While the social Web is the next frontier in understanding and predicting future consumer buying patterns, B2B sales and marketing are reaping benefits as well.
New solutions are emerging to help companies harness the power of social identity data to add context and color to customer account records in order to improve lead generation efforts by marketing, and ultimately, shape follow-up actions made by salespeople.
"Social media gives you a whole new dimension on leads," says Amnon Mishor, cofounder and vice president of products of Leadspace, makers of a SaaS B2B social lead targeting solution. "You can understand the true story about who this person is, what [his] role is in an organization, and what [he's] interested in."
Similarly, at company scale, sales and marketing can begin to evaluate the interests of employees at an organization, the products and technologies that are already implemented, and whether or not there are pre-existing social media connections to the company.
To take social prospecting one step further, Leadspace today rolled out Ideal Buyer Profiles, a fully automated feature letting marketers and salespeople tap into big data insights from the social Web in order to improve analytics around the buyer persona. Describing the platform as "Google for leads inside of CRM," Mishor says Leadspace, which is integrated with Salesforce.com CRM, combines data from social sites, as well as paid, public, and existing-lead databases to generate characteristics of the ideal buyer.
Once an ideal buyer profile has been created for a specific segment or product category, for instance, marketers can trigger targeted prospect lists based on how a lead's characteristics stack up against the ideal buyer profile.
"Now, you can start to create a very interesting internal discussion," Mishor adds. "Sales can now go to marketing and say, 'We've experienced great success with IT managers that are dealing with database issues' or 'marketing professionals who are specifically dealing with demand generation.'"
In addition to more-relevant targeting, one upstart company, Infer, which just raised $10 million in Series A financing, augments historic win/loss data housed in sales and marketing automation systems with statistical models it builds around information gleaned from social networks, financial statements, legal filings, and other Web data sources to help companies make predictive decisions about prospects and lead flow.
According to Satish Dharmaraj, a partner at Redpoint Ventures, which led the investment round, Infer combines Web search, machine learning, and personalization to "bring enterprises deep data science wrapped up in simple application services that anyone can use."
Similarly, Silverpop, a technology company that blends marketing automation with email, social media, and mobile buyer information, just raised $25 million from Escalate Capital Partners and Silicon Valley Bank to invest in automated, customer-driven sales and marketing capabilities. Last year alone, Silverpop onboarded 380 new customers, 60 percent of which turned to the vendor with the intention to grow revenue by focusing on buyer behavior, the company said.