Predictive Lead Scoring

Identifying and prioritizing high-qualified leads just got easier with predictive lead scoring. In a highly competitive marketplace, it’s crucial to provide your sales teams with qualified leads. Predictive lead scoring powered by predictive analytics software offers an accurate, reliable scoring model. The model’s predictions help prioritize great opportunities, adding tremendous value to your pipeline.

What is predictive lead scoring?

Predictive lead scoring leverages historical data (behavior data, intent data, firmographic data, and sales data) to predict the likelihood of a lead converting to a closed-won deal.

First, for predictive lead scoring to work properly, companies must analyze historical data (rejected leads and converted lead history). Then, that data can be used to develop an ideal customer profile (ICP). With this in mind, you can score new leads based on their similarities to your established ICP.

Why is lead scoring important?

According to leading research reports, only 25% of your inbound leads should advance to sales. To be sure, many unqualified leads enter the system, taking time away from better-qualified opportunities. In essence, lead scoring maximizes your sales team’s time with opportunities that lead to pipeline and closed-won deals.

How is predictive lead scoring used today?

Unquestionably, lead scoring and lead grading are very effective when used together to ensure the most qualified leads are passed from marketing to sales organizations.

  • Lead Score: The lead score is usually defined as the likelihood of a lead converting to an opportunity on a scale from 0-100. The highest likelihood of conversion is represented by a score of 100.
  • Lead Grade: Lead grade generally refers to quality from inbound lead sources. There are a number of factors that influence a lead grade. For example, these factors could include sourced channel, title, organization, company size, etc. Quality is typically scored with an A-F grade. After that, leads graded A, B, and, C are routed to sales. Leads graded D or F are either nurtured or archived as junk contact information.

As effective as scoring and grading leads may be, there are nonetheless a number of limitations. That’s especially true when you’re dealing with a high volume of leads and if sales teams aren’t updating CRM tools or following the same scoring definition. For this reason, many organizations have shifted from manual scoring to automated lead scoring using machine learning and advanced analytics.

Benefits of machine learning-based predictive lead scoring

Without a doubt, today’s companies have massive amounts of data at their disposal. Coupled with that data, the AI/ML approach to predictive lead scoring offers many benefits:

  1. Minimize Errors: Automated scoring and enrichment minimize errors from manual data entry. In turn, automation improves the accuracy of lead scoring and opportunity recognition.
  2. Data Patterns: AI identifies complex patterns in your data that are unrecognizable to the human eye. Uncovering these patterns allows you to discover new traits in leads, offering new opportunities in scoring leads and predicting opportunities.
  3. Virtuous Cycle: Using machine learning allows you to continuously update your scoring model. This ongoing refinement not only updates your scores with every new lead and opportunity, but also keeps your scoring model current as your business grows and your product portfolio changes throughout the years.

Key traits of a predictive lead scoring platform

  1. Own Your Data: With any lead scoring platform, you need full control over your data. In any event, be wary of any lead scoring platform that will tag your properties, score your leads, and mix your data with other brands.
  2. Data Connectors: The platform should connect with your systems of record to allow easy scoring and action on the scores. Also, connecting your predictive lead scoring platform to your marketing automation tools allows you to seamlessly push audiences to email or paid advertising platforms.
  3. Data Prep: Data prep accounts for roughly 80% of the time spent in creating predictive models. AutoML platforms eliminate resource requests needed in creating predictive lead scores.
  4. Enrichment Opportunities: For many seasonally oriented companies, working with third-party data enrichment providers (e.g., for weather data) is critical. When picking a predictive lead sourcing platform, understand your options for enriching your data to better score leads.
  5. Transparent Platform: Your lead scoring software must offer transparency in how the predictive algorithm scores leads and opportunities. Otherwise, you’re working in the dark and unable to adjust based on what you’re learning from your data and process.
  6. Customization: Predictive lead scoring should be customized to your business, KPIs, and business needs.

If you’re interested in adopting a machine-learning driven approach to predictive lead scoring, learn more today about how Pecan can help by requesting a demo.

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