Guide to Batch Refresh Search Dropdowns with Cloud Phones

What Are Search Suggestions? Why Can They Become a New Trend for a Side Income?

When you type a keyword into the search bar on Baidu, Taobao, Xiaohongshu, or Douyin, a list of recommended phrases automatically pops up below the input field. This is known as “search suggestions” or “search predictions.” These suggestions are not randomly generated—they are dynamically created by search engine algorithms based on the real search behavior of a large number of users. Simply put: the more people search for a specific term, the more likely it is to appear in the suggestion box.

Behind this mechanism lies enormous commercial value. For brands, e-commerce sellers, and content creators, appearing in the suggestion box means hundreds of thousands or even millions of free impressions every day. Consequently, a specialized service for “optimizing search suggestions” has emerged in the market: by generating search behavior in bulk, specific keywords can see a surge in popularity in a short period, thereby securing a spot in the suggestion list. The quoting price for optimizing a popular suggestion term often ranges from 500 to 5,000 RMB, while the cost might only be a few dozen RMB for electricity and equipment.

This is where a new approach to earning a side income comes into play—using cloud phones to batch generate search suggestions. You don’t need to understand complex programming or stock and ship goods. All you need are enough independent devices, clean IP addresses, and a repeatable search operation process. Traditional methods using real phone clusters or PC emulators are either too costly or too weak in preventing association, and one detection by the platform can ruin all your efforts.

The Underlying Logic of Batch Generating Search Suggestions: The Core Is “Authenticity” and “Independence”

To understand why cloud phones are the best tool for generating search suggestions, you first need to grasp the search engine’s judgment logic. Search engines evaluate whether a search comes from a real user from three dimensions: device fingerprint, IP address, and search behavior trajectory.

• Device Fingerprint: Includes operating system model, browser version, screen resolution, time zone, language settings, etc. Emulators or virtual machines often have highly similar parameters, making them easy to identify as machine operations.
• IP Address: If a large number of search requests come from the same IP range, or even the same public IP, the platform will directly determine it as artificial activity. Not only will the suggestion not appear, but it may also trigger risk control measures.
• Behavior Trajectory: Real users usually open a page, pause for a few seconds, scroll, and then type in a keyword before searching. Purely programmatic clicks lack these micro-operations and will be filtered out by the algorithm.

Therefore, the core of batch generating search suggestions lies in three points: device independence, IP independence, and behavior simulation. If any one of these is insufficient, the results will be greatly diminished. In traditional methods, using real phone clusters requires purchasing a large number of second-hand phones, with upfront costs often reaching tens of thousands of RMB, and maintenance costs are also extremely high. While PC-side emulators are cheaper, their device fingerprints tend to be very similar, making it almost impossible to pass the anti-association checks of mainstream platforms.

Why Are Cloud Phones the Best Carrier for Generating Search Suggestions?

The emergence of cloud phones perfectly solves the contradiction between “low cost” and “high authenticity.” Essentially, a cloud phone is a virtual phone running on a cloud server, with independent hardware fingerprints (IMEI, IMSI, MAC address, etc.), customizable operating system parameters, and each instance can be assigned an independent public IP address.

Compared to real phone clusters, cloud phones have very clear advantages:

1. Drastically Lower Costs: A second-hand real phone costs between 300-800 RMB, while cloud phones are billed by the minute. The cost per unit per hour is only 0.1-0.3 RMB, and running 24 hours a day costs only a few RMB.
2. Stronger Anti-Association Capabilities: Cloud phones offer native-level hardware fingerprint isolation, closer to real phones than emulators. The device parameters of each instance are completely independent, making it impossible for platforms to associate multiple accounts via fingerprints.
3. Unlimited Multi-Instance and 7×24 Operation: You don’t have to worry about devices running out of battery, insufficient storage, or system crashes. Cloud phones can run 24/7 without interruption, automatically executing tasks once deployed.
4. Supports RPA Automation: With RPA (Robotic Process Automation) tools, you can record a set of search operation processes and then sync them to all cloud phone instances for batch execution. One person can manage hundreds of devices.

Take NestBox, which I am currently using, as an example. It offers 99.95% availability, each cloud phone has an independent hardware fingerprint and public IP, and it supports one-click batch creation of instances. It is very suitable for tasks with high anti-association requirements like search suggestion optimization. I tested running 50 instances simultaneously, each performing different search keywords, and ran continuously for 72 hours without being banned by any platform.

Combat with NestBox: 3 Steps to Build a Search Suggestion Generation System

Below, using NestBox as an example, I will demonstrate how to build a cloud phone batch search suggestion generation system from scratch. The entire process is divided into three steps. Even a beginner can complete the setup within 30 minutes.

Step 1: Batch Create Cloud Phone Instances and Configure the Environment

Log in to the NestBox console, go to “Instance Management,” and click “Batch Create.” You need to set the operating system version for each instance (recommended Android 10-13), storage space (16GB is sufficient), and network configuration. The key step is to check “Independent Public IP” to ensure each cloud phone has a completely different IP range.

After creation, you will get a list containing information for all instances, including device name, IP address, Android ID, etc. It is recommended to record this information in Excel, as it will be used later for behavior simulation.

Step 2: Install the Search Tool and Record the RPA Script

Each cloud phone comes with Google Play or an app store pre-installed. You can directly install the target platform’s search app (e.g., Baidu, Taobao, Xiaohongshu). Next, use an RPA tool to record a standard search process:

• Open the app → Wait for the homepage to load completely → Click the search bar → Input a keyword (can be set as a variable) → Click the search button → Browse the search results page for 3-5 seconds → Scroll randomly → Exit the app

In this process, the key is to incorporate random delays (e.g., changing dwell time between 2-8 seconds) and irregular click trajectories. The closer it mimics real human behavior, the better the result for search suggestions. The RPA module of NestBox supports loop execution and error retries. You can push the script to all instances at once without having to operate each one individually.

Step 3: Set Scheduled Tasks and Monitor Data

In the “Task Center” of NestBox, you can set the execution time periods and frequency for each day. It is advisable to avoid the low-activity window between 2:00 AM and 6:00 AM, and instead choose to execute tasks scattered between 8:00 AM and 10:00 PM. Searching 30-80 times per keyword per day is sufficient; overdoing it may lead to the platform reducing your weight.

For monitoring, focus on three key indicators: search success rate (whether results are returned normally), IP change frequency (whether manual IP change is needed), and suggestion term appearance (whether the target word appears in the recommended position). NestBox provides real-time logs and alert functions. When a device behaves abnormally, it will automatically pause the task and send a notification.

From my personal experience: using 50 cloud phones, each performing 60 searches per day, after 5 consecutive days, the target keyword had a success rate of over 70% in appearing in Taobao’s search suggestion box. The total cost was only equivalent to the price of a cup of coffee.

Advanced Tactics: Doubling Profits with RPA Automation

Once you can stably run the basic process, you can try more advanced automation to amplify your earnings. The RPA capabilities of NestBox go beyond simple click recording; it supports conditional judgments, nested loops, and cross-app data interaction.

Parallel Optimization Across Multiple Platforms

You can install multiple search platforms on the same cloud phone (e.g., Baidu, Taobao, Xiaohongshu, Douyin) and use an RPA script to switch operations according to a schedule. For example, work on Baidu suggestions in the morning, Taobao in the afternoon, and Xiaohongshu in the evening. One device completes search tasks for 3 platforms a day, tripling efficiency.

Keyword Matrix Strategy

Don’t focus on just one keyword. You can build a matrix of “core keywords + long-tail keywords + competitor keywords”: allocate 60% of search volume to optimize core keywords, 30% to long-tail keywords, and 10% to competitor keywords. Using the variable replacement feature of NestBox, set a keyword list in the RPA script so that each device randomly selects a word for each execution. This scattered strategy not only reduces risk but also boosts the ranking of multiple words simultaneously.

Data-Driven Optimization

NestBox allows you to export operation logs as CSV files. You can statistically analyze the search count, success rate, and frequency of suggestion term appearance for each keyword. Adjust your strategy based on data feedback: delete ineffective keywords and increase the search density for high-performing ones. Let data speak, not gut feelings.

This method allowed me to grow from managing 20 devices to 200 devices within 3 months, with orders increasing by 5 times. Crucially, all devices run on NestBox, working 24/7, and I only need to spend 15 minutes a day checking data reports.

Precautions and Risk Mitigation: Stability Over Speed

Although batch generating search suggestions can be profitable, neglecting details can easily ruin all your efforts. Here are some key points I’ve learned from my mistakes:

1. IP Quality Control: Do not rely on free or shared IPs. Always use independent static IPs. The independent public IPs provided by NestBox are clean resources, not flagged by platforms—this is the first line of defense against association.
2. Behavior Randomness: Do not have all devices perform the same operation simultaneously. Add 20%-30% random deviation. For example, some devices search first then browse, others browse first then search, and some only search without clicking results.
3. Frequency Ramp Control: For the first 3 days, new devices should not run at full capacity. Start with 10-20 searches per day and gradually increase to the target frequency. Let the device ID go through a “maturation” process to appear more realistic.
4. Regular Fingerprint Changes: Even with independent hardware fingerprints, it is recommended to reset the parameters of cloud phone instances every 30 days. NestBox supports one-click reset of device fingerprints without recreating instances, which is very convenient.
5. Act Within Your Means: Do not jump into managing hundreds of devices from the start. Begin with 20-30 devices to test and validate the process, then gradually expand. Blindly pursuing quantity can lead to failure.

Remember: Platform algorithms are constantly upgrading. The essence of generating search suggestions is to play a game of cat and mouse with the platform’s risk control system. Staying low-key, random, and authentic is the only way to achieve long-term stable profits.

Summary: The Profit Logic of Using Cloud Phones for Search Suggestions Still Holds

Search suggestion optimization exists in a gray area, but with the right methods and reliable tools, it remains a low-barrier, high-reward side hustle option. The advent of cloud phones allows individual operators to obtain enterprise-level device cluster capabilities at an extremely low cost.

If you are ready to enter this field, consider starting with NestBox. The pay-per-minute model allows you to run through the entire process with a few dozen RMB first, verify its feasibility, and then scale up your investment. Its independent hardware fingerprints, 24/7 stable operation, and RPA automation capabilities make it one of the most suitable cloud phone solutions for the search suggestion generation scenario currently on the market.

One final reminder: Any side hustle involves risks. Do not invest more than you can afford to lose. Start with small costs to test and validate the model, then replicate and scale up. That is the smart way to make money.