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If data is labor, can collective bargaining limit big tech?

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There are plenty of reasons to doubt that the House Judiciary Committee’s antitrust report will mark a turning point in the digital economy. In the end, it lacked true bipartisan support. Yet we can still marvel at the extent of left-right agreement over its central finding: The big tech companies wield troublingly great power over American society.

The bigger worry is whether the solutions on the table cut to the heart of the problem. One wonders whether empowered antitrust agencies can solve the problem before them — and whether they can keep the public behind them. For the proposition that many Facebooks would be better than one simply doesn’t resonate.

There are good reasons why not. Despite all their harms, we know that whatever benefits these platforms provide are largely a result of their titanic scale. We are as uneasy with the platforms’ exercises of their vast power over suppliers and users, as we are with their forbearance; yet it is precisely because of their enormous scale that we use their services. So if regulators broke up the networks, consumers would simply flock toward whatever platforms had the most scale, pushing the industry toward reconsolidation.

Does this mean that the platforms do not have too much power, that they are not harming society? No. It simply means they are infrastructure. In other words, we don’t need these technology platforms to be more fragmented, we need them to belong to us. We need democratic, rather than strictly market processes, to determine how they wield their power.

When you notice that an institution is infrastructure, the usual reaction is to suggest nationalization or regulation. But today, we have good reasons to suspect our political system is not up to this task. Even if an ideal government could competently tackle a problem as complex as managing the 21st century’s digital infrastructure, ours probably cannot.

This appears to leave us in a lose-lose situation and explains the current mood of resignation. But there is another option that we seem to have forgotten about. Labor organization has long afforded control to a broad array of otherwise-powerless stakeholders over the operation of powerful business enterprises. Why is this not on the table?

A growing army of academics, technologists, and commentators are warming to the proposition that “data is labor.” In short, this is the idea that the vast data streams we all produce through our contact with the digital world are a legitimate sort of work-product — over which we ought to have much more meaningful rights than the laws now afford. Collective bargaining plays a central role in this picture. Because the reason that the markets are now failing (to the benefit of the Silicon Valley giants) is that we are all trying to negotiate only for ourselves, when in fact the very nature of data is that it always touches and implicates the interests of many people.

This may seem like a complicated or intractable problem, but leading thinkers are already working on legal and technical solutions.

So in some sense, the scale of the tech giants may indeed not be such a bad thing — the problem, instead, is the power that scale gives them. But what if Facebook had to do business with large coalitions representing ordinary peoples’ data interests — presumably paying large sums, or admitting these representatives into its governance — in order to get the right to exploit its users’ data? That would put power back where it belongs, without undermining the inherent benefits of large platforms. It just might be a future we can believe in.

So what is the way forward? The answer to this question is enabling collective bargaining through data unions. Data unions would become the necessary counterpart to big tech’s information acquiring transitions. By requiring the big tech companies to deal with data unions authorized to negotiate on behalf of their memberships, both of the problems that have allowed these giant tech companies to amass the power to corrupt society are solved.

Labor unions did not gain true traction until the passage of the National Labor Relations Act of 1935. Perhaps, rather than burning our political capital on breaking up the tech giants through a slow and potentially Sisyphean process, we should focus on creating a 21st century version of this groundbreaking legislation — legislation to protect the data rights of all citizens and provide a responsible legal framework for data unions to represent public interests from the bottom up.

TechCrunch

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5 Effective Ways to Run Facebook Ads A/B Tests

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Facebook Ads A/B Tests or split tests help them try different versions of ads with various campaign elements. This process helps them arrive at the best version for the organization’s target. 

A/B Tests offer a vast pool of resources to try out various versions. You may get caught up and lose your way to arriving at the best version in a limited time. To better understand this topic you can read the Facebook ad testing guide. Here are five effective ways to run Facebook Ads A/B Tests-

1) Start with the minimal number of variables

This approach will help you analyze the impact of a variable much better. The lesser the variables, the better will be the relevant results and more conclusive. Once you have various versions, you will need to run them through the A/B Significance Test to determine if the test results are valid.

2) The second way is to select the correct structure. 

There are two structures in A/B tests. One is a single ad test, and the other is multiple single variation ad sets. All the variations will go under one ad set in the first structure. Each variation will be under a separate ad set in the second one. Out of the two, the second one works out to be better and gives better results.

3) Use of spreadsheets is important to stay organized. 

These spreadsheets help collect and analyze data to get meaningful insights and arrive at data-backed decisions.

4) Do target advertising and set realistic time goals. 

One approach is to choose an entirely new set of audiences. Also, the data pool should be vast and not the same as some existing campaigns. The reason for choosing a different audience is that Facebook may mix up your ads and give contaminated output. 

Another approach to choosing the right audience is to pick geography. It works better, especially when you have business in a particular region.   

It’s also essential to set a realistic timeline for your testing. Facebook suggests one should run a test for at least four days, but you can choose to run the test for up to 30 days.   

5) Set an ideal budget. 

The concept of a perfect budget is subjective. But, you can fix it yourself, or Facebook can do that for you based on your testing data. A large part of the test budget is spent on avoiding audience duplication. If the same audience sees variations, it could affect the test results.

Besides these top five effective ideas, you will need to take a few more action points to make the testing process efficient. Make sure you put the website’s domain link and not the landing page link in the ad, as that doesn’t look good. Put appropriate Call To Action Button, such as ‘Learn More,’ ‘Buy Now,’ etc. It’s also important to see how your ad is coming across on various electronic gadgets- mobile, tablets, etc.

Another strategy that works is trying to engage the customer. You may add social engagement buttons such as ‘Like’ or ‘Comment.’ Use high-resolution images as they work better with the customers. Low-quality, highly edited images are often not liked and trusted by the consumers.

You can learn more about the audience behavior patterns with A/B test results. Conducting these tests on Facebook streamlines the entire process and makes it smooth for you. With the test results, advertisers and marketers can work on the creatives they need to utilize.

To sum it up, you can run an effective A/B test campaign within the specified budget. You don’t need to spend massive amounts to get your advertisement right. You’ll make the correct assumptions about the performance of variations with a good understanding of business and consumers.

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