Discover the game-changing potential of predictive lead scoring in this comprehensive guide. As businesses strive to maximize their sales performance, the strategic implementation of predictive lead scoring emerges as a crucial tool. Dive into the world of advanced data analytics, machine learning, and artificial intelligence to gain a deep understanding of how predictive lead scoring revolutionizes the sales process.

1. How to select the
best prospective
customers from
your marketing
campaign?

2. One the way is to adapt
Predictive Lead
Scoring(PLS)
using
Machine Learning
What is PLS?

3. As more and more prospects shows
interest in your product or services on
your campaigns, you have to be
selective in your pursuit on closing the
deals you can cater to
So PLS is a process of qualifying
prospects to best customers to better
the ROI of your marketing $
How to do PLS?

4. PLS can be done in 2 ways
A rule based scoring
1.
A score can be assigned to each steps of
the campaign journey of a prospect and
the total lead score can be arrived at
For example ,
Replying to
Connection
&
Follow
Social Media
Engagements
&
Comments
e-mail
communications
for interests
on your offerings
Engaging in
demos or
subscribing for
free trials
5
Points
10
Points
15
Points
20
Points
Add up the score based on the quality of
engagement with your sales team to
arrive at lead score
Is next one ML based?

5. Yes, it is ML based
2. Ready-to-convert classifier
Model
The model is trained on the past data
on campaign having customer profiles
and conversion status
This model can predict wheather new
lead coming through the campaign
process will convert or not (i.e.
True/False)
So in ML terminology, it is Binary
Classification Logistic Regression Model
Logistic Regression Model provides a
probability to convert and the sales
team can set the probability threshold
to higher side to perform the
classification

6. Steps to prepare a Ready-to-
convert classifier Model
Collect and prepare data
Split the data into training
and testing sets.
Train the model with training data
Evaluate the model with test data
Deploy & integrate the model with
campaign or CRM application
Refine the model with new data:
This is a powerful tool for
Predictive Lead Scoring
due to simplicity, interpretability, robustness,
scalability, and ability to give a probability to
find your next best customer.

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