Businesses, like the military, are mission-oriented organizations. While the military's missions typically revolve around national security, businesses' missions are focused on building market leadership and driving growth. These growth initiatives can come via organic or strategic expansion, new product launches, customer cross-sell and upsell, and more. Regardless of the mission at hand, actionable intelligence is key to efficiently accomplishing that initiative and ensuring the desired outcome. It is that critical edge needed to outflank the competition.
In this sense, intelligence breaks down into two interrelated components: the gathering of data and conclusions drawn from analyzing this data. For modern militaries, the two primary methods for collection include human intelligence, or HUMINT, and signals intelligence, or SIGINT. According tothe CIA, the former is "defined as any information that can be gathered from human sources." In contrast,美国国家安全局defines SIGINT as "intelligence derived from electronic signals and systems used by foreign targets."
While both intelligence gathering methods are valuable, certain factors have tipped the balance in favor of SIGINT over the past few decades. Human sourced intel has, by and large, been a one-to-one endeavor, inherently affecting its ability to scale. It's also susceptible to bias as data collected this way is typically tinged with the source's own perspective. Furthermore, as human populations grow exponentially, technologies multiply, and the world becomes increasingly interconnected, the volume, format, and speed of data creation have made SIGINT the more readily accessible data source.
The challenge up until the early '90s was less about collection and more about analysis. Reams of data are useless if they cannot be examined for patterns and insights. Breakthroughs in machine learning and artificial intelligence (AI) changed the game in this regard. These technologies were able to transform a universe of big data into actionable intelligence.
To draw parallels with the business world, and sales operations in particular, HUMINT would refer to information gathered on accounts by reps in the field, like insight into a buying group's motivations, information about an upcoming initiative, or changes in leadership and the potential implications. As with other forms of human-sourced intelligence, though, this data is susceptible to bias, which can manifest in the form of misplaced enthusiasm for an account that's not a good fit for a particular product or the premature dismissal of an opportunity based on a single negative experience.
随着企业的数字足迹扩展,他们leave a trail of datapoints ripe for collection and analysis, information beyond simple firmographics like company size, industry, and revenue. Businesses continuously emit signals, leaving clues in numerous places: website code can indicate technologies used, job board postings can show workforce investment trends, web search data can reveal spikes in buyer research, and the list goes on. Anaplan’s advanced预测的见解platform uses AI and natural language processing to automatically track over 4,500features, or measurable pieces of data, in real-time on more than twenty-three million businesses worldwide. Beyond the features mentioned above, this includes critical intent data, which can indicate topical interest surges within target accounts, for instance, when an account is searching for topics related to your solution.
Alone, this collection of third-party information is invaluable for enriching a CRM database, but in aggregate, and by pairing it with a business' first-party data, it’s a gold mine for predictive analytics. Using advanced machine learning algorithms, Predictive Insights can analyze this data to extract meaningful patterns and build predictive models for propensity to buy and other valuable use-cases.
Learn More About Predictive Insights
While government intelligence services across the globe have been implementing AI and machine learning to advance their SIGINT efforts and better understand the world's geopolitical currents, B2B enterprises have been a bit slower in their adoption. Though it has become a more common topic amongst business leaders, it didn't gain much momentum until the 2010s. This slow adoption was partly due to a lack of consumer-friendly platforms (and data science teams were rare), but it was also due to understandable resistance from would-be users. Prior to these technologies, business leaders have been delivering revenue based on hard-won insights gained through relationship building, fine-tuned intuition, and countless customer and prospect meetings. It can be difficult to change up your modus operandi and put your faith (and fate) in the digital hands of a computer algorithm. Despite its shortcomings and lack of hard data, HUMINT can stillfeelmore trustworthy. This same argument was made by more traditional-minded intelligence agency personnel, too, who worried that there was an over-reliance on SIGINT, which can occasionally lack subtleties of context.
It's important to understand, though, that the rise of signals intelligence hasn't been at the complete expense of human-sourced intelligence. It's true that HUMINT can provide texture, depth, and clarity to data that doesn't show up in a digital footprint, allowing intelligence professionals and business leaders alike to read between the lines. The best way to look at the insights derived from AI and machine learning is as a focusing mechanism. Human-sourced intelligence is resource-intensive work, but when used as a layer on top of signals intelligence—whether it's countries of greatest interest for intelligence agencies or high-propensity accounts for businesses—the payoff in terms of predictive accuracy can be massive. And by having SIGINT analysis running in the background assessing massive quantities of new information on a continuous basis, businesses can quickly identify new opportunities and focus resources more effectively.