HubSpot Dummy Data Generator

MKupermann — Tue, 06 Jun 2023 12:29:53 GMT

Dear community,

As a Diamond partner, we strive to provide our potential and current clients with realistic demonstrations of HubSpot scenarios and solutions. Authenticity plays a pivotal role in this endeavour, which is why we have developed an effective tool to generate demonstrative data.

We have created a Python script which generates an extensive Excel file, encompassing Companies, Contacts, Deals, Products and Tickets for G7 countries (although this list is easily expandable). This script can generate thousands of records, thereby providing a comprehensive and immersive experience for the client.

Feel free to amend the script to cater to your specific needs. You might choose to generate more fields, other records, or even Custom Objects. The possibilities are extensive, and the flexibility of this tool allows us to tailor each presentation to the unique needs of our clients.

Here is the code we have developed. We firmly believe in the power of sharing knowledge and resources, and hope this tool serves you well in your engagements.

Best regards,

Michael Kupermann
Managing Director Coders Unlimited / Ryze Digital

Here the Python code:

# This code has been created by Michael Kupermann (michael.kupermann@codersunlimited.com or michael@kupermann.com)

# The purpose of this code is to generate dummy data that simulates a realistic dataset for HubSpot CRM.

# This data can then be used for demonstrations, testing, or other purposes that require a representative dataset.

# You need to amend the HubSpot Sales and Service Pipelines before you import the data.

# Required Packages:

# 1. Faker: This package is used to generate the fake data for our dataset.

# 2. Pandas: This package is used to handle the data in a tabular format and to write the data to an Excel file.

# 3. DateTime: This package is used to generate realistic date data for the 'close_date' field.

# To install the necessary packages, you can use pip, the Python package installer.

# Open your terminal (or command prompt on Windows), and enter the following commands:

# pip install faker

# pip install pandas

# pip install datetime

# If you're using a Jupyter notebook, you can prefix these commands with an exclamation mark:

# !pip install faker

# !pip install pandas

# !pip install datetime

from faker import Faker

import pandas as pd

from datetime import datetime, timedelta

# Function to generate data for a given country. Here 100 companies with 10 contacts, deals, tickets for each company

def generate_data(country, company_rows=100, contacts_per_company=10, deals_per_company=10, products_per_deal=10 )

# Set the locale for Faker based on the country

if country == "Germany":

faker = Faker("de_DE")

elif country == "United States":

faker = Faker("en_US")

elif country == "France":

faker = Faker("fr_FR")

elif country == "Italy":

faker = Faker("it_IT")

elif country == "Japan":

faker = Faker("ja_JP")

elif country == "United Kingdom":

faker = Faker("en_GB")

elif country == "Canada":

faker = Faker("en_CA")

elif country == "Austria":

faker = Faker("de_AT")

elif country == "Switzerland":

faker = Faker("de_CH")

# Create a dictionary to hold the data

data = {

"company_name": [],

"company_domain": [],

"company_industry": [],

"company_address": [],

"company_country": [],

"contact_firstname": [],

"contact_lastname": [],

"contact_email": [],

"contact_phone": [],

"contact_address": [],

"contact_country": [],

"contact_function": [],

"contact_department": [],

"deal_name": [],

"deal_stage": [],

"deal_amount": [],

"deal_type": [],

"deal_source": [],

"close_date": [],

"ticket_title": [],

"ticket_status": [],

"ticket_priority": [],

"product_name": [],

"product_price": [],

"product_description": [],

"product_sku": [],

"product_quantity": [],

}

# Loop to generate data for each company

for _ in range(company_rows )

company_name = faker.company()

company_domain = faker.domain_name()

company_industry = faker.random_element(["Technology", "Healthcare", "Finance", "Real Estate"])

company_address = faker.address().replace('\n', ', ')

company_country = country

# Loop to generate data for each contact

for _ in range(contacts_per_company )

contact_firstname = faker.first_name()

contact_lastname = faker.last_name()

contact_email = faker.email()

contact_phone = faker.phone_number()

contact_address = faker.address().replace('\n', ', ')

contact_country = country

contact_function = faker.job()

contact_department = faker.random_element(["Sales", "Marketing", "Human Resources", "Engineering"])

# Append generated company and contact data to the lists in the dictionary

data["company_name"].append(company_name)

data["company_domain"].append(company_domain)

data["company_industry"].append(company_industry)

data["company_address"].append(company_address)

data["company_country"].append(company_country)

data["contact_firstname"].append(contact_firstname)

data["contact_lastname"].append(contact_lastname)

data["contact_email"].append(contact_email)

data["contact_phone"].append(contact_phone)

data["contact_address"].append(contact_address)

data["contact_country"].append(contact_country)

data["contact_function"].append(contact_function)

data["contact_department"].append(contact_department)

# Generate deal and product data

data["deal_name"].append(f'Deal-{faker.uuid4()}')

data["deal_stage"].append(faker.random_element(["Appointment Scheduled", "Qualified To Buy", "Presentation Scheduled", "Decision Maker Brought-In"]))

data["deal_amount"].append(faker.random_int(min=1000, max=50000))

data["deal_type"].append(faker.random_element(["New Business", "Existing Business"]))

data["deal_source"].append(faker.random_element(["Direct Traffic", "Organic Search", "Paid Search", "Social Media"]))

data["close_date"].append((datetime.today() + timedelta(days=faker.random_int(min=1, max=90))).date())

# Generate product data

data["product_name"].append(f'Product-{faker.uuid4()}')

data["product_price"].append(faker.random_int(min=10, max=1000))

data["product_description"].append(faker.catch_phrase())

data["product_sku"].append(faker.random_int(min=10000, max=99999))

data["product_quantity"].append(faker.random_int(min=1, max=100))

# Generate ticket data

data["ticket_title"].append(f'Ticket-{faker.uuid4()}')

data["ticket_status"].append(faker.random_element(["New", "Waiting on contact", "Waiting on us", "Closed"]))

data["ticket_priority"].append(faker.random_element(["Low", "Medium", "High"]))

# Convert the data dictionary to a pandas DataFrame

df = pd.DataFrame(data)

return df

# Define the list of countries for which we want to generate data

g7_countries = ["Canada", "France", "Germany", "Italy", "Japan", "United Kingdom", "United States", "Austria", "Switzerland"]

# Create an empty DataFrame to hold the generated data

result = pd.DataFrame()

for country in g7_countries:

df = generate_data(country)

# Append the data for each country to the result DataFrame

result = result.append(df)

# Write the generated data to an Excel file

result.to_excel(r"C:\~\~\~\hubspot_dummy_data.xlsx", index=False)

Re: HubSpot Dummy Data Generator

Jaycee_Lewis — Tue, 06 Jun 2023 21:03:26 GMT

Hey, @MKupermann 👋 Thank you very much for sharing! — Jaycee

Re: HubSpot Dummy Data Generator

itsndy — Wed, 11 Oct 2023 11:32:31 GMT

Good stuff @MKupermann . Just a couple of colons missing for the functions and loops. Otherwise it works fine.

Re: HubSpot Dummy Data Generator

MKupermann — Wed, 11 Oct 2023 11:35:51 GMT

Ah, sorry! Thanks for the feedback!

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