Conventional Lead Scoring Vs Predictive Lead Scoring

Lead scoring is process to rank your prospects against scale representing value each lead has to the organization. Lead scoring helps organization to identify most qualified leads. Most organizations design lead scoring model to identify sales ready leads. Lead scoring is beneficial to your business in many ways.

Conventional lead scoring

In conventional lead scoring points are given to certain actions such as demographics,website visit,webinar attendance, content download ,form completion etc. The challenge here is to which data points to be used for your lead scoring and what score to assign to these data points?

What points should be assigned to particular criteria is big challenge task For example Should we assign 10 points for ebook download or 5? Scoring to these data points is done without knowing a better way. Lot of guess work and gut feeling is applied while scoring these data points.. The number values are largely arbitrary

Which data points should we consider while designing our lead scoring model is another challenging task. This is mostly done by guesswork and gut feeling. scores are assigned to maybe a total of ten signals, based on rule of thumb or gut feeling, assumptions.

Conventional lead scoring model is based on mere guess work and assumptions.

Predictive lead scoring

Instead of relying on guesswork and gut feeling predictive lead scoring uses proven scientific and statistical methods to predict sales ready leads. Data science techniques like statistical analysis, look-alike modeling, machine learning are used to analyze data within your customer database and external data from the web. This method for designing lead scoring model is called predictive modeling. This process help you to define your qualified leads profile You base your lead scoring against this profile.

Unlike conventional lead scoring where you design lead scoring model on potentially arbitrary data points, predictive modeling will help you to design your lead scoring model based on ideal prospects according to characteristics of your customers. It will show you how to build your lead scoring on the data points that actually matter, so you no longer have to guess.

You can design predictive lead scoring model with the help of in house team of data scientists or you can hire services of consulting firms that specialize in this.

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