Building a Python-powered Revenue Engine

Life in the Singularity examines the impact Artificial Intelligence is having on our personal and professional lives. Our aim is to better understand and leverage the growing capabilities of AI.

In this post we’ll unpack how AI has begun to takeover one of the fastest growing functions - Revenue Operations.

In particular I will explain how Python can be used to power Revenue Engines.

A Revenue Engine is an integrated system that drives revenue growth by aligning and optimizing the efforts of sales, marketing, success & finance teams. It focuses on a seamless customer experience throughout the entire customer lifecycle and leverages data, technology, and cross-team collaboration to achieve revenue objectives.

The Revenue Engine comprises three key components: People, Process, and Platform.

1. People: People are the backbone of any Revenue Engine. This includes the teams that work together to attract, engage, convert, and retain customers. Aligning these teams and promoting a collaborative mindset ensures that they work towards shared revenue goals and contribute to the growth of the organization. Effective communication, clearly defined roles and responsibilities paired with a strong culture of collaboration are critical for the success of the Revenue Engine.

2. Process: Processes are the strategies, methodologies, and best practices that guide the day-to-day operations of the Revenue Engine. These include lead generation, lead nurturing, sales pipeline management, customer onboarding, and retention strategies. By streamlining and optimizing these processes, organizations can reduce inefficiencies, minimize bottlenecks, and ensure a smooth customer journey. It is essential to continuously monitor, evaluate, and refine these processes based on data-driven insights and changing market conditions.

3. Platform: The Platform refers to the technology and tools that support and enable the Revenue Engine. This includes CRM systems, marketing automation platforms, data analytics tools, and other technologies that help teams work more efficiently, make data-informed decisions, and deliver a seamless customer experience. A robust and integrated technology stack automates repetitive tasks, provide real-time data insights, and facilitate collaboration among teams.

When these three components - People, Process, and Platform - are effectively aligned and optimized, the Revenue Engine can achieve its full potential. This includes increased sales in the most valuable customer segments, improved customer satisfaction, and sustainable revenue growth.

Without powerful, predictable and efficient go-to-market machinery:

• market share is at-risk

• generating revenue creates excess friction

  • e.g. poor MQL to SQL conversion leads to pipeline generation and conversion underperformance

• retaining revenue is resource expensive

  • discounts

  • time costs

As builders and mechanics of the Revenue Engine, Revenue Operations (RevOps) teams are responsible for:

1. Alignment and collaboration between departments: By unifying the data, processes, and goals of each revenue team, RevOps aims to break down silos and improve collaboration across the organization.

2. Process optimization and efficiency: RevOps aims to streamline and optimize the revenue generation process by identifying bottlenecks, removing redundancies, and automating repetitive tasks.

3. Data-driven decision making and performance measurement: RevOps focuses on leveraging data and analytics to monitor performance and make informed decisions that drive revenue growth.

Python is the perfect tool to “wire up” the Revenue Engine.

Python can be used for this objective by automating data collection and processing signals from different departments, facilitating real-time information sharing, and generating insights.

With libraries like pandas and NumPy, Python can help in cleaning, analyzing, and visualizing data from various sources, enabling teams to make data-driven decisions.

Once you connect a CRM to a Python notebook you have the onboard computer of the revenue engine primed for:

1. Automation

2. Exploratory Data Analysis

3. Segmentation (Account, Opportunity, Lead, User)

Python can be used for process optimization by automating repetitive tasks and workflows. Libraries like Selenium and BeautifulSoup can be used for web scraping and data extraction, while tools like Airflow and Apache NiFi can facilitate workflow automation and process orchestration. Machine learning libraries like scikit-learn and PyTorch can also help in building predictive models to optimize lead scoring, customer segmentation, and forecasting.

Python can be used to create data-driven decision-making tools by performing data analysis, visualization, and creating predictive models. These tools can help in uncovering patterns and trends in data, improving forecasting accuracy, and identifying opportunities for growth.

Let’s examine how we can use Py to visualize Closed Won opportunities over the last 3-years:

import pandas as pd
from simple_salesforce import Salesforce
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

# Replace with your Salesforce credentials
sf_username = 'mmcdonagh@not_CIA.gov'
sf_password = 'PerfectPassword'
sf_security_token = 'abcdefghijklmnopqrstuvwxyz'

# Authenticate and create a Salesforce instance
sf = Salesforce(username=sf_username, password=sf_password, security_token=sf_security_token)

# Calculate the date 3 years ago from today
three_years_ago = (datetime.now() - timedelta(days=3*365)).strftime('%Y-%m-%d')

# Query Salesforce for Closed Won Opportunities in the last 3 years
opportunity_query = f"""
    SELECT Id, Name, Subscription_Start_Date__c, Amount, StageName, Total_ARR__c
    FROM Opportunity
    WHERE StageName = 'Closed Won' AND Subscription_Start_Date__c >= {three_years_ago}
"""
opportunities = sf.query_all(opportunity_query)

# Convert the results to a pandas DataFrame
opportunity_df = pd.DataFrame(opportunities['records']).drop(columns='attributes')

# Convert CloseDate to datetime and extract the year
opportunity_df['Subscription_Start_Date__c'] = pd.to_datetime(opportunity_df['Subscription_Start_Date__c'])
opportunity_df['Year'] = opportunity_df['Subscription_Start_Date__c'].dt.year

# Calculate Closed Won Revenue by Year
yearly_revenue = opportunity_df.groupby('Year')['Total_ARR__c'].sum().reset_index()

# Visualize Year over Year Closed Won Opportunities
plt.figure(figsize=(10, 6))
plt.bar(yearly_revenue['Year'], yearly_revenue['Total_ARR__c'])
plt.xlabel('Year')
plt.ylabel('Closed Won Revenue')
plt.title('Year over Year Closed Won Opportunities (Last 3 Years)')
plt.xticks(yearly_revenue['Year'])
plt.show()

And here is our result:

This Py file can be saved locally to a machine, or uploaded to a server and run on a schedule. The resulting reports, graphics, CSV, JSON can be automatically emailed or sent via Slack to a recipient list.

Python-executed AI can be employed to analyze customer feedback, social media mentions, and other unstructured data sources to gauge sentiment towards a brand or product. This can help businesses identify customer pain points, improve their offerings, and optimize the overall customer experience, ultimately contributing to revenue growth.

AI is extending incredible capabilities to companies willing to invest in upgrading their Revenue Engines with Python.

By leveraging Python's extensive ecosystem of libraries and tools, businesses can efficiently execute RevOps strategies and drive growth through improved alignment, process optimization, and data-driven decision-making.

The companies gaining market share going forward will increasingly utilize AI in the design and operation of their Revenue Engines.