8 Shining Examples of Influencer Marketing Campaigns
By Roza TsvetkovaAug 10
From toothpaste to technology, buying habits and trends in the
consumer packaged goods (CPG) sector are shifting.
Published August 21st 2014
Analyzing what people are saying on social media can be overwhelming. Scratch that, it is overwhelming!
At Brandwatch, we aim to make your job just a bit easier.
You may have noticed that recently we’ve launched our first #brandwatchtips video to show off the amazing capabilities of the Brandwatch platform.
Obviously, we think Brandwatch rocks, but we’re a bit biased. We often get asked about specific features we offer, so having a bank of tutorial videos with tips and tricks will hopefully make everyone’s lives easier.
This week we’ll cover how you can automate the segmentation of your social data.
Why, you might ask?
Most marketers agree that the art of slicing and dicing your data drives better results. Creating messages for each target group, changing sentiment to suit specific needs, tagging and assigning mentions and making sense your big dataset is a tough task. But with the help of our Rules, the segmentation game has changed for the better.
Here’s what you should do.
Within Brandwatch, you have a couple of ways in which you can create a Rule: within the Mentions & Search component or via the Rules menu under the Tools section.
From here you can also edit all of your existing Rules.
One of the most basic uses of Rules is to track and compare brands, product types, topics and other key terms within your query.
For example, by creating multiple Rules, all of the Ben & Jerry’s chatter is automatically classified into some pre-set categories, namely the ice cream flavors.
To achieve similar results, all you need to do is:
You can now filter any chart by this category in your Brandwatch Dashboard. Click apply, et voila!
Another clever use of Rules is to filter incoming mentions into buckets for particular teams within your company, such as customer service, product feedback, PR, marketing, press requests, community management. Have your teams also use this feature if you’re too busy to track the constant flow of mentions around your brand.
By telling Brandwatch to automatically assign mentions (e.g. pertaining to bad service) your team members will be alerted and be able to act on it as quickly as possible.
Follow these 5 steps listed above and select ‘Add Assignment’ when choosing an action. A list of all the users within your current project will then be displayed. Once assigned, prioritized and responded to, you will be notified if the task has been completed.
As reputation is everything, you might want to analyze the sentiment surrounding your brand or products too. Unfortunately, no sentiment analysis can ever be 100% accurate, but it can give an overall indication of the sentiment around your topic of interest.
Some industries, however, have specific jargon that our sentiment classifiers struggle to identify and classify correctly.
For instance, if you’re working in the pharmaceutical industry, chances are your customers may be talking about drugs, illness and emergencies. Typically, these topics are classified as negative, but you might find these neutral as they are not negative towards the brand.
Would you think this is a positive, negative or neutral mention?
What about this one?
What if we clicked on the link?Similarly, younger generations often use words like “sick”, “shit”, “wicked” which have a different connotation depending on the context.
In these cases, you might want to set up Rules for typically misclassified words to customize the sentiment of incoming mentions, meaning you increase the accuracy of the automated sentiment.
Selecting the “Changing Sentiment” action will allow you to automatically update the sentiment of all mentions that match your search. Remember, always give your Rule a name (e.g. Negative Mentions) before saving it.
Seen any irate customers or an influential tweeters posting about your brand? Chances are you want to deal with them quickly and differently.
By creating a Rule, for example, you could monitor the volume of mentions you get from various people, divided into groups, represented by the number of followers they have.
Here we’ve automatically separated incoming tweets mentioning Ben & Jerry’s into:
This means we can compare how different groups of people are talking about Ben & Jerry’s, watch their volume of mentions over time and assess the overall sentiment for each category.
Oh, and have you checked me out yet?