etc. Keep the list updated.

Then turn to the problem you are trying to solve. Mentally step through all the resources on your list and bring each of them into active memory. Try to think of a possible solution using each one. Keep going through the whole list. You’re bringing technical knowledge schemas forward while holding your problem knowledge in active memory.

Do any of the solutions stand out? Are there any that are truly outside-the-box? Are any of them impossible with current technology? That’s good. Do more research. You might find a breakthrough answer.

It takes time, commitment, and resources, but when you are passionate about the entrepreneurial process the effort will pay off big time.

Entrepreneurs get inside the mind of the customer to make the world a better place.

The goal of entrepreneurship is to enhance and improve the state of well-being experienced by customers. To achieve this goal, entrepreneurs aim to understand the customer’s mental model, and run creative solutions — potential futures — through it to simulate the customer’s new experience. It’s a counter-factual exercise, but entrepreneurs can improve their capacity, and their odds of success, with practice, commitment, and the use of some of the cognitive techniques Mark Packard recommends.

Additional Resources

“Contextual In-depth Interview Technique” (PDF): Mises.org/E4B_179_PDF

“Interpretive Value Learning” (PPT): Mises.org/E4B_179_PPT

Entrepreneurial Valuation: An Entrepreneur’s Guide To Getting Into The Minds Of Customers by Mark Packard: Mises.org/E4B_179_Book

178. Mark Packard On Entrepreneurial Valuation, Part 1: Value Learning

/0 Comments/in /by

Getting into the minds of customers is the universal need of everyone in business. A new book by Mark Packard, Entrepreneurial Valuation, provides a new understanding of how customers identify value in the constant, never-ending flow of the value learning cycle. Mark joins Economics For Business for a two-part episode on how entrepreneurs can better understand value in order to delight customers.

Key Takeaways and Actionable Insights

Getting into the minds of customers is the universal need of everyone in business.

The business world is enthusiastically adopting the insights of Austrian economics. They appreciate the unique economic perspective that can help grow and strengthen customer-facing businesses — and that means all businesses. Professor Mark Packard is presenting his insights on customers and how their minds work when choosing what to buy in a new book, Entrepreneurial Valuation, with the sub-title An Entrepreneur’s Guide To Getting Into The Minds Of Customers (Mises.org/E4B_178_Book). It’s a business book for every business and every businessperson.

The first step is to experience value as customers experience it. They learn it.

The purpose of business is to create value for customers. And for customers, the pursuit of value is everything. It’s life — a never-ending process of identifying what they expect to be valuable to them and trying to weigh up their choices between alternatives. Human beings are always valuing, all the time. In fact, Mark makes the point that we should think of value as a verb, not just as a noun. Value as a noun has a specific meaning: it’s an experienced benefit that constitutes a change in well-being from a state of unwellness to a better-off state. The benefit is the experience, and it can be ascribed to something that made us feel better off, which therefore has value.

Valuing — the verb form of value — refers to human beings constantly deciding what to do and what to choose based on their valuation process. And that process is learning — learning from previous value experiences, and learning from observing others. As customers, people are always asking: what makes us and others the best off we or they can be?

Entrepreneurs must have their own, complementary, value learning process: learning what customers value and, ideally, what they will value in the future.

Customers can be unsatisfied or dissatisfied. It’s important that entrepreneurs address these value states differently.

The default state for people is unsatisfied. We have unmet needs that we feel all the time. Mises called it a state of uneasiness. Needs like hunger can be satisfied in the short term, but the satisfaction degrades quickly. Needs like security or freedom or friendship may always be unsatisfied, or at least part of the time. There is always a state of greater well-being to aspire to.

Dissatisfaction is a different state. A customer may have applied their value knowledge — made a valuation — to predict a future value experience, and it falls short of their expectations. They made an error. This results in a feeling of dissatisfaction

Both states are opportunities for entrepreneurs: to meet a hitherto unmet need, or to substitute satisfaction for dissatisfaction via a new or better solution. It’s important to know the customer’s state of well-being and its source.

Customers have limited value knowledge and considerable value uncertainty, yet they must make value predictions.

Customers use the value knowledge they possess, from previous value experiences or observing others in the market, to try to predict a future improvement in well-being for themselves. What choices should they make to achieve this improvement?

How do they make the prediction? They perform a mental simulation of future value experiences. They imagine themselves having a future value experience with a particular product or service. Via the simulation, they form their predictive valuation: the benefit they expect to experience in the future.

When they actually use the product or service, they assess the actual value experience and compare it with the prediction, thereby updating their value knowledge. They ascribe to the product or service the satisfaction or dissatisfaction experience they feel. Or they might ascribe it to a set of circumstances or some other context. In any case, they have a new mental model: a new experience they can ascribe and use for future predictions.

Value learning is a cycle.

  • Self-assess to identify unsatisfaction and dissatisfaction;
  • Search for new value propositions with new satisfaction potential;
  • Compare the new value proposition with alternatives (and with others’ experiences);
  • Make an economic calculation: willingness to pay;
  • Purchase;
  • Usage experience — including objective value experienced in consumption and subjective value experienced as degrees of feelings of satisfaction (e.g., delight at exceeding expectations versus satisfaction at meeting expectations versus disappointment at failing to meet expectations);
  • Assess usage experience compared to value expectation;
  • Adjust value knowledge base and revise future expectations.

Austrian economics helps businesses get into the minds of customers to monitor and understand their value learning.

Economics is a much better discipline than finance on which to construct an approach to growing a successful business, because economics is the science of choice: how customers choose the ends they pursue and how they choose the means they perceive as best for attaining their ends.

It’s the Austrian school of economics that is most useful. Traditional economics believes that customers seek utility — what’s useful to them. But subjective value doesn’t reside in utility, it resides in the satisfaction that comes from the feeling of making the best choices. Behavioral economists believe that customers have a tendency to make poor choices (from the economists’ point of view) because of incomplete value knowledge.

But Austrian economists accept the customer’s mind as it is. The goal is to understand how customers choose and how they experience value in their everyday lives, how they negotiate value uncertainty, how they set expectations for the future and how they compare actual experience with expectations. What goes through their minds? To know that requires getting inside their minds, which is what Professor Packard is trying to help us to do with his new book.

Additional Resources

“Experiential Value Theory: How Customers Think About Value” (PPT): Mises.org_E4B_178_PPT

Entrepreneurial Valuation: An Entrepreneur’s Guide To Getting Into The Minds Of Customers by Mark Packard: Mises.org/E4B_178_Book

“Tools For The Value Learning Process” (PDF): Mises.org_E4B_178_PDF