Future Feature Prioritization for RPA

Context

A design thinking workshop was conducted among the RPA team in order to brainstorm new, innovative features for IBM RPA. After 15 new features were brainstormed by our team, I conducted a survey were conducted in order to get feedback on these features.

The objective was to determine what new features, via a prioritization exercise, would satisfy and delight RPA developers for a new iteration of the RPA dashboard.

The kano analysis was deployed in order to determine which features prospective costumers would be most satisfied by. A single feature was presented to participants, and subsequently:

  1. Participants were asked to rate the importance and likely usage of each feature

  2. Participants were asked a pair of questions about each feature:

    • How would you feel about having this feature?

    • How would you feel about not having this feature?

Features would then categorized based on answer combinations:

Features were then categorized into 4 groupings:

  • Must-be (M) – Features expected by customers

  • Performance (P) – Features that are satisfying

  • Attractive (A) – Features that excite and delight

  • Indifferent (I) – Features that don’t generate a reaction

The idea from the Kano Analysis is that you want to get all of the must-be’s, then performance, and if feasible, a few attractive features.

Tools: Alchemer

Methods Used: Kano Analysis, Survey

Participants: 50 RPA Developers

Timeframe: 3 weeks

 
“You are an RPA Developer within a company. Your company has just bought the latest RPA tool to automate your more basic customer service calls.

In the following survey, we will look at each individual tile on the dashboard and you will have the opportunity to provide feedback on our RPA dashboard.”
— Prompt from User Test

Each feature was categorized into one of the four categories and we had the follow breakdown of the 15 features:

  • 1 must-be’s

  • 8 performance

  • 4 attractive

  • 2 indifferent

Many insights were also uncovered. For example:

  1. It was observed that features that provided metrics were deemed to be of highest importance to the bot developers.

  2. There were a lot of excitement around the features; however, participants worried over the inaccuracy of some features.

For each feature, an importance score was calculated from the survey results; the feature was placed into a category, and provided a priority level… (1/2)

 
 

A rationale, along with participant quotes, were also provided, to help the team understand why each feature rendered the score it did. (2/2)

Readout

The results were presented to the team, outlining the categorization of each feature. I recommended that the 6 features that were considered performance and high priority to be prioritized on the roadmap.