#geolocation data

[odinsblog]

“Smart Cars” + Capitalism = a very dystopian future

Ford’s patent ranges from having a “self driving” car return itself to the dealership if you miss a payment, to having the car abruptly disable itself, to “minor inconveniences” like having the air conditioner or heater stop working until you voluntarily return the car.

Don’t think for one second that all carmakers aren’t thinking about doing the same things. Especially Tesla Motors.

Carmakers are already trying to monetize even the most basic features, like charging monthly fees for the ability to use your car’s seat warmers.

Anyway, nobody does more to radicalize people against capitalism more than greedy capitalists.

[odinsblog]

How automakers make use of data collected by connected cars is coming under scrutiny in California. On Monday, the California Privacy Protection Agency announced that it will review the data privacy practices of connected vehicle manufacturers. The agency is empowered to do so thanks to a 2018 state law, the California Consumer Privacy Act.

"Modern vehicles are effectively connected computers on wheels. They're able to collect a wealth of information via built-in apps, sensors, and cameras, which can monitor people both inside and near the vehicle," said Ashkan Soltani, CPPA's executive director.

"Our Enforcement Division is making inquiries into the connected vehicle space to understand how these companies are complying with California law when they collect and use consumers' data," he said in a statement.

Connected cars are fast becoming ubiquitous—it may well be impossible to buy a new car, truck, or SUV in 2023 that doesn't have at least one embedded modem in it. In the mid-2010s, many OEMs saw dollar signs at the prospect of monetizing data collected by their deployed vehicle fleets, and unlike with cellphones, it can be hard or impossible to disable location tracking in one's car.

"Under the California Consumer Privacy Act, geolocation is considered personal information. People have the right to say no to being tracked in their cars, but it is unclear if car companies are providing this right," said Justin Kloczko, a privacy advocate at Consumer Watchdog. "These companies know more about us than we know about ourselves, and they’re the ones in control of our personal information, not us," Kloczko said.

(continue reading)

[retailgatorsarticle]

Dynamic Pricing in Retail: How Data Scraping Drives Revenue Growth

Learn how dynamic pricing in retail drives revenue growth. Discover how data scraping helps monitor competitors, optimize pricing, and enhance product performance in real-time.

In the fast-paced world of retail, staying ahead of the competition is no easy feat. Pricing is one of the most crucial aspects that can determine a brand’s success. But here's the thing: static pricing is a thing of the past. Dynamic pricing—a strategy where prices fluctuate in real-time based on various factors—is rapidly becoming the norm. The question is: how can retailers effectively implement dynamic pricing? The answer lies in data scraping.

What is Dynamic Pricing?

Dynamic pricing is the practice of adjusting prices for products or services based on real-time supply and demand, competitor pricing, and other market conditions. Unlike traditional pricing models where the price of a product remains fixed, dynamic pricing allows retailers to maximize profits by responding to market changes instantly.

For example, an online retailer might adjust its prices based on:

Competitor pricing

Stock levels and availability

Customer behavior and preferences

Seasonal trends

External market factors (like inflation or demand spikes)

This approach enables retailers to offer competitive prices that attract customers, while also protecting their margins.

The Role of Data Scraping in Dynamic Pricing

Here’s where data scraping comes into play. For dynamic pricing to work effectively, retailers need accurate, up-to-date data. This is where scraping tools come in. By extracting large volumes of data from competitor websites, marketplaces, and various online sources, data scraping helps businesses monitor price fluctuations, track product trends, and stay ahead of the competition.

Data scraping enables retailers to:

Track competitor pricing: Scraping competitors’ websites provides valuable insights into how competitors are pricing similar products. Retailers can use this data to adjust their prices, ensuring they stay competitive.

Monitor product availability: Scraping helps track stock levels and availability in real time, so retailers can adjust prices to create urgency, offering discounts on slow-moving products or premium prices on high-demand items.

Understand market trends: By scraping data from a variety of sources, retailers can identify emerging market trends, customer preferences, and seasonal fluctuations. This allows for more accurate forecasting and pricing adjustments.

Analyze customer behavior: Data scraping also provides insights into consumer sentiment and shopping behavior. Retailers can adjust their prices based on how much demand there is for certain products or categories.

How Dynamic Pricing Drives Revenue Growth

Optimizing Margins With dynamic pricing, retailers can optimize their margins by adjusting prices according to demand. For instance, when demand is high, they can increase the price to capitalize on that, while lowering prices when demand slows down. This flexibility helps retailers stay competitive while protecting their profits.

Better Customer Experience Dynamic pricing helps retailers personalize their pricing for different customer segments. For example, loyal customers or those who browse frequently might receive discounts, while new visitors may see prices that reflect current demand. This personalization enhances the customer experience, making them feel valued, and can increase the likelihood of a purchase.

Real-Time Adjustments Dynamic pricing is all about real-time data. As soon as prices or stock levels change, the retailer can respond. Whether it’s a flash sale, a sudden drop in competitor pricing, or increased demand for a popular product, the ability to make pricing adjustments on the fly can be a significant competitive advantage.

Improved Inventory Management Through dynamic pricing, retailers can manage their inventory more effectively. For instance, if certain products are nearing the end of their shelf life or going out of season, prices can be adjusted to quickly move stock. Similarly, if there is a sudden spike in demand for a product, prices can be adjusted accordingly to maintain stock levels and maximize revenue.

Data-Driven Decisions One of the most significant advantages of dynamic pricing is that it’s entirely data-driven. Retailers no longer have to rely on guesswork when it comes to setting prices. Data scraping provides them with real-time insights into pricing strategies that are working and those that aren’t. With these insights, retailers can make better, more informed decisions about pricing, promotions, and discounts.

The Future of Dynamic Pricing with Data Scraping

As the retail landscape continues to evolve, dynamic pricing will become even more sophisticated. The rise of AI and machine learning will allow retailers to incorporate more data sources and make even smarter pricing decisions. Integrating predictive analytics with data scraping will allow retailers to anticipate market shifts and adjust their pricing strategies ahead of time.

Retailers will also increasingly use geolocation data, adapting their pricing based on the customer’s location, and customer preferences, offering hyper-personalized pricing strategies to further enhance sales.

Conclusion

In today’s competitive retail environment, dynamic pricing is no longer a luxury—it’s a necessity. The ability to adjust prices in real-time allows businesses to stay flexible, improve profitability, and enhance the customer experience. By leveraging data scraping, retailers can access valuable insights, monitor competitors, track market trends, and make data-driven decisions that fuel revenue growth.

Retailers who embrace dynamic pricing powered by data scraping will be better equipped to handle market fluctuations, customer demands, and competition. In a world where agility is key to survival, dynamic pricing can be the difference between thriving and falling behind.

[airsageofficial]

What is Spatial Data

If you are looking for more information on Spatial data and how it can help your business, look no further than AirSage.

[airsageofficial]

Location Intelligence 101

Companies can use location intelligence to make better decisions and increase ROI in the future. AirSage is your one-stop destination.

[airsageofficial]

Big Geolocation Data

Geolocation data provides information on audience and user segmentation and targeting, insight that can lead to improved strategy, & more.

[electronicprivacy-blog]

Mike Ward for the Austin American Statesman, 3/30/13: 

That has led to questions about whether the technology violates the constitutional right to privacy or whether it is simply a high-tech shortcut to solve crimes.

A large group of Texas legislators wants to ban the practice unless police obtain a court order, and the issue is pitting tea party activists and others who oppose intrusive Big Brother government against the police, who say it is a boon to solving crimes. The House bill alone has nearly 100 co-authors in the 150-member chamber.

...

“You’re going to completely gut law enforcement’s ability to build a criminal prosecution using electronic evidence,” warned Houston Police Department investigator Jimmy Taylor, echoing the sentiments of dozens of other authorities who testified against the bills at two public hearings last week.

Added Brian Tabor, with the Dallas Police Department: “We have a problem with raising the bar from reasonable suspicion (that does not require a warrant) to probable cause (that does). It takes away a valuable tool.”

...

“It started with the iPhone, and phone companies started putting in more and more towers to keep up with the demand for service — and as towers were added, the coverage areas became smaller and smaller,” said Christopher Soghoian, an industry expert and chief technologist for the American Civil Liberties Union. “As the coverage areas got smaller, it allows you to more accurately locate that signal.”

And that is where the rub between the right to privacy and police investigations conflict, said Scott McCullough, an Austin lawyer and former assistant attorney general who now represents phone providers in their dealings with police requests for cell location data.

“I’m here for privacy, about the people whose location information is requested and they never know about it,” he told the House committee, cautioning that current law provides no uniform standard on whether a warrant is required. “We don’t know what they (police) do with (the information), and we never know how they’ve used it.”

...

“The police can still get the information; they should just do it with a warrant — just like they do to get other information for investigations,” Hinojosa told the Senate Criminal Justice Committee.

...

[electronicprivacy-blog]

Security Ledger, 3/26/13:

(A) Shows the trace of an anonymized mobile phone user during a day. The dots represent the times and locations where the user made or received a call. (B) Shows the same user’s trace as recorded in a mobility database. (C) Shows the same individual’s trace when researchers lowered the resolution of their dataset through spatial and temporal aggregation.

Mobile phone use may be a more accurate identifier of individuals than even their own fingerprints, according to research published on the web site of the scientific journal Nature. 

Scientists at MIT and the Université catholique de Louvain in Belgium analyzed 15 months of mobility data for 1.5 million individuals who the same mobile carrier. Their analysis, “Unique in the Crowd: the privacy bounds of human mobility” showed that data from just four, randomly chosen “spatio-temporal points” (for example, mobile device pings to carrier antennas) was enough to uniquely identify 95% of the individuals, based on their pattern of movement. 

...

“We show that the uniqueness of human mobility traces is high, thereby emphasizing the importance of the idiosyncrasy of human movements for individual privacy,” the researchers write. “Indeed, this uniqueness means that little outside information is needed to re-identify the trace of a targeted individual even in a sparse, large-scale, and coarse mobility dataset. Given the amount of information that can be inferred from mobility data, as well as the potentially large number of simply anonymized mobility datasets available, this is a growing concern.”

The privacy of mobile data is an increasing concern for privacy advocates and for lawmakers.

Two bills introduced last week in the House and Senate would require law enforcement to obtain a warrant before affixing a GPS device to a vehicle or collecting mobile geolocation data from third party service providers, Wired reported.  And, in December, the U.S. Federal Trade Commission announced new guidelines for implementing the Children’s Online Privacy Protection Act (COPPA). Among other things, the changes expand the list of information that cannot be collected from children without parental consent to include photographs, videos and audio recordings of children and geo-location information.

“Unless you get parental consent, you may not track children and use their information to build massive profiles of online behavior,” said FTC Chairman Leibowitz.

The researchers who conducted the work on human mobility say that their work should further inform such legislation. ”These results should inform future thinking in the collection, use, and protection of mobility data. Going forward, the importance of location data will only increase and knowing the bounds of individual’s privacy will be crucial in the design of both future policies and information technologies.”