A new business initiative to address project health.
Kantata Pulse is a survey tool, purpose-built to support the professional services"Professional services" refers to any company whose primary output is a service or expertise rather than a product. Many of Kantata's customers are design & advertising agencies, legal firms, IT consultancies, accountants, etc. industry. As the lead designer on the project, I was involved in every stage, from the initial design sprint and customer interviews to prototyping, user testing, final asset delivery to the engineering team, and post-launch follow-ups.
When the project kicked off in February 2024, the timeline seemed generous and the financial goals were ambitious. We had never launched an add-on product before (with its own revenue targets) and our fiscal year ended in January, just 3 months after our scheduled launch. This meant we needed customers to start purchasing Pulse months before launch, something else we had never done.
Despite this, the project was very successful in the number of customers who signed on, if less so from a revenue perspective. The reasons for this, and how the team responded, is described in this case study.
Kantata is a SaaS company in the professional services automation (PSA)PSA software is designed to help professional services organizations manage projects and resources throughout the project lifecycle. Some of Kantata's competitors include Certinia (formerly FinancialForce), Workday, Wrike, and others. space. It offers two products, OX and SX, which provide time tracking, project management, resource management, billing, and financial reporting to professional services clients—any company that bills employees' time, such as design agencies, IT consultancies, and legal firms. The two applications are developed independently: OX runs on its own bespoke platform, while SX is Salesforce-native. I worked exclusively on OX.
During a period of economic uncertainty that challenged many of Kantata's customers, leadership identified the need for a new product offering to boost revenue amid declining renewals. Additionally, Kantata wanted to start developing products with a shared build once, use twice architecture that could be utilized by both OX and SX.
The initial problem we aimed to solve was related to project health. For years, we had been hearing from customers who wanted greater transparency and insight into the projects they were running on our platform. Our early assumption was that client surveys would be the most effective tool to gather these insights.
This assumption was quickly validated through interviews with customers and subject-matter experts. Some promising findings emerged:
We conducted a design sprint with SX and OX product teams to evaluate our interview findings, review competitors, and explore potential solutions. This revealed several key assumptions we wanted to test:
I created the initial prototype to validate our assumptions with customers and gain buy-in from senior executives. Depending on the audience, the prototype could be presented in different ways. The primary scenario described a future vision featuring a project manager at a firm that had been using Pulse for several years:
A high-level view identifies a project with declining client sentiment. The PM drills into the project details, finds a free-text survey response describing the issue, and ultimately receives guidance from an AI chatbot on how to address it.
The prototype successfully generated excitement about the project. Current customers responded enthusiastically, the sales team couldn't wait to present it to prospects, and the executive leadership team greenlit the project.
Much of our focus early on was in making Pulse as quick as possible to get up and running, with a minimum of setup that might slow adoption.
We defined an initial default state, which we referred to as the easy button: Without any preparation, a busy project manager could enable Pulse on their project with a single click. Our tool would automatically begin gathering valuable project health insights at regular intervals.
Clicking the easy button would:
Pulse launched with our initial default state intact, but over time, we recognized the need to balance simple automation with greater customization for users at more strategic levels within our customers' organizations.
Over the course of the project, I evolved the design by leveraging customer insights gained through continuous discovery, and close collaboration with product management, engineering, content design, and QA.
One component that evolved very little from the initial prototype were the Pulse surveys themselves. From the beginning, I imagined these as the shared component in the build once, use twice model we were using for the project.
I specifically designed these with a visual design independent of either platform. SX was on a slow migration from Salesforce Classic to Lightning, OX had it's own design system and component library. The surveys would be seen by our customers' clients, so needed a somewhat more consumer-oriented look and feel.
The Pulse Widget, displayed on each project's main page, shows recent survey scores, includes a toggle to start or pause survey campaigns (the easy button), and provides access to campaign settings.
We learned that our customers needed more independence between their client and internal team survey campaigns than we had initially planned. The updated Pulse widget allowed for each campaign to be started and paused separately.
I appreciate the axiom (often credited to Einstein) as simple as possible, but no simpler. My initial design for survey campaign scheduling was elegant but too simplified for real customer needs.
I moved all of the campaign settings to dedicated pages. This allowed for more granular control over schedule, and also allowed room for survey previews that update in real-time as settings are adjusted.
My initial prototype did not include custom question creation, but we learned soon enough that this would be a requirement for launch. I first designed a robust version, which was then scoped down to an MVP for launch.
Unforeseen delays caused front-end development to halt for a time. This put some key customization features on the backlog, and required a pivot to MVP versions of others for launch.
Customer interest in Pulse was high. Even with development delayed, my evolving prototypes generatied enough interest that several customers purchased the product even before release. By the end of the fiscal year, we had exceeded our goal for the number of customers using the new tool.
While Pulse was adopted enthusiastically by smaller commercial customers, our ambitious revenue goals would require the higher-dollar interest of larger enterprise customers.
| Key Result | Target | Actual | Variance |
|---|---|---|---|
| GA Launch | October 2024 | October 21, 2024 | -- |
| Customers with Pulse Active | 15 | 20 | 133% |
| Avg Active Campaigns | 10 | 17.2 | 172% |
| Avg Response Rate | 40% | 39% | 97.5% |
| Revenue | $250k | $226k | 90.4% |
The easy button was useful for smaller, scrappier firms where busy project managers (with many competing priorities) needed as simple a solution as possible. Larger firms, on the other hand, had operational leaders with interest in running more organized campaigns across larger departments. Some even had people dedicated exclusively to addressing this need. These teams needed a higher degree of configurability than we offered at launch.
Following launch, completion of features that would make Pulse useful for enterprise customers became our top priority. Here are a few that were in progress following launch.
Full customization of surveys has two components:
The creation of custom questions was available at launch (see above), but the ability to organize these questions into custom Question Sets was not. This was the first feature the team began work on after launch, and was scheduled for completion approximately five months later.
One of the biggest pain points people have in gathering qualitative data via surveys is poor response rates. My perspective has been that, once we had delivered the essential features for effective survey distribution, the next challenge would be in helping customers achieve better results from their campaigns.
Addressing this will require a multi-pronged strategy, but the idea here is to provide more transparency into what is happening with the emails sent by our system. This page displays the kind of data you might find in a traditional email marketing platform, such as bounce rates, email caught in spam filters, etc. Further explorations will include the ability to schedule automated reminders, and specific details on who has responded to the survey.
We enjoyed a high degree of collaboration on this project, which resulted in both a more enjoyable process and a better quality outcome. I also learned some valuable lessons.
We implemented a continuous discovery practice, meeting with customers multiple times per week throughout the course of the project. This provided a near-constant stream of input on whatever I was working on at the time, from high-level product direction early on, to specific interactions as we dug into details.
I ran design discussions with the product team twice a week: once to present features the team would be working on in the near future, and another to field questions that came up on current work. This was a valuable opportunity to gather feedback from people who hadn't always been given a chance to give it, including all levels of engineering, content design, and QA. The team's sense of psychological safety increased as they saw their ideas reflected in mockups and prototypes, which generated a palapable sense of trust.
There were no engineers involved in the discovery phase of the project. Although many eyes were on the project early on, there are some questions that executives, stakeholders, and customers (and even product managers and designers) don't think to ask. When we finally met with engineering to spec the data model, we had to make assumptions about certain details. When we later gained insight into those details, the data model required time-consuming changes.