UX Design Sprint: Glovo Lotto

Inspired by my previous competitor comparison between Glovo and Deliveroo products, I wanted to delve deeper into the overarching pain point I found across both products: deciding on restaurant to order take-away from. I set out to answer the HMW I ended on in my previous post:

How might we make the restaurant decision the most fun and stress free part of the user journey?

Photo by HalGatewood.com on Unsplash

The best way to explore this I felt was through a design sprint methodology, although as this was a side project I didn’t have many people to collaborate with, so some parts of the process were improvised. Below find the details of this mini project and the final prototype to play with!

Day 1: Mapping

As I had already got an idea about a specific problem/pain point from research, the scope of the mapping was already defined by this. Therefore the goal statement of this project was defined as:

“Glovo customers are able to make fast, enjoyable and reliable decisions.”

So, that was quite easy actually. Now to move on to the how…

How might we??

I used this exercise to explore as many options and ideas as I felt were relevant to the problem users (ahem, me), were facing. Then afterwards I filtered these down into a shortlist of the most valuable to the user and to this end goal. These are indicated with a star on the below images.

User journey mapping

Next I began creating user journey maps based on my previous experiences and anecdotes of friends of mine. These focused solely on the ‘restaurant picking’ part of the user journey, as this is the focus of this design sprint.

As I plotted these journeys out in Miro, there were beginning to be some common themes.

Users often needed external information to aid decision making on Glovo. I found that Glovo was expected to be the facilitator of good decision making, and when it was not able to fulfil this duty, this caused frustration to the user and resulted in them looking elsewhere for help. This could be using TripAdvisor or Google to get better idea of ratings or locality of restaurant.

Furthermore, cuisine type was not always determining factor of restaurant choice. This information backs up my previous finding from the heuristic evaluation: that the filtering system is too basic.

Also, contextual factors of the user were often big contributors to the flow of the user journey, for example if this was a group decision.

By delving into more examples of the ‘restaurant picking’ of the user journey, we were able to dig deeper into the multiple factors that are at play in this decision. Then, in the style of this sprint exercise Map & Target (but slightly adapted for this scenario), I distilled these down to the most voted, recurring important factors of the journey’s flow. These factors were both personal to the user’s preferences, and contextual, relating to the situation of the user.

To summarise:

  • Limited filter system does not reflect the multi-faceted mental process of decision making
  • The decision is often not made by one individual
  • Food related decisions are often stressful to make, users look for guidance in this decision

Day 2: Sketch

To begin the day, a lightning demo. I looked for inspiration from existing products that provide an interesting discovery and research features. I then created a mood board to show the highlights of filtering and research tools out there.

What I felt stood out most about these examples was the engaging and interactive elements of the research experience, this could be through text or visual design aspects. For example, asking questions: Are they in a rush? Are they interested in special offers? Do they want to interact with a map to find restaurants?

Also, personalised experience using popular searches, stored history of user orders, favourites or personal data info.

Four step sketch

Now to focus on solutions for the Glovo research and discovery process by going through the four step sketch method:

  • Notes (only writing)
  • Ideas (only drawing)
  • Crazy 8s (quick time sketching /ideation)

This example demonstrates different focal points of design ideas. I will explain briefly, from the top left: gamify the experience to make it fun, interactive and quicker; sharing the decision making process with friends using shared buildable shortlists; interactive maps; empathising with the users frustration; advanced filters through asking user questions about preferences; ability to save lists/preferences; sharing recommendations between friends; making the decision process faster, fun and exciting with a lucky dip delivery?!

Crazy 8s example
  • Solution sketches

Now to focus in on one idea and create a storyboard, placing the new feature in a specific moment or emotional experience of the user.

Storyboard example

Here we can see the moment of frustration illustrated by an argument between friends over dinner.

The solution given is to provide more intuitive filter options in order to refine the search.

Next give the user the opportunity to give the responsibility to Glovo to make this difficult decision for you by playing the Glovo Lotto!

No more arguments, Glovo becomes the mediator and stops the user going round and round in circles.

Lots of interesting ideas to go to sleep thinking about.

Day 3: Decide

Now to decide which ideas, or combination of, to move forward to the prototype stage. I used little sticky notes to “heat map” the best bits of the storyboards I had created.

Here were the hot spots:

  1. Turning frustration into fun — gamify the decision process
  2. Sharing the decision — shared, collaborative shortlists
  3. Making the qualified decision for the user — more diverse filter options

I decided to incorporate all of these elements in my final prototype for user testing. This would be, the Glovo Lotto feature! With some added features to address the three points above.

Day 4: Prototype

Heading straight over to Figma, I created a LowFi prototype for user testing. The story goes…

The user is frustrated after searching for 15 minutes to find the right choice for dinner. His partner is just as indecisive and they are both getting rather hangry. An automatic pop-up displays (after 15 minutes scrolling) asking the user if they want to play the GlovoLotto and get the job done.

If the user wants to, they click to move to the filter selector.

Next screen, the user can choose the filters they want to use in order to refine the Lotto. This can be a previously saved list, or a new choice of most common filters.

Once selected, click GO! and GlovoLotto takes over and takes the pain and frustration of decision making away with a qualified selection. If the user is not happy, they can spin again…

GlovoLotto prototype

If you want to take a spin, use this link to play the GlovoLotto.

Day 5: Test

The moderated testing was quite relaxed and informal, involving 4 adult participants. The structure for the testing as follows:

  1. Setting the scene — context of product pain point, “ You have been scrolling for 15 minutes trying to decide which restaurant to choose and are struggling to find what you want…”
  2. Introduce prototype, minimal instruction, allow participant to play around with it (Figma prompts enabled)
  3. Short, semi-structured interview — first impressions? what are the pros/cons of visual design/interaction design/conceptual design? would they use the feature & why?

Participant Observations Overview

Testing results overview

Next steps

Potential next steps and improvements based on user testing would be:

  • Shortlists were very well received, could be developed outside the GlovoLotto context (could also be shared between Glovo users)
  • Advanced filters also well received, again could be developed into main app functionality
  • Scepticism of random choice and giving up control… is this the right feature? Or do we need to build confidence in this feature?

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I am a research & marketing professional with a passion for understanding the human story behind the data.

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Geraldine Wastell

Geraldine Wastell

I am a research & marketing professional with a passion for understanding the human story behind the data.

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