Predictive lead scoring, explained simply
Posted: Tue Jan 21, 2025 4:06 am
In today's business world, a thorough understanding of the customer is a vital requirement for success. With the explosion of Big Data and Artificial Intelligence (AI), many companies are investing in tools that provide them with a more precise approach to their potential customers in order to carry out more effective marketing and sales strategies that increase ROI. Thus, technology has revolutionized traditional business methodologies, making them more competent, faster and ultimately necessary. This is the case of lead scoring, a traditional marketing method, now complemented by AI predictive models.
Traditionally, marketing and sales teams performed lead scoring manually. This method involved assigning scores to potential leads based on various attributes (demographics, behavioral tendencies, etc.). Based on this score, it was identified whether or not the lead was ready to close a sale.
This methodology had several drawbacks. First, data was classified based on criteria established by the organization and the ideal customer profile was created manually. Specialists relied on their own subjective judgment or previous data patterns to assess those actions that could be relevant to making a sale. As a result, real opportunities could go unnoticed or, on the contrary, sales teams focused too much on inappropriate prospects based on inaccurate scores. In addition, it had its limitations when analyzing voluminous data, as well as requiring more time and effort for a correct qualification of potential clients.
For example, a staggering 79% of B2B marketers have ghana whatsapp lead not even implemented traditional lead scoring, even though companies that excel at lead nurturing generate 50% more sales-ready leads at 33% lower cost.
This is where Artificial Intelligence comes into play. In a world characterized by Big Data and its constant growth, technological tools are required to ensure competitiveness.
What is predictive lead scoring?
Predictive lead scoring takes the traditional approach to the next level through Big Data and machine learning.
In this way, key behaviors of current and prospective customers are analyzed using predictive modeling algorithms in order to classify and scale them. From this, it is possible to identify which ones are more likely to convert, retain and purchase the company's products and/or services.
In other words, predictive lead scoring generates an optimal or ideal profile based on past purchasing behavior trends and then identifies which current prospects best fit this profile.
Predictive lead scoring
Benefits of predictive lead scoring
The benefits of predictive lead scoring methods are easily visible compared to traditional methodology. The main ones are:
Data-centric approach: In traditional methodology, the factors to be evaluated were based on the subjective judgment of marketing and sales executives and departments, with their biases, assumptions and guesses. The predictive approach eradicates this and relies on solid data to make its predictions.
Types of information that impact a lead's score
Reduction of human errors: Thanks to its data-centric approach and the use of AI tools such as machine learning, the possibility of subjective errors due to human biases or judgments is reduced.
Time optimization: The automated lead scoring system advantageously replaces the efforts that marketing and sales teams traditionally dedicated to this. Now their time can be used with greater impact on high-scoring prospects.
Fast and comprehensive results: By using AI tools, the results of predictive methods will of course be faster than manual ones. Also, by linking multiple data, it provides a more detailed profile of the prospect, qualitatively improving the accuracy of the scores.
Improved ROI: Predictive scoring methods calculate a score for all leads using machine learning algorithms. This allows you to identify and focus your efforts on sales-ready prospects and maximize your ROI.
Flexibility to cope with changing dynamics: Information changes rapidly within the customer base. Therefore, criteria outlined for scoring prospects a few weeks ago may already be outdated. Predictive methods allow for predictions to change as data changes. However, this is closely linked between marketing and AI. Marketing must report on these changes to ensure a good outcome from machine learning.
Harmony between marketing and sales teams: More accurate lead scoring brings together the efforts of both departments on the most highly qualified leads. Otherwise, many leads may arrive, but not many sales are closed, which can cause a conflict between the two teams, as marketing may not understand why they are providing a large number of leads that sales cannot close.
Traditionally, marketing and sales teams performed lead scoring manually. This method involved assigning scores to potential leads based on various attributes (demographics, behavioral tendencies, etc.). Based on this score, it was identified whether or not the lead was ready to close a sale.
This methodology had several drawbacks. First, data was classified based on criteria established by the organization and the ideal customer profile was created manually. Specialists relied on their own subjective judgment or previous data patterns to assess those actions that could be relevant to making a sale. As a result, real opportunities could go unnoticed or, on the contrary, sales teams focused too much on inappropriate prospects based on inaccurate scores. In addition, it had its limitations when analyzing voluminous data, as well as requiring more time and effort for a correct qualification of potential clients.
For example, a staggering 79% of B2B marketers have ghana whatsapp lead not even implemented traditional lead scoring, even though companies that excel at lead nurturing generate 50% more sales-ready leads at 33% lower cost.
This is where Artificial Intelligence comes into play. In a world characterized by Big Data and its constant growth, technological tools are required to ensure competitiveness.
What is predictive lead scoring?
Predictive lead scoring takes the traditional approach to the next level through Big Data and machine learning.
In this way, key behaviors of current and prospective customers are analyzed using predictive modeling algorithms in order to classify and scale them. From this, it is possible to identify which ones are more likely to convert, retain and purchase the company's products and/or services.
In other words, predictive lead scoring generates an optimal or ideal profile based on past purchasing behavior trends and then identifies which current prospects best fit this profile.
Predictive lead scoring
Benefits of predictive lead scoring
The benefits of predictive lead scoring methods are easily visible compared to traditional methodology. The main ones are:
Data-centric approach: In traditional methodology, the factors to be evaluated were based on the subjective judgment of marketing and sales executives and departments, with their biases, assumptions and guesses. The predictive approach eradicates this and relies on solid data to make its predictions.
Types of information that impact a lead's score
Reduction of human errors: Thanks to its data-centric approach and the use of AI tools such as machine learning, the possibility of subjective errors due to human biases or judgments is reduced.
Time optimization: The automated lead scoring system advantageously replaces the efforts that marketing and sales teams traditionally dedicated to this. Now their time can be used with greater impact on high-scoring prospects.
Fast and comprehensive results: By using AI tools, the results of predictive methods will of course be faster than manual ones. Also, by linking multiple data, it provides a more detailed profile of the prospect, qualitatively improving the accuracy of the scores.
Improved ROI: Predictive scoring methods calculate a score for all leads using machine learning algorithms. This allows you to identify and focus your efforts on sales-ready prospects and maximize your ROI.
Flexibility to cope with changing dynamics: Information changes rapidly within the customer base. Therefore, criteria outlined for scoring prospects a few weeks ago may already be outdated. Predictive methods allow for predictions to change as data changes. However, this is closely linked between marketing and AI. Marketing must report on these changes to ensure a good outcome from machine learning.
Harmony between marketing and sales teams: More accurate lead scoring brings together the efforts of both departments on the most highly qualified leads. Otherwise, many leads may arrive, but not many sales are closed, which can cause a conflict between the two teams, as marketing may not understand why they are providing a large number of leads that sales cannot close.