As marketers, we know that understanding buyers is critical in the B2B space because it fills the gap between buying intent and conversion. Despite this, most of us unfortunately fail miserably. One of the primary reasons behind this failure is a lack of adequate data to analyze one’s target audience. Fortunately, this is where syndicated data can be a significant game-changer.
In addition, syndicated data is a compilation of buyer data spanning across multiple industries. This type of data covers buyer’s intent, demographic, technographic, and firmographic details. This combination of external data that’s provided in a standardized, ready-to-use format offers an accurate picture of prospects. Many companies have made a fortune from syndicated data, specifically Nielsen and IRI.
Nowadays, most suppliers collect high-quality data, which requires a significant amount of money. Syndicated data, however, reduces costs. Although many buyers subscribe to the same information, you can split the cost of the process among yourself.
Furthermore, syndicated data is usually clean and ready-to-use because of routinized systems and standard procedures that collect and update the data. The more current the data, the more profitable it is. When it comes to syndicated data, it’s easier to formulate buyer’s decisions, which can be challenging to do with internal data.
Syndicated data suppliers regularly collect data about a specific product or the purchasing behavior of a particular market segment. This data isn’t customized to meet any company’s needs or solve specific buyer problems. Instead, it provides a standard, ongoing method to facilitate the flow of data. This stockpile of data is useless if you don’t know how to use it, and it’s important to keep in mind that insights offered by data are its sole value.
Additionally, AI and machine-learning elevates and brings syndicated data analysis to the next level. With these core tools, you can gain comprehensive and actionable insights such as the following:
- How has the brand performed last year?
- Why have sales continued to decline or continued to increase?
- Which segments have we ignored?
- What are competitors’ areas of success?
- How do you fill in any gaps and overtake rivals?
On the same note, advanced algorithms analyze syndicated data sources to identify how metrics like product quality and sales value relate. It also illustrates the interactions between metrics and outlines a hierarchy of relationships revealing the true drivers of brand performance.
Then there’s syndicated data, which provides context for understanding brands within a larger market. Whereas, AI analyzes underlying reasons for its success or failure. These in-depth insights can help businesses improve their execution rather than being limited to individual buyer data or lacking analytics tools with AI capabilities.
The combination of syndicated data and AI is well poised to grab the best opportunities in the market. Consider using it to consolidate a brand’s understanding of its performance within their framework, and then act accordingly. Also, do yourself a favor by reading our other blogs about syndication to improve your market position, and let us know your thoughts!