A. In the first part of this case study, I discuss a business- and user problem in the board game retail space, and its significance.
B. Second, based on my foundational research I identify various user segments and how they solve for this problem. I will also discuss solutions developed for adjacent categories (books, movies), and see how those solutions can be used as blueprints for boardgames.
C. Third, I will share my design process about the board game recommender interface.
The Problem: Game purchases are a risky investment
Purchasing games without adequate research is risky for the consumer – making it unlikely for them to buy games in the store without advance research . This is a significant issue because tabletop games (boardgames) are increasingly high-revenue generating business.
The US makes more revenue from board games than any other country ($2.48 billion), followed by China ($1.10 billion) and Japan ($0.46 billion).1
Research: validates the problem and maps user expectations
Methods:
- Preliminary survey of 52 respondents recruited through board game fan pages on Facebook to validate the problem the app is designed to solve
- In-depth interviews and concept testing of wireframes
- Competitive analysis
Insights and Personas
Shoppers need to invest time and effort into researching games before entering the store:
My study found that a common- and sometimes surprisingly challenging – issue for gamers was finding the right game to play. Many users have come up with several workarounds, however, all of these are performed as a preparation, and not in the store. These include:
- posting questions in Facebook groups
- visiting Reddit pages
- doing in-depth research on BoardGameGeek, the ultimate database for boardgames
- watching YouTube videos
- asking around among friends
95% of game shoppers consult at least 2 information sources to guide their purchasing decision, and close to 75% visit at least 3 sources!
The survey results show that the information on the game packaging and mainstream retail websites e.g., Amazon.com, are perceived as inadequate. Not a single respondent would make a shopping decision just based on those.
Game price determines the amount of research shoppers do
The extent of research consumers undertake is directly related to the game’s price – the more expensive the game, the more thorough their investigation before purchasing, supporting the problem statement that game purchasing is seen as an investment. Therefore, when designing detailed product information pages, it’s important to prioritize high-prized games.
Distinct shopper personas: BGG-users are more sophisticated in their information need than non-BGG users
There are systematic differences between BGG (boardgamegeek)-users vs. non-users.
Channel Preference
Satisfaction with retail websites
Both BGG and non-BGG shoppers are moderately satisfied with the information they find on the retail website (3.58 +/- 0.62 vs. 3.21+/- 1.18 on a 1-5 scale) but as the SE values (0.62 vs. 1.18) show, BGG shoppers are more in agreement with each other, whereas some non-BGG shoppers are highly satisfied, and other non-BGG shoppers are not at all.
TYPES OF information RELEVANT FOR DECISION MAKING
Evidently, some pieces of information are a lot more important when making a purchasing decision than others. What is more surprising is that certain crucial characteristics (such as how easy a game is to learn, or how much replay value it has) are rarely included in game product pages.
Starting with Minimal Viable Product
I originally planned to create a game recommender that would come up with ideas based on previous game purchases, however, I decided to start with an MVP
Here are a few examples:
DESIGN PHASE
Inspiration collection
Before putting my digital pen on the screen (a.k.a. starting working with Figma), I collected screenshots of mobile applications that solved similar problems: users trying to narrow down their search based on well-defined criteria using a mobile application.
Then I experimented with AI-based design tools such as Readdy, Design Pattern, and ChatGPT. Not surprisingly, their solutions provided general frameworks, relying on best practices in mobile design, however, most of the finer details that make the design work were subpar.
Using the ideas generated by these two sources, I used Figma to draw the wireframes first:
I originally designed two tabs separating the basic filters and the more advanced filters, but concept tests showed that users preferred to stay on the same interface and not change tabs. By abandoning the square-shaped cards, I was able to save on the UI real-estate, and avoid the need for tabs, and a more complicated user interface. This solution allows for adding more criteria (e.g., age recommendation, complexity) when further user testing indicates the need to do so..
Anticipating the need to look up games that one sees in a store, I included a search bar in a salient and expected place, on the top, on every page.
The product page
The product page contains the information that the generative research revealed users needed to make a purchasing decision. The UI allows for easy scanning by displaying tags (resource management, dice, strategy) and core pieces of information (number of players, duration of a game, etc) , with the ability to expand on tags.
The game overview, a brief description of game rules, and review further help the decision making. As the app is designed to be retailer-independent, there is no pricing information. There are potential AI-powered extensions to the app, such as a chatbot equipped with the game rules that can answer thorny questions, but that is more likely to be needed when playing than when deciding about a game purchase.
https://www.technavio.com/report/tabletop-games-market-industry-analysis
https://www.businessresearchinsights.com/market-reports/board-game-market-117710
