Introducing Data Upload API: 5 New Types of Data You Can Analyze in Brandwatch

“The key thing we were looking for was a new approach to easily uncover actionable insights and quantify sentiment among the thousands of conversations happening daily on our social network. By analyzing the data that we uploaded into the platform, we were able to readily discover unexpected trends, data-backed insights, and even influencers. The visualizations are amazing. They’re easy to generate, easy to understand and, importantly, make it easy to discover new ‘a-has’ and areas for further exploration. And they are a succinct way of summarizing important trends.”

– Beth Schneider, Director of Research, MyHealthTeams

Here are five examples of data you can upload to Brandwatch now.

1. Support data

This could be online support tickets, chat text, call logs, or any other recorded interaction your support team has with customers.

With the Data Upload API, you can bring data into your Brandwatch account so it can be viewed and analyzed just like the data already there. Each document needs to have at least a date/time stamp and a body of text. But you can also upload your data with other key pieces of information that data in Brandwatch sometimes has, such as:

• Language
• Author
• Gender
• Location
• Title (for example, the subject of an email or support ticket)

You can also upload up to 10 custom fields which can be anything you like. For example, with support data you might want to add things like:

• Company name
• Product that the customer uses (if you have several)
• Pricing plan
• Account owner / team
• Support team representative
• Solution (was the issue satisfactorily resolved?)
• Feedback rating

With all this data uploaded and labelled in Brandwatch Consumer Research, you can instantly start to understand the overall themes that are causing issues for your customers. Consumer Research will automatically identify key topics and phrases as well as sentiment and emotion and you can visualize how these change over time.

This can be invaluable information for your product or marketing teams. It helps them to understand and address the features of your products or elements of the purchase and onboarding process that are causing the most negative impact on customer experience.

You can also drill down further to uncover insights to help you optimize the support and services you are offering. Segment by region to compare your teams’ performance, or by customer profile so you can improve the services you offer to different types of customers.

You can even compare your support data with customer complaints or questions received via social channels to understand if issues discussed online actually reflect customer experience, or to identify where support services are failing customers.

2. Survey responses

Structured data such as survey responses tend to be easier to analyze where they are collected than other kinds of data. But there are still benefits to being able to upload and analyze these in Brandwatch.

With Brandwatch’s rules you can segment survey responses, either by categories you add as custom fields or keywords in the responses themselves, allowing you to focus on key topics. For example, if you’re developing a product improvement, you can understand what the main customer pain points are with that product or feature that you should be looking to address.

Here’s an example of the keywords most commonly used by ‘promoters’ of Brandwatch in our own NPS surveys:

3. Reviews

If you have customer reviews from websites or app stores you can upload these to understand how customers feel about your products.

By building a holistic view of your customer reviews, you can quickly identify where product improvements need to be prioritized or even aspects or features that your customers really love that you should be promoting more.

Uploaded data can be segmented using Brandwatch’s proprietary machine learning algorithm using Custom Classifiers, revealing hidden themes and trends within your reviews. Unstructured data like this can be hard to segment manually.

With Custom Classifiers, you can separate out reviews that focus on nuanced topics such as:

• Packaging
• Quality
• Cost
• Brand loyalty
• Suitability for purpose
• Delivery

To separate these out using keywords would be near impossible, but with AI it becomes easy to assess how these kinds of themes compare and how they change over time.

4. Community data

If you have an active community forum or a place where users can chat to each other, these conversations can be a highly valuable source of insight into what your most loyal customers think.

You might use this data in a similar way to support data, identifying issues that come up repeatedly in your community. But you’ll also find this to be a rich source of opportunities for all kinds of things, as people share their wishes and ideas.