Synopsis: We’ve discussed AI training as one use case for residential proxy networks, but text-based models are only one example of data-based decision making. Another can be found in price comparison. When determining prices, major retail companies regularly monitor the websites of their competitors as a part of their market research. Today we’ll examine what this process looks like for some of the biggest companies in the world, and how Grass can play an integral role in assisting them.

Introduction

As the number of people downloading Grass has taken off, one thing we’ve tried to do is keep users informed about what they’re participating in. Everyone loves the idea of free money, but we also want you to know what’s going on behind the scenes and why it’s important to us to offer this service. This series of articles is aimed at educating everyone on those subjects. To keep the train moving, let’s stay on the topic of who’s using Grass and why.

As you know, some of the buyers on residential proxy networks are AI labs. Last week we looked at how they use web data to train LLMs. But these labs are only a few of the many companies buying bandwidth these days.

The reality is often far less sensational, but it’s no less significant — obviously OpenAI uses big data, but companies like Honda, Motorola, and Walmart do as well. So with that said, let’s take a look at how data is integral to the functioning of nearly every business today — and how it will only become more valuable as time goes on.

Data and Pricing: A Case Study

Pretend you’re a business owner. Imagine you have a food truck, and every day you drive downtown to rk near a construction site. Lunch rolls around and the construction workers shuffle over for a sandwich. Some days you sell 50, some days more. The money comes and goes, but it’s an honest living. They seem to enjoy the turkey club.

One day, you notice something: on Mondays, you consistently sell over 100 sandwiches! After asking around a bit, you discover the true reason why. A beautiful deli has opened just two blocks away, and half of the workers prefer it. Ample seating. Friendly waitresses. Their turkey club won an award in the city newspaper. That part hurt. Here’s the catch — they’re only open Tuesday through Friday, so on Monday the customers are all yours and sales double accordingly.

Now, there are a number of choices you could make in light of this information. Maybe you could change your hours to open earlier than they do. Sell a few before they get started. Maybe you could lower the prices so your sandwiches are cheaper than theirs. In fact — maybe you could even lower your prices on Tuesday through Friday, but raise your prices on Monday.

What you’re doing here is called pricing strategy, and it’s an integral part of what makes a retail business succeed or fail. If you can measure a few key variables, you can determine the perfect price and defeat the competition. Fortunately, there’s another word for variables like this. That word is data.

Big Data, Big Business

Now, changing your prices in this way is a good example of a small business making data-driven decisions, but you may be wondering — why are we talking about this? Business owners have always strategized using the information at hand. Why does that matter to us?

It matters because this isn’t about sandwiches anymore. There’s you and your food truck, and the fancy deli down the street — and then there’s Subway. A business that brings in $769M a year in 104 countries. For companies this large, things have changed over the past decade in very important ways.

Companies like Subway make vast numbers of sales in extremely diverse environments, and sometime in the last ten years, they’ve all developed sufficient data architecture to store and analyze this information. Every month, they keep track of the number of sales they make, changes in what people are ordering, and what people are willing to pay for.

So while you might decide to buy some extra turkey when you notice that people like the club, they are performing complex data analysis and determining that club sales peak at certain times of year, then making inventory decisions based on these forecasts. The perfect amount of turkey at all times, so none goes to waste and profits are maximized.

Price Scraping

So how does Grass fit into this?

Factoring in a competitor’s pricing data — just like our food truck did — is a critical part of any major retailer’s pricing strategy. In your case, you can walk into the deli, look at the menu, and figure out what they’re charging for a sub. But how would a retailer like Subway possibly do this? Subway has 36,900 locations and they’re competing with Arby’s and Jimmy John’s. Not exactly easy to stroll in and check the menu.

The solution, dear anon, is online pricing data. Any company large enough to compete with Subway makes a huge chunk of their sales through online or mobile orders, and these prices are all available on the public web. Go check now — a 6” turkey sub in Boise, ID is $6.49. If you were Arby’s, now you’d know what to charge: $6.48.

The thing is, any company with this many locations will have to set prices depending on local conditions. $6.49 may be a fair price in Boise, but could people afford that at the two Subway locations in Guam? The price will probably be different there, so how could Arby’s figure out what to charge if they opened a new franchise nearby? Why, in order to do that, you’d have to scrape pricing data from the Subway website from as many different locations as possible, to get a comprehensive picture of what Subway is charging in each place.

Starting to sound familiar?

The highest volume use case for residential proxy networks, just like the one we’re building with your help, is price scraping. Every day, companies scrape the prices from their competitors’ websites so they can optimize what they charge themselves.

Simple enough in the case of a sandwich shop, but just think of what this looks like for Walmart. Millions of different products, many of which are sold online only. A menu price can only change so often, but in e-commerce, retailers can scrape a competitor’s prices, drop theirs by mere pennies, and instantly appear higher in aggregators that present the best option to potential customers. If you have ever clicked “Sort by Price: Low to High” before, you already understand why companies would buy bandwidth on Grass to scrape competitors’ prices.

Conclusion

Hopefully this explained another reason why networks like this are critical for business operations today — and why the market will only get bigger as more and more commerce takes place online. As the demand for data continues to balloon over time, it’s only fair that the people supplying bandwidth to these web scrapers should be fairly compensated. That is Grass’s central mission, and the system that you are helping us build.

Authored by Yield Aggregator (for Frogs Anon) & Wynd Network Team