G2

Verified User in Higher Education

Enterprise (> 1000 emp.)

8/23/2026

"AI-Driven Survey Tool with Room to Improve"

4/5

What do you like best about Whyser AI Research?

I really appreciate Whyser AI Research for its built‑in AI‑assisted thematic analysis for open‑ended survey responses, which fits perfectly for my PhD work at Universität zu Köln researching sustainable fashion consumer behavior. Back when I was solely using SurveyMonkey, I could collect quantitative survey data without issues, but every open‑text reply from participants had to be read, sorted and coded completely by myself. I still remember my second major survey round, where I gathered over 430 responses exploring people’s real‑world attitudes toward second‑hand clothing. I would have spent weeks buried in manual coding work, sitting late in the university office going through hundreds of free‑text answers one by one to pick out recurring viewpoints like cost concerns, green‑washing worries and personal sustainability motivations. With Whyser AI Research, once my survey finished collecting responses, the AI automatically scanned all open‑text inputs and created preliminary theme tags, grouping similar participant quotes together. This took most of the tedious first‑pass sorting work off my plate. What I value most is that I am not forced to accept whatever the AI outputs. I can go through every suggested theme, merge overlapping categories, split overly broad groups, and delete irrelevant tags to strictly match my own academic research framework. That way I keep full researcher control, which is critical for my dissertation’s academic credibility. Even so, there are notable workflow hurdles. There is no SurveyMonkey import function, so I rebuilt my whole survey instrument manually. CSV files frequently corrupt German umlaut characters ä, ö, ü in participant written answers, so I always pre‑sanitize datasets before upload. Advanced AI settings are only in English, and exported files still need heavy edits before they can be used in SPSS. I save all survey drafts, raw data backups on Nextcloud and talk through my analysis logic with my lab colleagues via Microsoft Teams. Overall, the integrated AI thematic analysis is the biggest advantage. It drastically reduces my manual qualitative workload and helps me stay on schedule with my PhD project under a limited academic budget. Review collected by and hosted on G2.com.

What do you dislike about Whyser AI Research?

A major limitation I face with Whyser AI Research is its AI’s tendency to over‑group loosely related participant comments together when handling nuanced academic open‑text responses. During my sustainable‑fashion survey, many respondents held mixed, complicated opinions. Some participants acknowledged that sustainable clothing is important while also pointing out high prices and poor product durability. Whyser AI Research lumped all these nuanced mixed‑perspective replies under one single broad theme. If I had trusted the raw AI output without careful review, I would have distorted my qualitative findings for my PhD thesis. I ended up manually splitting and re‑coding hundreds of entries to fit my research coding framework, which added quite a bit of extra work. The platform also lacks native SurveyMonkey imports, so I had to recreate my full survey, including skip logic and random question blocks from scratch. CSV import issues often garble German umlauts ä, ö, ü within participant free‑text feedback, corrupting parts of the text data and making some responses partially unreadable. I also cannot get SPSS‑ready exports directly; every dataset requires manual cleaning and variable reformatting before statistical analysis. Furthermore, the participant quota and screening logic options are fairly limited. I wanted to balance my sample across different age groups and shopping habits, but there was no automatic quota locking. I had to keep checking the dashboard multiple times each day and manually pause distribution whenever one subgroup hit its target size. This interrupted my other PhD tasks like literature review. Several advanced AI configuration menus are only in English, which caused me to misadjust filtering rules during early setup. I store all survey materials and old SurveyMonkey archives on Nextcloud, and discuss methodology with my research group on Microsoft Teams. All these gaps add significant manual overhead and require constant researcher vigilance to avoid flawed data for my dissertation. Review collected by and hosted on G2.com.

What problems is Whyser AI Research solving and how is that benefiting you?

I use Whyser AI Research to cut down on my heavy manual qualitative coding workload for my dissertation, something I struggled a lot with when I relied entirely on SurveyMonkey. SurveyMonkey only exports raw text answers and offers zero support for sorting open‑ended feedback. I still recall my second survey where I collected more than 430 responses about sustainable fashion consumption. Without AI assistance, I would have spent weeks reading and labelling every single free‑text reply by hand. That repetitive work took time away from literature review, thesis writing and other core PhD responsibilities, and I also worried that long‑hours manual coding could bring inconsistent personal bias into my thematic labelling. Whyser AI Research handles survey hosting and delivers AI‑assisted thematic analysis to give me a solid first‑pass overview of all open‑text responses. It groups similar participant opinions and suggests themes, which saves me weeks of tedious manual processing. This allows me to focus more on interpreting results and writing my dissertation instead of repetitive administrative research labour. That said, it does not eliminate all manual steps. There is no import for old SurveyMonkey surveys, so I rebuild each questionnaire manually. I still have to watch out for garbled German umlaut characters ä, ö, ü in CSV files and clean datasets before analysis. Exports still need rework to work inside SPSS, and sample balancing has to be done by monitoring response counts manually. All raw datasets and survey backups are kept on Nextcloud, and I coordinate research progress with my lab through Microsoft Teams. Whyser AI Research will not complete my full analysis for me, but it removes the most time‑consuming part of qualitative sorting. This keeps my PhD project moving forward under my tight academic timeline and student‑level budget. Review collected by and hosted on G2.com.