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What brands need to do if they want to break up with Facebook

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With more than 90 major advertisers and counting announcing plans to dump Facebook, a significant question lingers: Where will brands go next for their digital marketing needs?

The case for the breakup is clear: Brands want to distance themselves from third-party business practices that do not align with their values. Specifically, they are disenchanted by what even some members of Congress are calling Facebook’s “lackadaisical” approach to enforcing community standards, allowing an epidemic of paid political misinformation and hate speech to persist on the user-driven platform.

However, with Google, Facebook and Amazon representing just under 70% of global digital ad revenue, a clean break from the tech giants is easier said than done. Advertisers, like anyone facing a breakup, must look within. After all, they don’t want to make the same mistakes and they cannot just throw newly freed up advertising dollars at a new social network ad platform, where similar conflicts could easily follow.

With introspection, advertisers will see that this is more than just a war on disinformation and hate speech. A data war is brewing, pressuring businesses to diversify data sources. As brands compete to understand the needs and preferences of today’s consumers, consumers are concurrently responding with more guarded protection of their online data.

To win this war, brands must reclaim data autonomy and infuse their digital media strategy with more diversified data. But they cannot do it alone and they cannot do it within the current system.

Time to brandish holistic data

Whether Facebook adjusts its community standards to appease dismayed advertisers has yet to be seen. But in the interim, as advertisers walk out the door, it’s worth noting that Facebook’s reliance on online data may soon be obsolete anyway.

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One of the key differentiators for Facebook’s ad platform has been its ability to help level the playing field for smaller brands by cost-effectively captivating the right audiences. But the platform primarily draws insights from audiences’ behaviors online. The next wave of data-based marketing must employ tools that blend first-party data and qualified third-party data to offer a holistic view of customer behaviors, both online and offline.

Offline data sets, which include location intelligence, interactions, purchase history, contact information and demographics are lynchpins in the next digital media wave because they allow brands to develop a more human view of consumer data and create meaningful marketing moments. For example, location intelligence, an extremely potent tool that is currently helping brands pivot during COVID-19 disruptions and is even protecting public health, can drive personalized, alluring marketing campaigns with massive ROI opportunities.

The leading integrated data providers are managing extremely rich datasets, which increase in value daily as consistent tracking yields higher quality data. Such powerful and enriched data stacks offers brands visitor insights based on a specific location after an ad is interacted with on any device — requiring no guesswork for the marketing team. Brands are able to pinpoint exactly which messages resonate with which segments of their audience at which time. This precision ultimately helps them craft the right message for the target consumer — and deliver it at the exact right moment.

Marching orders for combat

Brands want to cut Facebook loose but where do they go next? How do they achieve data autonomy and make omnichannel strides in digital marketing? If the boycott movement is to succeed, revolutionary changes to the digital marketplace are needed.

A newly imagined system must be organized outside the proprietary grasp of any one single tech conglomerate. Otherwise, advertisers will lack ownership of the data they need to reach new audiences. Or they’ll once again get mixed up with similar paid political disinformation and hate speech across user-generated platforms, sending them straight back into the arms of Facebook.

Rather than rely on a single centralized social media platform, transparent media partners and publishers must come together on a shared central system that takes an omnichannel approach to building lookalike (LAL) audiences. A LAL puts advertisers in front of new audiences by finding users that, while they may be unfamiliar with their brand, are very similar to the buyer personas of their current customers. The LAL for each advertiser would be constantly tested and refined to keep pace with the rapidly changing marketplace.

Facebook currently operates on a LAL model but it is almost exclusively generated by online data from their users. The next step is expanding on this model and infusing offline and third-party data with a company’s first-party data, putting them in front of a LAL across a range of media partners and platforms. This will help build a core conversion audience, while constantly scaling new LALs for each brand.

Such a system would require collaboration, enlisting many players in a co-op style undertaking. For example, to get it off the ground, it would be helpful if about 20 of the large brands boycotting Facebook invest some of their newly freed advertising dollars to establish the data and publisher sharing co-op network.

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Once the advertiser framework is set, the co-op would need to identify media outlet partners such as news websites, blogs, apps, podcasts and social media outlets. The co-op would negotiate a performance-based publisher relationship for every player, effectively increasing content monetization for publishers’ content channels.

Reinventing the digital media landscape

This would be a transformational movement, galvanizing brands with data autonomy and increasing customer engagement across an entire network of media platforms — not just one platform. Each advertiser’s first-party data, which they’ve already given to Facebook, would be analyzed to isolate data overlaps within the co-op. This would essentially lay the foundation for building a core conversation audience, helping each advertiser tap new LALs.

Brands advertising with the co-op would gain access to more enriched, robust insights on consumers than Facebook could ever offer, leading to a higher return on investment for the $336 billion spend on digital advertising annually.

Most importantly, it would help brands future-proof their digital marketing efforts and grant them greater freedom in choosing where their advertising dollars are being spent.

That is how the war is won.

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. 

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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.

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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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