How brands can engineer social media content
IN the world of social media advertising, the biggest win for firms is when consumers are delighted by the content they see, want to engage with it and eventually buy something. Kartik Hosanagar, Wharton professor of operations, information and decisions, has co-authored research that takes a closer look at brand posts on Facebook to determine the type and mix of content advertisers should aim for to get results. The paper, “Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook,” which was co-authored with Dokyun Lee of Carnegie Mellon University and Stanford University’s Harikesh Nair, is forthcoming in the journal Management Science. Hosanagar recently joined Knowledge@Wharton to discuss his findings.
An edited transcript of the conversation follows.
Knowledge@Wharton: This paper looks at the increasing importance of something called content engineering. Can you explain what that is and how businesses have started to use it?
Kartik Hosanagar:
Content engineering or content marketing is essentially about designing the right kinds of content that might engage consumers. It’s becoming increasingly important on the web today because firms are producing so much content, be it on Twitter, Facebook, Pinterest and so many other social media platforms. Firms have a presence in all of these platforms and are constantly generating content. Some of that content sticks and some does not. The question is, can we figure out what sticks and generate content that customers want to engage with?
Knowledge@Wharton: You point out in the paper that reach used to be the focus, and now companies have moved more towards engagement.
Hosanagar:
Right. During the early days of social media marketing, a lot of firms were focused on acquiring followers or fans on these networks. For example, acquiring a lot more Facebook fans and spending a lot on advertising to get these Facebook fans. But over time, they realized that a lot of these so-called followers or fans weren’t actually engaging much with the content.
Now, the value of Facebook for most of these brands is ultimately that they can use these followers to instantaneously spread information about new products, about new promotions. When their brand-loyal customers engage with content by liking, sharing or commenting on it, the friends of these brand-loyal followers also learn about the brand. So, there’s the opportunity for viral spread of the message. But that wasn’t happening as much because while a company might have had millions of followers, only 1% of these Facebook fans were actually engaging with the brand. More recently, the attention of firms has moved from merely acquiring followers on social media platforms to actually getting these fans to engage with them because, ultimately, that’s where the value lies.
Knowledge@Wharton: This paper tries to get at that question of what content works best. I was surprised to learn that this is an under-studied area. Why would you say that is, and what do you try to do differently in the study to get at that problem?
Hosanagar:
Given that advertising has been around for a while and there is so much emphasis on advertising communication, you would think that there’s going to be a lot of work done that’s getting to the heart of how should we design content. We were surprised to find that there hasn’t been a lot of systematic study of the subject, and our conclusion is that’s largely because it’s a tough problem.
If you look at traditional advertising, a brand comes up with ads, a marketing person figures out what words to use in those ads, what imagery to use in those ads and that person makes a creative decision that we’re going to talk about the following product features but not these other aspects of our brand. Those creative decisions may well be intuitive. But at the end of the day, it’s very hard to test systematically and really assess whether this makes sense because we are talking about doing this at scale and with unstructured data. That has changed with the web because you’ve got so many firms creating lots of social media messages, and now you have the opportunity at analyzing this at scale by looking at hundreds of thousands and perhaps even millions of messages posted by various brands and looking at which ones people engage with.
We’re able to do that only recently because you have that kind of data at scale, but it’s a hard problem because you still have to process all that content and figure out what is in a message. Is this message about the brand’s product? Is it about the price? Is it about deals? Is it comparing its product to its competitors? Is it emotional? Is it humorous? Is it talking up a brand’s philanthropic initiative? All of this requires a lot of natural language processes and at scale, and we’re only now in the last few years starting to have both the data and the technology to do that.
Knowledge@Wharton: You found a really interesting data set to work with. Tell us about that.
Hosanagar:
We were fortunate to partner with a large social media analytics company. This company powers the analytics for a lot of large brands on Facebook. They shared with us a data set that spans a period of about six months and that is every Facebook post posted by 1,000-plus companies on Facebook. These Facebook pages belong to some of the largest corporations, so we’re talking about millions of followers associated with each of these brands. For each of these posts, we knew daily engagement information, meaning how many likes, Wharton, S3/ 2