In Cameroon, even in the big cities, residents have had to get used to the increasing power outages in their daily lives. This is where the LightCutOff app comes in: by the principle of crowdsourcing, LightCutOff invites users to geographically locate the places where they were affected by a power outage to warn others.
Interrogate LightCutOff's users' to uncover their needs, perceptions and past experiences related to the app. Findings would allow LightCutOff to identify improvement opportunities before investing in additional promotional efforts.
An exploratory meeting was held with the LightCutOff founder to discuss his view of the current challenges, and expectations towards the UX research. His input was analyzed to craft the following goals and sub-goals, which were used as anchor points during the research.
a) ... to find out about the state of the outages in the area?
b)… to signal an outage?
a) How is the application positioned in relation to other sources of information?
b) What level of motivation do users have to use the application?
a) Why would users favor web vs. mobile use?
b) In the event of a power cut, by what means do users plan to use the application?
Recruit 4 participants who fit the target user group
Convenience sampling through founder's network, to facilitate recruiting in Cameroun
30 minutes interviews via video call
Over 4 days: Oct. 8th to 11th 2021
Semi-directed interviews: using an interview guide for structure
To make the most out of the 30 minutes interviews, a robust interview guide was built ahead with sample questions for key themes. I kept the dialogue open with participants to let them share their experience, so the guide was mostly used as an anchor point.
For each participant, interview recordings and notes taken were reviewed to extract all relevant data. Using the tool Miro, each key user observation/statement was transcribed on a single post-it. Each participant gets its designated color.
One post-it = One manipulable data point
*Sticky notes blurred for user anonymity
The goal was to categorize and sort the previous sticky notes into groups, to help identify commonalities and differences in the data. Since my interview guide was strongly anchored to the research goals, I decided to use a deductive grouping method. Each research goal would become a category, to which sticky notes were assigned when they helped answer the question.
From the previous data analysis, I was able to provide the LightCutOff team with a detailed report on discoveries made for each research goals. Here is a summary.
All users mentioned a strong interest in the feature that allows them to view the status of outages on the map. We notice a trend, where users see great value in it when planning their daily activities to identify areas at risk of cuts.
Users understand that reporting outage are necessary to populate the database. However, they all mentioned that reporting outages was of little direct benefit to them. Some have identified that when faced with a power outage, several concerns are on their minds, and therefore reporting is not a priority.
Fortunately, it seems that the application still enjoys an exclusive advantage. The need for information on power cuts is very real, and users do not find this need fully satisfied elsewhere.
The main blocker to adoption remains in the lack of motivation to report outages. A low level of reporting can compromise the quality of the data, which can cause other users to lose trust in application.
All users agreed that they would favor a mobile use of LightCutOff, especially for use when traveling or outside their home. Mobile devices are also accessible to more people in Cameroon: the LCO team is currently working on the development of a web version.
Several mentioned that if they are experiencing a cut and need to save device battery, they are not interested in using resources on LightCutOff. But, all users claimed to have a good amount of cellular data which can be used in the event of a power outage.
We were able to formalize that the lack of motivation to submit reports of outages could remain an obstacle to greater use, but that at the same time, the need to stay informed about outages was at the forefront. I wanted to translate the impact of this finding into a Persona and an Experience Map, two visual tools that the LightCutOff team could use to keep the needs of their users at the center of the future developments.
Leveraging the same sticky notes from our user interviews, a new affinity diagram was produced to help understand better the profile of our participants (needs, objectives, motivations, etc.) Here an inductive method was used, meaning that the trends observed in the data guided the creation of groupings.
*Sticky notes blurred for user anonymity
The next step was to analyze differences in the categories of the previous diagram, and identify variables that could be visualized on an axis called a continuum (opposition between two facets of the same variable). Participants were then positioned on each continuum: this helped recognize users that have a similar point of view.
Participants P1 & P2 which were positioned similarly over 11 continuums: therefore having many behavioural similarities.
The Persona constructed should ressemble P1 & P2 in some ways, without being a direct copy of the true participants.
Consistent with the information presented in the Persona card, it is most likely that our Persona would use LightCutOff the most to identify if an area is affected by a power outage. This goal has therefore been selected for an in-depth analysis via experience map, where the objective will be to analyze the user's experience through the process.
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