Lucìa
In my everyday life me and my friends had the same problem: how can we find out supermarket’s sales in a simple way? In this situation was born Lucìa, which gave a chance to people to have access to all supermarkets’ sales; to give them the possibility to search a supermarket and see the actual sales in the shop.
RESPONSABILITY
Interviews
Design system
Prototyping
Wireframing
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People struggle to feel consumed and frustrated to search through multiple supermarket to find relevant sales.
In addition they feel dissatisfied with having to pay different delivery fees for each app they used.
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Develop an app that integrates all supermarket platforms, allowing users to search for offers in one centralised app.
Introduce a flat subscription model for deliveries across all supermarkets, similarly to Amazon Prime, so users can pay a single monthly fee for shipping, instead of multiple delivery charges.
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I conducted a remote user interview with 7 participants to understand grocery shopping behaviors. The study was quantitative, aiming to measure how factors like price, brand, and special offers influence people’s decisions, as well as their habits with online and in-store shopping.
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The interviews reveals that people care more about price and special offers than about brands. Most shoppers like to find deals in the store or use apps and leaflets, and more than half still prefer buying in person rather than online.
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Sale and price everywhere
If we clearly show prices and sales, then 85% of users who consider price important will continue buying through our app
Timely notifications
If we update offers with timely notifications, then 71% of users will continue using the app.
Display a brand
If products display a visible brand, then 57% of users will be more likely to use the app to discover new sales.
Premium users
If we create a discovery section exclusively for premium users, then we can generate revenue from the 57% of users who currently don’t buy groceries online.
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After defining the hypotheses as my final goals, I moved on to building the information architecture. Based on my research, I organized the findings into a clear structure, which helped me reorganise the information
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Most users find useful in the lending page these categories: leaflet catalogue, the best of the week, setting, current promotions, leaflet of the week. And in the personal area “your subscription”.
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44.4% of users expect animal food under "Non food"
44.4% of users look for it in the "Other" category
11.1% of users expect it in "Supermarket"
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Users confused about the placement of animal food, almost half of people aspect it to find into "other", the other half look under "non-food" and a small part of users aspect to find it into supermarket.
The current categorisations doesn't march the users' mental model so I considered to eliminate "non food" to reorganize "Other" into subcategories such as "food animal"
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^Food category
^Offers
^Supermarket
^Non-food
^Other
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Home
^Food category
^Offers
^Supermarket
^Other
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I was gone into different supermarket to analyse in person the type of category exists, and even how people find the different things.
Behavior insight: people rarely use the overhead sign, most of the person knows in which category are the things that they are in search.
Conclusion: categories need to reflect mental models and habits more than signage hierarchy.
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"Deals” section is more appropriate instead of “offers” label.
They use “All Categories” to separate all the supermarket aisles.
They use “Discover” label to highlight specific section for example gluteen free or brands
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"Stories" section to highlight what's the new newpaper
A section with seasonal assortments, who engages users based on their needs
I decided to split the original “Food category” into one labeled “Category”, when users click on this section, a dropdown appears displaying columns, each with a title.
These categories represent different subcategories or topics related to food and other things.
I created a "discover" section, this helps users explore additional content like supermarket in a more intuitive way. Additionally, I renamed the label “Offers” into "Deals" to make the terminology more familiar.
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To analyse the steps users might take through the tab bar, I set a users goal: finding the desired sale within the relevant section. The main action required to achieve this goal is opening the app.
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Through the research I realize that there are infinite possibilities but the best solution always comes from analyzing previous user behavior and needs;
The questionnaire process took about a week and a half to be completed, because I didn't set a deadline initially. I realised that it was much harder to get responses without a clear end date. In the end, the questionnaire went well, and I was able to collect enough data to analyze.
Before defining the architecture information, I did some research because I had never created an IA for an iOS app before. I explored the most commonly used platform from the users.