Applying Machine Learning for Intelligent Sales and Lead Scoring

An administrator accesses the settings for various Machine Learning features in SAP Sales Cloud Version 2. As an administrator, configure, train, and activate the Machine Learning models.

The administrators configure the following Machine Learning features using the path User Menu→Settings→All Settings→Intelligence Service.

With Lead Intelligence, identify open Leads with high potential to convert into an Opportunity. The system uses historical data from converted and declined Leads to train the machine learning model. As a result, the system builds a customer-specific predictive model and applies it to score Leads.

1. Open the "Lead Score" tab to get started with Lead Intelligence.
2. Check the "Readiness Report" to analyze the Lead data and determine if you get useful predictions for the Lead scoring.
3. Add custom fields to standard fields in machine learning scenarios and train models. Check that extension fields have enough data volume before enabling them at the machine learning scoring scenario level, as they won't affect the results.
4. Add, save, and train the model.
5. Activate the model.

Important factors for an administrator to consider:

• When activating the Lead scoring model, the Lead scores show the Leads. However, the scores don’t show until the initial scoring process is complete.
• The system updates and shows the Lead score pop-up daily. It triggers the update at midnight based on the data center’s time zone.
• The insights are available in the Leads score pop-up after the initial scoring run is complete, depending on the data volume.

The following demonstration shows how to set up the Lead Intelligence scenario.

With Lead Intelligence, sales representatives identify whether they can win or lose an Opportunity.

Opportunity scoring uses the machine learning model trained on past sales data to predict the probability of winning a deal. Opportunity scoring helps sales representatives prioritize their deals based on the winning probabilities.

1. Open the "Opportunity Score" tab to get started with Deal Intelligence.
2. Check the "Readiness Report" to analyze the Opportunity data to determine if you can get useful predictions for deal scoring.
3. Add custom fields to standard fields in machine learning scenarios and train models. Check that extension fields have enough data volume before enabling them at the machine learning scoring scenario level, as they won't affect the results.
4. Add, save, and train the model.
5. Activate the model.

Business Text Intelligence uses Natural Language Understanding (NLU) to generate actionable insights from appointment notes.

1. Open the "Business Text Intelligence" tab to get started.
2. Add, save, and train the model.
3. Activate the model.

The NLP Classification scenario includes the Sentiment output field, which indicates whether the survey vocabulary is positive or negative and the degree of positivity or negativity.

1. Open the "NLP Classification" tab to get started.
2. Add, save, and train the model.
3. Activate the model.

The Product Recommendation machine learning model offers actionable insights. Sales representatives use these recommendations to make quick up-sell and cross-sell offers to customers.

1. Open the "Product Recommendation" tab to get started.
2. Add, save, and train the model.
3. Activate the model.

The machine learning feature offers automated translation of incoming emails from the source language to the logon language.

1. Open the "Machine Translation" tab to get started.
2. Add, save, and train the model.
3. Activate the model.

The Profanity Check machine learning feature detects profane words and promptly issues warnings to keep a professional and respectful communication environment.

1. Open the "Profanity Check" tab to get started.
2. Add, save, and train the model.
3. Activate the model.