Investigating & resolving customer orders at a glance
Unifying features from seven disparate systems to create a better employee experience, cut resolution times in half, and launch a predictive triage algorithm
Client a global logistics company
Roles interaction designer, user researcher, presenter
Duration 3 months
Traits #artificial intelligence #employee experience #product design #web app
Note: Details of this case study removed for client privacy. Contact me for more on this and other projects.
“Where is my package?”
49 times out of 50, our client’s customers don’t have to ask this question. But sometimes one of their tens of thousands of orders gets lost or misdirected, leading to customer frustration and an average four-day investigation and resolution period. Our mission: to cut down resolution time, improve their employee experience, and become more proactive in addressing problems with orders.
On-site visits yield functional and cultural insights
A portion of our team flew to two user worksites to:
observe their physical work environment,
note the software tools (and any other tools) they use,
conduct individual user interviews, and
test a concept drafted by the prior Discovery phase of work.
This was one of the first times a visual designer was a part of the research trip, and she was able to pick up on things the interaction designers weren’t looking for. There was a grassroots culture of pride at the organization, with lots of past posters and artwork lining the walls; knickknacks and paraphernalia strewn across their desks. Legacy was important.
Interviewing users in-person, one-on-one proved valuable in subtle ways. We were able to notice hesitation at certain questions, and power dynamics around certain metrics, behaviors or, other teams in the organization. If designed just right, this tool had the possibility to lower walls between different parts of the company.
Surfacing the right information at the right time
Once we’d synthesized our research and pulled out some key insights, we began molding the existing prototype into their image, as well as identifying missed opportunities and net-new features:
Laid out the seven legacy systems they currently use side-by-side to compare data and construct the story of a given problematic package
Took note of how they communicate with their customers and asked how they wish they could
Plotted the many different types of data (agent notes, customer communications, location data, images, facility information, order details, customer details, and more) along a single axis that best helped the investigative process: time. A comprehensive “Facebook timeline” for a shipment.
Shaved off irrelevant or cryptic order information from legacy systems, while making details accessible with a single click
Worked with our developers to construct a predictive algorithm based on historical data, and designed the presentation of that data to point the agent in a helpful direction without being intimidating or implying false accuracy
Challenges converting raw data into actions
Surfacing the right information in the best contexts
Communicating algorithmic insights to the agent without removing agency or implying false confidence
Helping our main stakeholder build client-side support for the product against ingrained organizational bureaucracy and complacency
A tool by the agents, for the agents
Since we’d met a portion of the agents face-to-face, accepted written input after the initial interviews, and baked in elements they’d requested, we received lots of positive feedback about use and adoption of the tool. We also had enough cultural knowledge to include “easter egg” features that created some visual flourish, celebrated past organizational accomplishments, and just sent the message that “this is a nice thing, made for you”.
A domino-effect of better experiences
We were able to combine seven different antiquated resources into one focused, flexible tool that presented information contextually, proactively provided causation insights with an ~80% successful triage algorithm, dramatically improved the employee experience, and cut resolution times from four days to less than two. This resulted in happier customers, less wasted time in the field, and more time for agents to be a proactive partner instead of simply cleaning up messes.
This project directly led to at least four more streams of work with the client, including a 2.0 version of the tool and a downscaled version rolled out to almost the entire company.
(Header image by Ivan Bandura.)