Airlines, hotels, marketplaces, and food delivery services increasingly show different prices depending on the device used by the visitor. The problem is that checking this difference "manually" is almost impossible — your own browser has already accumulated cookies, search history, and fingerprints that distort the result. In this article, we will discuss how to organize a clean comparison of prices on mobile and desktop versions of websites without the influence of your digital footprint.
What is device-based pricing and why is it important
Device-based pricing is the practice where a website changes the price of a product or service based on the type of device from which the visitor arrives. Most often, the difference concerns mobile and desktop versions: the algorithm analyzes the user-agent, screen resolution, operating system, and based on this data selects the price, discount, or even the availability of the product.
For marketers and sellers, this is not an abstract theory. If a competitor shows a price on mobile that is 8-12% higher than on desktop, it directly affects your pricing strategy, advertising offers in Facebook Ads and Google Ads, as well as how you configure landing pages for different types of traffic. For arbitrageurs, it is important to understand what price a user will see when clicking on mobile ads — if it is inflated, the conversion to purchase drops, and the cost per lead increases unnecessarily.
Sellers on Wildberries, Ozon, and Avito face a similar challenge when monitoring competitor prices: the app and mobile site sometimes show a product cheaper than the desktop version due to "app-only" promotions. Without a correct comparison, it is easy to make incorrect decisions regarding your own pricing.
Why do websites show different prices on mobile and desktop
There are several reasons, and they are rarely publicly disclosed. First, dynamic pricing algorithms take conversion statistics into account: if it is known that mobile users are less likely to compare prices across multiple tabs simultaneously, they may be shown a slightly higher price — behavioral inertia works in favor of the seller.
Secondly, some platforms focus on geography and the internet service provider. Mobile traffic often goes through cellular operators, and the IP address of the mobile network is perceived differently by the system than the IP of a home provider or data center. This is especially noticeable with airlines and hotel aggregators, where the ticket price can differ by 5-15% depending on the type of network.
Thirdly, the design of promotional campaigns plays a role: mobile applications often receive exclusive discounts to keep users within the app ecosystem rather than in the browser. This is not exactly "hidden" device-based pricing, but it looks similar externally.
Finally, some differences are not directly related to the device but to the history of interactions: if you have already visited a hotel website five times from one IP and device, the algorithm may decide that you are a "hot" client ready to pay more, or conversely — offer a discount to encourage conversion. This is where the problem of your own footprint begins, which we will discuss below.
Which platforms most often change prices by device
The practice of device-based pricing occurs most frequently in several niches. Let's break them down in a table for easier navigation when planning monitoring.
| Niche | Typical Difference | Main Reason |
|---|---|---|
| Airline tickets and hotels | 5-15% | Analysis of network type and search history |
| Marketplaces (Wildberries, Ozon) | 3-10% | App exclusives, promotions for mobile |
| Food delivery services | up to 20% on delivery | Dynamic pricing based on demand and device |
| SaaS subscriptions | rare, but occurs | A/B testing of pricing plans |
| Avito and classifieds | varies | Paid promotion, display in the app |
For marketers testing ads in different regions through Google Ads or Yandex.Direct, it is important to consider that the price on the landing page may differ not only by device but also by the geolocation of the IP address — these two factors often work together.
Why your personal footprint hinders fair comparison
The main problem with manual price comparison is your own browser. It stores cookies, local storage, visit history, and device fingerprints (screen resolution, font list, time zone, graphics card version). If you first opened the site on a desktop and then, five minutes later, accessed it from your phone under the same Wi-Fi and the same Google account, the system can easily link both visits into one profile — and show a price adjusted to your previous activity, rather than the "clean" price for a new visitor of that device type.
The same problem arises with repeated checks: if you visit a hotel website every day from one IP, the algorithm may decide that you are hesitating and start gradually increasing the price, pushing you towards a quick decision. In the end, you are comparing not "mobile price vs desktop price," but "price for a regular visitor vs price for a new one" — and making incorrect conclusions.
To obtain objective data, you need to simulate two independent users — one from a mobile device, the other from a desktop — who visit the site for the first time, from different IP addresses, without shared cookie history and without overlapping fingerprints. This is what "comparison without your own footprint" means.
How to prepare a clean environment: anti-detect browsers
The simplest way to create independent digital profiles is to use anti-detect browsers. These tools allow you to create separate isolated profiles, each with its own set of cookies, fingerprints, time zones, and user-agents, not overlapping with other profiles on the same computer.
For the task of price comparison, profiles of two types will suffice: one with an emulated mobile user-agent and mobile screen resolution, and the other with a standard desktop set of parameters. Here’s how to set this up in popular tools:
- Dolphin Anty — when creating a new profile, select "Mobile" in the platform parameter, set the screen resolution to that of a smartphone (e.g., 390×844), and choose an appropriate user-agent from the built-in database.
- AdsPower — in the profile settings, there is a device type switch (Desktop / Mobile / Tablet) that automatically selects fingerprint parameters for the chosen type.
- Multilogin — allows you to create a custom fingerprint from scratch by manually specifying the user-agent, WebGL renderer, and touch event parameters typical for mobile devices.
- GoLogin and Octo Browser — both support ready-made templates for mobile profiles with emulation of touch input and accelerometer.
After creating two profiles — one mobile and one desktop — each should operate through its own IP address; otherwise, the site will still link visits by network address, even if the fingerprints differ.
The role of proxies: mobile, residential, or data center
The IP address is the second most important parameter after the device fingerprint. It should align with the logic of the scenario: if you are emulating a mobile user, the IP should also appear "mobile" — that is, belong to a range of cellular operators, not a data center. This significantly increases the reliability of the test because many dynamic pricing algorithms cross-check the type of network with the declared user-agent.
| Proxy Type | When to Use | Risk of Detection |
|---|---|---|
| Mobile Proxies | Emulating a mobile visitor, checking apps and mobile versions of websites | Minimal |
| Residential Proxies | Emulating a desktop visitor from a specific city or country | Low |
| Data Center Proxies | Mass fast monitoring without tying to network type | Above average for sensitive sites |
In practice, the optimal setup for the task "mobile price vs desktop price" looks like this: the mobile profile in the anti-detect browser operates through mobile proxies with IP rotation within the region, while the desktop profile uses residential proxies from the same region, so that geolocation does not distort the comparison result. This allows isolating the device factor specifically, rather than random differences due to different locations.
Step-by-step price comparison guide
Below is a working algorithm that can be repeated for any website suspected of device-based pricing.
- Create two isolated profiles in the anti-detect browser: one with a mobile fingerprint (user-agent, resolution, touch events), and the other with a desktop fingerprint.
- Connect different IP addresses to each profile: a mobile proxy for the first, and a residential proxy from the same region for the second.
- Clear cookies and localStorage before each new test to exclude the influence of previous visits on the result.
- Access the target website in both profiles at approximately the same time — prices on dynamic platforms (airlines, hotels, delivery) can change within an hour.
- Record the price, discount, and availability of the product in both cases — it’s best to take a screenshot with visible time and exchange rate if it concerns currency.
- Repeat the test 2-3 times at different times of the day and on different days of the week to exclude random fluctuations and obtain statistically significant differences.
- Compare the results and record a stable percentage difference — this will show the real pricing pattern by device.
This approach takes a bit more time than simply opening the site in two tabs, but it provides an honest result, undistorted by your own visit history.
Monitoring automation for e-commerce
If the task is not a one-time check but regular monitoring of competitor prices on Wildberries, Ozon, or Avito, manually repeating the steps from the previous section quickly becomes ineffective. There are ready-made parsers and price monitoring services available that do not require coding — just specify a list of product URLs and set up a schedule for checks.
Such services usually allow you to set separate request profiles for mobile and desktop user-agents, as well as connect proxy rotation so that each request appears as a new unique visitor. For parsing large product catalogs, data center proxies are often used due to speed and low cost for processing large volumes of requests, while for precise checking of "live" prices in the app — mobile or residential proxies that mimic a real buyer.
SMM specialists and marketers also apply a similar approach when testing advertising landing pages: before launching a campaign in Facebook Ads or TikTok Ads, it is useful to check whether mobile users see a different price on the landing page than desktop users — this directly affects the decision on creative selection and targeting device.
Common mistakes when comparing prices
Even with an anti-detect browser and proxies, it is easy to make mistakes that can nullify the results of the check. Let's discuss the most common ones.
- The same IP for both profiles. Even if the fingerprints differ, a matching IP address almost always links visits into one profile on the website's side.
- Geolocation mismatch. If the mobile profile operates through a proxy from one city, while the desktop one uses an IP from another country, the price difference may be explained not by the device but by geography.
- Checking at different times without considering dynamics. Prices for airline tickets and hotels change every few hours — comparing "desktop in the morning vs mobile in the evening" will yield incorrect results.
- Using a logged-in account. If you log in with your personal account on both devices, personalization based on purchase history will completely distort the comparison.
- Ignoring currency and locale. On international platforms, the price may be displayed in different currencies by default depending on the region determination — this is not always related to the type of device.
Avoiding these mistakes is aided by the combination of isolated profiles in an anti-detect browser with properly selected proxies for each scenario — mobile and desktop.
Conclusion
The price difference for mobile and desktop users is a real practice faced by marketers, marketplace sellers, and arbitrageurs when testing ads. To see it objectively, it is not enough to simply open the site on a phone and a computer — your own footprint from cookies, search history, and tying to one IP address will distort the result. The solution is to create two completely isolated profiles in an anti-detect browser, connect each to a separate IP of the required type, and repeat the check several times at different times.
If you are testing mobile versions of websites or applications, for reliable emulation, mobile proxies are better suited — they correspond to real IP ranges of cellular operators. For the desktop scenario and checking prices in a specific region, residential proxies work more effectively, while for mass automated monitoring of product catalogs on marketplaces, it is worth considering fast and affordable data center proxies. This comprehensive approach provides clean data on the real price difference without the influence of your own digital footprint.