How We Do Our Research at Product Scanner
Buying the right product shouldn't feel like a full-time job.
When we were trying to buy products ourselves, we found the process frustrating: too many tabs, conflicting opinions, endless reviews, technical specifications we didn't understand, and plenty of marketing disguised as advice.
So we built Product Scanner to solve that problem.
Our goal is simple: do the research for you, make sense of what real customers are saying, and help you make a better decision.
Our Research Framework
We follow a structured process to turn thousands of data points into simple, useful recommendations.
1. We define the product and what actually matters
Before looking at products, we identify the key questions a buyer should be asking.
For an air conditioner, that could mean cooling capacity, room size, ISEER, electricity consumption, noise, service and long-term reliability.
For an air fryer, it could mean usable capacity, cooking performance, temperature range, cleaning and ease of use.
We start with the buying problem, not the product.
2. We collect information from multiple sources
No single platform tells the whole story.
We bring together information from:
- Amazon and Flipkart customer reviews
- Reddit discussions
- YouTube reviews and testing
- Expert publications
- Brand websites and specifications
- Pricing and availability data
- Other relevant consumer discussions
Where possible, we use automated tools and structured data collection to process information at scale.
3. We clean and organise the data
Online reviews contain noise.
There are duplicates, irrelevant comments, incomplete information, promotional content and opinions that don't tell us much about the actual product.
We organise the information into meaningful areas such as:
Performance · Build quality · Reliability · Ease of use · Features · Common complaints · Value for money · After-sales experience
This allows us to compare products on the things that actually matter.
4. We don't treat every piece of information equally
This is one of the most important parts of our methodology.
Recent information matters.
A customer review from last month can be more relevant than one from three years ago, particularly when a product has received hardware, software or manufacturing updates.
Source quality matters too.
We consider factors such as whether the reviewer appears to be an actual customer, how detailed the experience is, whether the same observation appears across multiple sources, and how relevant the information is to the product being evaluated.
We use weighting and recency adjustments rather than treating every review as an identical data point.
5. We focus on actual customer experiences
We don't just count how many people said something positive or negative.
We try to understand what actually happened.
For example:
"Great product!"
isn't nearly as useful as:
"I've been using it for eight months and it still performs well, but replacement filters are expensive."
The second gives us something we can actually learn from.
We look for detailed experiences around:
- Real-world performance
- Reliability and durability
- Ease of use
- Recurring problems
- Customer service
- Maintenance
- Long-term ownership
6. We balance extreme opinions
Online reviews can be heavily skewed.
Some customers are extremely happy. Others leave a one-star review after a particularly bad experience.
Both are useful, but neither should automatically define the product.
We therefore look for patterns across a wider set of experiences and reduce the influence of isolated extreme opinions.
The question isn't:
"Did someone love it?"
or
"Did someone hate it?"
It's:
"What does the broader body of evidence suggest?"
7. We find the patterns
Once the information is structured and weighted, we look for recurring themes.
What do customers consistently praise?
What problems keep appearing?
Which features actually make a difference?
Are there trade-offs?
Is a product expensive because it performs better, or simply because it has more features?
This is where thousands of individual data points start becoming useful.
From Research to Recommendation
We then compare products across the factors that matter for that particular category.
Depending on the product, this could include:
Performance
Reliability
Features
Ease of use
Running costs
Price
Customer experience
Value for money
We don't believe there should always be one universal "best product."
Instead, we try to answer the questions buyers actually have:
What's the best overall?
The strongest balance of performance, price and ownership experience.
What's the best value?
The product that gives you the most for your money.
Who should buy it?
The type of buyer or use case where the product makes the most sense.
Who should skip it?
Where its limitations outweigh its strengths.
What's the catch?
The drawbacks you should know before spending your money.
Why Our Approach Is Different
A lot of product content starts with a handful of products and works backwards to justify a recommendation.
We start with the research.
We don't want to tell you what to buy because a brand has a great marketing campaign, because a product has the most features, or because one reviewer loved it.
We want to understand what the evidence says.
We combine product specifications with real customer experiences, expert analysis and discussions across the internet to create a more complete picture.
We don't just collect information.
We filter it.
We weigh it.
We find the patterns.
We turn it into a decision.
Our promise
Product Scanner exists to make buying products less overwhelming.
Less noise. More clarity. Better decisions.