Engagement Ladder

Engagement Ladder

There’s a figure that gets quoted about engagement; 1, 9, 90. Which is a ratio representation of engagement.  For everyone person who contributes content, 9 might like it and 90 will see it. It’s a little simplistic, and there are more forms of engagement now so it’s helpful to think of the engagement ladder.

Engagement Ladder

Starting from the lowest rung of the ladder

Seen / Read

How many people saw your image, watched your video, read your content. This is the lowest level of engagement as it requires the least amount of effort from your visitor. It’s roughly equivalent to reach, although you might want to consider how much of your content was viewed or read.

It doesn’t tell you much about the person’s attitude to your brand, or their likelihood to purchase. We’ve all read stuff we don’t agree with, sometimes because we don’t agree with it. To compare this to a classic sales funnel it’s at least awareness.

Liked / Facebook Reaction

The next rung on the engagement ladder is a like, a G+, a Facebook reaction. It’s low commitment, a one click easy reaction, Facebook reactions tell you a more. Personally I’m pretty quick to like posts on Facebook or Instagram, much less likely to do so on Twitter.  As likes are visible to others this level of engagement does indicate that the visitor has a possible interest in your brand – but be careful. Facebook rates all reactions the same, but a thousand “angry” reactions won’t translate to sales for your company.


The third rung is comments, or reactions to your posts. If you’re posting on social issues, as Banana Republic did in the screenshot below, you’re likely to attract a lot of comments.

It takes more effort to comment on a post, positive comments are a public endorsement of your brand. It’s going to take some effort on your part to analyse the comments, or to parse the sentiment analysis provided by social listening tools.

facebook comments


If a person shares a post, retweets, embeds your video, they’re increasing your reach as your content is now (potentially) reaching a new audience.  They’ve also added your brand to their online reputation, this doesn’t map easily to a step in the sales process, but sits between evaluation and decision. They’ve added your company to a mental list for possible future purchases.


Some of your content might included a specific Call To Action, or CTA. For many companies this is exactly how they sign up more customers or subscribers, you can see some examples of great CTAs in this article from HubSpot. (And I’ve just shared content from a brand I have never been a customer of, but I’m aware of them, and they remain a potential supplier if I’m ever in a purchase decision for their services in the future).

Your CTA might be a subscribe, follow, download, or purchase option.

Created Content

The ultimate brand accolade, when users generate their own content related to your brand. But it’s a tricky area, with brands needing to pay attention to copyright and privacy issues.

Spotify have taken the step of using the real titles of subscribers’ lists in their own ads, it’s a campaign strategy that is infinite since their users will always be creating new lists. It resonates with their audience really well – seeing your own list picked up for an ad is cool, or whatever the kids are calling it these days.

When your customers take the step of creating content around your brand and sharing it you can bet you’ve got the ultimate level of engagement.

Image: Ladder | Rich Bowen  |  CC BY 2.0

Sentiment Analysis

Sentiment analysis is the analysis of text by natural language processing or computational linguistic to understand subjective opinions.  It’s important when looking at social media as it goes beyond the qualitative measures such as engagement.

To give an example; your latest post generates huge numbers of comments which results in a high engagement score. But when you dig a little deeper you find that all the comments are negative, even angry, your post has infuriated your customers. It’s sentiment analysis that can automate this qualitative information source.

It’s a rich source of information for analysing online content about your company particularly ratings and social media. But it’s not easy to do.


If someone writes a post about Amazon are they talking about the retailer or the river? To make a good analysis for Amazon you would need to find a way to exclude posts made about the river. If your company name is also a normal word or family name this gets more complicated.


Even within one language words can have different meanings, depending on the source country; thongs means underwear in the UK, but footwear in Australia. Fanny is problematic in the UK, but not in the US. Schoon means clean to the Dutch, but beautiful to the Belgians. Coger is benign in Spain but obscene in Mexico.

Some population groups use slang that is difficult to untangle; in English “bad” can mean good, “wicked” could be positive and “groovy” could mean outdated.

So understanding the use of some words will require knowing the context, and that’s difficult to automate.


Some posts are clearly negative or clearly positive, but we use sarcasm which is hard to read.

“Restaurant X lost my booking for 10 people, brilliant”

Sometimes the slightest change in the subject of a sentence makes all the difference, this example came up on the wiki page.

“They would not let my dog stay in this hotel”

Which is somewhat negative, but explains hotel policy, and probably wouldn’t deter too many visitors. Changing just one word makes it very negative;

“I would not let my dog stay in this hotel”

Sentiment analysis in languages other than English is less robust, and some social listening tools don’t cope with languages outside Europe at all. The best social listening tools allow you to manually adjust sentiment analysis labels on a post because this is so tricky to analyse

When monitoring a company announcement a while ago, when we expected reactions in English and Dutch, we didn’t wait for the automated sentiment analysis from the social listening tools. We automated the social listening search to match keywords around the announcement and then manually watched the tweets and posts across the screen. This worked as we were covering the short period of the announcement and there were relatively few mentions. It’s not a long term solution.

Sentiment analysis will get better; it’s becoming a vital tool for understanding consumers’ opinions across digital media.

Image: Sentiment Analysis, my image