When Orange is not Just a Colour, and Other Challenging Queries

At Brandwatch, there are a few things we’re pretty pleased with ourselves about.

How satisfied our customers are with us is one (read this if you haven’t already and you’ll see why we’re so chuffed), the control we have over our technology is another (we do all our development in house and all our technology is proprietary).

One of the other really great things about our team here is the speed at which they can develop fancy new bits and bobs to make the platform really great – things that’ll help you get the most out of it.

Our search operators are something else we love to shout about. They help you really drill down to the nitty gritty in your Queries and get you the pinpointed search results you need.

Let’s say you work for a huge pharmaceutical company. You need to research what people are saying about specific treatment solutions, as well as general conversation about disease and health.

Searching for a specific brand of pacemaker is one thing, but trying to find mentions of ‘cardiac arrhythmia’ when searching for symptoms of a fast heartbeat brings back thousands of dodgy poems about love and a multitude of other medical problems is a totally different kettle of fish.

Using the operators to your advantage means you can cleverly whittle out all of the irrelevant chatter and get straight to the mentions that are useful to you and your business. Nobody said life was easy, but we think our operators can definitely make lighter work of a challenging Query.

You probably know about most of our operators. You might know how to use the OR and the AND to your advantage. The NEAR/ and * wildcard operators may be your go-to keystrokes to help you build your more advanced Query. And now, with the new hashtags: and at_mentions: operators, we are making it even easier for you. And it’s not even your birthday (except if it is. Happy Birthday)!

Let’s take a look at some challenging Queries that we’ve built that have really made our operators work for their money.

1. Context terms: When ‘orange’ isn’t just a colour

(orange NEAR/5 (mobile* OR broadband OR internet OR phon*))

NOT (fruit OR tasty OR TDM OR “credit card” OR site:debtsolutionsource.com)

location:uk

We wrote previously about how to write a useful Query about the telecommunications company, Orange. It’s a classic example, but a good ‘un.

Brand names can be very tricky to track accurately and relevantly, especially when they also share their name with a colour and the ‘9th Most Popular Fruit’ (according to a very scientific study by Gawker) – with over 800,000 mentions of the word ‘orange’ in a week, finding the relevant conversations about the brand was going to be a challenge.

Using a clever little combo of our nifty operators, we are able to wheedle out all mentions of fruit, along with all the spam that comes with being part of a brand that offers mobile phone contracts. We were also able to make sure that all the mentions of ‘orange’ were within five words of relevant context terms, using NEAR/5, such as mentions of phones, broadband, mobile or internet.

This means that the mentions are far more likely to actually be about the brand, rather than someone tucking into their mid-morning fruit of choice and live-tweeting it.

And we could keep building on this until you’ve managed to exclude certain spammy websites using site:, making the Query location sensitive and also adding in loads more relevant context terms – so by using a handful of those all-important operators, you are able to get straight to the heart of conversations that matter about Orange.

2. Capitalisation and characters: When you want to get ‘GOT’

raw:GoT OR raw:GOT OR hashtags:got OR (game thrones~5) OR “game of thrones”

We’re part of a world where if we want to know something – the first King of England, Bill Murray’s favourite sandwich, what people are saying about the last episode of Game of Thrones – we can find out. Just like that. But there are often some obstacles in the way.

Game of Thrones is an example of a leviathan of a TV show that users of social media can’t get enough of. The chatter around each episode is massive – but the hashtags or abbreviations people mainly use when talking about it online are #GoT or #GOT.

The problem with this is that obviously a search for ‘GOT’ on Twitter (or Google, for that matter) brings back millions of mentions. “I just got this amazing jacket”, “I got a puppy today!” or “I’ve got to go to bed now” might pop up. So how do we cut through all the noise and just hone in on the talk about what just happened on the show?

Our raw: operator is a handy little device that only searches for words in the exact case (upper or lower) that you want to search for. You can search raw:GoT OR raw:GOT and it will only search for exactly those with the capitals in exactly the right place, automatically excluding ‘got’. Easy.

Our new hashtags: operator is also a pretty great piece of kit that allows you to search specific hashtags within Twitter and Instagram, meaning you can cherry-pick all the #got mentions, as well as searching for ‘game of thrones’.

Another great thing about the hashtags: operator is that you can use it to track hashtag campaigns really easily, meaning easy comparisons and analysis of the impact over time in a way that isn’t possible anywhere else.

And, again, we could continue building this Query with context terms and further operators to make it even more refined. Powerful stuff.

3. Exclusion terms: Because you want ‘Cos’

Brace yourself… this is a complex one

“Cos shop” OR “Cos Store” OR “Cos shops” OR “Cos Stores” OR Cosshop* OR Cosstore*

OR (cos NEAR/0f (clothes* OR clothing* OR cardigan* OR sweater* OR jumper* OR “v-neck” OR s?irt* OR top OR tops OR tshirt* OR blouse* OR coat OR coats OR blazer* OR jacket* OR jeans OR trousers OR pants OR dress OR dresses OR bag OR bags OR handbag* OR purse OR clutch OR shoe OR shoes OR sandals OR heels OR outfit* OR onlinestore* OR “online store” OR “online stores” OR “is on-trend” OR “are on-trend” OR accessories OR accesorize* OR accessorize* OR necklace* OR ((summer OR autumn OR winter OR spring) NEAR/0f (look OR collection))))

OR ((clothes* OR clothing* OR cardigan* OR sweater* OR jumper* OR “v-neck” OR s?irt* OR top OR tops OR tshirt* OR blouse* OR coat OR coats OR blazer* OR jacket* OR jeans OR trousers OR pants OR dress OR dresses OR bag OR bags OR handbag* OR purse OR clutch OR shoe OR shoes OR sandals OR heels OR outfit* OR accessories OR accesorize* OR accessorize* OR necklace* OR ((summer OR autumn OR winter OR spring) NEAR/0f (look OR collection)) OR shop OR shopping) NEAR/0f ((from OR “is from” OR “was from” OR “bought from” OR at OR “got at” OR in OR “got in” OR “bought in” OR by) NEAR/0f cos))

OR (hashtags:cos AND hashtags:(asos OR hm OR h&m OR zara OR newlook OR fashion OR topshop OR riverisland))

NOT raw:(‘cos OR ‘COS OR ‘Cos OR Cos. OR cos. OR COS. OR cos:)

NOT (“Is it cos” OR “bag cos” OR “dress up cos”)

NOT url:(download* OR rifftheraff.com OR greenfoot.org)

Swedish clothing brand Cos is another example of a brand whose social chatter could get lost in the ether. Sharing their brand name with a popular slang and textspeak term for ‘because’ is definitely troublesome from a social media monitoring perspective.

Slang aside, there’s also the fact that the brand shares its name with a variety of lettuce and a Greek island. So how would you be able to make sure that any mentions of ‘Cos’ are relevant to the brand?

By creating a complex Query that ensures that ‘Cos’ is mentioned in relation to clothing and terms regularly bandied about when talking about fashion – for example ‘jacket’, ‘store’ or ‘trousers’ – and that excludes spammy/irrelevant sites, you’re able to get rid of any of the background noise that might mean you miss meaningful conversations about the actual brand.

We’ve used the NEAR/nf operator here, which means that the first word must come before the second word, and within the number of words specified in ‘n’. For example, ‘cos NEAR/5f jacket’ would find mentions where jacket was within 5 words away from cos, where cos comes first. Using NEAR/0f means that the two terms must sit side by side in the specified order. Clever.

As for the slang – how best to eliminate that? In steps the raw: operator again, this time combined using the exclusion operator, NOT. By specifically asking the tool to ignore any mentions of ‘cos with the apostrophe, plus excluding common phrases using the word as slang, we’re removing all the abbreviations of ‘because’, and therefore making the search results even more pertinent.

So, that’s three examples of how to get round tracking some tricky brand names – and of course, we could expand these Queries even further to make the data even more relevant. The opportunities with operators are endless.

Get help with your Queries

If you have any tough Queries you’re stuck on, why not join us for our Twitter Q&A on Friday 2 May 3pm BST/10am ET. We’ll be answering any questions you have about Queries and operators – join in using #brandwatchtips

Plus, we’re hosting two webinars all about advanced Query tips and using our new operators. For more info and to register, click here:

30 April 3.30pm BST

1 May 11.30am ET

Using Brandwatch to create complex Queries that help you really target the right phrases, brands and conversations is a really powerful way of making sure you’re listening in on the right discussions. Have you ever worked with a really tricky Query? How did you succeed?

Oh, and Bill likes a smashed avocado and sprout sandwich, by the way.