Last year, we shared our four main tips for tracking data with keywords. And this remains one of the areas we get more inquiries for. How do you make your searches more effective? How do you adjust the search? So, we're here to help guide you to success!
Let's start with the basics -- what exactly is a keyword? A keyword is basically a search term. It doesn’t always need to be a single word. It can be a sentence; it can be a hashtag or a mixture of letters and numbers. When you start a keyword search, you begin searching all the available platforms for every mention of the term to be analysed and visualised.
Searches can get more advanced when you use a query. So, what is a query? A query is used to search social media and real-time data sources for mentions of your topic/project. Often to get the best results and pull in the most relevant and interesting data, one keyword is not enough. In this case, you may need to use multiple keywords, keyword filters or exclusions. So, basically, the full set of keywords and filters is known as a query.
Now, if you want to get more advanced, you can use a filter. A keyword filter allows you to filter your keywords through another set of keywords. This enables you to narrow down the number of mentions you are pulling in and look at more specific data. For example, if you wanted to look for all mentions of Nike trainers, your query would be the keyword “Nike” and you would put “trainers”, “trainer” and “sneakers” in the Keyword Filters section. This way you would only pull in mentions of Nike in the context of trainer conversation. Simple, no?
One final layer is a keyword exclusion. A keyword exclusion stops keywords coming into query results. Sometimes you may find a keyword brings unwanted data associated with another keyword. So, if you put a term in the keyword exclusion, it will stop mentions of your keyword in unwanted contexts. For example, if you are looking for mentions of Nike but don’t want to see any sales related conversation, your query would be the keyword “Nike” and you would put “sale”, “sell” and “deal” in the Keyword Exclusions section. This way you would only pull in mentions of Nike which are not related to selling items.
Now, that we have the basics down, let's get to actually constructing your query. The best way to start is to ask yourself what are you really looking for. When constructing your query, it is generally best to have an idea of what kind of data you are looking for. Start with these questions:
Are you looking for data around a brand, a product, a general topic or a specific campaign?
If you are interested in a brand, do they use any specific hashtags or phrases in their social media communication?
Does the name of the brand have other meanings?
Then you need to know how to choose your keywords. If you are looking for a specific brand, it is best to start with their social platforms. Here you can see how they refer to themselves on social networks and work out if they are using any specific campaign hashtags. If you put their exact account names in as keywords, you will pull in all the directed messages towards their account. You may find though that a brand’s name has multiple meanings, in which case you can always use keyword filters to narrow down your search.
Using our Nike example, you would first take a look on Twitter search; and you will see that all the mentions of Nike refer to the brand and that they have a large number of Twitter accounts outside the main @Nike Twitter account. So, as well as “Nike”, you might want to also add @NikeStore and @NikeLab to get a wider picture of the Nike conversation.
As with most things, the key to success is testing! So, we encourage you to test those keywords. The quickest and easiest way to test a keyword is to put it in the Search Twitter box on Twitter and click the Live tab. This way you can get an overview of what kind of content your keyword will pull. If you see lots of irrelevant data, you should consider changing your keyword or using keyword filters.
Now, on occasion, you'll find that your query brings in irrelevant data. Not to fret. There are a number of ways you can deal with this. For instance, if you wanted to measure Apple, a brand whose name has multiple meanings not all related to the brand, you might find a lot of mentions of fruit. In this case, you will want to use keyword filters. As you will most likely want all mentions of Apple’s products, you can put “iPhone” and “iMac” into the keywords and the keyword filters so you get all mentions. But as you would only want mentions of Apple in the context of the brand rather than the context of fruit, you would put Apple into the keywords but put terms related to Apple such as “computer” and “Tim Cook” in the keyword filters so only mentions related to the brand are pulled in.
Sometimes a brand’s name may have other meaning in different languages. For instance, the baby milk brand SMA is a word in Indonesian. To prevent from bringing in lots of irrelevant Indonesian tweets, you can set the language filter to English and all the Indonesian posts will not be aggregated with what you're seeking.
Then, we have what we all love -- spam. Very often a brand can be the victim of sales spam, hundreds of posts from re-sellers. You may want to individually block problem users, or you can put key sales keywords such as “eBay”, “sell” or “sale” words into the keyword exclusions.
At the end of the day, setting up a query is simple if you have the tools and a little bit of knowledge. And of course, our team is happy to help at any point. Now, go forth and query!