Customizing posts with the new Facebook API update

In an attempt to fight misleading news on their platform, Facebook has recently changed the way you can use their API to edit previews of posts. While before you could specify the header, preview text, and the image, this is no longer possible. Instead, Facebook picks the relevant information from the website that the post links to.

This article presents a solution to continue to post customizable post previews for certain contexts.

Facebook takes action to fight fake news

To fight misleading posts with false preview texts and misleading images,  Facebook has started to disable the ability for Pages to edit the previews of the links they post – both for the Page composer and the API.

Says Facebook (July 18, 2017):

By removing the ability to alter link metadata (i.e. headline, description, image) from all link sharing entry points on Facebook, we are eliminating a channel that has been abused to post false news. (…) Specifically all API versions will no longer support this capability, and Page admins will no longer be able to make these edits in Page composer. (source)

Now I’ve been doing exactly that with Jazzity: Everyday a cron job would run a script that took a few snippets from a data base and post them to Facebook using the API:

postPicture($post_message, $post_link, $post_picture, $post_name, $post_caption, $post_description);

The function postPicture (we’ll go into more details later) would take six parameters, which will make up the post as follows:

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Calculating your Marketing ROI – a practical guide

Calculating your Marketing ROI sounds super simple, right? You just take the income, divide it by the marketing expenses, and boom! – there’s your marketing ROI. Super simple – or is it?

Well, the truth is, when you google “marketing roi” you will find that there is a huge number of different definitions out there. So many, in fact, that Forbes Magazine has called this situation “ROI Anarchy”.

In this article, we’ll look at what a marketing ROI calculation should take into account in order to deliver meaningful insights to evaluate a campaign.

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How to tell if your results are significant – a practical guide

Marketers frequently face a situation like this: In a survey it is found that 57% of women prefer product A, while 60% of men prefer product B.

In this article I will show how marketers – using only simple statistical analysis tools available in Microsoft Excel – can quickly and easily decide whether or not they can draw meaningful conclusions from such a result, or whether they may be making fatal mistakes by interpreting random noise as valid data.

stats mofo

Marketers frequently face a situation like this: In a survey it is found that 57% of women prefer product A, while 60% of men prefer product B.

Some marketers will just go “Great, statistics prove that women prefer product A, and men prefer product B. We’ll market product A to women then and product B to men.”.

But is this really always a valid conclusion? Couldn’t it also be that the difference is purely coincidental? After all, we haven’t asked all people, but only a subset of people: those participating in our survey. So maybe if we took another sample, and asked different people, the results would be different? May well be!

Statistics to the rescue!

As is often the case, statistics can provide a solution. Before delving into the details, let’s look at another, more formalized example. Dice!

Suppose we take two dice, and we want to know if one of them is loaded, i.e., we want to know if one of the dice yields better values than the other. Let’s start by throwing them 10 times each. This is what the results may look like:

Example 10 dice

Well. What do we get? Let’s look at the mean value for each die. As a reminder, if the two dice were fair dice, there would be an equal likelihood of one in six for each number to turn up. More formally, the expected value would be 3.5 (=1/6*1+1/6*2+…+1/6*6).

So what do we get for our dice? For die 1, the mean is (4+4+4+…+3)/10 = 4.40, the mean for die 2 is (5+2+1+…+1)/10 = 3.20.

So is die 1 better than die 2? Well the average is higher, of course, but as you will intuitively suspect, 10 throws is quite a small number of throws to draw any meaningful conclusions.

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