SMART WATER MONITORING SYSTEM

My Role: User Researcher and Strategic Designer

Project Duration: Four Months (2019)

Client: [confidential]

Methods: Cognitive Walkthrough, Contextual Inquiry, Kano Model Questionnaire and Analysis, Competitive Analysis, Thematic Analysis, Experience Mapping

Output: Findings and Recommendations Report

Synopsis: Balance was approached by a company with a desire to enter the smart home/IoT space. As a plumbing company, the product they were interested in entering with was a smart water monitoring system. In order to understand the experience of an IoT homeowner, I was made the project lead to help map the user experience between eight stages in the customer journey: trigger, research, purchase, estimation, installation, setup, usage and maintenance. The final output was a list of 13 prioritized recommendations for the digital and industrial product.

Research

Interviews

The process followed a semi-structured interview technique that includes the following methods to elicit the information needed to develop the design tools outlined in this document.

Cognitive Walkthrough: A method that had the participant recount their experience with the DRiY system from trigger (what made you decide to get the device) through detailed usage of the product.

Contextual Inquiry: A method where the researcher shadows the participant through their experience with the installation, setup and initial usage of a water monitoring product (competitive products Phyn & Streamlabs).

Kano Model Questionnaire: A highly structured questionnaire to help understand the perceptions and prioritization of future features of the system.

This project included 18 Remote Interviews including 8 homeowners, 4 property managers, 6 plumbers.

This project also included 3 Contextual Inquiries with 3 homeowners to watch the entire experience of installing and using the product first hand.

Competitive Assessment

A competitive assessment was conducted to provide the team a baseline understanding of the water monitoring space as well as how the market supports key features of the product and the needs of its users and installers. It provides valuable information that can help identify opportunities for our client to more aggressively compete and/or differentiate from current offerings.

Findings from the competitive assessment were used to inform the final recommendations for this project.

Artifact data is intentionally blurred

Personas

Example

Based on the interviews/workshop we set up three personas. We referred to them throughout the entire product development process.

Role-based personas were the method selected so that we could focus on key interactions that take place between users throughout the product journey. These personas were created from patterns found throughout the user interview process.

Artifact data is intentionally blurred

Customer Journey

Example:

To understand how customers find and interact with the service we created a Customer Journey Map. The map included the different phases of the journey as well as discrete “thinks”, “feels”, and “dos” for each phase. The journey maps were framed through the perspectives of the personas.

Results

Project Research Report

The final output from this project was a research report with 13 prioritized recommendations for the water monitoring system. I presented this report to a team of executives from our client. The presentation and findings were very well received.

Lessons

One of the biggest lessons in this project was the importance of early strategy. There were numerous times during this project was forced to rethink our approach because the initial scope was unrealistic. The initial ask was for our team to perform 24 different interviews with 6 different segments. Unfortunately this was not possible to recruit for, which we learned about a month and a half into the project.

Another lesson is not to utilize guerilla research techniques when all else fails. During a trip to Nashville, TN for this project, our team spent hours waiting around for interviewees to appear who ended up not showing up at all. This time could have been better spent finding random homeowners to speak with, as the compensation may have been enough to ask them for an hour of their time.

The major lesson learned for me in this project was managing the egos of multiple groups of stakeholders. When presenting the results of this project, we had to keep in mind that offending anyone about the existing project was a bad idea. Thus, presenting the research in a way that was optimistic was a real struggle, especially when discussing a product ridden with usability flaws.

Overall, this project was the most difficult project I’ve worked on and it was a great learning experience.