User Experience Design

LinkedIn's Utilization of Small Print as a Shield for Bad Behavior [Bad UX]

Product Background

  • While a significant violation of the Product Manager’s Hippocratic Oath, companies regularly shield bad behavior behind small type, Terms of Service updates sent at midnight on Fridays, and unclear or outright false information.

  • It is unfair to the user to both assume that they can completely understand the information presented to them and therefore make an informed choice or even care enough to spend time to do so.

  • While LinkedIn has a history of disregarding user’s privacy and disrespecting data fair use, they have quietly rolled out 3rd party ad retargeting based on LinkedIn contact and profile data without adequately asking for consent or informing users of the ramifications.

    • Last Week - LinkedIn violated data protection by using 18M email addresses of non-members to buy targeted ads on Facebook (Link, PDF)

    • 2015 - History of LinkedIn Dark Patterns (Link, PDF)

    • 2014 - LinkedIn Feature Exposes Email Addresses (Link, PDF)

  • A (poorly structured) argument against this post is whether LinkedIn’s behavior is any worse than any other company. While I would argue they are a significant offender, most other technology companies are similarly bad actors, but that doesn’t support lesser condemnation of LinkedIn, it simply supports condemnation of the behavior and industry as a whole.

UX Experience & Detailed Discussion

  • Upon a recent login to LinkedIn, I received the following notification, alongside what I would assume to be millions of other users.

  • Let’s break this update down piece by piece:

    • As it appeared initially to the user, all of the information from “Information you’ve shared with other companies” down to If you select “Agree” was hidden from the user, and was only revealed after clicking the carrot. This is a dark pattern to start.

    • Allow LinkedIn to keep showing you relevant jobs and ads.

      • This is nefarious because if LinkedIn is already doing it, why do they need my approval again? If they need my approval, then they must be doing something different. This sentence preys on users continuing to agree to what has occurred in the past.

      • In addition, it also implies that if you don’t agree, you may not be shown relevant jobs in the future, which most users wouldn’t like. However, this is not precisely what is occurring, it is just that LinkedIn won’t use additional data to make those recommendations more relevant, not that the recommendations won’t be relevant at all. These are two very different things.

    • Information You’ve Shared With Other Companies

      • “When You show interest (Like…” - The like here implies that there may be other ways in which I may show interest to a company and that I am agreeing to potentially unlimited, undetermined ways by agreeing to this. I could theoretically click on the link provided but there is no guarantee that I’ll find the information there.

      • “When you visit their websites, some companies may use tools (like cookies)…” - Again there is the implication that there may be other ways that companies use and that I am agreeing to whatever those are, which LinkedIn has no control over and how they will use/misuse my information.

      • “Also, if you provided consent directly to a company you trust to use your data for ad relevant, LinkedIn may rely on that consent when showing you ads.” - This translates to “If any company that has a direct incentive to obtain your consent to show you targeted ads is somehow able to obtain that consent, through whatever means or dark pattern they can, then LinkedIn will blindly trust that 1 or 0 signaling your consent and show you ads even if you click decline here that are more heavily personalized and targeted, which will have higher cost structures and will make us additional revenue.

      • Finally, highlighting the Accept & Continue button in blue, which is the default action color for users on the site, abuses user trust in LinkedIn and pushes users towards that selection by default, likely increasing conversion rates.

    • Most users likely will not understand that this one click will allow their data to be used across LinkedIn’s entire retargeting ad network. The gravity of that situation is not proportional to the copy or visuals of the dialogue.

LinkedIn Ad Opt In.png

Detailed Discussion

Upon further digging through their latest privacy policy (Link, PDF in case of Revisions) and LinkedIn’s additional primer on 3rd Party Ad Targeting (Link, PDF in case of Revisions) as of this post, there are a number of additional data points that are collected that users may not realize. While the below are by no means exhaustive of the data items I believe are being misused, they illustrate the complexity and effort required to understand how a users data is used, and how most users have no idea.

LinkedIn (and others) also do a poor job, either intentionally or unintentionally, describing the interactions of data points that they collect. As a result, while they may be following the letter of their privacy policy, they may be doing so either by omitting the scenarios where data is combined, or describing them poorly.

In the below examples, text from the policy is italicized, while my comments are just below:

  • Different Services & Device Tracking - Logging Sites after LinkedIn Visit

    • Section 1.4 - Cookies, Web Beacons & Other Similar Technologies

      • As further described in our Cookie Policy, we use cookies and similar technologies (e.g., web beacons, pixels, ad tags and device identifiers) to recognize you and/or your device(s) on, off and across different Services and devices.

      • Fair enough, but what does across different Services and Devices mean? Let’s go to section 1.5

    • Section 1.5 - Your Device & Location

      • When you visit or leave our Services (including our plugins or cookies or similar technology on the sites of others), we receive the URL of both the site you came from and the one you go to next. We also get information about your IP address, proxy server, operating system, web browser and add-ons, device identifier and features, and/or ISP or your mobile carrier. If you use our Services from a mobile device, that device will send us data about your location based on your phone settings. We will ask you to opt-in before we use GPS or other tools to identify your precise location.

      • I would hazard a guess that most users don’t understand that the site they go to after LinkedIn is also logged by LinkedIn. It is fairly standard practice to log which pages users are on before exiting, but I’ve rarely seen the logging of pages after exiting an application.

  • 3rd Party Advertising

    • Section 2.4 - Advertising

      • We do not share your personal data with any third-party advertisers or ad networks for their advertising except for: (i) hashed or device identifiers (to the extent they are personal data in some countries); (ii) with your separate permission (e.g., lead generation form) or (iii) data already visible to any users of the Services (e.g. profile). However, if you view or click on an ad on or off our site or apps, the ad provider will get a signal that someone visited the page that displayed the ad, and they may through the use of mechanisms such as cookies determine it is you. Advertising partners can associate personal data collected by the advertiser directly from you with our cookies and similar technologies. In such instances, we seek to contractually require such advertising partners to obtain your explicit, opt-in consent before doing so.

      • Potentially my favorite section in here, with key bits underlined.

      • We do not share your personal data with any third-party advertisers or ad networks for their advertising except

        • So LinkedIn doesn’t share my personal information with 3rd party advertisers, except that it does exactly that thing.

      • Advertising partners can associate personal data collected by the advertiser directly from you with our cookies and similar technologies. In such instances, we seek to contractually require such advertising partners to obtain your explicit, opt-in consent before doing so.

        • LinkedIn seeks to obtain that consent, but it is not clear whether they will allow targeting even if they do not obtain proof from the advertiser of explicit, opt-in consent.

    • How Businesses & Websites Can Use Third-Party Data for Advertising on LinkedIn

      • There is an additional and entirely separate page on Third Party data usage (the second link in the intro paragraph above and here), the text of which is not included in the primary privacy policy.

      • This policy essentially allows LinkedIn to serve retargeted ads across its entire network as well as across 3rd party sites with which it has a retargeting partnership.

      • While this is similar to other retargeting relationships/providers, what is not clear is what unique situations LinkedIn may be able to exploit due to the network information effects that LinkedIn is the recipient of.

      • For example, if a colleague uploads my email via the LinkedIn address book feature, is that email used for retargeting even though I never consenting to that being a retargeting data point? I would assume it is likely the case, but this scenario is not detailed implicitly or explicitly in these documents to my knowledge.

Improving Uber Express Pool [Product Case Study]

Product Background

  • Uber Express Pool is a recent addition to the Uber ride hailing application that allows the user to pay less for their ride if they agree to:

    • Walk several blocks from their exact pick up location for pick up.

    • Allow drop off that is several blocks from their exact drop destination.

    • Wait up to 5 minutes for a driver to be assigned to their ride. I’ve also seen this as low as 2 minutes as Uber experiments with different wait / matching periods.

  • Rides can often be significantly cheaper than even Uber Pool if the user has a wider tolerance for walking and arrival time.

  • The ability for a rider to optimize according to time (Uber X) or money (Uber Pool -> Uber Express Pool) is incredible from a utility maximization standpoint and allows Uber to more optimally match individuals based on their money/time preferences for greater utility for all parties (driver included).

Potential Improvements

Uber Express Pool (UEP) isn’t perfect however, and could be improved in several ways, some of which are relatively simple and provide immediate value to the user, while others will require significant engineering work to solve. My comments below stem only from my experience as a user and do not take into account potential constraints that the Uber team is operating within.

  • Optimizing Pick Up & Drop Off Zones for Challenging Terrain

    • As anyone who has walked up San Francisco’s steep hilly streets, a pickup and drop off location even one block North or South can present quite a physical challenge.

    • Currently, based on my use of the product, Express Pool assumes that a pick up / drop off location is similarly/equally accessible to the user regardless of which direction / distance it is from the user’s current location. In other words, the pick up zone is calculated based on the user’s Current Location + Some (X) Distance from that location, combined with the most optimal pick up points that Uber has determined over time.

    • Unfortunately, this does not take into account the terrain/geography of the Pick Up Zone.

    • For example, for those of you familiar with San Francisco, the Intercontinental Mark Hopkins (Aka Top of the Mark) is located at the top of very steep hills in nearly all directions. However, as can be seen in the screenshots below, Uber Express Pool doesn’t take this into account.

    • It’s pickup zone for Express Pool extends two blocks N/S/E/W even though the 2 blocks south are incredibly steep descents. This can be seen in the Google Street View Images below.

    • A potentially readily available solution that would improve this aspect of UEP would be to calculate the estimate elevation change from one point to another, or at the least, use calculated walking time or Walk Scores. This would prevent UEP rides where the rider is going up or down steep terrain.

Google Street View facing South on Mason Street (Red Dot)

Google Street View facing North on Mason Street (Green Dot)

  • Optimizing Pick Up & Drop Off Zones w/Additional User Information

    • While I understand the objective of requiring the user to provide as little information as possible, UEP could improve the pick up and drop off experience by being more transparent regarding the locations of other passengers or attempting to better group passenger pick up points, particularly in busy areas.

    • Example 1 - Airport Pickups - Group by Zone

      • While there are now usually multiple airport pick up zones that the user can specify, on my previous UEP ride from the airport, I selected Section 1-2 and my matched Pooler selected zone 3-4, and we were both picked up seperately.

      • While I understand that Uber is trying to minimize walking distance with bags at the airport, it is actually far more convenient to group pickups at the same zone due to traffic and loading times.

    • Example 2 - Drop Offs - Allow User Input / Select Drop Offs

      • While this would likely only apply in the scenario in which a rider is being dropped off and the car is full (I.e. there is no possibility of the car being rerouted to pick up another passenger), it would be a net positive user experience to reveal the drop off location to the user as soon as possible.

      • There have been a number of occasions where first hand knowledge has led me to exit the ride earlier than the Drop Off Point because the drop off point is actually sub-optimal to where I ultimately want to go, and it is a better outcome if I exit somewhere else on the route.

      • Per the two photos below, it would have been faster for me to exit right as my UEP ride right before turning North on to Columbus, and walked the remaining distance, rather than wait in traffic for the right turn, and left turn.

      • Revealing the drop off point sooner, and allowing me to make a choice of a drop off point that is already on the route, would result in a shorter ride for me, shorter ride for my fellow Poolers and more revenue for the driver and Uber.

      • This logic could also be applied to user selectable Pick Up points.

Oakland Airport (OAK) PIckup Zones

UEP Drop Off Zone in San Francisco

UEP Drop Off Point Reveal

  • Optimizing Route Pricing by Allowing Uber X / Non Shared Rides to Add Riders During Ride

    • Another potential route to increase the number of pool rides would be allowing riders to select non-shared rides initially (Uber X / Black / Etc.) and if an individual is an optimal match for the route that the original rider is traveling, allow that rider to be added to the Uber X ride and give the original rider a discount just as they would have had if they had selected Uber Pool to begin with.

    • This would add liquidity to the pool of potential drivers for Uber Pool, as well as add certainty to the route when matching Uber Pool riders with potential drivers.

  • Optimizing Route Pricing by Allowing Shared Rides to “Upgrade” To Non-Shared Rides During Ride

    • Another potential route for increasing revenue would be to allow Pool/Shared rides to “Upgrade” to a non-shared ride after being picked up.

    • It could be that after pick up, those users realize they are later than they’d like, and would be prime targets for upselling to a non-shared ride that gets them there faster.

The Value of Design & UI/UX


  • At this point, you understand and can articulate the value that the product management function provides to your company.

  • While design and UI/UX can stand on their own within an organization, they are typically situated within Product Management alongside User Research due to the close collaboration required.

    • Note that design and UI/UX can often be different individuals all the way up to different teams depending on the size of your organization.

  • Design’s primary objective is to ensure that the solutions that the product team identifies as needing to be built are created in such a way so that they are readily accessible to the relevant customer personas.

  • Pay Now or Pay Later - Good design reduces downstream costs by minimizing eventual customer support requirements, product documentation and customer churn.

Detailed Discussion

Let’s first better define the objectives of design:

  • Accessible Solution Definition - A solution can be defined as accessible if it enables the user to obtain their desired solution at the lowest cost.

    • Costs include:

      • The time it takes for the user to learn how to obtain their desired result

      • Additional financial burdens (data packages, add ons)

      • Pre-work the user must undertake (properly formatting data)

      • User mental memory usage and management (remembering data or inputs from one location to the next)

      • Time/work necessary to recover from user errors (form data being lost and not being autosaved)

      • Time required to reach out to support or read product documentation

    • More colloquially, an accessible solution is one where the user doesn’t have to read the manual to obtain value. These solutions or products “just work", are a pleasure to interact with and use, and make the user feel great about the experience.

    • it is important to note that a user’s willingness to invest time/energy/resources into your product does not have a linear relationship with how well it is designed, it is typically exponential in nature.

      • For instance, if a product is 2x as hard as a competing product to use and obtain a useful solution, that product will likely see 4x+ declines in usage/new customers/etc, given that the value of the solution is equal.

  • Value in Creating Accessible Solutions

    • Good design, and by extension accessible solutions, are valuable to both the customer and the company. Too often do companies assume that the cost of bad design is borne by the user alone, and decide that investing in design is an unjustifiable expense.

    • Unfortunately, poor design simply shifts the cost from a smaller upfront cost of design headcount to larger downstream costs of additional support staff, more complicated product documentation, and lower customer retention.

    • Good design is also a product differentiator as it is the language in which users interact with your product. Users form a relationship with your product based on how it made them feel while using it (frustrated or accomplished, in charge or out of control, intelligent or incoherent). These feelings matter because when your account representatives or customer support team interact with each user, those interactions will be framed by how the product made each user feel, which is primarily driven by the product’s design.

      • Side Note - Net Promoter Score, while incorporating a number of different factors about a customers interaction with a given company, is significantly driven by product design. NPS focuses on asking whether the customer would recommend the company on a 1-10 scale and this is highly driven by how the customers feel. Design is a leading indicator to how customers ultimately feel about your product.

      • Thought Experiment - Take a sheet of paper and draw a line down the center, and on the left side write good design at the top and on the right side write bad design, and write out as many examples of each as you can in the next 5 minutes. Next, write out how each of those products/experiences made you feel. Finally, write out how you would rate the overall company 1-10. You’ll find that your rating is less informed by the solution the company provided and more informed by how you feel towards the company.

Now that we understand why good design is important and the costs associated with bad design, let’s put these guidelines into practice.

  • The Good Design Trade Off Formula

    • Design teams often get bogged down in attempting to create the “perfect solution”. In reality, the perfect solution doesn’t exist because of a multitude of constraints (money, time to market, headcount, legacy code, legacy product decisions, pressure from marketing/sales/CXO, differing customer personas).

    • The first step towards good design is recognizing and appreciating that your team will find never find a perfect solution.

    • The second step is delineating the trade offs that exist, which qualities are worth optimizing, and the decision’s rationale.

    • As part of this process, it is important to remember that a decent solution with incredible design/accessibility is often significantly better than a great solution with poor design/accessibility.

    • This can be expressed as the following formula: Solution Value = (Utility of Solution/Utility Discount Factor) * Accessibility of Solution

      • The Utility Discount Factor is an amount you believe appropriate relative to design for your specific industry. The UDF is not a constant as it varies depending on the industry that you operate in as well as your beliefs as to how to weight Utility compared to Accessibility.

      • Let’s explore how varying these components affects a product’s results:

        • If your Utility is low, but your Accessibility is high, you can still obtain a relatively high score if Utility is less important than Accessibility (High UDF >1).

        • Underdesigned - If your Utility is high, but your Accessibility is low, you can still obtain a relatively low score if Utility is less important than Accessibility (High UDF >1).

        • Overdesigned - If your Utility is high, and your Accessibility is high, your design resources are being utilized efficiently if you have a very small (UDF <1) as the solution value will be driven to a greater extent by your UDF than your Accessibility efforts. This is often seen when a given provider has monopoly power for a given market.

    • This formula and framework are meant to put quantitative guardrails around what can be a qualitative subject and can be particularly helpful when discussing trade offs with stakeholders.

      • A further modification could be made to add a Accessibility Discount Factor to weight Accessibility in a similar way to Utility. This addition can make the formula overly complex, but for advanced teams/select cases in can make sense where Utility and Accessibility need individual discount rates. For example, product managers may want specific weights relative to specific customer personas. Expressing those weights in UDF/ADF factors allows more ready comparison of decisions across personas.

      • For completeness, the formula would then be as follows: Solution Value = (Utility of Solution/Utility Discount Factor) * (Accessibility of Solution/Accessibility Discount Factor)


Each of us encounters good and bad design everyday as we interact with the objects around us. While there are several examples below specifically focused on discussing the concept of accessibility in design, for additional examples see both the Good UX and Bad UX series of posts. For even more comprehensive language around good design, pick up a copy of The Design of Everyday Things by Don Norman.


Examples of underdesign or instances where accessibility is under resourced should be taken with the perspective not of being critical of the product teams, but of areas where those products could be improved if additional resources were obtained. It is difficult to ascertain design decisions and tradeoffs made without understanding internal constraints.

With that in mind, an interesting case study is Apple’s design choice as to the new keyboard on its Macbook / Macbook Pro / Macbook Air line of laptops.

  • The new design (at left) is widely regarded as possessing a poorer typing experience than the previous keyboard design due to its lack of key travel (how far the key presses down upon being hit) as well as causing significant repairability issues due to keys sticking when dust or small particles are lodged under the keyboard.

  • This design was largely seen as a requirement to making the new thin form factor a possibility. Compare this to the legacy Macbook keyboard or desktop keyboard (pictured at right), both of which were highly regarded for their typing experience.

  • This is an example of underdesign because if you ask most Macbook owners how they feel about their computer, they are likely to tell you about the poor typing experience, ignoring the positive changes that were made in terms of screen brightness, thinness, weight, battery life, and trackpad.

  • This is also a great instance of individual product attributes having different Utility Discount Factors - in this case, the keyboard has a high discount factor as Accessibility matters most.

Apple Macbook Pro (Late 2016) - First iteration of the new keyboard aside from the Macbook, with very little key travel.

Apple Wired Desktop Keyboard - The longstanding keyboard design largely used across all devices, with significantly more key travel.


Examples of overdesign or instances where accessibility matters little can be seen in Microsoft Office applications, Adobe Creative Cloud, and Bloomberg Terminal as they maintain monopoly power in their respective markets. They can afford poor design and its associated additional costs because they can price their products so as to capture a significant portion of customer value. For Microsoft/Adobe/Bloomberg, users must invest considerable time in understanding how to use the product, allocate additional financial resources beyond the initial license cost to make use of the product, remember a great deal of information from screen to screen, spend significant time recovering from errors if the user doesn’t understand proper data storage procedures, and have lengthy conversations with customer success specialists to achieve their goals.

Adobe Photoshop 2019 - This is the default preferences pane and application layout. To Adobe’s credit, they are featuring additional learning resources directly in the application.

Microsoft Excel 2018 - This is the default preferences pane and application layout. As a user, if I don’t know what I am trying to accomplish, how do I figure it out?

Good Design

Good design is like a cold drink on a hot afternoon - it is great because of how it makes you feel. Examples of good design include instances where utility is lower than what is possible but accessibility is high as well as where utility is very high and accessibility is moderate but better than competing solutions. As previously mentioned, more examples can be found here.

Sub-Optimal Utility, High Accessibility

Amazon’s Alexa / Echo products captured a significant share of the voice activate speaker market due to their immediate accessibility. After initial setup, anyone could use the product because Alexa / Echo meet the user on the user’s terms. Alexa / Echo don’t require the user to read the manual, allocate additional resources to use the product, require the user to remember what they said previously, or dig into product documentation unless the user wants to dig deeper of their own accord. Alexa / Echo’s generalist design is likely at the expense of maximizing specific utility for consumers were it to focus on specific verticals or industries or customer types. This was the correct design choice however as it allowed Amazon to gain maximum market share which in turn improved the Alexa / Echo product via training data.

Amazon Echo

Amazon Echo Dot

High Utility, Moderate Accessibility

Salesforce CRM and its associated suite of applications have high amounts of Utility, but their good design lay in the fact that they were hosted in the cloud compared to most of their on premise competitors. While the UI/UX of Salesforce is mediocre at best, Salesforce focused on the design attribute that mattered most: widespread “literal” accessibility and a consumer-like configurability. Previously, CRM systems were costly to deploy, use, configure and update. Salesforce focused on reducing the costs of those four attributes while assuming that the Utility provided would make up for moderately better accessibility. This assumption has largely proven correct as Salesforce has continued to grow and take market share from significant competitors (Microsoft, Oracle).

Anticipating & Preemptively Addressing User Needs at Costco & Inside Safari [Good UX]

Product Background

  • Good products are similar to good teachers - they know the subject material so well that they anticipate where students/users will face challenges and provide guidance at exactly the right time.

  • Acting on this principle is often difficult for product teams because they understand the product so well, it is challenging to determine where a basic user might stumble or take an action that is sub-optimal.

  • If we as product managers can find those moments, they are often moments that surprise and delight users.

UX Experience

Two great examples of this, one from Costco, and one from Safari on MacOS:

  • Costco Gasoline Pump Open / Busy Indicator

    • Forgive the blurry picture, but at a Costco in San Diego, an Open/Busy LED illustrates to cars waiting in line which pumps are open or busy.

    • This is often difficult for a car not in the next position to determine for their line or the lines adjacent to theirs.

    • In theory, this proactively answers the question of whether the car two pumps ahead of mine has just pulled out, speeding up the process of gas buyers filling the next available pump station.

    • This is the first and only Costco, let alone gas station, where I have seen this indicator.

  • Safari User Prompt When Bookmarking Google

    • If as a user I try to create a bookmark for Google, Safari prompts the user that they can already search Google via the address bar without adding a bookmark.

    • This is interesting in a couple respects:

      • It is contextually aware of the goal that the user is trying to accomplish with the bookmark (searching), not just the content of the bookmark (Google).

      • It recommends the faster way of achieving the goal (searching) via the address bar.

      • And still allows the user to create the bookmark if they so choose.

    • Google appears to be the only search engine where this occurs (Yahoo, Bing, Yelp all did not trigger the prompt). This could actually be due to Safari obtaining a referral fee for searches from the address bar to Google whereas they do not receive any fee if it is from a Google address.