I See Sensors Everywhere
Life in the Singularity is dedicated to making sure we get the most out of living with artificial intelligence.
As an investor and business operator our principal focus is using AI and AI-enabled technology such as automation to make our lives:
less risky
more productive
peaceful & prosperous
We’re going to work with live code (typically Python) and unpack how technologies intersect. We’re going to build tools to help you gain the largest benefit from the incredible emergent capabilities.
One of the keys to Life in the Singularity is the ever-presence of sensors and the ubiquitousness of insights from their data.
A high-resolution information environment is required to drive effective automation.
That makes our lives easier.
We can use Python to build sensors that read changes in business conditions.
We can also use Py to automate the processing of these signals.
Based on readings from these sensors (threshold violations, trend formation / breakdowns, etc…) Py can then take action and:
Email
SMS
Slack
Write to G-Sheet / Excel
The magic starts when you layer different sensor and alert patterns, such as an escalating alert based on logic that uses more channels + targets progressively higher roles (AE, Sales Manager, CRO, President)
Let’s explore a working example of Python code.
I’ll post the code itself at the very bottom to keep things readable for non-nerds.
Whats going on here?
Simple.
This Py script is:
Connecting to SF
Calculating the number of created Opportunities in the last 12-weeks
Sending an alert if the company isn’t generating at least 20 deals per week
Talk about a useful sensor.
Having your finger on the heartbeat of the business - 24/7 with automated alerts that can be sent across any channel.
As mentioned earlier, creating a framework of escalating alerts can be a great way to ensure visibility and follow-through.
Consider how much better your life will be with sensors, AI and automation studying these in real time.
Sales Metrics: Key sales metrics such as new leads, conversion rate, deal pipeline, average deal size, and sales cycle length can give insights into how well your sales department is performing.
Customer Churn Rate: A high churn rate might indicate dissatisfaction among your clients. This is an extremely crucial metric in B2B businesses due to their typically high customer acquisition costs and long-term contracts.
Financial Health: Monitor cash flow, profit margin, expenses, and revenue growth rate. Understanding the financial health of your business is crucial for long-term success.
Customer Lifetime Value (CLTV): This gives an estimate of the total value a customer brings to your business over the duration of their relationship with your company.
Net Promoter Score (NPS): This measures customer loyalty and satisfaction. A sudden decrease in NPS could indicate rising customer dissatisfaction.
Customer Support Metrics: Average response time, ticket resolution rate, and customer satisfaction score can show how well your support team is managing client issues.
Product Usage Metrics: Depending on your product or service, metrics like daily active users, feature usage, and session duration can indicate how well your customers are engaging with your product.
Inventory Levels: If your business relies on physical products, automated alerts about low or surplus stock can help optimize your inventory and avoid disruptions.
Accounts Receivable: Automated alerts about late payments can help ensure you take timely action to collect what you're owed and maintain healthy cash flow.
Employee Satisfaction and Productivity: Keeping track of employee engagement, productivity, and attrition rates can provide early warning signs of problems within your team.
Sensors integrated with AI and automation provide a host of benefits for businesses, which can positively impact both the efficiency and effectiveness of operations.
Let’s explore the primary benefits:
Increased Efficiency: Automation reduces the time taken to execute repetitive tasks, freeing up human resources to focus on more complex, strategic responsibilities. This can dramatically increase overall operational efficiency.
Cost Savings: Businesses can save on labor costs with automation. Moreover, predictive maintenance facilitated by sensors can save substantial costs by predicting equipment failures before they happen.
Improved Decision Making: AI and machine learning models can process and analyze vast amounts of data faster than any human team, providing valuable insights and informing strategic decision-making processes. This can lead to better business decisions and improved outcomes.
Enhanced Customer Experience: AI can be used to enhance customer experiences with personalized recommendations, faster service, chatbots for 24/7 customer support, and more. This can increase customer satisfaction and loyalty.
Productivity and Scaling: Automation and AI can easily scale to meet growing business needs. Unlike humans, machines don't get tired or suffer from decreased productivity after long hours. This can also allow companies to be more productive without needing to significantly increase their workforce.
Reduced Errors: Machines are less likely to make errors than humans, especially in repetitive tasks. This can increase the accuracy of work, reduce the need for corrections, and enhance overall product or service quality.
Data Accuracy and Consistency: Integrated sensors and AI can provide accurate, consistent data collection and analysis, reducing the potential for human error in data management.
Better Risk Management: Predictive algorithms and AI can identify potential risks and threats, providing businesses with the opportunity to address these before they turn into major issues.
Improved Supply Chain Management: Sensors, AI, and automation can improve inventory management, procurement, and logistics, leading to a more efficient and reliable supply chain.
Enhanced Security: AI algorithms can help enhance cybersecurity efforts by identifying suspicious activities and potential threats, thereby protecting sensitive information and reducing the risk of cyber threats.
Sustainability: Automation and AI can help businesses reduce waste, optimize energy use, and create more sustainable practices.
These benefits layer atop each other which magnifies their impact.
Implementation requires my favorite programming language - Python.
Python is a powerful and versatile programming language that can be used to implement integrated sensors, AI, and automation:
Data Analysis and Machine Learning: Py is one of the most popular languages for data analysis and machine learning, thanks to libraries like NumPy, Pandas, SciKit-Learn, TensorFlow, and PyTorch. These libraries can be used to train machine learning models for predictive maintenance, risk management, customer personalization, and more.
Web Scraping and Data Gathering: With libraries like Beautiful Soup, Scrapy, and Selenium, Python can be used to automate the process of gathering data from the internet, or the CRM. This could include scraping competitor prices, collecting data for market research, or monitoring news feeds for relevant information.
Automation: Python can be used to automate a wide variety of tasks. For example, you can use libraries like Selenium or Pyautogui for browser automation, use Python's built-in libraries to automate file management tasks, or use libraries like Airflow or Luigi to build complex data pipelines.
Internet of Things (IoT) and Sensor Data: Python is a popular language for IoT devices due to its simplicity and the existence of libraries that interact with hardware, such as PySerial for serial communication, gpiozero for Raspberry Pi GPIO (General Purpose Input/Output) pins, and more. Sensor data collected from IoT devices can be processed and analyzed using Python. If your company uses booths at trade shows there is a lot of data to capture.
Web Development and APIs: Python can be used to develop websites and APIs, making it a good choice for developing web-based dashboards, data services, and more. Libraries like Flask and Django can help in this.
Chatbots and Revenue Intelligence: Python's Natural Language Processing (NLP) libraries like NLTK, SpaCy, or packages like ChatterBot can be used to develop revenue intelligence chatbots.
Cybersecurity: Python is also often used in cybersecurity, for tasks like network scanning, malware analysis, and penetration testing.
Python is the tactical tool of AI.
Sensor placement is a key element to enable whatever strategy you have in mind.
As you weave sensors throughout your life your ability to orchestrate better outcomes will increase. You will have more time to correct negative outcomes and maximize the impact from positive ones.
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Full Code in All its Nerdy Glory
import datetime
from simple_salesforce import Salesforce
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
# Replace with your Salesforce credentials
sf_username = 'mmcdonagh@not-CIA.gov'
sf_password = 'Majestic12'
sf_security_token = 'X'
# Authenticate and create a Salesforce instance
sf = Salesforce(username=sf_username, password=sf_password, security_token=sf_security_token)
# Date 12 weeks ago
date_12_weeks_ago = (datetime.datetime.now() - datetime.timedelta(weeks=12)).isoformat() + 'Z'
# Query for opportunities created in the last 12 weeks
query = f"""
SELECT COUNT(Id)
FROM Opportunity
WHERE CreatedDate >= {date_12_weeks_ago}
"""
result = sf.query_all(query)
opportunity_count = result['totalSize']
# Check if the number of opportunities is less than expected
if opportunity_count < 20 * 12:
# Set up the SMTP server
s = smtplib.SMTP(host='smtp.gmail.com', port=587)
s.starttls()
s.login('matt@revsystems.ai', 'AppPassword')
# Create email
msg = MIMEMultipart()
msg['From'] = 'matt@revsystems.ai'
msg['To'] = 'matt@revsystems.ai'
msg['Subject'] = 'Salesforce Opportunity Alert'
message = 'Less than 20 opportunities are being created each week on average for the last 12 weeks.'
msg.attach(MIMEText(message, 'plain'))
# Send the email
s.send_message(msg)
s.quit()