Consider this scenario: A regional bank is evaluating providers to help it migrate its financial management applications to the cloud. The cross-functional decision-making team is spread across offices and communicates heavily over tools like Skype. In addition to the time team members spend evaluating the websites of you and your competitors, they also search, read blogs and publisher websites, tweet, share research, and meet to discuss in person.

Research from Google/Corporate Executive Board and SiriusDecisions suggests this buyer decision-making process is the new normal and that means your marketing team is challenged to not only have adequate visibility into these activities but to also effectively influence each prospect across as many channels as possible. Oh, and then there is the sales team hungry for new leads down the hall…

Today, a new category of predictive intelligence beyond lead scoring provides marketers with the data, insights, and recommendations they need to understand today’s disjointed buyer’s journey and drive measurably better sales results.

It’s Not Magic, It’s Math

Through a combination of data sources and modeling methods, predictive intelligence tells marketers which companies are in the market to buy, which products and services they need, and when they are likely to make a purchase. The accounts and contacts are then scored and that information is delivered directly into systems like marketing automation, CRM, CMS, and ad buying tools for companies to take immediate action.

What might seem too good to be true is built on machine learning and data science. Predictive intelligence ties together data from sources like marketing automation, CRM, past bookings, buyer profiles, and web activity across your site and thousands of others. The blend of static data (e.g., company size, revenues) and behavioral data (e.g., prospects downloading white papers) not only identifies which companies are in market to buy now but also uncovers accounts previously unknown who are looking for what you sell. One SiriusDecisions ABM Campaign Of the Year award winner says these early insights on what prospects do before they reach a salesperson gives their company an “unfair advantage.”

Applying Predictive Intelligence Data to Marketing and Sales Channels

Let’s revisit our bank: Through email campaigns over the last several years you have obtained several contact records inside your marketing automation database. From what you can tell its interest is limited to a case study registration a quarter ago. And what you don’t know is that the bank’s IT team is moving closer to a vendor recommendation for the application migration. Team members attended multiple webinars in the last month, spent days researching across the web, and held a lengthy conversation on a tech forum. Is your typical quarterly investment in email, lead gen, and advertising enough to identify and create a sales opportunity with this company? Predictive intelligence can help ensure it is by tying together all the activity into one predictive score.

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Let’s look at some of the ways marketers can use predictive intelligence data to improve nurturing and create opportunities with companies like our example bank.

  • Call center: Create call scripts tailored to your prospect using company and category intelligence and improve lead qualification rates.
  • Marketing automation: Route prospects to the right campaign and ensure communications are properly personalized based on company size, industry, job role, and level of interest in your products.
  • Advertising: Only advertise to key companies and adjust messaging and landing pages based on purchase intent and company attributes.
  • Retargeting: Stay visible to your prospects after they reach your website. Tailor messaging according to the intent they show at your website and across the rest of the B2B web.
  • Social media: Only target ads to prospects at key companies when they are on LinkedIn, Twitter and Facebook.
  • List acquisition and lead generation: Educate and nurture prospects from key companies while building your database with net new contacts.
  • Website personalization: Make each visit as useful as possible by integrating audience data from predictive intelligence scoring into your content management system, personalization and testing tools.
  • Sales calls and conversion: Help reps prioritize the right prospects and arm them with the intelligence to secure meetings, RFPs, and close deals.

The use cases are many and the impetus is here: savvy buyers warrant savvy marketers. Before launching another half-baked campaign where your prospects run you, consider how predictive intelligence can help you gain insight into your audience so you can blow away your benchmarks.

Data sources, modeling techniques, systems integration, marketing tactics: What areas would you like to learn about most? Let us know, and we’ll follow-up in future posts.