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Evaluation of Predictive Lead Scoring Options

#Demand Generation

B2B marketing organizations with revenue generation as their focus have always regarded lead scoring as an important component of the lead management process. Over the last 3 years or so, many of the more mature companies have started evaluating or implementing predictive lead scoring to further improve the quality of leads being sent for tele or sales qualification.

There are three key options available for companies considering predictive lead scoring options:

  1. Use Predictive Lead Scoring Platforms

  2. Use Intent-based Scoring

  3. Use AI/ML-based modelling tools to develop predictive lead scoring models

Each of these options have their advantages and disadvantages.

Below, we present a simple evaluation of the 3 options to help organizations choose.

  Core Benefits Pros Cons Sample Brands
PLS Platforms
  •  Look-alike modelling of prospects against customer traits
  •  Profile and activity info about customers/prospects overlaid with external info through market scan + proprietary database
  •  Combination of internal and some external signals
  •  Data enhancement through proprietary data
  •  Low coverage of non-English markets, SMB segments
  •  Only partial customization of models
PLS Platforms
AI-based Intent Scoring
  •  Intent-based prioritization of accounts
  •  Environmental scan to collect intent and use AI-based predictive ranking
  •  Extensive external intent information
  •  Data enhancement through proprietary data
  •  Low coverage of non-English markets, SMB segments
  •  Limited customization for the organization’s requirements
  •  Limited overlay of companies’ interactions or transactions
AI based Intent Scoring
ML/AI Platforms
  •  Customizable modelling with ML capabilities
  •  Custom data including structured and non-structured data to create a customized model
  •  Customized modelling
  •  Regular optimization
  •  Ability to handle structured and unstructured data
  •  Trial and error for initial set-up
  •  Dependent on data volume and quality
ML/AI Platforms

As the above comparison shows, each option has its advantages and disadvantages. So, it is important to relate these strengths and weaknesses to the specific context in which the company is operating – what are the most important segments/markets, what is the existing data quality, volume availability, etc. to weigh the options. Similarly, the level of control that the organization might want for the model design could lead to different conclusions.

So, it is important to identify these key criteria and score each of these options against those criteria based on the business context to arrive at the optimal solution.

To hear more about our approach or to help you derive the most out of a PLS solution that you might have selected.

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