Analytics or Channel Analytics

When the source of the data is exclusively the social channels that we add into an analytics tool, we can stick to the term “analytics.” Data sources that are included under “analytics” are the content that the channel publishes, interactions related to the content published, number of followers, and some information on these followers.

It is important to make it clear that the term “analytics” alone is often used as a name to this category or data set, for the lack of a better term. It is also the term used in the market by the tools that offer this specific data set. All the types of analytics we are looking at refer to the official terms used by the market to describe or define them.

The reason behind the different analytics types on the market is mainly related to the way that social media networks are structured. There are different data points on social networks feeding different tools and platforms on the market, or even different features of a same tool, as we see happening with hybrid tools.

The social media networks offer many different connection points for their data. Some tools only connect to one or a few of these connection points. So by watching the labels that the market gives to these tools, we are also filtering the tools by the kind of data we need so that we can quickly find the best tool for the job.

Inside this analytics category, we also find data from the “native analytics” tools. These are the tools offered directly by the social media networks. Most social media networks offer some kind of analytics directly to their users. Usually, they are not complete or are not easy to work with on a professional level, hence, the growth of the third-party tools.

This does not mean, however, that you will find all of the data offered from native tools present inside third-party tools. Many times, the networks offer a set of data that is exclusive to users that connect to them directly. This usually happens while a social media network is still structuring its technology to then enable third parties to connect to it and pull data. It is demanding for one system to be open to external programs to pull data from it. As a social network matures (i.e., builds up its technology, hires more engineers, enhances its software, etc.), it is likely that it will offer direct analytics connections to third parties and be ready to handle the demand of external programs pulling its data.

So a third-party tool that may be missing a certain data set does not necessarily need to be considered a bad tool; it is important to investigate further information before making such a judgment. It is never a bad thing to ask the makers of a social analytics tool why they don’t include certain data. We usually learn a lot by asking such questions, especially about the technical aspects of social media data.

The goal with the analytics tool, therefore, is to have a strategic approach for the performance of the channel and the growth of the community around official channels. The concept of community is explored further in the book, and in this case, it refers to the fans or followers of a brand, people that have the brand or a channel as a common interest point.

With an analytics tool, an analyst can easily understand important points, such as what the community is interested in and the best way to deliver that content to them.

Professional analytics tools allow you to perform competitive benchmarking, features that let us compare our analytics to other pages and channels. These can be our competitors, or partners, or any page that we are curious about and wish to learn more about what they do. This feature is very powerful, and within an analytics tool, it can be an integral part of the strategy, perhaps even the main reason to use analytics.

With such tools, we are usually dealing with 100% of the data from a channel. So when we reach any insights, we know that we have evaluated 100% of what happened within our channel or any external channel we are looking into. This is important, because we can approach the analysis from any angle we like. We can see what is working best and also what is the worst. This can truly help us shift our strategy into a better performance and make the correct references to the overall business strategy.

Social Media Listening: Keyword- and Mention-Based Analysis

Social media “listening” received this name because it relates to the analyst being able to “hear” what the market is saying about the brand via social media channels. Many marketers also refer to it as social media monitoring.

The process starts with a keyword-based search, and the data source in this case is any possible source where a mention of the brand (or any keyword searched) is found. It does not need to be a channel that belongs to the brand; usually it is not. It is most of the time content generated by profiles and sources external to the brand.

Please note that I make reference to brands, but listening and analytics in general can be used for broader market research as well, and different approaches that are not related to marketing, such as academic research. There are remarkable studies on happiness and other more philosophical or sociological themes that use social media analytics and listening to find results. This book does not address the depth of academic research, but if you are an academic, please be encouraged to get in touch, and we can explore those topics together and exchange resources on that.

Listening is a process that can be easily related to what a search in Google can do. It is similar to Google in the way that it is performing a search all over the Internet, but it is focused instead on finding information from social media channels. Some listening tools go beyond social media and gather information from news channels and from nonsocial channels such as websites. The objective of such additional sources is to enhance the context of the analysis, which can be fascinating.

The process in listening is based on using keywords or expressions for our search. The listening tool then crawls the Internet for the best results it can find. This means that the results in listening are often a sample of what is out there. They are not 100% of the mentions of a brand on social media or on the Internet. This is because the Internet is too broad of a space and not a problem caused by the type of study or by the listening tools. For most studies, a sample is good enough to indicate actionable points and insights.

Within official brand channels, it is likely that brands can retrieve all the comments and content from their audience or community. Therefore, when the objective is to be more precise on the analysis, an organization can create incentives for the audience to come into the official channels to express their thoughts. Listening is initially aimed at covering cases in which the official channels are not the channels that people are using to talk about the brand—cases that will likely happen a lot, despite activity within official channels. Some organizations like the beauty retailer/brand Sephora have very strong incentives for clients to be part of their internal social networks and forums, giving them unprecedented insight to their audience.

Listening tools face challenges to gather the data they need. Many social media networks are not open for listening-type searches, and the Internet is a big space to begin with. So a good listening tool is often judged by how far its search can reach and how many extra insights it can offer on top of the search.

After the trigger from keywords (the search results), most listening tools then add a few processes to enrich the data with more details.

It’s important to note that these processes that go beyond the keyword search results are usually run by calculations and the technology of the social media listening tool, not by gathering the extra information directly from the social networks, or even directly from the description that people insert into their profiles.

What does this mean?

Let’s look at an example.

The user performs a simple search for the keyword “Toyota.”

Some of these results are pulled directly from the social media network themselves, and others are generated by the social listening technology.

The results generated by listening tools are a mix of data from social media networks and calculations or processes that run on the listening platform. Social media mentions and sources, in this example, come directly from social networks. The remaining points are processed by a tool.

Let’s take a deeper look.

Advertising Analytics: Focus on Conversions and ROI of Paid Social Media Campaigns

Social media is strictly an advertising channel for many brands. Many marketers treat it as simply that and are very oriented to conversions and the return on investment (ROI) of their campaigns. This is not right or wrong, it is a valid strategy, like any other. If a brand is seeing results from such an approach, great. Other strategies can offer great results as well, so the everlasting debate on paid vs. organic social media will never cease to exist.

I am personally inclined toward the creation of true communities, so I also value the organic performance very much. I also understand that when a company reaches a certain huge scale, it becomes increasingly difficult to manage “true” communities, and easier to just treat social media as an advertising channel with “true-like” content posted to it, or in other words, pretending that it is building a true community when it’s really not. This book will not dive too deep into the content strategy itself, but I would advise you to take that critical look at the nature of the content posted and the effect on the audience. Does the audience think it is a “true community?”

Paid promotion on social media is useful for everyone trying to get more exposure than the channel offers organically. Paid promotion can be a good addition to an organic strategy. It can be used in crucial moments to help drive the awareness of the channel. Social networks have an array of options for marketers to invest in, and some of these options work better than others for each specific case or campaign.

This is where advertising analytics come in, the information that it delivers is focused on showing to the marketer what is working best and why, based on results from the direct investment on specific content.

CMS Analytics: Measuring the Performance of the Content Management Team

Another big aspect of social media is the management of content on a professional level. When we have a brand identity to maintain, many different social media channels to publish into, and an ongoing significant amount of very specific content that we wish to publish, we need tools to help us get it done.

These content management systems (CMS) also come with metrics of their own. These metrics typically display the performance of each team member and allow you to mark content to better analyze the progress of each campaign. Here, we sometimes have a mix of traditional channel analytics and analytics on the individual team members or content campaigns. So we can eventually find a metric such as “engagement per team member” to see who in our team is creating the most engaging content.

CMS tools, being such a core element for a professional social media content team, usually include features from different types of social media analytics tools. They are usually what we call a “hybrid” tool, as opposed to a “dedicated” tool that has one unique focus when it comes to analytics. There are advantages and disadvantages related to hybrid and dedicated tools; we will look at that throughout the book.

CRM Analytics: Customer Support and Sales via Social Media

Usually, when we think of CRM, or customer relationship management, we don’t immediately think of social media. This may happen because of how hard it is to maintain a long conversation through a social media channel and how easy it is to lose track of the conversations we had. Usually, conversations related to customer support and sales that start on social media quickly move to other channels in order to be continued.

The truth is that many brands are already using social media for longer conversations and are taking the conversation all the way to a successful conversion or resolution of a problem before inviting the client to use a different support channel.

Beyond common CRM, companies are creating bots to interact with people through social media channels. These bots can do many things, from selling products to directing customer support requests and even entertain people with games. Also known as chat bots, these virtual entities rely on different kinds of technologies to interact. They range from simple and basic sets of possibilities to systems that learn from the interactions and evolve into delivering more.

A very common social media channel for customer support workflow is Twitter. Many brands have special teams dedicated to speaking with clients via Twitter. Facebook is also a network where many brands try to dedicate a team to engage with people within the comments section of their content or to answer questions posted to the brand’s timeline or via chat. The challenge on Facebook is that the content can be longer, not as limited as on Twitter, and the volume of comments within Facebook content is usually too much to handle. In general, the volume of community interaction is what primarily limits CRM on social media.

Facebook is trying to help on this aspect of engagement by creating messaging tabs for conversations within comments sections of posts, so that people can continue the conversation without leaving the comments section of a post. This feature was available before only when using direct messages; it was a chat feature. So there is an awareness about this issue, and there are many different initiatives to help brands relate to their communities on social media and to push social channels to become truly engaged communities. Facebook is also working on giving users the ability to praise comments of others, so a comment can receive a like or any other reaction that the platform offers. This type of initiative from the social networks will help social managers understand the sentiment in audience-generated content and community behavior by monitoring reactions within the comments section, since they cannot track what happens within direct messages between users.

Some CRM tools try to integrate social media in different ways. They use it to enhance the information that a brand has about its clients, to open a new lead to the sales team, to keep the conversation going directly from within the CRM, and to keep the historical information of the relationship registered.

The metrics here vary by task, ranging from the number of questions answered by customer support and how fast the team can answer them, to the lead score in sales, where a mix of metrics are used to determine if one person is a potential client or not.

It is likely that CRM technologies will add more from social media as they evolve. One of the main challenges they face is the collection of data. Because personal profiles are usually under privacy regulations, only a few social networks, such as Twitter, offer personal data at a public level; but not everyone uses Twitter. Independent of challenges, the idea of influence and influencers is one that is likely to merge into CRM tools; companies can then better understand how influential their clients are, correlate that to their purchases and interactions history, and engage people in more meaningful ways.