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Facebook plans to track which stores you shop at, report data to advertisers

Privacy advocates and Facebook have been at odds almost since the service made its public debut, and the company’s latest plans to expand its advertising service aren’t likely to play well with anyone who values controlling their own digital foot print.

Facebook has added new measurement and information tools that are designed to make it easier for FB users to find businesses relevant to their interests, according to Adweek, while simultaneously handing those businesses an unprecedented amount of information about the customers that walk through their door.

Here’s how the system works: If you have location services enabled on your phone, Facebook will track which local ads it serves you, as well as your response to those advertisements. If you visit a partner store after seeing an ad for the company’s products, Facebook will know it. This itself isn’t necessarily new; Google debuted a similar service back in 2014 to track whether or not ads drove foot traffic to specific businesses. What does appear to be new, however, is Facebook’s ability to track whether ads result in actual sales. Adweek reports:

Along with measuring foot traffic, Facebook is also adding a way to connect which ads lead to actual sales—at least at the cumulative level—in stores or even over the phone. An Offline Conversions API will allow businesses to match transaction data from a customer database or point-of-sale system with Ads Reporting. The tool will also let businesses gather insights about demographics of the people who make a purchase…

Facebook is also adding a store locator option for local ads, which will allow people to navigate their way to the nearest store from within the ad itself. The feature shows information such as address, hours, phone numbers and estimated travel time.

A demonstration for how the latter function will work is embedded below:

As for Facebook’s visit tracking and data collection, the company doesn’t plan to share individual visitor information with any of its partners. The problem is, such information may be relatively easy to extract, depending on which demographic data the company chooses to share.

For example, if a company knows that an unidentified male between the ages of 25-34 entered the store at 3:45 PM and left at 4:20 PM, Facebook could compare the latter timestamp against cash register logs to see if a male checked out by credit card around 4:17 PM. If someone did, that person’s name can be run through other commercially available databases to determine if they’re a probable match. This type of data mining itself isn’t unusual — it’s the way corporations create detailed behind-the-scenes profiles on their customers.

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