The only tool you need for your marketing work is free.

If you think marketing is a stage where creativity and gut feeling call all the shots, get ready for a script you didn't see coming. Marketing isn't just an art anymore. It's a science too.

Python is the tool you use to process data and also to build automations that save you precious time. And if the word "code" makes you nervous, relax.

With digital interpreters like Stablecode, ChatGPT or Code Llama, you can tap into everything Python does without becoming a coding scholar.

So buckle up: the next few paragraphs could turn your work routine into a fascinating and remarkably efficient adventure.

What Python Can Do for Marketing

Maybe you've always told yourself that marketing is for creative types, people who know how to talk and sell. Well, it's time to update your playbook. With Python, you step into territory where creativity meets scientific precision. We're not just talking about chatbots that keep your customers company when you can't, or analytics tools that tell you what your audience likes and doesn't like. No, we're going much further.

Python lets you automate processes that used to take hours of manual work.

Want to collect data from websites for your market research? Python does it for you. Interested in understanding the emotions hiding behind your customers' comments? Python has the right tools. And if you're more ambitious, machine learning lets you predict market trends before they go mainstream.

If you think this is all just talk, wait for the next sections, where we get into the details of each of these. We'll show you how Python can be more than a utensil: a real extension of your marketing skills. And the best part is that Python keeps evolving. Every day, a developer community of millions of people around the world adds new libraries and tools.

So if you're wondering whether it's time to rethink how you do marketing, the answer is a resounding yes. And if you're ready to set off on this adventure, keep reading. Because what comes next isn't just a list of functions and commands. It's a new way to think about marketing and to practice it.

Workflow Automation

If routine is wearing you down and repetitive tasks make you feel like a hamster on a wheel, Python is your way out of that loop. With a few lines of code, you can automate processes that have always eaten up your time and energy. We're not talking about simple shortcuts. This is a real shift in the way you run your marketing work.

Python lets you connect different platforms and software to each other, through APIs. That means you can, for example, have your CRM talk to your email marketing software, fully automatically. And while the machines work for you, you can focus on what really matters: thinking, strategizing, creating.

But automation isn't only about efficiency. It's also about precision. With Python, you remove the risk of human error in sensitive tasks like data analysis or audience segmentation. So you work faster, and more accurately too.

If this sounds like science fiction, I promise you it isn't.

Take a simple but powerful example: sending emails.

Say you have an Excel sheet with thousands of customer email addresses and you want to send each of them a personalized newsletter. The best part is that you can do all of this without paying for expensive email marketing tools.

All it takes is code like this, ready to copy, paste and run:

import pandas as pd
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import time

# Leggi il file Excel con i contatti
df = pd.read_excel("contatti.xlsx")

# Configura il server SMTP
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login("tua_email", "app_password")

# Numero di email da inviare per ogni "batch"
batch_size = 5
pause_time = 10  # Secondi di pausa tra ogni batch

# Itera attraverso la lista di email
for index, (idx, row) in enumerate(df.iterrows()):
    email = row['Email']
    nome = row['Nome']  # Assumendo che ci sia una colonna 'Nome' nel tuo file Excel

    # Crea l'email
    msg = MIMEMultipart()
    msg['From'] = "tua_email"
    msg['To'] = email
    msg['Subject'] = "Questo è un messaggio di prova"

    # Corpo dell'email
    body = f"Caro {nome}, questo è un messaggio di prova."
    msg.attach(MIMEText(body, 'plain'))

    # Invia l'email e stampa un messaggio di successo
    server.sendmail("tua_email", email, msg.as_string())
    print(f"Email inviata a {nome} ({email})!")

    # Pausa l'esecuzione dopo ogni batch
    if (index + 1) % batch_size == 0:
        print(f"Aspettando {pause_time} secondi prima del prossimo batch...")
        time.sleep(pause_time)

# Chiude la connessione con il server SMTP
server.quit()

You'll find the step-by-step procedure in the courses area, under quick win.

Web Scraping

Let's talk about a field where Python really shines: web scraping. If you've ever spent hours collecting information from websites by hand, you know how tedious and time-consuming it can be. But with Python, the job changes completely and hours become seconds.

Web scraping with Python is like having an army of analysts ready to gather data for you, except Python doesn't make mistakes, doesn't get tired and works at unimaginable speed. Say you want to analyze customer reviews across different products or keep an eye on your competitors' prices. With Python, you can do all of it automatically, accurately and incredibly fast.

But let's not stop there. This isn't just a way to speed up a task that would otherwise be slow and laborious. It also lets you do things that used to be practically impossible because of human limits. Picture having to analyze thousands of pages of reviews or compare prices across hundreds of websites. With Python, that's possible, and doable in a reasonable amount of time.

And if you think you need to invest in expensive software or platforms for all this, you're dead wrong. Python and its libraries are open source, which means they're free for anyone who wants to use them.

And for anyone with ethical doubts: web scraping is legal, as long as you follow a few rules of good conduct. So besides being effective and powerful, it's also an ethically acceptable way to gather information.

The code looks more or less like this. It's just an example, a starting point. If you want the detailed procedure, you can ask for it in the AI Warriors community

import requests
from bs4 import BeautifulSoup

url = 'URL_del_tuo_prodotto'
response = requests.get(url)

soup = BeautifulSoup(response.text, 'html.parser')
reviews = soup.find_all('div', {'class': 'recensione'})

for review in reviews:
    print(review.text)

With Python and a handful of lines of code you don't even have to write yourself, you've just turned research that could have taken hours into a job of a few minutes. And while Python works for you, you can focus on how to use that data to improve your products or your marketing strategies.

Text Analysis

In marketing, understanding your customer is the key to success. But how do you do that when opinions multiply at dizzying speed? This is where Python steps in, with a solution as elegant as it is powerful: text analysis with Python.

Python libraries for Natural Language Processing (NLP) let you run complex operations on huge amounts of text in a flash. For example, you might want to analyze customer reviews to find your product's strengths and weaknesses. And we're not talking about expensive tools: Python gives you this for free.

To run sentiment analysis on customer reviews, the code could look something like this:

from textblob import TextBlob

recensione = "Questo prodotto è fantastico!"
analisi = TextBlob(recensione)

if analisi.sentiment.polarity > 0:
    print("Recensione positiva")
elif analisi.sentiment.polarity == 0:
    print("Recensione neutra")
else:
    print("Recensione negativa")

In a few seconds, Python will tell you whether the review is positive, neutral or negative. And just like that, you have a small but powerful lens for looking at your customers' opinions.

That's the power of text analysis with Python: it lets you pull valuable insights out of data that would otherwise be far too big to analyze by hand. All without paying for expensive tools.

Data Analysis

In modern marketing, data is the universal language. But data on its own is just a pile of meaningless numbers if you don't know how to handle it. With Python, you can put those numbers in order and also make them talk. You start by organizing and cleaning the data, steps without which any attempt at analysis is doomed to fail. Once you've untangled that mess of numbers, Python gives you the tools to analyze them in depth. That ranges from spotting trends and patterns to calculating ROI, all the way to building detailed reports.

But data analysis isn't just about figures. It's also about presentation. With Python, you can turn the data you've analyzed into charts and visualizations that make your findings clear to anyone, from the marketing team to the board of directors.

With Python you get a full arsenal for turning raw data into information you can act on. It's like having a team of analysts on hand, ready to give you the answers you need to make informed decisions.

Here's an example of what code could look like that merges sales data from different years, stored in separate Excel sheets, into a single data set. After combining them, you drop the columns you don't need to clean up the set. Finally, you save the clean, organized data to a new Excel sheet, ready for further analysis.

import pandas as pd

# Caricare dati da diversi fogli Excel in un unico DataFrame
df1 = pd.read_excel("dati_vendite_2021.xlsx")
df2 = pd.read_excel("dati_vendite_2022.xlsx")
dati_totali = pd.concat([df1, df2])

# Eliminare colonne inutili
dati_puliti = dati_totali.drop(['Colonna_Inutile'], axis=1)

# Salvare i dati puliti in un nuovo file Excel
dati_puliti.to_excel("dati_vendite_totali.xlsx", index=False)

So Python gives you an efficient way to turn raw data into tidy information, a solid base for processing the data you'll build your marketing strategies on.

Technical SEO

If you think SEO is just about keywords and content, get ready to change your mind.

Today, technical SEO is a full-grown beast that needs attention on several fronts. Problems like a slow load time, broken links, and poorly designed data structures can sink your website's visibility faster than you'd think.

Enter Python. With Python, you can examine the data structure of your site or your competitors' sites, analyze page load speed and even find and fix broken links. It's like having a technical SEO expert inside your computer, always ready to spot and solve problems you might not even know you have.

Say you want to find and fix broken links on your site. Here's how:

import requests
from bs4 import BeautifulSoup

# Scaricare la pagina web
pagina = requests.get('https://www.tuosito.com')
soup = BeautifulSoup(pagina.content, 'html.parser')

# Trovare tutti i link
for link in soup.find_all('a'):
    url = link.get('href')
    risposta = requests.get(url)
    
    # Verificare se il link è interrotto
    if risposta.status_code != 200:
        print(f"Link interrotto trovato: {url}")

Once again: simple, powerful, efficient.

Building Internal Tools

Time is life and precision is everything.

That's the starting point for understanding what Python is really worth for building internal tools. With this language, you can build solutions that save you precious time by removing the need for repetitive manual work. But it's not only about efficiency. The tools you build with Python are as precise as a Swiss watch, keeping to a minimum the risk of errors that can cost you dearly.

Another advantage is customization. Every company has its own needs, and with Python you're free to build tools that do exactly what you need. You can schedule them, chain them together and even connect them to Artificial Intelligence for more advanced solutions. And it doesn't stop there. You can generate automatic reports and send them to your team or your clients, and plug your Python tools into the ones your company already uses, all at no extra cost.

Say you need to run a workflow that downloads reviews from a website, cleans the data, runs sentiment analysis and finally sends a report by email. Here's how to chain all these scripts into one.

import requests
from bs4 import BeautifulSoup
from textblob import TextBlob
import smtplib
import pandas as pd

def scarica_review():
    # (Questo è un esempio ipotetico, il codice specifico dipende dalla struttura del sito)
    recensioni = []
    pagina = requests.get('https://www.sito.com/recensioni')
    soup = BeautifulSoup(pagina.content, 'html.parser')
    for recensione in soup.find_all('div', class_='recensione'):
        recensioni.append(recensione.text)
    return recensioni

def pulisci_dati(recensioni):
    # Rimuovere caratteri speciali, ecc.
    recensioni_pulite = [rec.replace('\n', '').strip() for rec in recensioni]
    return recensioni_pulite

def analisi_sentiment(recensioni_pulite):
    sentimenti = [TextBlob(rec).sentiment.polarity for rec in recensioni_pulite]
    return sum(sentimenti) / len(sentimenti)

def invia_email(report):
    email = "tuamail@example.com"
    password = "tuapassword"
    destinatario = "destinatario@example.com"

    server = smtplib.SMTP('smtp.example.com', 587)
    server.starttls()
    server.login(email, password)

    messaggio_completo = f"Subject: Report Analisi Sentiment\n\n{report}"
    server.sendmail(email, destinatario, messaggio_completo)
    server.quit()

# Scaricare le recensioni
recensioni = scarica_review()

# Pulire i dati
recensioni_pulite = pulisci_dati(recensioni)

# Eseguire l'analisi del sentiment
sentiment_medio = analisi_sentiment(recensioni_pulite)

# Generare e inviare il report
report = f"Sentiment medio per le recensioni: {sentiment_medio}"
invia_email(report)

Thirty-three lines of code (in production it might be a few more), hours of your life back.

Python makes you more efficient and precise, and it also gives you a level of flexibility and customization that's almost impossible to find in off-the-shelf tools. It's like having your own R&D lab, always open and always free.

This is just a short introduction to this fantastic, fascinating world, and Marketing Hackers is your limitless pill.

Whether it's study groups, courses, standard operating procedures, research, patents or biohacking, this platform brings all the knowledge together in one place, so you can break out of the office cubicle routine and put the power of a world of robots to work for you.

Join us.