This AI solution exposes weak points in your competitors' products. Why not build upon it?

At a previous stage, I engaged Synthetic Users to generate user feedback for ColdSnap, my fictitious AI-generated software concept. It went exceptionally well; by now, this artificial user's product assessment is my only?proof of concept. SyntheticUsers did a great work of emulating my target audience and providing detailed feedback that helped me decide whether the product has the potential to become appealing to users.

I received input on the following:?

• strategies users tested to deal with the problem that ColdSnap is addressing
• challenges resulting from consumers' existing approach to their problem resolution
• opinions?on the usefulness of the proposed solution
• improvements suggestions
• concerns and queries
• cost recommendations
• general rating

But what I did not receive was a functional review. I'm not sure I could even have expected that given how poor my product description was and how few details it contained. It must have?been so since I still didn't have a functional product structure.

So I decided to narrow my search and provide as the input to SyntheticUsers a list of ColdSnap probable software features (generated by ChatGT), together with a general description. I expected users' comments to contain some?feedback on the software components this time. And I got exactly what I hoped.

Again, the feedback I gained from SyntheticUsers was quite descriptive, and because I was testing the free beta version, I only got a few of it. But?what if I hired this application to generate?hundreds of user feedback? How would I be capable of assessing and drawing conclusions from users' opinions on a mass scale?

While SyntheticUsers was still in development and didn't provide any analytics dashboard, I opened a search engine. I googled for any AI-based solution suitable for synthesizing large amounts of feedback and providing key conclusions?that are statistically significant.

I discovered Kraftful that intends to assist with user research. Nevertheless, unlike SyntheticUsers, Kraftful is a ChatGTP-based summarising tool that analyses input feedback and provides synthesized findings.

Kraftful is simply a must-use program that can provide insight into the weaknesses in competitive solutions.

The innovation is being used by global brands?such as 谷歌 , Meta , Canva , Atlassian , and? Netflix . It suggests?various use scenarios where it might be very effective.

1. Summarizing and synthesizing feedback that might be uploaded via text or directly from an app or data source.
2. Monitoring user sentiment while evaluating feedback comments over time.
3. Processing?feedback from competitors' users and making use of it to stay ahead.

In the experiment (check my?previous articles for more details of its earlier phases), when using AI tools to ideate, validate, and design the MVP of an AI-generated product, I considered exploring?Kraftful for two purposes:

1. Synthesizing AI-generated feedback from previous SyntheticUsers testing.
2. Analyzing competing software feedback to discover some constructive advice to consider when deciding to build on.

Synthesizing feedback generated by SyntheticUsers.

Getting started with Kraftful is pretty simple—initially, the program prompts you to select a suitable data source. As I?previously noted, it might be as effortless as a text file upload, scraping opinions from?Google Play or App Store, or integrating directly with several major programs?(including, among other options, Zendesk , HubSpot , Salesforce , G2 , or even Twitter ).

Because I don't have a finished product but would like to try out a hypothesis instead, I chose to paste the feedback generated by SyntheticUsers and see what would happen.

In nearly no time, the application provided outcomes grouped in?two categories filled with information that each product owner would greatly appreciate:

• top feature requests
• main complaints

I suspect that this is precisely what most researchers are attempting to uncover as a result of product user research.

Building extra value based on competitive software feedback.

Since I couldn't find any direct?alternatives for ColdSnap at that moment, I decided to choose one of the most successful applications that may be operating similarly to my AI-generated product and see what I could learn from its reviews.

NoWaste was the first app I found during a quick and superficial search.

Then I had to find the application in Kraftful's app?browser?window. It was effortless. Again, I received a list of top feature requests, most loved features, and statistics presenting how relevant each position was for users.

If I had a paid subscription to Kraftful, I would also receive a list of the most common complaints. It was exactly what I was expecting to see. I will not pay that price if I am only experimenting now. But, considering that some of you may be working on an authentic product with a real budget, Kraftful is simply a must-use program that can provide insight into the weaknesses in competitive solutions. And this is what I find really interesting and valuable in building a new product. As you expand on that, your product MVP might be a terrific response to real users' requirements that your competitors can't meet.?