NetBase Quid is always evolving and improving as new tech is available, and as artificial intelligence continues to improve. This means better, and more accurate results for your consumer and market intelligence endeavors, including these expanded datasets.
Accuracy is critical as more businesses adopt analytics to inform their strategies, as these stats attest:
- 58% of enterprises see a significant rise in customer loyalty and retention through consumer analytics.
- Worldwide spending on big data and business analytics solutions was forecast to reach $215.7 billion by the end of 2021, an increase of 10.1% since 2020. And the CAGR is projected to increase 12.8% by 2025.
- As of 2020, the global consumer and market intelligence adoption rate was 26%.
And as this growth continues, having market and social analytics tools that grow and expand along with it will be essential. Let’s see how NetBase Quid is evolving and helping your brand keep up!
Naming That Makes Sense
Proper segmenting is crucial for understanding the different aspects of any conversation. There’s lots of insight to sort through, so it’s important to have conversation clusters that are easy to scan as you kick off your analysis!
In our latest update, we released enhancements that generate more accurate cluster names. These names are more meaningful and relevant to the clusters they represent, reducing the need for merging and renaming clusters. This saves brands time and delivers maximum results to inform consumer and market intelligence endeavors.
Below, we have cluster names for a Quid Social network on the topic of breakfast. On the left you can see conversation names before the update, and on the right, the improved clustering. You can see how the terms “along with,” “A.M. To P.M.,” and “Peanut Butter” were not very helpful. However, on the right, we can see how Quid Social replaced these with “Products,” “Recipe,” and “Restaurants.” These names are more representative of the underlying posts.
For some clusters, you may see comma separated names such as “Bacon, Cheese, Toast.” This, once again, more accurately represents the posts in the clusters
Additionally, node names have been updated. Previously the node label’s source setting defaulted to ‘node name,’ which meant that it used the post title to name the node. This resulted in node labels that seemed irrelevant to the analysis, but were actually very relevant upon deeper analysis.
With this update though, the node label source setting uses the body of the post to sort out labels that are more representative of the overall content of the post. This eliminates the need for brands to review the body of each and every post to understand relevancy. And this saves time, which can better spent on consumer insights and market intelligence discovery instead of tedious data clean-up.
This is particularly beneficial with forum data. Forums are growing, likely due to the feeling of community felt on them. And brands are noticing. Reddit, for example, experienced a 192% increase in ad revenue in Q2 of 2021 alone. And having a social media tool that stays updated will be increasingly critical as social media forums continue to grow
Understanding Author Interests & Professions
In consumer research, demographics play a huge part in understanding your audience – and psychographic details go deeper into who they are. It takes Maryanne, a woman who lives in New Jersey, and categorizes her as not only that, but a whiskey connoisseur who loves dancing, and her dogs.
There are now two new values in the Nodes Represent setting in the control panel: Author Interests and Author Professions. Now, brands can organize nodes to represent the interests of the authors, as well as their professions. This makes discovering new, highly relevant intel, easier than ever.
Applying these values is simple. Select the Nodes tab>Nodes Represent>Author Interests or Author Professions:
And it will look something like this
Family occupies the largest interest in this example, with Travel close behind. This is a quick way to segment your audience and discover content that they care about. And this will help brands design campaigns and offerings that will resonate with them.
Nodes are linked based on semantic similarity, so an author with a Soccer interest mentioning Hulu can be linked to another post by an author with a Dog interest also mentioning Hulu. This helps you identify common links your consumers may have that aren’t necessarily categorized as interests – giving you yet another in!
Expanded Company Dataset Intel
In the realm of market intelligence, and specifically as it relates to competitive intelligence, you need intel that you can trust, and that cuts through the noise rapidly. Timing is key, particularly when tracking budding startups that may be worth your investment.
Helping with this, our coverage has been expanded to include the funding stage data from S&P Capitol IQ. If funding stage data is not available for an S&P Capital IQ-sourced company, we’ll use Crunchbase funding stage data. And this intel appears in the Information panel, event reports and company profile reports.
This is exciting news for investors, as tracking funding events of promising startups early on can be game changing. But then most of this intel is, when brands are confident of the accuracy of the insight – which NetBase Quid customers are! There’s a noticeable difference when working with a tool that evolves right along with the best available tech and AI.
If you’d like to learn more and see why industry leaders switch to our offerings, reach out for a demo!