These days, finding out how many mentions your brand is getting online is really quite simple.

Register for a trial of a social media monitoring tool, set up your query and there you have it: your brand has been mentioned X times in the past month.

Trouble is, that doesn’t tell you much about anything and, presumably, you’re going to want to know more…

Whether you’re a complete novice or consider yourself a bit of a guru when it comes to social media monitoring, you’ll probably be familiar with this daunting task. Whatever the size of your returned dataset, you need to make sense of it and organise it into something useful.

Different ways of organising your data

As Brandwatch has evolved, the meaning behind the data has remained a key emphasis of our product development. Much more important than the number of mentions your brand gets is what exactly those mentions are about. Which topics do people talk about in association with your brand? Which topics do you want people to talk about? What kind of people are talking about your brand and why are they talking? Are there particular elements of your brand’s reputation that you are concerned about?

Before you dive in to the data, it’s important to decide what you want to get out of it. This means determining what your different categories and topics might be.

For example, you might not only be interested in organising by the subjects of the conversation, but also by the types of people talking (e.g. customer, prospect, brand, competitor, news, commentator etc), or by their motivations for talking (e.g. review, compliment, complaint, referral, advice etc).

Brandwatch features:

We have three different tools which allow you to organise your data. Some people prefer different methods to others and there are slight nuances to each which make them suited to different objectives.

1. Tags

Comma separated tags are a simple way to sort and label content that is also commonly used in other environments such as blogs, social bookmarking and mail clients.

In Brandwatch, Tags are extremely useful for quick-sorting data. As well as manually tagging individual mentions, in the Mentions component, you can filter your data by a full text-search and then bulk-tag or bulk-delete all the results in one.

It’s a very efficient way to label lots of data and makes a great start on organizing those many hundreds of mentions. Tags can also be entered in the full-mention view as you work through each mention. In the Chart component, Tags can also be used to create charts displaying sentiment, page-type or chronological break down.

2. Topics

These are initially presented in the form of an auto-generated wordcloud. These are great for picking out key subjects within a set of data but they also have another very handy feature.

You can choose to create your own Topics, which are essentially queries within a query.

What’s really useful about this feature is that once you’ve created your topics, you can leave them in the Topics component and they will keep classifying new mentions in your query’s data based on the Topics you have defined.

3. Categories

Categories are perhaps the most powerful and flexible option we offer to organise data. They differ from Topics and Tags due to their two-tier nature, for example a category (tier 1) might be Holiday-Type and the sub-categories (tier 2) to choose from would be sports, family, romantic and retirement.

This is a very powerful feature when you want to organise data in more than one dimension. For example, you might want to dive into the mentions categorised as family (for Holiday-Type) and see how they are broken down in terms of Persona (a second Category) – how many of the mentions are categorised as being authored by a customer, a prospect, or a detractor? This kind of drilling-down is very useful to pick out particular cues for action and also observing trends.

You can add as many Categories as you want and as many sub-categories for each of them. There are also default Categories already set in the dashboard for “Assign”, “Priority”, “Status” and “Checked”. These are useful for users that require basic work-flow activity, particularly for reputation management or social CRM.

Categories are typically assigned in the full-mention view, and we will also be adding a bulk-categorise feature in the near future. As with Tags, the Chart component also allows you to break down data by Categories over time or by sentiment or page-type.

Automatic vs Manual:

We are always developing the features that help our users automate processes which take them the longest, and the above tools offer some pretty fast and powerful options to do this.

However, certain types of classification and categorisation still require manual input. For example, the Topics feature is great for auto-labelling future mentions based on finding certain terms, but when it comes to determining what kind of person it was who wrote a certain mention, human judgment still can’t be beaten and this means manual use of the Categories tool would be required.

Don’t be daunted…

Whether or not you’re a Brandwatch user, hopefully these tips give you an idea of how to start getting more out of your data.

Whatever tool you use and whatever the scope of your project, to start moving beyond the “buzz monitoring” stage you’ll need to first lay out your objectives and determine what you want to learn or achieve – possibly even going as far as listing your categories and sub-categories.

If that planning stage is done comprehensively, it should be a lot clearer how best to start organising your data and turning it into something meaningful with the tools described above.