The Role of AI in B2B Sales Success
A lot of the big, flashy work in Artificial Intelligence (AI) happens in the B2C world. You can’t go a few hours without reading another story of how AI is working on huge problems with huge datasets.
But in a recent article in Harvard Business Review, authors Thomas Davenport and Stephan Kudyba point to the role that artificial intelligence tools can play in B2B success.
Using the example of Autodesk, which uses AI technology from EverString Technology, they discuss how machine learning and analytics can mine the broad data that lays scattered throughout the internet and help find key audiences. They note that unstructured data like that in LinkedIn, blog posts, online filings, and other such employee-generated information provides B2B organizations a way to more accurately classify organizations and individuals. This allows sales teams to go beyond traditional classifications such as revenue, capitalization, employee size, and industry type (or SIC codes) to find prospects that matter.
This use of AI doesn’t surprise us at all, given that some of our customers are doing similar work, though in a much more targeted way. Rather than trying to sift through all the information on the internet to find nuggets of gold, customers like Thermo Fisher Scientific use Attivio’s AI-powered search and machine learning technology to find specific sales targets.
A major challenge Thermo Fisher faces is its sheer size and scope. The company sells such a vast array of scientific equipment through various sales teams and channels, that it’s often difficult even for internal teams to understand how to sell all of what the company offers to a particular customer.
In one use case, the company starts with a Freedom of Information Act request to retrieve copies of federally funded grants. The grant applications lay out specific types of experiments and equipment that will be used to complete the project. Thermo Fisher’s team ingests that information, and then uses Attivio technology to make that information completely searchable. Taking it one step further, the system creates email alerts to let specific sales people know about opportunities with scientists, often providing key information about the amount of the grant and who owns it, enabling sales teams to take immediate action without having to sift through reams of data.
The key to making it run, however, is not just the machine learning, but the natural language processing that can distinguish between names, organizations, types of research, and anything else that’s specific to this type of operation. It’s about going beyond a database and finding opportunities that can be acted upon immediately.