User Analytics API Integration
Unearthing less obvious but very important customer insights.
Duration
Sept 2021 - May 2022
My Role
Product Designer
Platform
Responsive Desktop SAAS App
Not tracking important user insights or metrics.
The organization's ability to proactively help its customers was being limited by a lack of analytics.
I conducted user interviews to discover the company's informational deficits.
The further the customer got in the pipeline between the initial prospect call and launch, the less the company knew about the customer despite increasing employee contact.
"Given a certain fleet size, what percentage of those vehicles are out of service at any give time?
Are we delivering on our promise of
reducing out-of-service time?"
- CEO
"I don't know if they actually start using the system after their go live date.
Its not something we can confirm over the phone either."
- Client Trainer
"The system has a ton of features.
We don't know what parts of the app customers use more than others."
- Sales Rep
So what? A lot of businesses don't properly track metrics outside of sales.
A primary source of income for Collective Data is support contracts. That's source of revenue is opposite of support labor costs. The less support issues you have, the more that support contract money can be used to grow the business rather than put out fires.
Quality customer insights lead to a user experience that prevents common support ticket causes.
I led the project from end-to-end to design and implement an API integration to gather important user metrics.
Proof of Concept
Utilizing the in back-end app-editors and access to live hosted customer enviroments, I was able to successfully poll how many SAAS customers had logged in over the past day. Filtering the data is critical.
I demo'd the proof of concept to leadership to discover any oversight (leadership was also a software engineer and why the user login datapoint was even available in the first place). Once I was given the green light and encouragement, I started to design the informational architecture.
Data Points
A crucial qualitative measure for our API data-points is their diagnostic value in identifying problems. The higher this value, the more likely and severe the problem. Conversely, low diagnostic value requires further investigation.
For instance, user login frequency is a clearer sign of potential issues than the number of vehicle repair orders submitted in a day.
While new customers might not place many orders, they should be logging in.
On the other hand few or no logins relative to licenses is alarming.
Next Steps
Before I left Collective Data for new opportunities in July 2022, the functionality to receive insights was added to the internal CRM systems and the ability to send that information was added to customer's apps via scheduled update.
From there, the next step was to iterate on what information we are receiving from customers, create email notifications for customer success management, and ramp up client trainers in it's functionality.
The result would be new internal processes deployed in tandem with the view to help customers when problems were detected early.
Case Studies
Easily access library materials on mobile devices with WorldCat Find.
Impact :Collaborated with product management and engineering to scope, drive alignment, design and launch iOS and Android app in 8 weeks.