listening247® : Social Media Listening
listening247® is the only social intelligence platform that has been specifically developed for multilingual high accuracy annotations and the easy integration of unstructured data from sources other than the web.
It offers the highest semantic and sentiment accuracy on the market. Most people call this discipline Social Media Listening, Social Listening, Social Media Monitoring, or Active Web Listening though the latest term to describe it is Social Intelligence.
listening247® is a platform designed for organisations to manage their unstructured and structured data, not only from social media, but also from their own data sources such as call center conversations, chat message exchanges, emails etc.
Social intelligence can help a company understand what is being said about own and competitive brands, companies, stock tickers, persons' names.
Social intelligence can help an organisation:
We harvest data from different sources such as Twitter, Facebook, Instagram, blogs, fora, videos, review websites and any other public online source.
Harvesting irrelevant posts (noise) from these sources can be a common problem when using an automated seach-based dashboard. listening247® enables its users to clean the data and reduce the noise substantially.
Once we harvest the online conversations about own and competitive brands within the product categories of interest, we do semantic and sentiment annotations in order to help decipher the relevant stories in the big data. We provide sentiment not only at the brand level, but within topics, sub-topics, and attributes by brand, company and stock ticker.
Manual data annotation is conducted by our network of native language annotators or the users. If we do not already have a machine learning model trained for a product category, in a few days/weeks, the listening247® users or DigitalMR can create a comprehensive one in the language of your choice. Finally, we make it easy to decide how to action the social media listening data in the form of various data delivery mechanisms e.g. data through an API or FTP, online dashboards, excel tables, CSV or JSON files with annotated data, that make the production of PPT reports fairly easy.
Social media has helped provide consumers the platform to share their experiences of brands and services en-mass. When consumers are discussing a brand online – the brand should be “listening”. This is a great way to “keep your finger on the consumer pulse”.
It’s now more vital than ever for organisations to understand what is being said about them, so that they can effectively manage their brands and their reputation online.
Social intelligence (or social media listening) is a highly cost effective way to measure what is being said about your brand and where those conversations are taking place; it has become a key tool in informing social media research and marketing strategy. Social listening can now be integrated with your surveys and other internal data.
There are many social media monitoring companies that monitor “what” is being said about brands online. However, the role of social media research is to also understand “why”.
Most pure technology and software companies tend to focus on data: gathering it in vast quantities and cranking it out via dashboards without giving a second thought to data relevance and accuracy. But data is of no value unless it is clean and accurately annotated. That’s why, in addition to harvesting posts or ingesting data on listening247® from external data sources it is a pre-condition to make sure the noise is eliminated leaving only the signal accurately annotated for sentiment, emotions, topics, demographics etc.
With its proprietary approach to creating machine learning models, listening247® can annotate for topics and sentiment in any language – assessing what is being said about your company, whether it is good, bad, or neutral. High levels of sentiment precision are notoriously difficult to achieve by purely automated processes. Many technology companies in this space find it difficult to define their levels of accuracy or refuse to do so and most achieve at best only around 60%.
We combine our highly sophisticated sentiment and semantic analysis algorithms with real research know-how, using accurately annotated training data (by humans usually) to further annotate and refine customer machine learning models, that achieve sentiment and semantic accuracies over 80% at post level.
Our suggested approach is to:
DigitalMR monitors millions of online conversations across open-access social media sites such as Twitter and Facebook, forums, blogs and comments on e-commerce sites and online stores. We not only measure the number of posts posted by consumers on the internet and separate them by topic, but we also conduct sentiment analysis – stating whether these posts are positive, negative or neutral.
We aggregate these findings to produce reports that can help you make better business decisions.
We offer a full range of reporting options: from syndicated to fully bespoke, which accommodates your input in report set-up, content and frequency of reporting, plus customised additional analyses of your choice.
We currently have off-the-shelf syndicated reports for a variety of financial services such as alternative data for funds, retail banking, insurance, asset management, online trading.
Reports include detailed analysis on brands, product categories and stock tickers, including share of voice, product features, Net Sentiment Score and competitive benchmarking.
Brands can use accurately annotated unstructured data from websites and other social media in the following ways: