Python has a major focus in SEO but what about PPC? The two disciplines are often treated as polar opposites but they share common goals and adding a bit of Python to a PPC campaign can do wonders for improving conversions, CTR, and time spent.
But before we have a look at how Python can boost your PPC performance, we need to outline what the language is all about.
What is Python?
Python is a programming language created by Guido van Rossum in the 1980s and publicly released in 1991. van Rossum wanted Python to emphasize code readability with five philosophical pillars:
- Beautiful is better than ugly
- Explicit is better than implicit
- Simple is better than complex
- Complex is better than complicated
- Readability counts
Its structure and syntax help users to write logical code regardless of project size.
Is it easy to learn?
How do I install it?
As Jacob Fairclough said, Python can be difficult to install for some users. But that depends on your operating system.
For most Mac users, Python comes built-in so you can use your Terminal to access it. That’s not the case for Windows users. The recommended way is via Anaconda as this also installs a lot of useful libraries to use (which I’ll explain in more detail later).
Python techniques to help your PPC campaigns
In the words of Aristotle, “for the things we have to learn before we can do them, we learn by doing them”. And Python is no exception. Practicing Python in SEO is common practice and it’s the same for PPC.
Understanding other languages is important but Python can save professionals a lot of time by automating jobs that would normally take hours.
The amount of data you can obtain from a PPC campaign can grow very quickly so a way to organize and automate it into a logical structure would make everyone’s lives a lot easier in the long run.
Two of the biggest applications of Python are AI and machine learning and they’re also the main bridges between the language and PPC. As Danielle Strouther said in her article AI For PPC Is Only Useful If You Use External Tools, “using AI for PPC is no longer an option. It’s a necessity.” So that’s what we’ll look at – integrating Python with external tools and software.
Other things you can do with Python that can help you with PPC management includes:
- Data scraping
- Data analysis and mining
- Data visualization
- Natural language processing (NLP)
Python + Google Ads
We all know how laborious Google Ads management can be. So Google created an API for its ad platform so users can automate a wide range of PPC-related tasks. You can find a list of them on the Client Library page.
One programmer created a script for KPI reporting which would come in handy for clients, shareholders, and colleagues.
Python + Google Search Console
When you write PPC ads, you want them to convert so your ROI can be as high as possible. Search data from Search Console can help find areas to improve or examples of success to capitalize on.
Passion Digital created a script that analyses search queries from Search Console to gain insights to improve SEO and PPC performance. It does this by finding keywords and phrases with poor conversion rates and CPA using those terms.
Python + Excel/Google Sheets
One of the most common Python workflow combos involves Excel and Google Sheets.
As data can be exported as CSV files and spreadsheets by most external tools, it’s easy to import them into a spreadsheet program. And Python loves data.
The list of ways to use Python and Excel with PPC data is exhaustive. You could use it to project future trends, CTR prediction, campaign creation, keyword generation, bid modifying, account structure analysis, customer match lists, geolocation targeting.
Python + Google Data Studio
Google Data Studio is a powerful tool for data visualization and it’s free to use. So combining it with Python means a streamlined approach to data viz and reporting.
There are also paid tools like Panoply which can integrate Data Studio and Python along with a multitude of services like Salesforce, Zendesk, and Google Analytics. Suddenly you have a large network of data from every department – sales, dev, customer support, project management, design, web analytics. Phew!
You can even spy on your competitors using Python and create PPC reports and graphs with Data Studio to show the results.
Python + Google
The SERPs are more than just a display of results. They can be used as their own data source and give an insight into how well you and your competitors are doing.
With APIs like Serpstack, you can extract data about ads from any SERP and analyze things like position, title and description optimization, sitelinks, and displayed URLs. You can also leverage this with organic results to find new potential keywords you can bid on and improve your campaigns.
Python + Facebook
In 2017, Facebook made its Prophet open source. The forecasting tool is accessible through Python and R (another programming language) and is optimized for businesses to forecast trends, whether they’re hourly, daily, weekly, or seasonal.
It’s highly advanced and mainly for large scale business use but if you have the expertise and the resources, Prophet has the potential to streamline major paid campaigns.
Useful libraries, modules, and APIs
Vanilla Python can do most jobs but its power lies in all the libraries, modules and APIs you can use. Although they all share similarities, they’re all different additions. A module is a Python file containing functions, variables, and methods, a library is a collection of modules and pre-defined functions that let you perform actions without writing the code yourself, while an API is an interface set of standards and instructions.
Here’s a list of some useful ones you can use.
- Pandas (library) – Pandas is an open-source library that makes data structures and data analysis tools. You can make tables, create ordered and unordered data series and “dataframes”, join, merge, and split them. It’s probably the most flexible data analysis tool to have if you’re using Python.
- CSV (module) – This module goes hand-in-hand with pandas as it allows you to export data into a CSV.
- Requests (library) – Requests is a must if you’re scraping web data. It sends requests to HTTP pages, allowing you access to pull anything from a webpage. If you’re planning to scrape SERP data, it’s an essential library to use.
- Beautiful Soup (library) – Beautiful Soup is the companion to requests, letting you take out everything inside an HTTP page.
- Serpstack (API) – The serpstack API allows you to scrape Google SERP data in real-time and at scale and it lets you export the data in JSON and CSV formats (depending on your account level).
- Google APIs (API) – Google being Google, they have a library of APIs you can use for all kinds of things.
- TensorFlow (library) – One of the best libraries for machine learning.
- SciKit Learn (library) – Another machine learning library for predictive data analysis
Learning a programming language can seem daunting but Python is one of the easiest and most accessible languages out there. Its automation and analysis capabilities have a wide range of uses and it can help to simplify complex data and automate time-consuming tasks. Nobody wants to make their jobs harder!
If I had to give some takeaway advice to remember while you learn, I’d say:
Don’t let FOMO get to you
When I started learning Python, I got carried away with jumping into projects without knowing all the techniques. Everyone on Twitter was making amazing scripts and I was still learning about lists and loops. But then I realized I was never going to get to their level by copying and pasting when I didn’t understand. So I went back to my course and focused solely on that.
Fully understanding the basics is the only way you’ll be able to build up to the advanced techniques. And there’s no expiration date on education.
Practice, practice, practice
Most courses come with practice examples. Aside from those, you should always test what you’ve learned. It doesn’t have to be part of a big project, just something small so you can get the hang of the techniques.
Learning something new isn’t always easy. You’ll get frustrated when things don’t work and you may need some time away if it gets too much. But never lose your curiosity. Programming languages have so many applications and some haven’t even been discovered yet. Stay curious and you might find one.
Find others who are learning
Communities are great places to improve your learning. Here are some great places to collaborate and grow with Python:
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12 Tools and Resources for Software Developers in Insurance
If a developer designs a system for Big Data analysis or creates programs for processing and analyzing application data for mobile gadgets, in any case he cannot do without analytics tools and services. Big Data is understood as the basis of the business of insurance companies that depend on information, that is, probabilities, statistical data, customer information, and so on.
Today, for programmers whose responsibilities include insurance software development, many useful tools have been created that are suitable for their needs and corresponding to their skills.
TOP-12 tools for programmers of insurance companies
Every developer who works on coding for insurance products will need the following 12 “helpers”:
- BitDeli. It is a startup that has been operating since November 2021. With its help, programmers are able to analyze various data using a variety of metrics through Python scripts. An important plus of it is that the scripts can be of different levels of complexity, which will depend on the needs of the developers. They can even be self-taught. Suitable for analytics. The solution is easy to use.
- Continuity. It is a platform that was created with the aim of pulling businesses to the same level as the companies of its creators – Yahoo and Facebook. The guys created a data structure to implement a new level of abstraction over complex connections in HBase and Hadoop. The main advantage of the platform is the facilitation of Big Data development processes for programs that are designed to work with external and internal audiences.
- Flurry. This application is in the “store” format, which is intended for the creation of mobile programs, as well as for the analysis of their data. This allows the application to be improved.
- Google Prediction API. Suitable for developers who have the necessary knowledge to work with the Prediction API. This interface will provide a wide range of diagrams and topics, and will also allow the application to give correct answers.
- Infochimps. We are talking about a platform, despite the fact that the brand today is trying to become a company and become even more successful. As for the platform for the programmer, he gets the Wukong framework, which has a key task – to interact with Hadoop and its data, while using Ruby scripts.
- Keen IO. In 2012, this tool was recognized as the best and most effective in its category, and it is used in analytics by mobile application developers. Its plus lies in its ease of use. You need to apply just one line, which is inserted into the source code to be able to track all the necessary information from the programs.
- Kontagent. A tool for processing huge amounts of information.
- Mortar Data. It is a cloud service that has managed to replace MapReduce with a combination of Python and Pig. It differs in simplicity and clarity in operation.
- Placed Analytics. A tool that provides programmers with ready-made products. With its help, it is possible to obtain complete information about the use of the application by customers: where, when and how long it lasted. The data is especially useful for app owners and advertisers.
- Precog. It is an interactive environment for developing insurance analytics products based on Quirrel, an open query language.
- Spring for Apache Hadoop. A tool that greatly simplifies the creation of programs that use Hadoop, and also provides integration with other Spring applications.
- StatsMix. It is a tool with which developers can collect and analyze data received from programs using only the languages they have learned.
Take a look at the Fireart site for more interesting and useful information. The main thing to remember is that analytics not only assesses the quality of traffic, looks for ways to increase conversion and reduces the cost of attracting customers, but also determines the most effective advertising channels, compiles portraits of visitors and their behavior patterns on sites, identifies site shortcomings up to technical errors.
From Creation to Stellar ROI
Reaching the right customers with your Google Ads campaigns is critical to increase conversions. While it’s possible that scattershot advertisements could catch the interest of Internet users, it’s far more likely that this general ad approach will result in a disconnect between dollars spent and sales made.
To help ensure your ads are reaching the people in the right place, it’s worth leveraging a function in the Google Display Network (GDN) known as affinity audiences. Using these audiences helps pinpoint customer segments that may be more likely to purchase your products, in turn driving more effective and efficient ad spend.
But what exactly is an affinity audience? How do they work, how can you create your own — and what can you expect once you dial in the ideal customer segment? Let’s find out.
What are Affinity Audiences?
Affinity audiences are used by the Google Display Network to deliver your ads to relevant locations online. Given that Google’s network reaches more than 90 percent of Internet users worldwide, it’s well worth the time and effort to understand and apply these audiences at scale.
But what is an affinity audience?
Let’s break the term down into its component parts. Audience is easy — it’s the group of people that will see your ad. Affinity, meanwhile, is defined as “a feeling of closeness and understanding that someone has for another person because of their similar qualities, ideas, or interests.” The result? An affinity audience is a group of potential customers that share similar interests or qualities (similar to a buyer persona).
Using affinity audiences allows your brand to better align ad campaigns to buyers who are interested in what you have to sell. For example, if you’re in the coffee-making business but also have a focus on reducing plastic waste, your affinity audience might contain both people who love coffee and those who love the environment. Groups with both of these qualities are far more likely to buy your product than either group individually.
Affinity targeting, meanwhile, is the process of identifying the ideal affinities that align with your product or service. Consider the coffee example above. While targeting buyers who love coffee helps improve your brand placement, it also puts you in direct competition with a host of other brands all producing similar products. Additional affinity modifiers that narrow your focus — such as sustainable growth processes, fair labor practices, or environmental priorities — can help set your brand apart.
Do note that it is possible to get too specific with your audience targeting. For example, if your coffee brand targets audiences that prefer beans from a specific region that are collected, packed, and shipped in a specific way, you may end up with a handful of very loyal customers but almost no broader appeal. As a result, targeting needs to narrow the focus without preventing you from reaching the greater public.
What are Custom Audiences?
GDN and the Google Ads platform contain a host of pre-built Google affinity audiences — also called segments — that you can use to focus your marketing and advertisements. These include everything from pet lovers to do-it-yourselfers, TV comedy fans and users with an interest in news and politics.
But they can’t cover everything. You may have a product or service that doesn’t dovetail with existing segments — here, custom affinity audiences can help.
Understanding Custom Affinity Audiences
Custom affinity audiences are those you create yourself in your Google Ads platform to align with the interests of your target customer base. While Google will suggest different potential segment tags depending on what you input, it’s worth taking the time to do some market research before diving into the custom affinity process. This lets you pinpoint the audience preferences that align best with your brand.
You can create custom affinity audiences related to four criteria: Interests, URLs, places, or applications. In general, places and applications are the least useful of the bunch. Here’s why. In our coffee example above, there aren’t a lot of coffee-related applications that would set your audience apart. And while geography has some impact on buying behavior, it’s usually not enough to justify an entire segment.
Interests and URLs, meanwhile, can help you dig down and identify potential affinity options that may be shared by your target market at large.
How to Create Affinity Audiences
Ready to create your own affinity audience? Follow these steps:
- Log into your Google Ads account.
- Select “Tools and Settings”, then “Audience manager.”
- Select “Custom Segments.”
- Enter segment name and interests.
- Save your new segment.
Let’s tackle each step in more detail.
1. Log Into Your Google Ads Account
First, log into your Google Ads account. Here, you can see any active campaigns along with the associated affinity audiences.
2. Select “Tools and Settings”, then “Audience Manager”
Next, head to “tools and settings” in the upper-right-hand corner and then find “Audience manager” in the drop-down menu.
3. Select “Custom Segments”
Now you’ll see a list of any data segments you’re currently using to target prospective buyers. To create an audience or segment, click on “Custom Segments” and then the blue “+” icon.
4. Enter Segment Name and Interests
Now, give your segment a name and add a few potential interests. For example, if you enter “coffee”, Google will return interests or purchase intention ideas such as “coffees to make with an espresso machine”, “how to make coffee with coffee beans” and “coffee makers that make different coffees.”
5. Save Your New Segment
Finally, save your new segment with use for ad campaigns. You can create as many segments as you like until you’ve covered all relevant market bases.
The Impact of Effective Affinity Audiences
Ideally, affinity audiences lead to a definitive result: Increased ROI.
Here’s why: When your ads are shown to audiences that are interested in what you’re selling, they’re more likely to click through and purchase your products. As a result, the money you spend on advertising is directly offset by the conversions driven by these ads, in turn creating positive ROI. More generic campaigns, meanwhile, may still increase overall sales but not enough to balance out the spend required to reach larger audiences.
The right audience makes all the difference. Targeted, customized affinity audiences help you reach the people that want to buy your products, in turn boosting conversions and making your overall ad spend more cost-effective. Custom affinity audiences further narrow your market targeting, increasing the likelihood of revenue and reducing the gap between what you spend on ads and what you get in return.
The HubSpot Blog’s 2022 Social Media Marketing Report: Data from 310 Marketers
In our recent Marketing Trends survey, we learned that social media is the most effective channel marketers leverage, as well as the channel they use most.
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