Whiteboard Pictures App

Antoni Botev
7 min readMar 28, 2020

The client wants an app that allows taking photos of office whiteboards, enhancing them, and organizing them differently. They spend a lot of time in meetings, drawing on whiteboards, lots of lists, diagrams, etc., and then taking images with cell phone cameras, which they send to each other via email. The app you design should significantly enhance this experience (add value), and it should be working on both mobile and desktop, with a primary focus on the mobile experience.

Mission: Figure out what this app should provide regarding features and functionality, how it should work and feel, and design some of the most common and essential screens and interactions. We need to see wireframes level design for interactions and general functionality (UX) and the core screens to show a graphical design approach (UI).

Problem exploration

At the outset of the project, I didn’t have a clear idea or specific goals for the managing whiteboard pictures experience. Without pre-existing insights, I started to pick up pieces and explore how people behave.

Non-linear process

When challenging a specific problem, I don’t follow a strict order of steps. Still, I try to double-check backward every completed step before moving forward and fix the inconsistent statements if needed. It’s nearly impossible to predict if the core architecture components fit the best, so I constantly go back and iterate. This way, I set a better problem definition and improve the solution every iteration.

Gaining a deep understanding

I could not learn much by conducting secondary research because it’s a particular topic, and my time was limited, so I stepped forward to the primary research.

User interviews

I made a list of appropriate potential participants. I conducted a short interview with some of them to uncover more about their behavior and dig deeper into the “saving something for later” mental model.

I picked five people, aged 25–34, a woman and four men, and sketched a list of questions to ask.

The goal was to observe by asking “why?” rather than ask for pain point statements or feature suggestions. Therefore I assumed a set of problems and interaction patterns I needed to observe and focus on to gain more empathy.

  • acting in urgency
  • shooting pictures in urgency
  • saving potentially important photos for later
  • working with currently existing tools
  • what’s their limit for “too slow”
  • what’s their limit for “too complex”
  • what are their clues when they try to find something

Having those highlights in mind, I created a list of questions. I intended to ask about 10 to 15 questions to avoid the casual talk becoming boring or annoying. Still, I had a list with about 30 questions, including possible follow-ups in case I needed it.

Understand the entirety of the experience

Looking at common human behaviors when trying to solve a similar problem

When asking them, I started with more common questions describing them in a context different than “shooting a picture of a whiteboard sketch” because I aimed to go beyond the tactical into the more emotional territory to seek emotions, stories, and motivations.

  • When you see something important, something like a billboard on the street or while you’re on the highway. You want to save it, what do you do?
  • Do you take notes? How?
  • Do you take pictures? Do you send them?
  • How do you keep it for later?
  • Do you use any apps? Why? What do you like about those apps?
  • Do you know about any better apps?
  • Why don’t you use them?
  • Do you always find it later when you need it, weeks later?
  • Why don’t you see it?
  • Is there something that slows you down? What is it?
  • Do you see anything confusing?
  • What is it?
  • What would help you?

Digging deeper

  • Do you remember what you didn’t find last time?
  • What do you remember about it?
  • How did you try to find it?
  • Where and how did you search? What did you try?
  • How would you describe it?
  • What’s your opinion on why you were not able to find it?
  • What would you do if you saw it now? Again, how would you save it this time?

Exploring some emotions closer to the topic

  • Do you draw some schemes or sketch or draw on a whiteboard?
  • Do you save them after that?
  • Do they help you? If they don’t, why?
  • How do you keep them?
  • Why do you think they don’t help you?
  • How do you share it with the rest?
  • What do you think is the fastest way to share it?

Unexpected interaction pattern highlights

I synthesized and cataloged the needs and the problems that I’ve uncovered.

Most prominent pain points

It’s not surprising users prefer fast apps but it seems to be everything for saving important notes and organising them kind of apps.

Most meaningful insights uncovered

Interviews helped me to reveal some unexpected insights and began pointing to a more precise problem hypothesis.

Setting the initial problem hypothesis

People lose important notes and pictures because they have no time to describe and categorize them when shooting.

Competitive apps exploration

Users’ favorite apps seem to be the native OS tools because they provide shortcuts to save information like screenshots or taking pictures. They mentioned the iOS Camera app, Photos app, Notes app, and iMessages. Gmail clients and Google Docs are also standard options because they are popular options usually used in daily tasks. The popular everyday To-Do task management tools like Trello, Wunderlist, and Google Keep were mentioned when explaining why they intended but never got used to them because taking pictures is more effortless.

Reframing the problem assumption

The app should feel as close to the default camera app as possible. It should provide intelligent optical picture recognition and tagging. A powerful search option and detailed filtering should work and feel seamless. Based on thorough research about the most popular information chunks drawn on the whiteboards, the app should focus on recognizing, tagging, and sorting specific common whiteboard elements like charts, diagrams, numbered lists, bullet lists, foreign languages, and more visual clues.

Jobs to be done stories

It’s a long process, so I need something as a guide to help me stick to the initial idea of all insights I explored. I prefer the jobs-to-be-done approach, so I populated the sheet with user needs as a situation, motivation, and expected outcomes in the form of a list.

Prioritize ideas which are the most efficient & feasible

  • Fast shooting
  • Powerful search
  • Intelligent recognition and tagging
  • Smart categorizing and sorting
  • Compatible with native OS apps
  • Simple sharing options

Mapping the flow

Going visual

UI Prototype

Based on the sketches that illustrate the problem solution, I created a simple MVP prototype to help me test it and see how it feels in users’ hands to validate if it works and helps them. The most prominent interaction pattern is the switch between the two significant screens: the home “camera” screen and the list with all pictures stored. My idea is to use a floating button at the bottom right corner, which allows users to quickly switch between taking a new photo or finding an old one. I believe the camera screen should come first because people tend to be more impatient when they need to save something than when searching for an old image.

Future improvements

I still need to conduct a lot more usability testing for each step to validate my assumptions and iterate again if needed.

I would spend more time developing a desktop experience that should include more features as users tend to be more patient.

With recent developments in optical character recognition and speech recognition technologies, and as voice chatbots are getting more precise and human-like, we see some opportunities for improvement by adding “intelligent” capabilities. Rather than having to navigate complicated menus or change multiple variables on the device manually, a simple voice command provides input. The system would respond with the correct feedback by adding the most accurate tags and descriptions.

As predictions and recommendations become more and more prevalent, we should explore using machine learning to adapt to the users’ styles.

Conclusion

I believe the research, problem exploration, and definition could be much more precise. Still, I tried to focus on the most feasible and vital steps to gain empathy for the most prominent pain points and dig deeper into the users’ behavior, needs, and emotions.

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