Taking Account-based Marketing to the Next Level with Predictive Marketing
We know how hard it is being a marketer today.
That’s why we are bundling smart ideas and tactics from elite marketers in our organization and beyond into a new series of playbooks. These playbooks are designed to help diehard marketers like you align predictive marketing with today’s buying cycle. By applying these straightforward suggestions and proven strategies throughout the prospect/customer lifecycle, you can take your marketing to the next level.
And that’s exactly what our latest ebook, the Predictive Playbook on Account-based Marketing, was created to do. We teamed up with Jon Miller of Engagio, Peter Isaacson of Demandbase and Nicolas Draca of LinkedIn to gather insights into ABM: what it is, how to overcome common challenges, best practices for success, how to measure the results and what the future holds.
(Editor’s Note: Read on for a preview of the ebook, or download the complete playbook here.)
While marketing automation has empowered marketers to take their demand-generation efforts to new heights, the software is not built to support account-based marketing. As a tool designed around the concept of marketing to an individual contact, marketing automation limits the types of data that marketers can collect, and the scoring and segmenting they can do. That’s where predictive marketing comes into play.
What is predictive marketing?
Predictive marketing considers all available data from both internal and external sources (e.g., CRM, marketing automation, blogs, websites, government sites, social media channels) and applies modern data science to answer questions such as:
- Who is going to be my next customer?
- How do I convert them?
- How can I find more of these ideal customers?
Truly knowing your entire marketing universe requires insight into both individuals and accounts. Blending account and lead scoring helps to provide a complete view of all available buying signals. Just like individuals, companies exhibit digital body language. For instance firmographic data may tell you a company fits the right industry profile or size. But what most marketers miss are the account-level buying signals such as growth trends, hiring patterns, government grants, patent filings or technology usage, just to name a few.
Having insight into account-level scores may give you a head start. Account-level activities are often the earliest buying signals, possibly preceding contact-level activities by weeks, months or even years.
Tapping into a Wealth of Data
With predictive analytics, you can score and segment your customer base on any attribute, including things like company-growth indicators, social activity, technology usage, funding events, credit score, job data and more.
Marketers can do ABM without predictive analytics by consolidating contact and account information into a single view, devising a scoring model and developing an ABM strategy. However, marketers that do ABM most elegantly and effectively use predictive analytics to score on three dimensions: fit, behavior and intent. By understanding these dimensions, you can take the key attributes of your ideal account and run segmentation to find more companies that are a good fit.
Go beyond simple scoring
Predictive analytics analyzes all the contact, opportunity, account and prior conversion data contained in marketing automation and CRM systems on three dimensions:
- Fit – Considering demographic details such as industry and title, what is the likelihood of this account being our customer?
- Behavioral – Analyzing the activities – such as reading a blog post, opening an email, watching a demo – that indicate an intent to learn more or being ready to purchase, what is the behavioral score of contacts associated with the account – whether prospects or customers? And how likely are these individuals to be our customers?
- Intent – Does the account demonstrate a need for our products or intent to purchase?
Traditional lead scoring models are usually based on measuring a combination of fit and behavior, factors that typically make up the ideal customer profile. However, a company may be the perfect fit and exhibit positive behavioral signals (e.g., consumed content), but that doesn’t mean it has a problem that your company can solve with its products. In other words, that account might have a need you cannot fulfill – or might not have a need at all.
To address this gap, a predictive scoring model goes a step further to also consider the account’s demonstrated need or desire for the product your company offers.
To tap into the full marketing universe when choosing target accounts, it’s critical to first understand that universe, and then prioritize and rank your efforts.
Learn more about how predictive marketing can turbo-charge your ABM efforts in our latest ebook.