Data Sources and Approach
We gather information from a variety of sources to identify buyer intent, creating a robust picture of your prospects’ behavior. This includes data from:
- Ad Exchanges: Tracking ad engagement and impressions.
- Publisher Networks: Monitoring interactions on various media sites.
- LinkedIn: Analyzing connections, posts, and activity.
- Proxy/Cached Website Traffic Logs: Understanding website visits and engagement.
By analyzing these sources, we monitor web search keywords, IP addresses (about 15% of which are tied to specific businesses), target URLs, and LinkedIn profiles. This results in a dataset of around 1 billion data points daily.
Signal Mining for Precise Insights
Our analysis goes beyond just collecting data; we perform signal mining to extract meaningful information tailored to your business's strategy. This means focusing on:
- Your specific list of competitors.
- SEO keywords prospects use to reach your website or competitors’ sites.
- Visits to your competitors’ LinkedIn pages or engagement with posts.
From this data, we derive signals with varying levels of accuracy—some may indicate weaker interest, while others suggest strong buying intent. The key is identifying a surge of relevant signals over a short period, which helps predict where and when your next customer might emerge.
Example: Understanding Buyer Signals
Consider this sequence of events:
- 10/12: A group of decision-makers in NYC visits a competitor’s website.
- 10/14: The same group searches Google using terms related to your product.
- 10/16: A research analyst in Chicago downloads a best practice document from your website.
- 10/18: The analyst comments on a LinkedIn post about your competitor.
With this sequence, our system can:
- Estimate a 95% probability that the prospect is entering a buying phase.
- Highlight the NYC-based decision-makers as the primary target, rather than focusing solely on the research analyst.
This approach enables you to direct your efforts where they are most likely to succeed, increasing your chances of winning the deal.
Avoiding False Positives
Contrast this detailed sequence with a single interaction:
- 10/16: A research analyst in Chicago downloads a document from your website.
Alone, this could be a false positive—just a researcher gathering information without real purchase intent. Many solutions only track such isolated events, leading to wasted time on prospects with no intention to buy. Our method focuses on analyzing patterns and multiple signals to avoid such missteps.
Conclusion
Our approach allows you to see the bigger picture of your prospects' behavior by leveraging diverse data sources and real-time analysis. By focusing on tailored signals and analyzing patterns, we help you predict buying intent with greater accuracy, so you can prioritize the right opportunities and improve your sales outcomes.