Flow AI is a company that is dedicated to develop tools that maximize sales-reps daily activity. Turbo is their lead source tool that use AI to present qualified accounts and help speeds up the customer acquisition process.
As a UI/UX Design Intern at Flow AI, I led the design of the AI feature and the tool integration feature for Turbo. In the course of three months, I work alongside a full-stack team, an AI team and 2 other designers.
Before launching, we sent out surveys and invited users to test out Turbo, receiving a SUS score of 72.5.
The process is iterative and we work under lean UX which breaks my design process into 3 steps:
Think - Explore the problems that users are experiencing and consider how I could solve them with my design. Make - Start designing the product by creating sketches, wireframes, and prototypes. Check - Find out how users respond to my design and gather feedback from project stakeholders.
In today's hyper-competitive business environment, sales representatives face an increasingly daunting task of identifying and converting high-quality leads into customers.
This was quite a challenge for me since I have no prior knowledge to the sales industry.
I decided to do a competitor analysis to understand the sale stack tools and the customer acquisition process.
Then I interviewed our stakeholder, who is also a sales-representative. He provided me with a more holistic view of what a sale-reps day-to-day look like.
I map out a simple user journey to help myself visualize the pain points during the customer acquisition process.
These ideas are valuable as they were derived from user pain points and could help is design for success.
So then I beginning designing our first feature which is AI prompt. My original thought was to design this like as a conversational AI. I researched on different ways of interacting with a language model, including using a quality meter to suggest improvements and a checklist that updates in real time while inputting.
After some deliberation, we move forward with Idea A.
After talking to the AI prompt engineer we agreed that we need to do a testing to see if the conversation of AI can generate results that meet users' expectation.
In order to validate this idea during an early stage, and also ensure this would work on the engineer side, we conducted a testing where we ask 2 users to ask AI the same thing.
Then I quickly realized that people’s conversational style are extremely different. When I ask them to input a sentence for the target they are looking, they constructed the sentence very differently, which eventually led to inconsistent results.
After analyzing the keyword AMP we are using, we found out that the search engine needs a set of specific parameter. We came up with a long form by discussing which parameters needs to be prioritized.
After clarifying how our AI model works, and also considering the complex nature of account prospecting, I sequenced the long form and ask users to complete only 1 step on each page.this iteration is extremely crucial to the product development because we were able to identify the constraints that our AI model and look for solutions that could meet users' needs and feasible on the technical side.
Based on feedbacks we got from the survey and the product matrix I created before, I have several improvements in mind including improve onboarding experience, expand our tool selections, set goals and reminders for users...etc.
I tried so hard in the beginning to understand problems and the challenges, and also a lot of ambiguity along the way. I learned how to effectively communicate in these circumstances and never stop asking questions!