To conserve resources and optimize investment, a business must determine which potential opportunities are most likely to result in conversions and evolve into successful deals and determine which opportunities are at risk. This Hot Lead predictive analytics use case describes the value of predictive analytics to prioritize high-value leads and capitalize on an opportunity to convert a lead into a relationship by identifying key patterns that contribute to successful deal closures. Use these tools to identify the leads that are most likely to result in conversion and provide the most benefit to the enterprise. This technique can be used in many industries, including Financial Services, B2C and B2B. For more info https://www.smarten.com/augmented-analytics-learn-explore/use-cases.html

1. Hot Lead Prediction
Predictive Analytics Use Case

2. Hot Lead
Sample Application
Description
In order to conserve resources and optimize investment, a
business must determine which potential opportunities are
most likely to result in conversions and evolve into successful
deals and determine which opportunities are at risk. By
analyzing factors such as Opportunity Amount, Region, Client
Size (by Revenue and Employee Count), Competitor Type, and
Deal Size Category, Hot Lead predictive analytics helps sales
teams prioritize high-potential leads and enables businesses
to forecast the likelihood of capitalizing on an opportunity to
convert a lead into a relationship by identifying key patterns
that contribute to successful deal closures.

3. Hot Lead
Sample Application
Target
Opportunity Result
(Loss/Win)

4. Hot Lead
Sample Application
Influencing
Factors
Most Impacting Factors:
• Opportunity_Amount_USD: Represents the monetary value of the
opportunity, directly affecting the likelihood of closing and
conversion.
• Total_Days_Identified_Through_Closing: Represents the time
period from identifying the opportunity to closing and conversion,
and indicates lead engagement and urgency.
Other Contributing Factors:
• Region, Route_To_Market, Client_Size_By_Revenue,
Client_Size_By_Employee_Count, Competitor_Type,
Deal_Size_Category

5. Hot Lead
Sample Application
Algorithm(s)
The Classification analytical process is used to classify
numeric and/or categorical data into two or more groups
based on predefined categories.
• Higher classification accuracy (>=70%) means the results are
reliable and accurate.
• Lower classification accuracy (<70%) means the model
needs to be rebuilt using different input parameters.

6. Hot Lead
Sample Application
Model
Visualization

7. Hot Lead
Sample Application
Model
Visualization

8. Hot Lead
Sample Application
Model
Visualization

9. Hot Lead
Sample Application
Model
Visualization

10. Hot Lead
Sample Application
Interpretation

11. Hot Lead
Sample Application
Interpretation

12. Hot Lead
Sample Application
Model
Summary

13. Hot Lead
Sample Application
Result
• Likelihood/probability of a given opportunity result.
• Flag containing opportunity result with ‘Loss’ and ’Won’ values.

14. Hot Lead
Sample Application
Single Apply
To predict the lead based upon the selected parameter values,
APPLY functionality is used.

15. Hot Lead
Sample Application
Mass Apply
Predict lead for multiple records and compare actual result with
the predicted ones.

16. Hot Lead
Sample Application
Mass Apply
Model
Validation
Summary

17. Hot Lead
Sample Application
Key Influencer’s
Analysis

18. Hot Lead
Sample Application
Simulation

19. Hot Lead Prediction
Predictive Analytics Use Case
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Smarten – Hot Lead Prediction Use Case - 2025