Spending your time doing the right thing

Hotline spent a big part of their time finding to the right information before starting to evaluate the issue. In a large system with a lot of data, users had many roads to take to solve an issue.

    Our goal was to:
  1. Reduce the time spent on finding to the right data
  2. Increase efficiency once they found it.

My role was to research, synthesize and create a solution to present to stakeholders and developers.

We knew customers call hotline with a large range of issues that all require a different approach. Our hypothesis was that there is a set of data that would cover 70% of the calls and it would reduce the average time spent greatly.

Quantitative

Finding the most value for the least effort

We extracted lists with the most common problems and their average problem-solving duration. We filtered out the extremes that was dependent on unique situations in their market. We selected 9 common problems with high average problem-solving duration to be able to more effectively compare the countries effectiveness on each problem.

Our research from statistics can only tell us the first part, the quantitative part. Next step is to get a more in-depth understanding of each selected problem.

Qualitative

Following the clues and investigate the leads

We narrowed it down to 3 countries with highest concentration of selected problems. We planned and performed interviews, user tests and observations with candidates from each location.

We tracked solution routes, expectations and their own motivation for that route.

The complexity thickens

We analyzed their problem-solving flow together with comments on what they were expecting to find with each action. We compiled a list with the most used and effective metrics based on all solutions. During this process we discovered that the setup of hotline departments varied between countries. The responsibility and also the technical knowledge could vary and our solution might not be applicable everywhere.

A number of the metrics require a broader knowledge of the machine than some countries have on their first line support. This issue required us to grade the metrics and solutions by availability.

    We grouped metrics by:
  1. Which level of support
  2. Complexity
  3. Impact.

  4. We boiled the proceses down to one solution per problem to test viability with our existing system.

Prototyping a problem-solver for problem-solvers

I created a series prototype components with the problem-solving user flow and sets of data. I conducted user test remotely on different markets and went through a couple of iterations before we started development.

Simplified prototype
Another simplified prototype

We did not add new information, we made it more visual. It gave all users important information at a glance and more accessible in-depth analysis. The solution was greatly appreciated by all users and have increased the efficiency for Hotline support, as well as Technical experts.

We added a Label feature to make it possible for users to add a temporary message on each machine.
We changed the structure of Identifiers to make the userflow unified.

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Upgrading TOMRA Connect continuously

Enterprise UX Designer

My work at TOMRA includes a wide portfolio of products but the main one is TOMRA Connect. It's an application that covers a vast amount of ground in monitoring a fleet of more than 70 000 Reverse Vending Machines, in 50 countries and with thousands of users.

Strange machines from the north

UX Analyst UX Researcher

When Australia started their deposit system together with TOMRA I was on the ground to research how well the system performed. I was tasked to observe and interview end users, test our internal systems with our brand new employees and how we could improve the roll out of machines on the other side of the world.

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