Even the best guess is still a guess!
Merely a decade ago, B2B marketing relied heavily on tried-and-true methods: trade shows, industry events, print advertising (in trade magazines), direct mail, and telemarketing were the stalwart channels of traditional marketing. The goal was to secure face-to-face interactions with prospects. Personal connections were the cornerstone of relationship-building and brand recognition.
Fast-forward to 2023. B2B buyers have undergone a seismic shift in their preferences. They actively seek out independent research and product discovery through self-serve digital processes. At every turn in the buyer’s journey, digital touchpoints generate intent signals, resulting in an overwhelming surge of data.
In this era of abundant touchpoints, can we still afford to rely on gut instinct, persona documentation, and subjective interpretation of customer behavior for lead scoring? This is the traditional lead scoring model as we’ve known it.
Is predictive lead scoring the answer
A shift in buying behavior and the proliferation of touchpoints are compelling marketers to adopt a more data-driven approach to capture prospects’ interest. With a multitude of signals pouring in from various sources, manual lead scoring no longer makes sense.
Over the past decade, AI has found increasing applications in marketing processes, including lead scoring systems. Many marketers today utilize machine learning algorithms to decipher historical and real-time data, gaining valuable insights into the buyer’s journey. This approach is known as predictive lead scoring.
But how does it differ from traditional scoring methods, and is it superior?
The Evolution of Predictive Lead Scoring
Predictive lead scoring surpasses traditional lead scoring by harnessing advanced analytics, reducing bias, and offering more precise, data-driven insights into lead quality. This results in enhanced resource allocation efficiency, increased conversion rates, and improved overall sales and marketing effectiveness.
While predictive lead scoring may be suitable for marketing efforts with lower ACVs (Average Contract Values), a shift towards more enterprise-focused strategies, such as Account-Based Marketing (ABM), necessitates a broader perspective that goes beyond individual leads and contacts.
In these scenarios, the number of stakeholders involved increases significantly, and buyer journeys become more complex and intertwined, making it impractical to focus solely on individual personas. This calls for the emergence of buyer group-focused playbooks.
Your ABM program needs more than predictive lead scoring
The primary objective of implementing account-based marketing programs is to engage with the complete committee of buyers within an account. Yet, many organizations continue to rely on lead scoring.
But can lead scoring, even when using predictive or AI-based approaches, truly enable you to achieve your ABM goals?
Let’s examine how predictive lead scoring stacks up against predictive account scoring.
The Leap to Predictive Account Scoring
Predictive account scoring is the way forward
B2B marketers have traditionally used lead scoring as a metric for gauging marketing success. Creating gated assets, expecting website visitors to pick up the pre-decided trail, and fall into the form-fill trap. This playbook will not serve the modern revenue marketers.
B2B marketing is rapidly evolving to keep pace with the ever-changing landscape of buyer behavior. Whether it’s demand generation or ABM, B2B marketers must urgently transcend mere lead scoring, whether traditional or predictive, to not just meet but exceed their revenue goals. With a growing number of organizations swiftly adopting smarter scoring systems, the imperative now lies in embracing predictive account scoring for immediate success.
