‘Predictive’ Deal Scoring With ML: Crystal Ball in Sales Process!

For years, Sam was a legend within our sales department. He possessed an almost supernatural ability to predict which deals would close, leaving colleagues scratching their heads.

His secret? Not magic, but a relentless dedication to data.

Sam meticulously logged every detail of his client interactions, from the obvious (deal size, company name, Financial stability) to the seemingly trivial: email frequency, call durations, client engagement cues, industry news mentions, trial metrics and even the weather of the day he met his clients.

One afternoon, amidst a sea of spreadsheets, Sam realized, “There has to be a smarter way to extract insights from this data.” He had heard whispers of machine learning (ML), the buzzword that promised to revolutionize data analysis.

He sought out Maya, our lead data scientist, and with a mix of excitement and trepidation as he had always avoided IT given a choice.

“Maya,” he asked, “can we build an automated ‘Deal Scoring’ system using these parameters?” He listed his key metrics:

?? Customer Interaction Frequency: How often do we connect?

?? Customer Engagement Quality: Are clients truly engaged during interactions?

?? Industry Trends: How well do their industries align with market growth?

?? Client Financial Health: What’s their financial stability?

?? Sales Cycle Length: How long has the deal been in the pipeline?

?? Decision-Maker Involvement, Competitive Landscape, and More.

Maya, initially intrigued by Sam’s data-driven approach, dove into the challenge. She cleaned the data, trained an ML model, and tested it against historical deals.

The results were astounding!! The model could predict deal closures with a level of accuracy that surprised them both.

Sam, the sales visionary, not only discovered his predictive tool but also revolutionized the company’s sales approach.

What is Deal Scoring?

Deal scoring is a systematic method of evaluating sales opportunities using predefined criteria to predict their likelihood of success.

It transforms complex data into a single, actionable score, empowering sales teams to prioritize effectively.

Deal scoring’s ability to combine many different data points into one actionable score is extremely powerful.

List a Few Factors Used For Deal Scoring.

Should We Assign Weightage to Each Deal Score Factor?

Yes, weightage assigned to each factor in a deal scoring model is crucial for several reasons:

1. Not all factors are created equal:

• Some factors have a significantly more significant impact on deal closure than others. Weightage allows you to reflect this reality in your scoring system.

2. Customization:

• Weightages allows you to customize your deal-scoring model to align with your specific business goals, industry, and sales strategy.

3. Improving Predictive Accuracy:

By emphasizing the most important factors for your Company, you can create a more reliable and effective scoring system.

4. Providing Actionable Insights:

• Weightage helps sales teams analyze the factors most critical for deal success.

5. Monitoring:

• By monitoring how different weightages contribute to deal outcomes, you can refine your weightages and improve the model over time.

How Are Weights Assigned to Deal Factors?

? Define a Scoring Scale: Choose a scale (e.g., 1–10, 0–100%).

? Assign Base Weights: Use impact levels (High, Medium) as a starting point.

? Fine-Tune for Your Business: Adjust weights based on your company’s priorities. For Example —

• If your company is in a highly competitive market, then Competitive Landscape/Differentiation should be weighted higher.
• If your product relies on a healthy economy, then Economic/Industry conditions should be weighted higher.

? Refine and Iterate: Analyze historical data to see how well your scoring system predicts deal outcomes.

How Does ML Transform Deal Scoring?

1?? Dynamic Weightages for Deal Score factors:

• ML enables dynamic weighting of scoring factors, adjusting their importance based on real-time data, changing market dynamics and customer behavior, thus ensuring the scoring system remains relevant.

2?? Personalized Insights:

• ML can provide personalized insights for each deal, highlighting the specific factors driving or hindering its progress.

3?? Risk Identification:

• ML can help identify potential deal risks, such as customer churn or financial instability, allowing for proactive mitigation.

Few ML Models Used for Deal Scoring.

Here are four ML models are commonly used in deal scoring with their strengths and weaknesses:

• Logistic Regression: For simple predictions.
• Random Forests: For complex data and feature importance.
• Neural Networks/GBM: For high accuracy and complex patterns.
• SVM: For complex boundary classification.

Choosing the proper machine learning (ML) model for deal scoring involves considering several factors related to your data, goals, and resources.

? For simple deal closure prediction and understanding factor relationships, start with Logistic Regression.

? For complex data and feature importance analysis, consider Random Forests.

? For high accuracy and the ability to handle large datasets and complex patterns, consider Neural Networks or GBM.

? For complex boundary classification, consider SVM.

The final deal score output should be designed to be clear, actionable, and easily integrated into your sales workflow.

Example Output:

Providing a comprehensive and actionable deal score output can empower your sales teams to make informed decisions and close more deals.

Conclusion:

Predictive deal scoring powered by machine learning is revolutionizing the sales process, transforming gut instincts into data-driven precision. By intelligently analyzing engagement, financial health, competitive factors, and market trends, ML-driven scoring systems empower sales teams to focus on the right opportunities, mitigate risks, and optimize strategies in real-time.

With dynamic weighting, personalized insights, and advanced risk identification, machine learning ensures that sales teams are always one step ahead. Choosing the right ML model — whether Logistic Regression for simplicity, Random Forests for feature importance, or Neural Networks for deep learning — can unlock unparalleled accuracy and efficiency.

As businesses embrace AI-driven deal scoring, they shift from reactive to proactive selling, where data and predictive intelligence back every deal decision.

Thank you for reading. I would greatly appreciate your comments and suggestions.