Streamlining the Rightsizing Workflow
Compology
-
Outcome
Increased adoption of savings recommendations
-
Role
Product design, user research and visual design
-
Timeline
6 months
-
Tools
Figma
UserZoom Go
What exactly IS rightsizing?
Looking at sensor images, Compology uses machine learning to analyze the amount of trash added to a dumpster over time and recommends an optimized service schedule, ensuring customers are always paying the lowest amount possible for their waste collection needs. We’re talking tens of thousands of dollars in savings across an entire fleet of dumpsters.
We assumed if there are savings to be had, people would jump to realize them.
Something wasn’t quite right
We launched the first version of this feature and weren’t seeing the usage we expected or wanted. The recommendations were beautifully laid out with supporting data visualizations in a core area of the app, we’d tested the flows, and users were excited about it, but for some reason, they weren't accepting the recommendations.
This was not ideal, as the more savings customers realize using Compology, the more likely they are to renew and expand their contracts. If we can show high realized savings across our customer base, potential customers are more likely to trust us and buy the product. So the fact that they weren’t… was a problem.
Deep into the user research forest
I ran a series of interviews with current users as well as a handful of our customer success managers (they are a glorious fountain of information) and we found out a few things:
The volume of recommendations made it daunting to act on all of them.
Stakeholder approval meant it took a lot of time to switch schedules for a container.
Users wanted to prioritize the highest value recommendations (dollar amount, largest customer) and/or group recommendations in such a way that they were able to get mass approval rather than one by one.
Not really a “you can lead a horse to water, but can’t make it drink” situation and more of a, “you can lead a horse to water, but if you force it to drink the whole lake through a firehose it might just choose to stay thirsty” situation.
The end result was a user journey map which you can see below.
Maybe this will help?
With this in mind our objectives for the project became:
Help users find the highest value group of recommendations.
Help them get approval for those recommendations in one fell swoop.
Help them keep track of which recommendations they’ve looked at and accepted.
Which led us to a wireframe like this -->
So close, just a little tweak
From there I narrowed in on a design like this:
And then ran a round of usability testing with 5 current Compology users that were in charge of running the rightsizing program for their organization. We chose this method to make sure that the design we proposed actually met their needs and was easy to use. And we learned:
There were certain data points users needed in order to make decisions.
Users wanted to curate which columns of data they share with their customers or haulers.
Overall, participants conveyed a preference for this new in-app experience because of the:
Ability to easily filter and drill down by customer or hauler
The easier to view layout
Ability to mark the status of the recommendation
Voilà
We tweaked the design based on the testing feedback and here is where we landed.
What I learned
It’s important to take a step back and look at the bigger picture of what your user is doing. Cool, the flow is intuitive and easy to complete, but how many times are they going to have to do it? How does it fit into their overall workflow? Is doing this flow a priority for them compared to everything ELSE they need to do?