Getting the best ROI on your Ad campaigns requires more than just Google Ads skills, sharing learnings and changes with your team is equally important. And when using more than one Ad platform or using multiple accounts it is paramount that you keep track of the ad campaigns performance across the board.

working together

When it comes to generating sales and profiles, companies rely heavily on marketing and advertisements. In today’s business landscape where data is the name of the game, it is crucial that information within the company is well-managed. This is where effective knowledge management comes in. 

What is knowledge management?

Knowledge management is the process of identifying, gathering, storing, retrieving, and sharing information across an enterprise; it is limited to the knowledge assets of an organization. An effective knowledge management system is essential to any enterprise. 

It should be well-noted that there are two types of knowledge: explicit knowledge that is easily codified, and tacit knowledge that is derived from experience and practice. This differentiation is translated to different ways of storing and sharing this knowledge within the organization.  

Knowledge management involves gathering and retaining knowledge from various sources and distributing it to the people within the company that need it. It is especially helpful in marketing as it provides a way to keep tabs on customer data, and keep product information and best practices all in one place. 

From a marketing point of view, knowledge management is important because it keeps everyone abreast of up-to-date information stored within an easily accessible repository. This ensures that everyone is on the same page, especially during important projects and campaigns; and that customers will be given the same data when calling about products or services, regardless of which customer service representative answers the query. 

Benefits of Effective Knowledge Management to Ad Campaigns

A knowledge management tool is a great way to streamline and organize the processes involved in your ad campaigns. When data is gathered, stored, and transferred effectively within the company, communication and processes run smoothly.

Knowledge management tools provide a platform to store and connect all your data and make them easily retrievable. They have editing capabilities that are intuitive and fix formatting so you don’t have to. Some have functionalities that organize your data into folders that provide context as to their contents so that team members don’t have to hunt for the data that they need; all of it is already arranged and ready for consumption in several keystrokes. All your data from consumer reports to best practices to customer information are kept secure and accessible within the knowledge management platform. 

Weekly planning notes are easier to create and maintain in knowledge management tools, as well. Your team can edit their entries, plug in their progress, and collaborate easily and efficiently. For team leads and project managers, keeping tabs on the progress of the team’s assignments has never been easier. Even conceptualizing your campaigns during team meetings, asking for feedback, or doing your post mortems can be streamlined.

Managing Ad campaigns performance results

Keeping track of ad campaigns performance across ad platforms or even multiple ad accounts within the same ad platform can be hard. Not to mention the need to filter the right data out of the system(s) to prepare your monthly, weekly or daily report for colleagues, your boss or your client(s). Using external tools like Cervinodata can save you hours per week and tons of frustrations. It’s easy to use interfaces allows you to connect the right ad accounts, start the data collection and deliver the data you need to the most popular reporting destinations like Google Data Studio or Klipfolio. Cervinodata even offers a multi-data feed, where the data is streamed to multiple locations at the same time (like the simple Google Data Studio report for your client or boss AND to your custom built machine learning application you built yourself as a hobby project on the side).

Conclusion

Whether you are in the planning stage, execution stage, or are now evaluating the effectiveness of your ad campaign, a truly effective knowledge management tool can make the work easier, and drive members to collaborate better and contribute positively to the overall success of your project. 

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Post created in cooperation with Slab

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In the world of computer science, there are many programming languages, and no single language is superior to another. In other words, each language is best suited to solve certain problems, and in fact there is often no one best language to choose for a given programming project. For this reason, it is important for students who wish to develop software or to solve interesting problems through code to have strong computer science fundamentals that will apply across any programming language. This blog Python vs. Java provides a brief overview comparison between these two programming languages.

Programming languages tend to share certain characteristics in how they function, for example in the way they deal with memory usage or how heavily they use objects. Students will start seeing these patterns as they are exposed to more languages. This article will focus primarily on Python versus Java, which are two of the most widely used programming languages in the world. While it is hard to measure exactly the rate at which each programming language is growing, these are two of the most popular programming languages used in industry today.

One major difference between Python and Java is that Python is dynamically typed, while Java is statically typed. Loosely, this means that Java is much more strict about how variables are defined and used in code. As a result, Java tends to be more verbose in its syntax, which is one of the reasons we recommend learning Python before Java for beginners. For example, here is how you would create a variable named numbers that holds the numbers 0 through 9 in Python:

numbers = []

for i in range(10):
numbers.append(i)

Here's how you would do the same thing in Java:
ArrayList numbers = new ArrayList();

for (int i = 0; i < 10; i++) {
numbers.add(i);
}
Another major difference is that Java generally runs programs more quickly than Python, as it is a compiled language. This means that before a program is actually run, the compiler translates the Java code into machine-level code. By contrast, Python is an interpreted language, meaning there is no compile step.

Python vs Java: Usage and Practicality

Historically, Java has been the more popular language in part due to its lengthy legacy. However, Python is rapidly gaining ground. According to Github’s State of the Octoberst Report, it has recently surpassed Java as the most widely used programming language. As per the 2018 developer survey, Python is now the fastest-growing computer programing language.

Both Python and Java have large communities of developers to answer questions on websites like Stack Overflow. As you can see from Stack Overflow trends, Python surpassed Java in terms the percentage of questions asked about it on Stack Overflow in 2017. At the time of writing, about 13% of the questions on Stack Overflow are tagged with Python, while about 8% are tagged with Java!

Web Development

Python and Java can both be used for backend web development. Typically developers will use the Django and Flask frameworks for Python and Spring for Java. Python is known for its code readability, meaning Python code is clean, readable, and concise. Python also has a large, comprehensive set of modules, packages, and libraries that exist beyond its standard library, developed by the community of Python enthusiasts. Java has a similar ecosystem, although perhaps to a lesser extent.

Mobile App Development

In terms of mobile app development, Java dominates the field, as it is the primary langauge used for building Android apps and games. Thanks to the aforementioned tailored libraries, developers have the option to write Android apps by leveraging robust frameworks and development tools built specifically for the operating system. Currently, Python is not used commonly for mobile development, although there are tools like Kivy and BeeWare that allow you to write code once and deploy apps across Windows, OS X, iOS, and Android.

Machine Learning and Big Data

Conversely, in the world of machine learning and data science, Python is the most popular language. Python is often used for big data, scientific computing, and artificial intelligence (A.I.) projects. The vast majority of data scientists and machine learning programmers opt for Python over Java while working on projects that involve sentiment analysis. At the same time, it is important to note that many machine learning programmers may choose to use Java while they work on projects related to network security, cyber attack prevention, and fraud detection.

Github repositories

Both Python and Java have a large crowd of followers that share pre-built pieces of code that are ready to use. This usually takes a lot of time. For instance, Cervinodata offers a Github repository for Python and a Github repository for Java that allows the user to bring the online marketing campaign statistics from multiple ad platforms (and Google Analytics) together with a few lines of code. This is particularly handy for technical analysts that want to create code to monitor the performance of campaigns from multiple platforms or want to insert online campaign data into a machine learning process. In this example, Cervinodata takes care of all the api versions of the ad platforms (like Facebook Ads, Google Ads and Bing Ads) and the Github repository makes it easy to get started running.

Python vs Java: Where to Start

When it comes to learning the foundations of programming, many studies have concluded that it is easier to learn Python over Java, due to Python’s simple and intuitive syntax, as seen in the earlier example. Java programs often have more boilerplate code – sections of code that have to be included in many places with little or no alteration – than Python. That being said, there are some notable advantages to Java, in particular its speed as a compiled language. Learning both Python and Java will give students exposure to two languages that lay their foundation on similar computer science concepts, yet differ in educational ways.
Overall, it is clear that both Python and Java are powerful programming languages in practice, and it would be advisable for any aspiring software developer to learn both languages proficiently. Programmers should compare Python and Java based on the specific needs of each software development project, as opposed to simply learning the one language that they prefer. In short, neither language is superior to another, and programmers should aim to have both in their coding experience.

Python vs Java

• Runtime Performance – Java is the Winner
• Ease of Learning – Python is the Winner
• Practical Agility – Tie
  Mobile App Development – Java is the Winner
• Big Data – Python is the Winner

This article originally appeared on junilearning.com

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