Why historical data protects you against poor product choices on bol
Lars HurkmansCo-founder20 February 2026Temps de lecture 5 minutesHistorical sales data protects you against poor product choices on bol by adding context: without context, every estimate is a snapshot. With data spanning multiple years you recognise seasonal patterns, distinguish structural growth from one-off spikes, and see how price and competition develop.
You've found a product that looks good. Sales are high, competition is limited, the margin checks out. You order 5,000 euros worth of stock. Three months later it turns out the product only sells well in September and October, and stands still the rest of the year. You're stuck with stock you can't get rid of. You could have seen that, if you'd had historical data that went back far enough.
How do you recognise seasonal patterns in sales data?
By looking at sales data that goes back multiple years. Some products sell all year round, others peak in a specific period and then drop sharply. You only see that difference if your data looks back far enough.
A product like a garden hose sells well from April to August and almost not at all in winter. That's predictable. But many seasonal patterns are less obvious. Computer glasses can peak around the back-to-office period in September. A specific type of storage system sells better in January, when people tidy their homes after the holidays. A wildlife camera peaks during hunting season.
If you only have data from the last three months, and those three months happen to be the peak period, you'll draw the wrong conclusion about the product's annual potential. You don't see the rest of the year, so you overestimate it.
In MarktMentor you view sales data per product spanning multiple years, set per week, month or quarter, so you immediately see whether a product is evergreen or seasonal and how strong the peaks are. From the keyword analysis you see the same pattern reflected in consumer search behaviour: when do people search for this type of product, and how does that differ per year? That distinction between evergreen and seasonal is also where the framework for product research starts.
Growth trend or one-off spike: how do you tell the difference?
You can only make that distinction with historical data that goes back far enough. A product that sold 200 times last month could be a growing product or a product that had a one-off spike. Maybe there was a TikTok video that temporarily made the product popular. Or maybe a competitor was temporarily out of stock, shifting sales to this product.
With enough history you can tell those two apart. If a product has sold more every quarter than the previous one for the past two years, that's a growth trend. If it's been stable for two years and suddenly jumps in one month, that's a spike you need to investigate further before buying stock based on it.
Without that context you make decisions on a snapshot. With that context you make decisions on a pattern.
What does the price development tell you about the competition?
Historical price data shows how the price has developed over a longer period, and that tells you something about the competition. Sale prices on bol change continuously: competitors adjust their prices, new sellers join, others stop. The price you see today isn't necessarily the price you can expect in three months.
Is the average sale price structurally declining? Then there's probably increasing price competition and your margin is shrinking. Is the price stable over multiple years? Then the market is mature and predictable. Does the price fluctuate strongly by season? Then you need to align your buying and pricing with that.
The same applies to the number of sellers. If 5 sellers were active on a product a year ago and now there are 15, that tells you something about how competitive the market is becoming.
Why does the tool you choose now determine what data you have later?
Because historical data about your own performance builds up from the moment you start with a platform. Your position development per keyword and your market share per category can't be added retroactively. This point is often overlooked when choosing a tool.
If in a year's time you want to know how your market share has developed, how your organic positions have shifted, and which products are structurally growing versus standing still, you need to start now. Switching to a different platform means leaving that built-up history behind.
That's not a reason to never switch. But it is a reason to make your first choice deliberately.
When does historical data become unreliable?
When the methodology behind the data changes partway through. Historical data is only valuable if it's based on a consistent method. If the way sales estimates are calculated changes in the meantime, you can no longer reliably compare the data from before and after that change.
An example. You look at a product and see that it sold 120 times in March last year and 80 times this year. It looks like the product has become less popular. But if the tool changed methods in the meantime, the difference could just as easily be the result of a different measurement method. You don't know, and you can't find out.
This is a concrete thing to pay attention to when choosing a product research tool. Don't just ask how much historical data is available, but also how long the current method has been in use. Data collected over a longer period with the same method is more reliable than more data based on changing methods.
MarktMentor has worked with the same data method for years. Today's data is collected on the same basis as data from years ago. That consistency makes it possible to make comparisons over a longer period with confidence in the outcome.
Why is historical data the foundation for every product choice?
Because every serious product choice comes down to the same question: is what I see now representative of what I can expect? Historical data is therefore one of the most underrated factors in a product choice on bol, and not a luxury for advanced sellers. It's the foundation under market research, whether you're researching a product for the first time or evaluating your existing range.
You can only answer that question with data that goes back far enough, is collected with a consistent method, and lets you recognise patterns you'd miss in a snapshot.
Summary: what historical data points you to
- Seasonal patterns. Only visible with data spanning multiple years. Three peak months can mask that the product stands still the rest of the year.
- Growth or spike. Structural growth is a line that keeps rising over quarters. A stray spike stands out against a stable base and requires further investigation before you buy.
- Price development. A structurally declining average price points to increasing price competition and a shrinking margin; a stable price to a mature market.
- Sellers. A growing number of sellers on a product shows how competitive the market is becoming.
- Consistent method. Data is only comparable if the measurement method hasn't changed partway through. Ask how long the current method has been running.
- Your own history. Data about your own performance builds up from day one and can't be added retroactively, so make your first platform choice deliberately.