The IKEA effect describes how people come to overvalue things in which they have successfully put effort into. This cognitive bias can pose risks to organisations in the form of sunk cost effects and “not invented here” syndrome.

![ikea-effect](//images.ctfassets.net/vwq10xzbe6iz/1o8egJQ2qvuZqLQjsPpOSf/5c7e44c7d77fc37f809a05fe47b6d357/ikea-effect.png)

This article explains when and why the IKEA effect emerges and introduces four ways you can defend against it.
1. [What is the IKEA effect?](#what-is-the-ikea-effect)
2. [Pitfalls of the IKEA effect](#pitfalls-of-the-ikea-effect)
3. [Four ways to avoid the IKEA effect](#ways-to-avoid-the-ikea-effect)

### <a name="what-is-the-ikea-effect"></a> 1. What is the IKEA effect?
The IKEA effect is a finding that people tend to ascribe a higher valuation towards self-assembled products compared to objectively similar products which they did not assemble.

A study by Norton, Mochon and Ariely ([2011 paper](https://www.hbs.edu/ris/Publication%20Files/11-091.pdf)) found participants who self-assembled an IKEA storage box (Builders) valued the purchase price 63% higher than those who inspected the same pre-assembled box (Non-Builders). Similarly, the Builders also rated liking the box 55% more than Non-Builders. 

__IKEA Effect Experiment A - Perceived value of IKEA storage box by Builders vs Non-Builders__

|   Measure   | Builders (Self-assemble)     | Non-Builders (Inspect pre-assembled)     | % Difference     |
| ---------- | ---------- | ---------- | ---------- |
| Willingness To Pay (0-100 cents scale)       | 78       | 48       | 63%       |
| Liking of Box (1-7 scale)       | 3.8       | 2.5       | 52%       |

*Participants: N = 50*

In another experiment one group of participants were asked to create origami (Builders) and another asked to inspect their creation (Non-Builders). Similar to the first experiment, the Builders valued their creation 360% higher than the Non-Builders who ascribed close to no value to the origami (“nearly worthless crumpled paper”).

__IKEA Effect Experiment B - Perceived value of origami creation by Builders vs Non-Builders__

|  Measure    | Builders (Self-create)     | Non-Builders (Inspect creation)     | % Difference     |
| ---------- | ---------- | ---------- | ---------- |
| Willingness To Pay (0-100 cents scale)       | 23       | 5       | 360%   
*Participants: N = 106*

### <a name="pitfalls-of-the-ikea-effect"></a> 2. Pitfalls of the IKEA effect
The IKEA effect explains broadly why people may come to fall in love with their own ideas. With the effect strongest when people expend a greater amount of their own effort and perceive their effort as successful.

The IKEA effect has negative implications for organisations more broadly as a contributor to two key organisational pitfalls:
- __Sunk cost effects__: people continue to devote resources to failing projects in which they have previously invested
- __“Not invented here” syndrome__: people refuse to use perfectly good ideas developed elsewhere in favour of their own, often inferior internally-developed ideas

Here are some examples of how the pitfalls could occur:

| Team     | Effort     | Pitfall ⚠️     |
| ---------- | ---------- | ---------- |
| Fund Manager       | Creating a company financial model with industry and market share forecasts       | Perceiving company to be more valuable than it may be even when forecasts are not met       |
| Sales Executive       | Developing a sales slide deck on a unique selling proposition       | Selling the solution instead of listening for customer struggles       |
| Product Designer       | Building high fidelity prototype of MVP product       | Over Indexing visual design feedback and distracting from focus on user goals       |
| Growth Marketer       | Developing Account Based Marketing strategy based on LinkedIn research       | Avoid comparing cost of acquisition vs other more effective initiatives like SEO and direct prospecting       |

### <a name="ways-to-avoid-the-ikea-effect"></a> 3. Four ways to avoid the IKEA effect
Here are four ways you can avoid and defend against the IKEA effect.

#### 1. Acknowledge your unconscious bias
In the IKEA effect study, participants were found to be unaware of their unconscious bias. 92% of the participants when asked said they would pay more for pre-assembled products compared to self-assembly. This was in direct contradiction of their actions in the study. 

Acknowledging you are prone to the cognitive bias of the IKEA effect can be the most effective way to mitigate against it. When completing an effort on a solution or idea, admit to yourself that you probably love it more than other solutions, because you worked on it.

#### 2. Spike rough prototypes
The IKEA effect study found that the effect was strongest only when the effort was successful. Failure to complete tasks had corresponding negative psychological consequences. People ruminate more on tasks they failed to complete than on those they successfully completed, leading to negative affect and regret.

Spike rough prototypes and MVPs to test ideas early. Embrace sharing ideas that you are embarrassed with so it will be easier to cull.

#### 3. Talk to customers
Framing your solutions and efforts towards the customer struggle can help mitigate your bias against any one solution.

Talking to customers is the best way to achieve this. Learn how to conduct user interviews to surface customer's struggling moments and understand why they don't love their current solutions ([example user interview questions](https://mannhowie.com/user-interviews)).  

#### 4. Run growth experiments
Growth experiments can help mitigate against [confirmation bias](https://mannhowie.com/cognitive-bias#confirmation) by setting the discipline to state the problem statement, hypothesis and success criteria upfront before expending effort on a solution. Learn how to run your own growth experiment in this tutorial ([How to run a growth experiment](https://mannhowie.com/growth-experiment)).

The Build, Measure and Learn framework popularised by Eric Ries in his book [The Lean Startup](http://theleanstartup.com/book) explains how to apply a learning and feedback loop to measure the success of products and solutions.

__Sources__
- Norton, M. I., Mochon, D., & Ariely, D. (2012). [The IKEA effect: When labor leads to love.](https://www.hbs.edu/ris/Publication%20Files/11-091.pdf)
- Ries, Eric. [The Lean Startup. (2011)](http://theleanstartup.com/book)


When and why the IKEA effect causes people to overvalue their own ideas. Examples of organisational pitfalls of the IKEA effect. 4 ways you can defend against the IKEA effect: 1) Acknowledge your unconscious bias; 2) Spike rough prototypes; 3) Talk to customers; 4) Run growth experiments

ikea-effect

The IKEA Effect - Why people fall in love with their own ideas

The average is the most commonly used metric we use to understand data and infer insights - it is also the most dangerous.
![beware_average](//images.ctfassets.net/vwq10xzbe6iz/12kJw6jBqfvjjSCHyMxUNm/2dbf7edf379236641095dffe09c670fb/beware_average.png)

# The Pitfall of the Average
The US Air Force began life in the late 1940s with a high rate of aircraft accidents and pilot fatalities (excluding combat losses) with 23.6 aircraft destroyed per 100,000 flying hours. This dramatically reduced to 4.3 by the end of the 1960s driven largely by the work of one man, Lieutenant Gilbert S. Daniels who saw the pitfall of using “the average” to design solutions.

> The tendency to think in terms of the “average man” is a pitfall…it is virtually impossible to find an “average man” in the Air Force population.
[Gilbert S Daniels (1952 US Air Force Report)](https://apps.dtic.mil/sti/citations/AD0010203)

US Army engineers had standardised the design of pilot cockpits to conform to the average dimensions of a 1926 pilot. This was revisited in the 1950s by Lt. Daniels who measured the bodily dimensions of more than 4,000 pilots and found that zero pilots matched the average dimensions.

The US Air Force responded and demanded that its manufacturers design its cockpits to be adjustable to individual dimensions vs the standardised average. As a consequence, both pilot performance and accident/ mishap rates dramatically improved.
![us_airforce](//images.ctfassets.net/vwq10xzbe6iz/6mtEbxaIXNU60Svdu9uGsO/3c61d041d241f4040e7269db2552b8eb/us_airforce.png)

# A Modern Example
A modern example of the pitfall of the average would be to look at user activity. Imagine we build software that helps users perform inspections and we are tasked with improving user engagement.

Looking at the data we find that the average count of monthly inspections performed by our active users is 32 inspections per month:

![table_inspections](//images.ctfassets.net/vwq10xzbe6iz/3SfYJ8CwfGsBPFptTVL3jB/44bd12fd8ec35a7fc0c8efe608a47ed3/table_inspections.png)

Knowing this, we can deduce that our users on average perform 32 inspections per month or approximately one inspection a day. We begin building solutions such as daily notifications to help remind our users to perform their daily inspection.

__Stop.__

Reducing a large data set into a single number can often mask useful information and give us the wrong answer. Instead we can look at the distribution of our data and graph the same data as a histogram to reveal how often each different value occurs in a dataset.
![histogram_inspections](//images.ctfassets.net/vwq10xzbe6iz/1UixJwtgp0xcrWIvV6I6J2/ce429dc02a478f3c49abb41f28b113bc/histogram_inspections.png)

Now looking at the distribution of our data we can see that none of our users perform the average number of 32 inspections per month. Instead we see that 60% of our users perform 10 or fewer inspections per month and a large group (+30% of users) perform 70 or more inspections per month.

The distribution of our data could suggest that the usage of our software differs widely by different cohorts of users. One cohort could be using it to perform monthly scheduled safety checks vs another cohort using it to perform factory line QA checks. 

Next time when using the average, pause before making any conclusions and ask: "How many instances of the data actually reflect the average?". 

__References:__
- [THE “AVERAGE MAN”? - Gilbert S Daniels (1952 US Air Force Report)](https://apps.dtic.mil/sti/citations/AD0010203)
- [Trends in U.S. Air Force Aircraft Mishap Rates (1950-2018) - Thomas Light, Thomas Hamilton, Spencer Pfeifer](https://www.rand.org/content/dam/rand/pubs/research_reports/RRA200/RRA257-1/RAND_RRA257-1.pdf) 
- [The End of Average - Todd Rose (2015)](https://www.penguin.com.au/books/the-end-of-average-9780141980034) 


The average is a myth. Understand the pitfalls of the average and how it can conceal useful information. How the US Air Force threw out the average and saved lives. Use distributions to better understand your data.

beware_average

How the US Air Force Ditched the "Average" and Saved Lives

![whose_problem_is_it](//images.ctfassets.net/vwq10xzbe6iz/7zzVyJsGJgxnmhrfEGmEI7/dc069116327d27b573068efce0725579/whose_problem_is_it.png)
Here are 4 simple questions you can ask to help identify and solve the right problems:
1. [Whose problem is it really?](#1)
2. [What is their problem?](#2)
3. [Where does the problem come from?](#3)
4. [Do we really need to solve it?](#4)

To understand the importance of identifying and solving the right problems, imagine you are in the strategy team of a B2B software company presented with a situation:

> “New free user signups are strong but paid conversion is dropping. Cost of acquisition is being bid up by marketing competition. Our development team is under-resourced and focused on solving existing user retention which is making great headway. The Board is reluctant to expand the marketing budget given the uncertain funding environment.”

__What is to be done about this situation?__

Our first instinct is often to jump directly to solutions, these may include:
1. Promote the benefits of our paid features earlier on
2. Send marketing campaigns to our free users to upgrade
3. Reduce our free user allowance
4. Increase our paid user allowances
5. Introduce referral bonuses
6. Create new features that our users are willing to pay for
7. Target new free user signup channels

__Stop.__

Before jumping to solutions we should begin by asking our 4 simple questions.

## <a name="1"></a>1) Whose Problem is it Really?
Let us look at each of the stakeholders and restate the problem statement based on the situation from their perspective:

| Stakeholder     | Problem       |
| ---------- | ---------- |
| Free Users       | How can I get the most value out of the product?       |
| Marketing Team       | How can I achieve my user signup target with the given marketing budget?       |
| Development Team       | How can I keep my engineering resourcing focused on the current roadmap?       |
| CEO       | How can I convince the market that my strategy is working and keep stock high?       |
| Board/ Shareholders       | How can I ensure we have enough funding to get through current uncertainty?       |

By defining the problem owner we can already see markedly different solutions.

## <a name="2"></a>2) What is Their Problem?
Once we have identified who has the problem we can look deeper to what is their true problem (if any). Examples below could include:

| Stakeholder     | Problem     |
| ---------- | ---------- |
| Free Users       | I don’t want to get locked into paying for something I won’t use       |
| Marketing Team       | The cost of acquisition is getting too high for us to achieve our signup targets       |
| Development Team       | I don’t have sufficient development resources to work on multiple projects       |
| CEO       | Word may get out that our paid conversion is dropping and share price may fall        |
| Board/ Shareholders       | Market conditions are not conducive to raising new funding now       |

## <a name="3"></a>3) Where does the problem come from?
Once we have identified what is the true problem, we need to locate where the problem stems from. Examples below could include:

| Stakeholder     | Problem     |
| ---------- | ---------- |
| Free Users       | Past bad experiences from having to cancel or request refunds       |
| Marketing Team       | We set too ambitious new user signup targets vs. paid user targets       |
| Development Team       | The projects we are tasked with are often unscoped and variable        |
| CEO       | None. Our strategy is focused on high user engagement and not yet monetisation       |
| Board/ Shareholders       | We want to avoid dilution by not raising money at a lower valuation to fund growth       |

## <a name="4"></a>4) Do we really need to solve it?
Once we have identified who has the problem, what is the problem and located where the problem comes from we are in a final position to ask whether we really need to solve the problem. 

| Stakeholder     | Problem     |
| ---------- | ---------- |
| Free Users       | Yes. Test and promote a pause payment request to free users within first 12 months       |
| Marketing Team       | Yes. We want marketing focused on identifying new free user channels with lower cost of acquisition (we accept there will always be marketing competition)       |
| Development Team       | No. We want development team focused on retention which is a bigger problem       |
| CEO       | No. We are aligned on strategy       |
| Board/ Shareholders       | No. We are not asking for more marketing funding       |

Next time you are faced with a new problem, before jumping to solutions try to stop and ask yourself these 4 simple questions: 
1. Whose problem is it really? 
2. What is their problem? 
3. Where does the problem come from? 
4. Do we really need to solve it?.

__References__

[Are Your Lights On?: How to Figure Out What the Problem Really Is](https://geraldmweinberg.com/Site/AYLO.html) by Donald C. Gause and Gerald M. Weinberg (1982)


Ask: 1) Whose problem is it really? 2) What is their problem? 3) Where does the problem come from? 4) Do we really need to solve the problem?

whose_problem_is_it

How to Solve the Right Problem - 4 Simple Questions to ask

Product, marketing and sales teams can learn to run growth experiments to validate new strategies in the real world before incurring significant resource investment.
![guess_vs_experiment](//images.ctfassets.net/vwq10xzbe6iz/40uy9hD2VDsloevHEODX5m/7231e7f18856fa8c00e0f9eaef7347ec/guess_vs_experiment.png)

[Confirmation bias](https://mannhowie.com/cognitive-bias#confirmation) is a psychological tendency where we seek information that validates our existing beliefs as well as avoiding contradictory information. This bias often causes teams to waste resources on initiatives that validate existing views but are ineffective in the real world.

This article explains the 4 key steps of a growth experiment and a practical example of how you can run your own growth experiment quickly and cost effectively:
1. [Hypothesis](#hypothesis)
2. [Trigger](#trigger)
3. [Action](#action)
4. [Measure](#measure)

Imagine we are in the product marketing team of a B2B software startup that provides a tool for capturing customer complaints called Capturo.

## <a name="hypothesis"></a>1. Hypothesis
Set a hypothesis early. Start by identifying a customer struggle and business outcome of what you are trying to achieve. Form your own explanation as to what is causing the customer struggle and come up with a solution as to what could ease that struggle. [User interviews](https://mannhowie.com/user-interviews) are an effective tool at this stage if you have the time.

It is important at this stage to avoid the pitfall of falling in love with your explanation and solution. Focusing on the struggle will allow you to run a new growth experiment if the current one fails.

### __Example Hypothesis__
__Struggle:__ Our new users are observed to create a lower number of customer complaint forms on the platform than our existing users which is leading to lower product engagement and complaints captured.

__Explanation:__ We suspect two core reasons: 1) New users are unfamiliar with the types of questions to ask to gather complaints; 2) New users lack the time or know-how to digitise their existing questions into our platform.

__Solution:__ Our proposed solution will address the first explanation of new users not knowing what questions to ask. We will research and propose the most frequently asked questions to address customer complaints by service type (e.g. contact centre, restaurant, consultation).

__Hypothesis:__
> “Teaching new users the most commonly asked customer complaint questions relevant to their business will help them create more forms on the platform and gather more customer complaints”

## <a name="trigger"></a>2. Trigger
Establish a trigger point to communicate with your customer around their struggle and your proposed solution. The most effective mediums are email and calling customers directly. In-app banners, notifications and user flows are expensive if you do not already have the infrastructure in place. As this is a growth experiment we do not need to worry about solutions that don’t scale yet. 

The three most important components of a trigger:
1. __Audience:__ identify your target audience. Start with bigger groups, segmenting at this stage will be time consuming and take longer to gather results
2. __Message:__ keep consistent with your original hypothesis. There is no need to A/B test on a small scale but avoid message variation overkill
3. __Timing:__ identify the best time to contact your users to get their attention. Be sparing for when you trigger again due to lack of response

### __Example Trigger__
__Audience:__ our growth experiment will be to email new users who have created two forms or less in the platform from their first three months of joining

__Message:__ “Real examples of customer survey questions [Dental Practices] are using in Capturo” or “Top 6 customer survey questions [Dental Practices] ask in Capturo”

__Timing:__ Send when admin users are most active on the platform (for example mid-week mornings)

## <a name="action"></a>3. Action
Establish an action that you can measure based on the trigger. This can include opening a link to an article, registering interest via a form, logging in to the platform to perform an activity, emailing or calling back to respond.

Without creating an action it will be difficult to measure the success of your growth experiment.

### __Example Action__
Create a webpage link in the email which takes a user to the top customer survey questions.
E.g email link.
“View top 6 customer survey question examples”

## <a name="measure"></a>4. Measure
Measure and track the performance of the trigger and action stages. Start simple and use a spreadsheet to track key results. Free or cheap email tracking plug-ins are an effective way of tracking email open and click-through rates.

Once you feel you have gathered enough insight (can take a few weeks) assess the results and determine if your hypothesis was correct. Within the same experiment you can go back and modify the trigger and action to gather more data if needed.

### Example Measure
Create a spreadsheet with columns to track the following:
User contact email and id
- Email message sent
- Date and time sent
- Date and time opened email (use email tracking tools)
- Date and time opened link (use email tracking tools)
- Already sent (Y/N)
- Created a new template (Y/N) (check manually each week)

After measuring the performance assess engagement metrics for example:
- Total users contacted
- Average contact count per user
- % email open rate
- % click through rate
- % users creating new template

__Happy case__
If the % click through rate and % users creating a new template is good we have quickly validated that knowing the right questions to ask can help boost form creation. We now have an improved business case for investing more resources in-app to build onboarding tours in the form creation stage.

__Sad case__
If the % open rate is poor after adjusting for message and timing then the solution of our hypothesis is likely incorrect. We can go back to our original hypothesis and form a solution for our second explanation (e.g. already know what questions to ask but don’t have the time to create a new form). 


4 steps to a successful growth experiment: 1) Hypothesis; 2) Trigger; 3) Action; 4) Measure. Practical example for how teams can run growth experiments quickly and cheaply.

guess_vs_experiment

How to Run a Growth Experiment - Learn the 4 Steps

![fininte state machine](//images.ctfassets.net/vwq10xzbe6iz/6JNh1Rpi6a1VUnTux03TzW/c69b8b54a1be48ec820cec8e721163af/fininte_state_machine.png)

Finite State Machines allow cross-functional teams to visually design complex logic before committing expensive resources to design and code implementation.

It is a powerful tool for product teams and business owners to communicate on customer requirements in order to identify rabbit holes and poor UX early in the process.

They have been widely used in electrical engineering and in the logic design of electronic devices for decades. 

To illustrate the power of Finite State Machines, imagine we have been briefed by the business to build a customer feedback form widget.

## Phase 1 - Simple Feedback Widget
The first phase of our brief is for a simple feedback widget that will do the following:
- Ask for positive or negative feedback
- If positive display a happy success message
- If negative display an apology
- Allow user to cancel the feedback form

We can visualise this simple application using Finite State Machines below:
![phase1](//images.ctfassets.net/vwq10xzbe6iz/3aRVidpZK6c6qevtvEWLfB/9a09666917aeeddc3170c9a4ef80cfcf/phase1.png)

Our application has 4 states:
1. __START__ - our initial state where a user can click to begin the survey
2. __RESPONSE__ - where a user is asked for a positive or negative response and where they can cancel to go back to START
3. __POSITIVE CONFIRM__: an end state upon a user clicking POSITIVE
4. __NEGATIVE CONFIRM__: an end state upon a user clicking NEGATIVE

Our 4 states are also characterised by 4 events and transitions which are conditional upon the current state the application is in.

Our application also triggers actions such as saving the rating and updating the message upon certain events and entering different states.

Once the logic and user flow has been agreed we can easily convert this to design and code stage. In the examples below we use the open source library [XState](https://xstate.js.org/docs/) by [David Khourshid](https://github.com/davidkpiano) to codify our Finite State Machine and use ReactJS to render our UI.

<iframe src="https://codesandbox.io/embed/feedbackphase1-stkhm?fontsize=14&hidenavigation=1&theme=dark&view=preview"
     style="width:100%; height:500px; border:0; border-radius: 4px; overflow:hidden;"
     title="feedbackPhase1"
     allow="accelerometer; ambient-light-sensor; camera; encrypted-media; geolocation; gyroscope; hid; microphone; midi; payment; usb; vr; xr-spatial-tracking"
     sandbox="allow-forms allow-modals allow-popups allow-presentation allow-same-origin allow-scripts"
   ></iframe>

## Phase 2 - Negative Feedback Reason
The business has now come back asking for us to capture the main reasons for negative feedback.

Our application now must:
- Ask the user to select the main reason for negative feedback (Service, Product, Price)
- Also allow a user to select POSITIVE again if they accidentally selected NEGATIVE

With Finite State Machines we can easily modify and expand our logic without breaking any previous logic:
![phase2](//images.ctfassets.net/vwq10xzbe6iz/38VI5AsXioynO833DlaAzM/9f8c7edb547374d6044b6bff26400c85/phase2.png)

Our application has a new __NEGATIVE FEEDBACK__ state which allows a user to select from three of the main reasons for the negative response (SERVICE, PRODUCT or PRICE). Upon selecting an event the reason is captured and the state transitions to the NEGATIVE CONFIRM end state and displays an apology message.

We have now also added a new POSITIVE event which a user can click on to transition directly to the POSITIVE CONFIRM end state.

Our team can now confidently update our application with the new business logic:

<iframe src="https://codesandbox.io/embed/feedbackphase2-lg6sy?fontsize=14&hidenavigation=1&theme=dark&view=preview"
     style="width:100%; height:500px; border:0; border-radius: 4px; overflow:hidden;"
     title="feedbackPhase2"
     allow="accelerometer; ambient-light-sensor; camera; encrypted-media; geolocation; gyroscope; hid; microphone; midi; payment; usb; vr; xr-spatial-tracking"
     sandbox="allow-forms allow-modals allow-popups allow-presentation allow-same-origin allow-scripts"
   ></iframe>

## Phase 3 - Advanced Feedback
The business comes back wanting to direct users to contact sales for pricing discussions if they select PRICE as a negative response. In addition they want to capture any other reasons for negative feedback.

Our application must now grow in complexity to handle the following:
- If user selects PRICE as a negative response, send to different end state with pricing discussion CTA
- New OTHER option for a user to select with ability to leave comment

Once again we modify our Finite State Machines to expand our logic
![phase3](//images.ctfassets.net/vwq10xzbe6iz/6wb9cvXQflR8QIBGul2SOU/b8e775fd2c86f028a3c4652602219969/phase3.png)

Our application has now added two new states:
1. __PRICE CONFIRM__: end state when a user selects PRICE as a negative response. We will display a contact number for pricing discussions. Creating a new end state provides us with flexibility if the business may want to display discount offers or ask the user for more contact information in the future
2. __COMMENT__: when a user selects OTHER as a new reason for their negative response. This state includes three events where a user can UPDATE COMMENT to capture the reason, SUBMIT COMMENT to transition to end state and CANCEL to go back and select a different reason

Once again, our Finite State Machine is able to handle increasing complexity without breaking previous logic. Our designer and developer team can now confidently work on implementing the new logic:

<iframe src="https://codesandbox.io/embed/feedbackphase3-8y5dx?fontsize=14&hidenavigation=1&theme=dark&view=preview"
     style="width:100%; height:500px; border:0; border-radius: 4px; overflow:hidden;"
     title="feedbackPhase3"
     allow="accelerometer; ambient-light-sensor; camera; encrypted-media; geolocation; gyroscope; hid; microphone; midi; payment; usb; vr; xr-spatial-tracking"
     sandbox="allow-forms allow-modals allow-popups allow-presentation allow-same-origin allow-scripts"
   ></iframe>

## Future Complexity
Our application will naturally grow in complexity as business and user requirements evolve. 

Examples may include:
- Ask for feedback for positive response
- Invite user to add users after a positive response
- Ask user to provide the name of the poor service representative
- Introduce a passive response in addition to positive and negative
- etc.

With Finite State Machines we can confidently continue modelling this advanced complexity into our design and visually see how it interacts with previous functionality.


How Finite State Machines allow cross-functional teams to visually design complex logic before committing expensive resources to design and code implementation.

fininte state machine

Finite State Machines - A Common Tool for Product Owners, Designers & Developers

Why do most startups fail to find product market fit? 
![beware_solutions_seeking_problems](//images.ctfassets.net/vwq10xzbe6iz/2rGuWsynYvglJNeJeWcQoz/b11da185061ef24f957efe1f9683d4f8/beware_solutions_seeking_problems.png)

Because our [confirmation bias](https://mannhowie.com/cognitive-bias) creates tunnel vision towards our existing solutions. Instead we should frame our focus on solving the user’s struggle.

Here are 5 questions you can ask in user interviews to validate if your startup has found product market fit.

Imagine we are a startup building solutions for mortgage brokers.

## 1. What progress are you trying to make in X?
__*Example:*__
__Q.__ What progress are you trying to make in finalising loan applications?

__A.__ I’m looking for my team to close more loans and shorten the bank approval times.

## 2. What is your biggest struggle when trying to make progress in X?
__*Example:*__
__Q.__ What is your biggest struggle in getting bank approvals completed on time?

__A.__ Document gathering with our customers. A single important lost or incomplete document can slow the whole process down and cause frustration amongst both lenders and borrowers, both of whom are our customers.

## 3. Tell me about the last time you encountered this struggle?
*__Example__*
__Q.__ Tell me about the last time you encountered this struggle? (create a timeline of the event as if you were in their shoes)

__A.__ Last month one of our new starters had a backlog of 10 applications with one of our newer lenders. An applicant customer did not submit a key piece of information that the new lender required but this only came to light a couple of weeks after the applicant had already submitted all the documents we asked. The lender was frustrated that we missed the information (as they said they had asked us for this before) and the applicant was livid as they had already terminated their rental lease.

## 4. What solutions have you previously tried?
__Example:__
__Q.__ What solutions have you previously tried to avoid or manage this struggle?

__A.__ We’ve used shared cloud storage solutions like Google Drive and Dropbox, onboarding checklists to make sure we don’t miss things and our CRM tool to keep track of emails.

## 5. What didn’t you love about those solutions?
__Example:__
__Q.__ What don’t you love about those solutions you’ve previously tried?

__A.__ Some of the tools create more work for our team in managing the document workflow and version control, we still have to review all the documents to make sure they are complete. As a result we still rely heavily on email to follow-up and confirm next steps with both lenders and customers but then it’s easy for us to miss things and drown in emails.

## Bad User Interview Questions
User interviews to find product market fit should be framed around the user’s struggle and not solution ideas. 

Bad user interview questions include:
- What ideal solution would you like to solve your struggle? ❌
- Do you have any feedback on our solution? ❌
- Would you use this solution if we offered it? ❌
- What do you think we could do to improve your process? ❌
- Do you know anyone else in your company who would use our solution? ❌

## User Interview Insights
Following the insights from our 5 user interview questions we are now better able to frame the struggle for our mortgage broker manager as:

> “Communicating between lenders and customers the missing information needed to complete a loan application”

Solutions we can now explore to solve the problem could include:
- Assignees for onboarding docs (whose court is it in now) 💡
- Automatic email reminders of incomplete docs 💡
- Auto-logging emails to capture a digital chat history 💡
- Identify which docs have not been updated in a while 💡

Framing the challenge around the user struggle allows us to continue iterating on solutions to help users make progress instead of being fully invested into one solution. 

⚠️ Beware solutions seeking problems.


5 user interview questions to validate product market fit: 1) What progress are you trying to make?; 2) What is the hardest thing about making progress?; 3) Tell me about the last time you encountered the struggle; 4) What solutions have you previously tried? 5) What didn’t you love about those solutions?

beware_solutions_seeking_problems

5 Questions to Ask to Find Product Market Fit - User Interviews

Inversion is a mental model of framing a problem from an opposing objective in order to solve the original problem.
![inversion](//images.ctfassets.net/vwq10xzbe6iz/61qV3DFcVLXMoW1R899hWB/db5a16bc87fc56ff29624d857bbed49f/invert_image.png)
It is a favourite trick of famous value investor Charlie Munger. He gives an [example of how the inversion process](https://youtu.be/_uacTl8Lv_U) helped him in his early career become a better meteorologist:

> “When I was a meteorologist in World War II, they told me how to draw weather maps and predict the weather. What I was actually doing was clearing pilots to take flights. 
> 
> I just reversed the problem. I inverted, __I said:__ __suppose I want to kill a lot of pilots. What would be the easy way to do it__? And I soon concluded that the only easy way to do it was to get the planes into icing conditions they couldn’t handle or to get the pilot into a place where he’d run out of fuel before he could safely land. 
> 
> So I made up my mind I was going to stay miles away from killing pilots by either icing or getting them sucked into conditions where they couldn’t land. I think that helped me be a better meteorologist in World War II. I just reversed the problem.
> 
> Algebra works the same way. Human beings should do the same thing in their ordinary walks of life. Just constantly invert. You don’t think about what you want. You think about what you want to avoid. When you think about what you want to avoid, you also think about what you want. And you just go back and forth all the time.”

This article looks at how others have used the inversion process to innovate big ideas and new solutions in both life and growing start-ups.

## Productivity
In solving the problem of decluttering one’s life, Marie Kondo frames the challenge of effective tidying __not as what we want to get rid of__, but instead __choosing what we want to keep__.

The big idea of the [KonMari Method](https://konmari.com/what-is-konmari-method/) is to keep things that spark joy: “Does this spark joy? If it does, keep it. If not, dispose of it.” 

By reframing the problem she was able to develop her tidying techniques as “involving only two essential actions: discarding and deciding where to store things.”

## Product Innovation
In solving the problem of product innovation, Bob Moesta and the late Clay Christenson frame the question __not as what new features do customers want__, but instead understand __what struggles they experience with their current solution__. 

The big idea behind their innovative [Jobs To Be Done (JTBD) framework](http://jobstobedone.org/) is understanding the struggles a customer faces in making progress in their lives and innovating around those struggles.

By reframing the problem in understanding the struggle, Bob Moesta innovated his JTBD framework to look at “four forces of progress” where progress only occurs when:

> (__Push__ of the current struggle + __Pull__ of the new solution) > (__Habit__ of the old solution + __Anxiety__ of the new solution).

## Project Management
In solving the problem of delivering meaningful work on time and on spec, Ryan Singer from Basecamp frames the challenge __not as how long will it take to execute__, but instead __how much time do we want to spend__. 

The big idea of [Shape Up](https://basecamp.com/shapeup) is to focus less on estimates and more on appetite in order to narrow down problems and design outlines of solutions that fit within constraints of appetite.

By reframing the problem based on appetite, Basecamp’s Shape Up techniques focus on setting bounded solutions using fat marker sketches and addressing risks and rabbit holes early on. All the techniques are designed to set guardrails based on appetite to de-risk projects from derailing or growing in scope.


How Charlie Munger used the inversion process to save the lives of pilots during WWII. How inversion helps frame innovative solutions in productivity, product design, and project management.

inversion

Invert, Always Invert - Find out how to kill pilots

Here are three learnings you can apply from the fields of psychology and neuroscience to increase user engagement and build habit forming products.
1. [Deliver unexpected rewards](#reward)
2. [Repeat easy to perform actions](#repeat)
3. [Trigger to target environment](#trigger)

![hero habit](//images.ctfassets.net/vwq10xzbe6iz/6iFtiJ8t1d4ZM3mYnPzwmh/6c687f8baa2bd41a217e0311ad13a86a/hero_habit.jpeg)

## <a name="reward"></a>1. Deliver unexpected rewards
The human brain learns behaviours which deliver rewards that exceed predictions. Incorporate unexpected rewards in your product:
- __Congratulation messages__: congratulatory pop-up messages after a user performs a correct initial key action (e.g. creating your first post 🎉) 
- __Social feedback__: invite other users to send shout outs or add reactions to a user after completing an action (e.g. notify emoji reactions 🐐) 
- __Bonuses__: bonus credits, coupons and points (e.g. inviting users, sharing photos) 

Negative rewards that disappoint predictions can also cause people to unlearn behaviours. Ensure your product’s features do what users expect them to do. Identify and respond to bugs as early as possible for first time users. Retain users by continuing to find opportunities to deliver rewards that exceed predictions (under-promise and over-deliver). 

### Why?
Studies have shown that dopamine neurons in the brain are activated by rewards that exceed predictions rather than the actual reward itself ([reward prediction error coding](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4826767/#:~:text=Reward%20prediction%20errors%20consist%20of,rewards%E2%80%94an%20evolutionary%20beneficial%20trait.&text=The%20dopamine%20signal%20increases%20nonlinearly,and%20codes%20formal%20economic%20utility.)). The presence of dopamine helps neurons wire together and develop new behaviours including habits.

Dopamine neurons are a major contributor to addictions like gambling, and drug abuse. The variable nature of rewards causes people to develop habitual behaviours even in the absence of achieving the desired goal. Recovering heroin addicts often talk about chasing their first unexpected high and continuing to use even if they can’t get high.

#### Dopamine Reward Prediction Coding - Wolfram Schultz (2016)
![dopamine](//images.ctfassets.net/vwq10xzbe6iz/20nlsCNoAI1iESPNNQLB4P/64c73830b0f89950b5e41a6797cc3e64/dopamine.png)
> Demonstration of reward prediction error in brains of monkeys. Top panel shows increased dopamine response to an unpredicted reward (R). Middle panel shows lack of dopamine response to predicted reward (R) and bottom panel shows depressed dopamine response when a predicted reward does not occur. (From [Hard to Break](https://www.amazon.com.au/Hard-Break-Brains-Habits-Stick/dp/0691194327) adapted from [Schultz et al](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4826767/#:~:text=Reward%20prediction%20errors%20consist%20of,rewards%E2%80%94an%20evolutionary%20beneficial%20trait.&text=The%20dopamine%20signal%20increases%20nonlinearly,and%20codes%20formal%20economic%20utility.))

## <a name="repeat"></a>2. Repeat easy to perform actions
Incorporate features that are simple to perform, require limited decision-making and can be repeated (in a pleasant manner):
- __Single actions__: reduce complex multi-step tasks into single actions (e.g. Amazon 1-click ordering)
- __Feedback__: gather user input with predefined responses (e.g. slack comment reactions)
- __Less options__: favour less choices, create habit and reduce decision making (e.g. an Approve button vs transition status)

### Why?
Performing an action or task for the first time requires focus and planning. Repetition of actions in a consistent context progressively increases the formation of them into habits. 

A [study of healthy habit formation](https://centrespringmd.com/docs/How%20Habits%20are%20Formed.pdf) also found that the complexity of tasks impacts the development of habits. Participants who selected more complex tasks to develop as habits, such as exercise after meal, took 1.5 times longer than simpler tasks such as drinking after meal.

#### How are habits formed: Modelling habit formation in the real world - P Lally, C Van Jaarsveld, H Potts, J Wardle (2010)
![repetition](//images.ctfassets.net/vwq10xzbe6iz/1h121jkDKnIR21Rv7KaeNX/c51ddb6b56bc5250e2fe7a92c021c72e/repetition.png)
> Example of increases in automaticity scores based on daily questionnaires (42 max score answering “I did the task automatically”) over time. Most participants reached an asymptotic score representing habitual performance following repetition. More complex tasks took longer to achieve automaticity than simpler tasks.

## <a name="trigger"></a>3. Trigger to target environment
Habits are more easily learned based on the context at the time. Deploy triggers that take advantage of consistent context and cues your users find themselves in:
- __Time and Place__: when and where are users best placed to use your product? You are competing for their attention so target an ideal context (e.g. send early morning reminders to get them before they go to work)
- __Platform__: should they be performing the task on mobile, desktop, tablet via email? Focus triggers based on the relevant platform (e.g. use mobile notifications to get users to upload photos and email notifications to view and share reports).

### Why?
The brain’s two memory systems include both the conscious and the habitual (lizard brain). Habitual memories are contextual. A famous example comes from [Claparede’s Pinprick Experiment](https://www.researchgate.net/publication/226796062_Implicit_learning_and_implicit_memory_in_moderate_to_severe_memory_disorders) on an amnesiac patient. One day, he hid a pin in the hand he used to greet one of his amnesiac patients. Later, the patient had no memory of the pinprick or even greeting the doctor. When asked why she pulled her hand back she said “Sometimes pins are hidden in people’s hands”. 

In a study observing the [treatment context of spider phobia](https://www.sciencedirect.com/science/article/abs/pii/S0005789402800351), patients who were successfully able to fight their phobia had their fear return when confronted with the phobic stimulus in a context different than the one used during the initial therapy. The study suggests that new behaviours become easier to learn in a similar context and easy to lose when confronted with a different context.


How to build habit forming products: 1) Deliver unexpected rewards; 2) Trigger based on environment; 3) Repeat easy to perform actions

hero habit

How to Increase User Engagement - 3 Learnings from Psychology

By recognizing the 5 key principles of User Experience (UX) design as popularised by [Don Norman](https://www.amazon.com.au/Design-Everyday-Things-Revised-Expanded/dp/0465050654/ref=asc_df_0465050654/?tag=googleshopdsk-22&linkCode=df0&hvadid=341744103972&hvpos=&hvnetw=g&hvrand=1891756520801654644&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9071871&hvtargid=pla-416263148589&psc=1), you will be able to see thoughtful vs bad design:

1. [Affordances](#affordances)
2. [Signifiers](#signifiers)
3. [Mapping](#mapping)
4. [Feedback](#feedback)
5. [Conceptual Model](#model)

First let me tell you about my pedestal fan. Then, we will apply these principles to look at [how YouTube designs their hugely popular mobile app](#youtube).

![fan](//images.ctfassets.net/vwq10xzbe6iz/5fcOhrcQydI8c2c3K6glFu/e3ab1076ddff5c6307bbd77ca15593fe/fan.png)

## <a name="affordances"></a>1. Affordances
My fan has the basic affordances of:
- Blowing air by rotating its blades;
- Adjusting height;
- Changing speed; and
- Auto swivelling

Affordances represent how a person can interact with an object. The relevant person here matters. The controls and blades are at the top of the fan meaning a child can’t reach. The affordance only exists for people above a certain height.

## <a name="signifiers"></a>2. Signifiers
My fan has three signifiers that influence its features:
- A screw handle for adjusting height
- A knob for changing speed
- A plug for enabling auto-swivel 

Signifiers are signs, hints and directions of what actions and affordances are possible. 

Let’s look at each of them:

### Screw handle for adjusting height
![height](//images.ctfassets.net/vwq10xzbe6iz/5o2lRwsRu3F15SFSiP5KWL/3f6b486b8cf1271895040871d8056dba/height.png)
The height control takes advantage of the perceived affordance that screw handles are for turning with the convention of clockwise, anticlockwise for the loosening and tightening to lock in the height. So far so good.

### Knob for changing speed
![control](//images.ctfassets.net/vwq10xzbe6iz/72gjyhJbHQzLvJee48CL3j/5d486c47ec02b2c23516eb09ef1f5119/control.png)
The speed control knob also takes advantage of the perceived affordance that knobs are for turning. The convention of numbers and turning from left to right indicates the level of power. The arrow signifies the current speed. The deliberate choice of the number zero limits the need to require an additional control to power on/ off the motor. Still good.

### Plug for enabling auto-swivel
The auto swivel is where the design gets confusing. The ON/ OFF signifier risks confusing the operation of the swivel with the power of the fan itself. Whilst a plug has a perceived affordance of pushing and pulling, convention is not clear as to which is ON/ OFF (ask around). I am always confused with my swivel.

An alternative design could be to use the similar knob control for speed with a text signifier of FIXED/ AUTO.
![control-alternative](//images.ctfassets.net/vwq10xzbe6iz/2X89cYodShgkzp78lIBusr/d56e21a1da124fc95bd77ec3af0aa53b/control-alternative.png)

### Accidental Affordances & Signifiers 
![fan-sock](//images.ctfassets.net/vwq10xzbe6iz/4WlnxmHmx6WOZkyh6aoh1d/a3e8a5f6f7ea76e2fa83067abb74980b/fan-sock.png)
Affordances and signifiers can also be accidental. Take the image above of a sock placed on top of the fan. It signals the affordance of the fan also being able to be used as a clothes drying stand. Be aware of these signs in products and explore designs to prevent or promote unintended use cases.

## <a name="mapping"></a>3. Mapping
Mapping is the spatial relationship between two separate things.

Controls should be close to the items being controlled. Our fan passes this design test. The speed, power and swivel controls are on the fan motor and the screw handle is at the center of the stand.

The natural mapping of left to right for speed control is also good.

## <a name="feedback"></a>4. Feedback
Immediate or timely feedback is important to communicate to the person what has just happened and what they should do.

The sound of the fan getting louder is a cue that turning the knob to the right increases fan speed.

The movement of the motor when the plug is pushed or pulled is a cue whether it is in FIXED or AUTO. However, this is difficult to perceive as there is a delay which prompts me to push up and down, shaking the unit and further confusing whether it is auto swivelling. Yes I hate this plug.

## <a name="model"></a>5. Conceptual Model
Conceptual models are how we understand an object to work based on our past experiences or what has been communicated to us.

Most people are familiar with fans and do not require an operating manual. Our fan has good signifiers and perceived affordances to help us understand and navigate its controls.

However, take a Dyson fan. It relies on a remote which if lost requires the person to press and hold a button at the base of the unit to turn the speed up to maximum and then down to mininmum. This is a deviation to the prior conceptual model and has caused much consternation amongst Dyson owners.
![dyson](//images.ctfassets.net/vwq10xzbe6iz/2a5KbLyNJwIhPxPGJpD7cd/e01edaa5c13c6a876a7df28054d28d77/Screen_Shot_2021-01-18_at_10.39.14_pm.png)

## <a name="youtube"></a>YouTube User Friendly Product Design
Let us look at the mobile design for the globally popular YouTube app. We will apply the 5 key principles of design to recognize deliberate design decisions.

![youtube-mobile-app-design](//images.ctfassets.net/vwq10xzbe6iz/cKzZ4mfE3ylijqZ2qiOfc/362b09e6382f5f17f19e91106d4c2c17/youtube-mobile-app-design.png)

Affordance | Signifiers
--- | ---
Play/ Pause | Toggle play and pause icon
Next/ Prev Video | Backwards and Forward arrow icon. Natural mapping
Comments | Comment count and expand icon inviting viewing. Placeholder inviting users to post. Sometimes it previews the most popular comment (YouTube spends a lot of time growth hacking comments to drive user engagement)
Like/ Dislike | Thumbs up and down icons. Convention pioneered by Facebook (don’t reinvent the wheel)
Share Video | Icon with explicit text. There is no standard convention between Android, iOS and Microsoft users as a share icon. For important features, when in doubt, be explicit.

In addition to the affordances above, the mobile app has hidden affordances of being able to fast forward/ backwards 10 seconds by double tapping in the natural mapping direction. Also if you click and hold a video it will open its features for you to stop following or not be interested in a video. These are useful features that are found through discoverability of active users. 

![youtube-discoverability](//images.ctfassets.net/vwq10xzbe6iz/16MJiKyqnkZp1ivTZIrCOT/cea7321b8afa209bf26d61adbb7d8a06/youtube-discoverability.png)

This is likely a YouTube design decision to hide visible signifiers that allow users to reduce their watch time and skip channels. By understanding user friendly design, you are also able to design unfriendly experiences to increase friction for features you may wish to dissuade people to use.

#### References:
[The Design of Everyday Things by Don Norman](https://www.amazon.com.au/Design-Everyday-Things-Revised-Expanded/dp/0465050654/ref=asc_df_0465050654/?tag=googleshopdsk-22&linkCode=df0&hvadid=341744103972&hvpos=&hvnetw=g&hvrand=1891756520801654644&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9071871&hvtargid=pla-416263148589&psc=1) - *Learn more about the above 5 key principles of design and the two additional principles of Discoverability and Constraints.*


Learn the 5 Ways to Make Your Products User Friendly: 1) Affordances, 2) Signifiers, 3) Mapping, 4) Feedback, 5) Conceptual Model. Recognize deliberate design from the hugely popular YouTube mobile app.