Competitive Analysis of RPA User Bases

Context

As an emerging RPA project, it was necessary for our team to have a better understanding of our competitors in the market and who uses the particular competitors. For example, some of our competitors were claiming that their products were being used by non-technical business professionals. However, in speaking to a number of our customers, I was skeptical that this was the case.

The objective of this research was to conduct a user-centered competitive analysis to provide clarity for questions that exist around our competitors, particularly who is currently using RPA tools.

Tools: G2, TrustPilot, LinkedIn

Methods Used: Secondary Research, Data Scrapping

Timeframe: 1 Month

#1 Analysis of User Reviews

For the competitive analysis, I looked at 4 RPA competitors:

 
 

I looked at the 25 most recent user reviews for the 4 RPA competitors on G2 and Trustpilot. For example, on G2 in the image above, the site neatly identifies the following:

  1. What the users like about the tool

  2. What the users dislike about the tool

  3. What users are using the tools for

#2 Linkedin Data Scrapping

In collaboration with the IBM Marketing team, we were also able to data scrape Linkedin for profiles that listed our competitors as skills on their profiles.

Using this method, we got approximately 150,000 Linkedin users who listed at least 1 of our competitors on their profiles

Readout

In the readout to the team, I was able to identify the positive and negative themes of our current competitors, as well as the job roles that were currently using those products.

We were able to identify the themes that were important we prioritize in our product… (1/2)

 
 

…as well as unmet needs and the biggest opportunity area for our product. (2/2)

 

With the Linkedin data scrapping, we were also able to see that the professionals who were using RPA products were very technical users (e.g., robotics engineers, software engineers, RPA consultants).

Less technical roles such as business analysts and managers made approximately 1-2% of all users.

A result of the playback was an understanding among the team that our current persona did not match up with the most common users of our RPA competitors. Therefore, less importance was applied to our competitior’s claims