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. 

Image source

Post created in cooperation with Slab

Start a free Cervinodata trial here: https://www.cervinodata.com?utm_source=rss&utm_medium=rss

The post How Your Ad Campaigns Can Benefit from Effective Knowledge Management appeared first on Cervinodata.com.

Python is one of the most popular languages for coding in the world. The majority of top U.S. colleges even use Python in their introductory coding classes: teaching students to use Python to build simple games, parse information from web pages, and even conduct language processing! Still, many parents and students alike often wonder — how is Python actually used in companies or various fields? Read on to learn how Python powers today’s leading industries, and plays a major role in numerous computer science disciplines.

Why Python?

Python is a high-level and general-purpose coding language — meaning that it’s easier to understand and can be used to solve a variety of problems! With its simple syntax, high readability, usability across operating systems, Python is a valuable language to both learn and use for a variety of applications. It’s therefore not surprising that Python is prevalent in not only today’s job market, but also in common technology we use everyday. Python programming is a skill that can be used in virtually any industry, as well as in positions far beyond just software development. From industries like finance, healthcare, and insurance, to fields like aerospace to entertainment — Python-based tech is driving innovation and new solutions. In our What is Python 101 guide, we introduced Python as well as a few of the fields Python is used in such as tech, finance, and biotech. But in this article, we’ll be taking a deeper look at even more of Python’s broad uses — specifically in the following areas:

• Web development
• Data science
• Machine learning
• Artificial intelligence

Applications of Python

Web Development

While HTML, CSS, and Javascript may be the common choice of tools when it comes to web development, Python is also a great choice. Web development refers to the building of websites on the internet, from static, plain text pages to dynamic ecommerce sites and interactive internet applications. Coding is crucial in web development to build out site functions, optimize user experience, and ensure that that information is organized properly. Python’s web frameworks such as Django and Flask have become popular options for website development. Python is a great option for backend web development, and is used by many companies today. These open-source frameworks allow users to build web applications more efficiently due to Python’s ability to handle vast amounts of data and automate tasks. In fact, companies such as Instagram, Pinterest and Google incorporate Python in their backend web development.

Data Science

Python is also a major language of choice when it comes to data science and data analysis. In the age of big data, we come into contact with data frequently in our everyday lives, with technology such as navigation (GPS), online shopping, energy use, and wearables such as smart watches. The average household contains around eleven devices and that number is likely to increase in the future! With so many devices, there is a vast amount of data that businesses need to process in order to provide their services. Python-based tools are often used to visualize, process, and analyze large amounts of data. Data science relates to processing data and inferencing trends — and Python is incredibly useful in this area due its intensive packages like scipy, numpy, and pandas. Additionally, the matplotlib package can be used to build data visualizations. This makes Python a popular choice in fields like bioinformatics that also involve a lot of data and modeling. These Python-based tools have become popular due to their ease of use and scalability. For example, Netflix uses scipy and numpy for numerical computing to manage user traffic across the platform.

Machine Learning

By analyzing vast amounts of data, machine learning algorithms can find patterns and apply those patterns to make sense of future behavior. The most obvious use of machine learning in products is recommendation systems. Companies such as Netflix, Spotify, and Youtube collect user activity to make predictions and suggest playlists and videos. Scikit-learn and tensorflow are Python libraries that enable the use of algorithms for classification, clustering, and regression on large data sets. The high performance and extensive functionality of these libraries make them essential in any exploration of machine learning.

Artificial Intelligence

Machine learning is actually just one facet of artificial intelligence. AI refers to the intelligence of machines and the development of computer systems to perform human-oriented tasks such as perception or decision making. One prominent example of artificial intelligence is in ridesharing apps, like Uber and Lyft.
Python is used in artificial intelligence for apps like Lyft or Uber. The AI system can then predict things like user demand and estimated arrival times.
Uber utilizes AI for predicting user demand and estimated arrival times (among other features) and much of this is accomplished through Python. In addition to some of the previously mentioned Python libraries, AI functionality such as prediction models and neural networks can be created through other libraries like keras and pytorch.

Python is Everywhere!

Among popular programming languages, Python is one of the most diverse in its applications. Moreover, it’s deeply integrated within our everyday life as well as today’s leading companies. The range of careers that involve Python include software engineer, web developer, data scientist, product manager, business analyst and more. Regardless of industry, companies in this technological age rely on data-driven decision making and Python is the perfect tool to do just that.
If you or your child are interested in the fields or roles listed above, then learning Python is a great choice!

Cervinodata for Python

For applications related to online marketing campaigns from ad platforms Python code can get quite complex when fetching data from multiple ad platforms. With Cervinodata you can connect to multiple ad platforms at once, and only need to keep the Cervinodata api up to date. This will save you about a day per month (because you do not have to keep track of changes in the Facebook Ads api, The Google Ads api and Google Analytics api. Cervinodata handles that for you). Furthermore, your Python code can be kept very simple, just use the Cervinodata for Python client on Github: https://github.com/Cervinodata/clientapi-python?utm_source=rss&utm_medium=rss. See more details about how the Cervinodata api for Python works here: https://www.cervinodata.com/application-programming-interface/python-api/?utm_source=rss&utm_medium=rss

Want to Become a Python Programmer?

One of Python’s greatest strengths is helping new coders learn basic concepts and become familiar with programming! There are also many resources widely available to learn Python, for new coders of all ages.

Python Classes for Kids

At Juni Learning, we teach Python to aspiring young coders ages 11-18, with the help of experienced coding instructors. From solidifying coding basics, to building algorithms and analyzing datasets, these courses explore the many different applications of Python and teach students how to build their own coding projects. Read more about our coding courses and curriculum, or get started with our Admissions Team to learn which course is best for your child’s coding journey.

More Coding Languages

Python is a versatile, popular language that’s growing in versatility with more users and creators. Its convenient readability, versatility, and software development prototyping capabilities make it a go-to language for many cases.

This article originally appeared on junilearning.com. 

The post What is Python used for? | Major Industries appeared first on Cervinodata.com.

Interested in getting your child into computer programming but unsure of where to start? (By the way Not to mention that when you are a Beginner Python coder yourself, this is a great to start as well ) You’re not alone. Finding simple programming project ideas that interest pre-teens and teenagers can be tricky, especially when learning a programming language for the first time. If your child has any interest in Harry Potter, this short tutorial will introduce them to programming with a simple, fun project. We will use Python, a popular programming language, to draw a symbol any Harry Potter fan will recognize: the Deathly Hallows. We’ll walk you through all of the steps below!

Getting set up

Programs can be written using many different software tools. Fortunately, one of the simplest to set up is Repl.it, which allows us write, run, and save our code in the browser. Start by signing up for a repl.it account.

Now that you’re logged in, create your project by clicking on the +new repl button at the top of the page:

Choose the programming language Python (with Turtle) and name your project Deathly Hallows. Now, we’re ready to start writing some code!

Starting with Python Turtle

For this project, we are going to use the Python module turtle. In Python, modules are like add-on tools we use to give our programs extra functionality. In this project, we will use turtle to help us draw graphics on the screen.

When we use turtle in Python, we create a Turtle object on our screen. Then, we issue commands in our code to the Turtle, by telling it to move, turn, and change color.

Thus, at the beginning of our code, we need to import the turtle module, and we need to create the Turtle object we will use. The Turtle can have any name, like bob. The name of the Turtle is called a variable, and variables in programming are always one word (no spaces) and usually start with a lowercase letter. Here is the code we start with to accomplish these two things:

import turtle

bob = turtle.Turtle()

 

Now, we can try giving our Turtle a command. Let’s try telling bob to move forward 200 steps. This is how we do that:

bob.forward(200)

Try running your code. There’s a line on your screen! We just gave bob the command to move forward by 200 pixels. Wherever the Turtle moves, it drags a pen behind it, leaving a line. In programming, when we issue commands like this, we are calling a function.

Let’s say we want to have bob draw a square. The next thing we need to do is to turn left by 90 degrees. Following the same pattern as before, our code should now look like this:

bob.forward(200)

bob.left(90)

Notice that the Turtle’s head is now pointing down, as it is ready to move forward in a new direction! How do you think we would complete the code to draw a square?

bob.forward(200)

bob.left(90)

bob.forward(200)

bob.left(90)

bob.forward(200)

bob.left(90)

bob.forward(200)

bob.left(90)

Notice that our code is getting repetitive. We like to avoid making our programs repetitive, because it makes them more prone to small mistakes (what if one of the numbers had been written incorrectly?). Luckily, it is very easy to repeat code in programming by using loops.

What specific code do we need to repeat here? The Turtle is going forward and turning right four times total. Thus, we need to repeat those two lines of code four times.

In Python, we use a for loop to repeat certain lines of code a certain number of times. To tell the computer we’re about to repeat some code, we start with the line for i in range(4):. The number 4 represents the number of times the code inside the loop is going to repeat, and it’s important to remember the colon at the end of the line. The code that goes inside the loop has to be tabbed or indented, so that the computer knows what to repeat, like this:

for i in range(4):

    bob.forward(200)

    bob.left(90)

 

Now we have an efficient program to draw a square! To make sure we really understand our code, let’s try to modify it to draw a triangle with three equal sides. Experiment with your code to get it right!

Hint: How many times do we need to repeat our code? How many sides does a triangle have? If you were walking in a triangular shape, how much would you need to turn at every corner?

Keep experimenting until you land on something like this:

for i in range(3):

  bob.forward(200)

  bob.left(120)

You’ve successfully created the first shape in our Deathly Hallows symbol!

Next, we will draw the circle. Let’s move bob to the middle of the bottom side of the triangle, where we will start drawing the circle. Write a line of code that tells the Turtle to move forward just slightly less than half of the length of your triangle side. For example, if each side of our triangle is 200 pixels, then we would write below our existing code:

bob.forward(95)

To draw the circle, we need to think of a circle as a shape with many very short sides. The steps are then similar to how we drew our square and triangle: draw a line, turn, draw a line, turn, etc. However, this time we need to cover 360 degrees with many more shorter sides. To make the math simple, let’s repeat our code 36 times, turning 10 degrees each time. You can experiment with how far the Turtle moves forward each repetition to make the perfectly sized circle for your graphic:

for i in range(36):

  bob.forward(10)

  bob.left(10)

We’re almost there! The last piece we’re missing is the vertical line to the top. You should be able to experiment with our commands to move forward and turn to put this last line in place.

Hint: We need to move forward a tiny bit to put our Turtle right in the center of the bottom side of our triangle, turn the head to face up, and move forward to draw the vertical line. After some experimenting, our code to do so looks like this:

bob.forward(6)

bob.left(90)

bob.forward(172)   

And here is the result:

If you need your online ad campaign data in a Python project, try out Cervinodata for Python here: python api

Final Program

Here is the code to draw the Deathly Hallows symbol in Python in full:

import turtle

bob = turtle.Turtle()

bob.speed(1000)

for i in range(3):

  bob.forward(200)

  bob.left(120)

bob.forward(95)

for i in range(36):

  bob.forward(10)

  bob.left(10)

bob.forward(6)

bob.left(90)

bob.forward(172)

About this Post

This article originally appeared on junilearning.com and was republished here with their permission.

The post Beginner Python: Draw a Harry Potter Symbol appeared first on Cervinodata.com.

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

The post Python vs Java: Uses, Performance, Learning appeared first on Cervinodata.com.

This time, the focus will be on the design elements and features related to both tools. Many agencies a

The post Google Data Studio vs Klipfolio (Part 3) appeared first on Cervinodata.com.

Google Data Studio vs Klipfolio

After our visit to Measurecamp London in September we came back with more background information about how agencies and analysts perceive the differences between Google Data Studio vs Klipfolio. So here’s part two of this blog post.

During one of the sessions we asked participants to compare Google Data Studio vs Klipfolio. Although the categorisation is a bit different than our blog post part one , it is still similar in content/results. See the summary below.

⊗ = More like these is better.

Read Part one of the blog “Google Data Studio vs Klipfolio” here

The post Google Data Studio vs Klipfolio (part 2) appeared first on Cervinodata.com.

We have published a new comparison between Data Studio and Klipfolio. Go check it out.

Why Klipfolio is 10 times better than Google Data Studio

As a data and dashboard company we have built and deployed countless Klipfolio dashboards in the last 5 years. We currently manage over 750 Klipfolio dashboards for about 200 clients. After the introduction of Google Data Studio in 2016 we expected demand to change from Klipfolio (a paid solution) to Data Studio (fee to use), but this has been only very limited. Here’s a genuine Google Data Studio vs. Klipfolio comparison.

The reason is simple: Free comes at a price. Depending on your needs you can choose your preferred dashboard tool, but in the end Klipfolio has (as it should) a lot more to offer. Based on our experience with both Data Studio and Klipfolio, we can give some advice on which tool to choose. If you are looking for a quick and dirty way to show some key metrics (likely coming from a from a google sheet) in a good but not really fancy looking dashboard, Data Studio is a decent choice (as is Klipfolio by the way).

But if you are looking for a more professional looking and more advanced dashboard, Klipfolio is definitely the better option. And that’s not an easy thing to achieve. After all, Google has a multi billion budget to invest.

3 stages of building a dashboard

When working with a dashboard platform, there are basically 3 stages you are going through:

1. Getting data in your dashboard
2. Visualizing your dashboard
3. Creating scale with your dashboards

Below we will give you 10 reasons why our clients keep using Klipfolio and did not make the switch to Data Studio.

Getting data in

1.     Ways to connect your data – Non-google data connections

Klipfolio offers over 400 native integrations with other platforms (including our Cervinodata integration https://www.cervinodata.com?utm_source=rss&utm_medium=rss ) to get data in. Data Studio only has 18. Although the Data Studio does offer a Data Studio community connector with 138 partner connectors, many of those connectors are for the same channel (there are 18 Facebook Ads connectors).

And even if the number of connectors grows in Data Studio, it is still so much easier to get data into Klipfolio. In particular the core connectors, see the screenshot below, offer a variety of very easy ways to get your data in.

Klipfolio core connector & service connector page

2.     Data blending

Both Data Studio and Klipfolio give the option to merge multiple data sources to one datafile. But Klipfolio offers more ways to combine data, because you can also combine multiple data sources in one graph or table or combine data sources with advanced formulas. Klipfolio is more flexible that way in daily use.

And if you need to blend many data sources into one data file (like all the analytics views for your 20 + international websites) it is always better to pre-process the data before putting it in a reporting or dashboard tool. Tools like Supermetrics (https://www.supermetrics.com?utm_source=rss&utm_medium=rss) or Cervinodata (https://www.cervinodata.com?utm_source=rss&utm_medium=rss) can make your life a whole lot easier.

Visualizing your data

3.     Snap to grid / responsiveness

Both Data Studio and Klipfolio offer ways to “snap to grid”. But again, in Klipfolio this is way easier to do that. With “Lay out” it’s easy to choose from a multitude of predefined grids, but you can also simply define the number of columns you need and let the klips snap to the grid. Not just by column, Klipfolio also helps you align vertically by showing support lines (like in Powerpoint).

Klipfolio lay out options

Data Studio offers the option to define canvas size in pixels, and a grid with tiny squares to align your klips on, but these small squares make you dizzy if you look at them for a long time (i.e. when building a big dashboard). Also, there is an “align” option in Data Studio, but clicking the wrong alignment (horizontal instead of vertical, for instance) will mess up your dashboard (you can undo, of course). It works, but not as user friendly as Klipfolio.

One key thing Data Studio is missing is responsiveness. That’s bad. Klipfolio will scale seamlessly from a desktop size to a TV screen size and tablet size and back. Data Studio will force you to zoom in manually. If your client has to adjust the size the dashboard to the size of his screen, it does not leave a great impression. Especially when he / she sees the report for the first time.

4.     TV screen mode

It is possible to show a Data Studio dashboard on a TV screen but showing multiple pages in a carousel is not an easy thing to set up. I am pretty sure it is possible, but after some looking around in Data Studio and searching online, I did not find what I needed. In Klipfolio, you can just click the Full screen (TV) Mode and press play. You can also adjust the time each page is shown before moving to the next page.

5.     Mobile app

If you wish to see your performance on the go, Klipfolio offers a Mobile app version. It is possible to see a regular Klipfolio dashboard in the mobile version but Klipfolio will scale all the klips to the mobile screen. This includes the font, so your axis and metrics might get very small. Since, most of the time you just want to see one or two key numbers when en route, It is advised to create a separate dashboard for mobile use. This is easy to do and looks great.

6.     Indicators

One of the key uses cases of a dashboard, on a TV screen or other format, is to highlight the key metrics that need the reader’s attention. Klipfolio offers various ways to add indicators that highlights the right data (like hitting your target or overspending you budget). Data Studio does not offer indicators.

If the out of the box indicators are not enough, In Klipfolio you can boost the look and feel of the indicators through a custom style sheet. We do this for most of our clients. How it works is beyond the scope of this blog, but we think it gives the finishing touch and makes the dashboard look even better. See the screenshot below or check out a live demo here

Klipfolio indicators

Link to live demo: https://app.klipfolio.com/published/7e5780605d8ca7d45d37150b98aa8264/multi-channel-advertising-dashboard?utm_source=rss&utm_medium=rss]

7.     CSS / Stylesheets

We use the stylesheet option in Klipfolio to customize the designs of most of the dashboards we deploy and maintain. What’s very handy with the CSS style sheet option in Klipfolio is that you can fully customize the design to your needs (like you would do with your website) and change the design on many places at once. For instance, if you are an online agency and have 30 clients with 70 dashboards, you can easily change your font size or color: Just change the CSS in one place and, voila, the font size or color is changed in all your dashboards.

One last cool feature of the Klipfolio CSS is that you can also change the way the headers, navigation menu, etc looks, so you can make it perfectly on par with your brand guidelines. In Data Studio it is possible to use and re-use a reporting template, but I did not manage to find an easy way to change the styling in all the dashboards at once.

8.     Types of visualization

Beyond the basic visualizations both Data Studio and Klipfolio have (like Tables, Line charts, bar charts, spreads and bullets), Klipfolio offers a few additional visualizations that we think are essential: The gauge charts and the mini charts within a table.

Data Studio offers one version of gauge charts, the bullet chart, but they are limited in customization. Klipfolio offers the same visualization, but with dynamic ranges that can change color if a threshold is reached (it works like an indicator). Klipfolio also offers the bullet chart as part of a table. This gives you the opportunity to provide more context without overwhelming your reader.

Data Studio vs Klipfolio gauge charts

Deploy and manage dashboards at scale

9.     API connection for scalable deployment & management

For more advanced users and larger organizations with development capacity, the Klipfolio API is very powerful. It allows you to basically change every element in Klipfolio, automated. This ranges from copy/pasting a dashboard into another client and replacing the content of a data source with the right data, to changing the access for many users at once.

In Klipfolio you can even automatically replace a complete set of dashboards with a newer version. For instance, if you decide you want to add the data of the previous period to a couple of graphs, but you have 30 clients and 70 dashboards you would go nuts if you had to do this one-by-one. With the Klipfolio API you can just copy/past. Done.

10.  SSO, domain aliasing, etc.

Both Data Studio and Klipfolio offer ways to embed a dashboard. But again, Klipfolio is way more advanced. With SSO (Single Sign On), you can for instance give access to dashboards that are seamlessly integrated within other platforms. Users get to see only what they need to see, depending on their rights in Klipfolio, so no need to create duplicates.

Another nice feature is domain aliasing. Klipfolio allows you to create your own url (like dashboard.yourcompany.com ) to give your users or clients a better brand experience (see also CSS).

Conclusion

We definitely need and use all of the 10 things and not just because we set a high standard of design quality for our dashboards. In terms of telling your data story, Klipfolio just has more to offer. We occasionally do custom building of dashboards in Data Studio, but as soon as we need more than just the basics, Klipfolio is our preferred dashboarding solution.

And to help our clients (mostly agencies) to deliver custom design, professional and easy to interpret dashboards at scale, we wouldn’t know where to begin without the API power Klipfolio offers. Looking at the pace of change (Google Data Studio vs Klipfolio), I am pretty sure we will not change our minds any time soon.

Note

This article has been written with the support of Klipfolio and has also been published on the Klipfolio website in a slightly adjusted form, check it out here

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