Why AI tools aren’t moving the profit needle and why the real leverage lives between them
For the last two years, the enterprise AI market has followed a familiar pattern. A new tool launches. It promises to automate a specific task—sales emails, support replies, invoice parsing, demand forecasts. Enterprises buy it. Pilots run. Dashboards light up. Usage grows.
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Monetization: Price is right — Monetization strategy (Part III)
Pricing is an iterative process. Best way to get it right is to keep doing qualitative research, quantitative analysis, and roll out A/B testing to confirm your hypothesis.
A/B Testing : How to calculate sample size before launching your test
“How long are we planning to run the test? Do we have a significance yet?”. This is not an unusual situation. In fact all product managers run into this issue.
Metrics: Twyman’s law - Any figure that looks interesting or different is usually wrong
The more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another
Metrics: ‘Satisficing’ Metric — Not all metrics need to be optimized
Discover the ever so tricky science behind the biggest unsolved challenge of Marketing
A/B Testing: What to do when you do not have enough traffic on your site? (Part I)
A/B testing and decision making using statistical tools is as much a science as an art
A/B Testing: What to do when you do not have enough traffic on your site? (Part II)
There are a few others issues that product managers and analytics have to deal with, most important of them all is the opportunity cost of running an A/B test.
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