Relesys is a company that specializes in communication and management performance platforms that bridges the gap between HQ and the non-desk workforce.

My role

User research, interaction design

Workshop facilitation

Critique sessions

Feature validation

Results

Design proposals for future development of the “mood score” feature aimed at helping store managers understanding their employees motivation

Relesys is an one-in-all app that streamlines communication throughout a company

Relesys serves clients in 15 different countries, supporting them in 40 languages with 450,000 licenses across 20,000 stores and users in 100+ countries. With a strong presence in the Nordics and EMEA, Relesys continues to increasingly attract clients from across the globe, as they break into new, dynamic markets.

The Relesys app platform unifies communication, training, and operations for non-desk workers through a mobile app. It helps companies engage and unite their workforce by centralizing information and providing tools for daily tasks, performance management, and engagement. The platform is modular and includes features like gamification, task management, and analytics to streamline operations, improve employee performance, and foster a stronger company culture.

Validating a new feature before release

With designers busy with developing UI and other features, I was tasked to use my UX expertise to validate the new" “mood score” feature developed by Relesys to minimize the efforts and time needed for management to discover lack of motivation at a frontline worker level.

The feature lets frontline workers score their mood after each shift and gives management insight into an average “mood score” over a weekend/month.

Context & users

In the new Relesys app design, frontline employees can complete daily mood surveys (Called Work Mood) to indicate their current mood and view their team's average mood score. Managers have access to the same view as well on the Relesys mobile app.

The users of the feature are the following:

  • Store managers Age 25-30 Y/O full timers Work in grocery stores Comes from different countries

  • Frontliners Age 18-25 Y/O full timers and part-timers Work in grocery stores Comes from different countries

Defining the feature purpose

Although the feature came as a suggestion from a customer, Relesys had an idea that this feature would bring in new customers and further digitalize the frontline-management communication through quantitative data.

My purpose early on was to establish and uncover knowledge gaps in the current design process. Together with Relesys, we made a mission-phrase/feature purpose from the managers perspective since they were the focus until I joined the project. We ended up with the following:

“As a store manager of a grocery store, I would like understand better my team mood and motivations and take actions to increase my employees’ engagement on the job”

Uncovering knowledge gaps using reverse engineering helped steering the direction towards a critique

To understand the knowledge gaps that existed in the development of the feature, I facilitated a workshop expanding on the mission phrase. The purpose of this was to discover if our designers and PMs had different understandings on the subject - which they had.

I tasked the designers and PMs to reverse engineer the mission phrase and we found that several knowledge gaps existed. We then reverse-engineered the “mood score” UI and had great discussions about customer needs, user wants and product direction.

Questions to uncover

From the reverse engineering brainstorm, we came op with the following knowledge gap questions:

  • Which effect does it have on frontline workers and store managers to watch their teams average mood score for the week? 


  • How does it effect frontline workers in the age of 18-25 to think about their mood daily or weekly?


  • What does work mood actually mean? -and is there a difference in how store managers and frontline workers define “good mood”?


  • What should store managers do with this knowledge? Could the data potentially create more division than what was before? Is it our responsibility?


  • Will frontline workers answer honest if they are in doubt of how the data will be used? “Am I a bad worker if my mood is bad?” Will I risk being fired? 


  • How will it change the overall mood and teamwork between store managers and frontline workers, when creating a simplified daily Wellbeing Assessment?

While engaging with frontline workers we discovered that “work mood” is highly subjective

While other designers and PMs started talking to customers, I became responsible for uncovering the question: “What does work mood actually mean? -and is there a difference in how store managers and frontline workers define “good mood”?”

After interviewing a handful of frontline workers, we learned that “work mood” is highly subjective and varies even more between part-time and full-time workers. Although some themes appeared such as part-time workers having less at stake when it came to performance and work motivation and gen Z workers’ “work mood” being less tied to actual work and more to school, friends, family, etc. Leaning into the subjectivity of “work mood” I made the following research scope for further development:

Mood is a subjective state of mind/feeling. If we want to create a tool that lets store managers understand their teams mood and motivation through a quantitative measuring tool, we first need to uncover what mood means for frontline workers and if it varies from how store managers define it

Part-time workers and managers have vastly different expectations to what work needs to offer

Store managers in general thought the “work mood” tool would be exellent, especially for understanding their part-time workers and how to improve their routines and work conditions. However, the part-time workers in general did not care that much about their mood at work. Work was necessary to earn money to buy clothes, going out and afford housing (ranked in that order). They generally wanted good work conditions and a manager who met them with understanding and flexibility during exam periods. Otherwise mood for them had almost nothing to do with work. With a cultural probe we found that mood is determined way more by friends, famility, school, sports and partners.

In the analysis report I wrote to Relesys, I wrote that the data collected with a “work mood” feature might be flawed in a way that it can widen the gap between store manager and frontline worker even more. Considering that managers and frontline workers have different understandings of what “work mood” is, it could also be that the way HQ could percieve the numbers based on an entirely different idea. This could potentially create tension upwards and result in store managers rooting out the ones with “bad” moods. Even if that did not happen, some frontline workers could be scared and flaw the data on purpose, fearing to loose their job.

Feedback to Relesys

Validation report summarized

The general feedback I gave to relesys can be summarized in the following 4 points:

  1. “work mood” can be interpreted in many ways - making it a less viable as a metric for uncovering motivation to increase engagement at work.

  2. Work motivation and purpose can vary a lot when you ask someone who is committed to a career and someone who is only planning to be in a given period of time.

  3. Simplifying the frontlines response-method might help collecting more data, but the data has a high chance of being flawed

  4. As we can not increase the motivation of frontline workers by decreasing the number of customers or making sure their friends are there, we need them to answer to specific questions store managers can act upon

The new knowledge gaps to consider/answer included in the report:

  • Are we trying to simplify the complicated when we ask for an emoji or a number as input to a question our users’ might view differently?

    • and do we risk creating more confusion with this feature when the definition varies between a store manager and a frontline worker?

  • Is measuring ones mood, the best way to increase employee’s engagement on the job?

    • What data/questions would a store manager need answered in order to make changes to increase front workers mood, motivation and their engagement on their jobs?

  • Should you add steps and clarify the purpose of what data you want to collect?

    • Will answering more detailed questions create a reflection-load on frontline workers?


My main suggestion for further development was to rename the feature and change the input from emoji to text. Qualitative data is excellent for getting a general feeling, but when the matter is as subjective as mood is, it will most likely be more effective to interview rather than looking at numbers. Taking into considaration that most store mangers most often do not meet their frontline workers that often, mediating a connection through the “work mood” tool could prove to be the best way to “start talking” and increase motivation and ultimately conversion rate.

Main suggestions for further development

I also highlighted the importance of not making questions modular to the companies interested in the “work mood” feature. Instead, I told them to carefully consider and define questions in collaboration with customers, to get a clear indication of what they want to improve. This leads to my other point to “only ask questions that store managers can act upon to increase frontline workers’ mood, motivation and engagement at the job” and to “make tools and suggestions available to store managers to prepare them on how to act on the quantitative feedback”. Only this way you can make sure to minimize the risk of misunderstandings appearing between people who already have widely different ideas about what “work mood” is and what it should be.