Lead scoring based on the principles of predictive analytics is the definition of the fact, which customers are ideal for working and the construction of a simple lead score based on these facts.
Essentially, while lead generation is the activity of acquiring the contact information of prospective buyers, lead scoring models are primary components of the process that assist businesses in sorting the leads. In the use of predictive analytics, it is possible to ascertain the scoring models of the leads that are likely to deliver, and therefore, these models are likely to be good and quite effective.
To make the lead scoring model for the companies with the kind of data that will really predict enough leads, one has to identify major factors the most linked to the customer acquisition first. The variables, which can be employed for the assessment of customer preferences, includes website activity, and customer’s age, gender, buying histories etc.
Once the factors have been identified, the businesses can use the technique of data mining to assign to the leads a likelihood of a customer based on the extent to which they mirror the ideal customer.
The scoring model can be updated, improved over time as more data emerges, and the model changes due to the shift in customers’ behavior, and therefore, the businesses can adapt their marketing techniques based on the results of the improved scoring model and drive customers to the targeted marketing segment. Marketing automation applied to the creation of lead scoring models helps businesses understand their customers better and focus their energy on leads that stand a higher likelihood of culminating in a sale—a key to increasing the probability of success of a lead generation strategy.