Emerging technologies coupled with digital data are making it easier for B2B marketers to identify businesses and their buying intent.
More specifically, by combining machine learning for business identification and modeling for business intent, as well as using blockchain for web visitor intelligence, it’s possible for B2B marketers to gain an expanded view of what a business does for a far more precise sales and marketing approach.
Modern businesses have access to technologies and operational constructs that allow them to work quicker and more efficiently than they could have in the past. For instance, the sharing economy has created captive workforces that are not employed directly by the company they work for, and cloud computing has brought the time-to-value for a web business from months to hours. These changes have disrupted the ways in which marketers can identify the B2B buying needs for a prospect they are interested in targeting.
This is where machine learning and natural language processing can help by more effectively weeding through company web and social data in order to better understand it. For example, marketers can apply natural language processing in order to classify a business as a cloud services company and not a web-based file-sharing company. These nuances matter when marketers are looking for a precise segment that might be interested in managed cloud services and not cloud-based apps.
At the intersection of identifying information about a business and its digital activity data lies business intent. A view into what a business does (i.e., SIC codes or keywords) will give you a sense of the type of products a company will need to buy in order to provide a product to its end customers. For instance, a simplistic view of a company’s intent would examine the business it is in (e.g., washing machine manufacturing), determine the upstream raw materials needed to generate its product (e.g., motors, metals, belts, etc.), and then model intent that way. Or it might examine the sales and administrative needs of a company (e.g., CRM, HR/staffing, etc.) to determine intent.
But the issue with looking only at firmographic data is that it makes a marketing effort very point-in-time and not necessarily relevant to overall buying needs. To understand real-time buying intent, we need to tack on business activity data. This gives us, for example, a sense of the type of individuals a company is hiring or their IP-centric web browsing behavior. This helps determine intent and can be a differentiator when analyzed with natural language processing (to parse relevant phrases) and machine learning (to understand buying behavior after certain criteria are met). This process creates a much more precise view of business intent at the moment when a prospect is actively looking to buy.
The last mile of modern marketing involves more precisely understanding who your ads are being served to. Emerging technologies such as blockchain can potentially incent consumers to verify their identities and intentions before an ad is served. (Think of blockchain as a spreadsheet that you share with your friends, and all of you have to agree before a change is made.)
This would result in marketers having precise visibility from the time they identify a prospect, understand their intent, and finally serve an ad. While this is still an emerging area, there is a lot of investment being poured into designing an incentive mechanism for consumers and creating fail-safe infrastructure.
Modern marketers have a variety of tools and technologies to assist them in their campaigns. Emerging precision marketing techniques are giving them a more refined view of their customers, allowing for better targeting. Machine learning, digital data, and blockchain technologies are poised to fundamentally change the way marketers get messages to audiences in a more precise way.