Analyse data with excel

Data analysis in Excel is provided by construction of a table processor. A lot of the program’s resources are suitable for solving this task.

Excel positions itself as the best universal software product in the world for processing analytical information. From a small enterprise to large corporations, managers spend a significant part of their working hours analyzing their businesses activity. Let’s consider the main analytical tools in Excel and examples of their use in practice.



Excel analysis tools

One of the most attractive data analysis is «What-if Analysis». It is located in «DATA» tab.

Analysis tools of «What-if Analysis»:

1. “Scenario Manager”. It is used to generate, change and save different sets of input data and the results of calculations for a group of formulas.



2. «Goal Seek». It is used when the user knows the result of the formula, but the input information for this result is unknown.



3. «Data Table». Used in situations when it is necessary to show the effect of variable values on formulas in the form of a table.

«Data Analysis». This is an Excel add-in. Helps find the best solution for a particular task.

Other tools for analysis:

Analyze data in Excel using built-in functions (mathematical, financial, logical, statistical, etc.).



Summary tables in data analysis

Excel uses summary tables to simplify the viewing, processing and consolidation of data.

The program will treat the entered information as a table, but not as a simple information set. But firstly you should format lists with values according to next steps:

1. Go to the «INSERT» tab and click on the «Table» button CTRL+T.



2. The «Create Table» dialog box appears.



3. Specify the range of data (if it already exist) or the expected range (in which cells the table will be placed).

Set the check-mark in the box next to «Table with titles». Press Enter.

The specified default formatting style applies to the specified range.

You can compose the report using the «PivotTable».

1. Activate any of the cells in the values range. We click the button «PivotTable» («INSERT» — «Tables» — «PivotTable»).



2. In the dialog box you specify the range and place where to put the summary report (new sheet).



3. The «PivotTable Fields» opens. The left side of the sheet is the report image; the right part is the tools for creating the summary report.



4. Select the required fields from the list. Determine the values for the names of rows and columns. The report will be built on the left side of the sheet.

Creating a pivot table is already a way for analyzing information. Moreover, the user selects the information he needs at a particular moment for displaying. Then he can use other tools.

Analysis «What-if Analysis» in Excel: «Data Table»

This is a powerful tool for information analysis. Let’s consider the organization of information using the tool «What-if Analysis» — «Data Table».

Important conditions:

• data must be in one column or one line;
• the formula refers to one input cell.

The procedure for creating analysis:

1. We enter the input values in a column. Enter the formula in the next column one line higher.



2. We select a range of values including a column with input values and a formula A3:B12. Go to the «DATA» tab. Open the «What-if Analysis» tool. We click the «Data Table» button.



3. There are two fields in the opened dialog box. Since we create a table with one input we enter the address only in the field «Column input cell:». If the input values are in lines (not in columns), we will enter the cell’s number in the field «Row input cell: » and click OK.

• Download enterprise analysis system
• Download analytical finance table
• Business profitability table
• Cash flow statement
• Example of a point method in financial and economic analytics

When using the features Excel, to analyze the enterprise activity, we use information from the balance sheet and income statement. Each user creates his own form, which reflects the features of the company and important information for decision-making.

Overview of Excel

Excel is basically a spreadsheet that Microsoft developed for the different operating systems such as Windows, macOS, Android and iOS. It comes equipped with diverse functionalities such as calculation, graphing tools, pivot tables and a macro programming language called Visual Basic for Applications. It forms a part of Microsoft Office.

In the actual application, the world of business has embraced Excel as it is smooth, effective and flexible in the way it can be used. Nearly all major businesses make use of Excel in one way or the other. It suits any and every kind of business processes whether it’s sales, marketing or anything else. It’s such an integral part of businesses because it can be customized and it can produce effective results quite quickly without any specific technical expertise.

Since data is imported into Excel most of the times, it’s interesting how Excel itself can be used to carry out data analysis.

But before we go to data analysis, let’s understand what it entails…

What is data analysis?

While data is of vital importance and the world has become data-driven, data in the raw form is not quite useful. In order to use data to derive actionable intelligence, it needs to be inspected, cleansed and transformed. This kind of a process is what is called Data Analysis.

There is no single way to accomplish this. There are a variety of ways to carry out data analysis. These diverse ways of data analysis are used in different fields such as business, science and even social sciences. In fact, data analysis is something that contemporary business world thrives on. Data analysis is leveraged in order to glean business intelligence to drive business growth.

Data mining is also an exercise of data analysis but it focuses on discovering new knowledge for predictive rather than descriptive purposes. As far as statistical applications are concerned, data analysis can be bifurcated into descriptive statistics, exploratory data analysis (EDA) and confirmatory data analysis (CDA).

While EDA is all about identifying new features in the data, CDA endeavours to confirm or prove the existing hypotheses wrong.

Predictive analytics is an exercise of applying statistical models for predictive forecasting or classification. In order to extract and classify information from textual sources, text analytics, on the other hand, makes use of statistical, linguistic and structural techniques.

These are all variations of data analysis. Data integration is something that is needed prior to data analysis. Data analysis is also connected with data visualization and data dissemination. Sometime, people use the terms data analysis and data modeling interchangeably.

Why Excel for data analysis?

You know how navigating through data could be a nightmare in itself.

It’s quite tricky to explore and process data when you are looking at large chunks of data. Analyzing it could very well be a unique challenge. However, Excel can come to your rescue.

Excel contains functions that can process a large amount of data quite effectively and easily. While different tasks of data analysis could be tricky, Excel functions are quite easy and anybody can use them and analyze the data.

It’s not necessary either to remember all the functions. You can simply Google it and find out the function you need for data analysis tasks.

For the sheer speed, simplicity and accuracy of it, Excel is not just useful but imperative for data analysis. It can save your valuable time and effectively enable the data analysis without any hassle as well.

How to carry out data analysis with Excel?

You might wonder how data analysis actually works. Here’s an overview of the step-wise process of data analysis for you:

Specifying Data Requirements

In order to carry out effective data analysis, it is imperative to specify the data requirements right at the outset. Let’s say that the data pertains to population. If that be so, the specific variables such as age, income etc., need to be specified and obtained. The data obtained could be in the form of numbers or categories.

Data Collection

Once the variables are specified, the information regarding the variables needs to be collected. It can be collected from various sources and made available for further process. This data may not contain any insights in the present form. Therefore, it needs to be processed and cleaned.

Data Processing

The data that is collected needs to be organized for further analysis. This would entail structuring the data in a particular way so that it becomes compatible for various analysis tools. For instance, you may need to place the data in rows and columns in a table for further analysis either in a Spreadsheet or Statistical Application. You may even need to create a data model as well.

Data Cleaning

While the data may get organized, it may, however, be incomplete. It could still contain duplicate items. A few errors may also creep in. Data Cleaning is the way to correct these errors and make the data accurate. There are different ways to clean the data. Suppose it contains financial data, it will surely have totals. These totals can then be compared against authentic published data or some other parameters. In this way, the data can be cleaned.

Data Analysis

Once data passes through various phases such as processing and cleaning, it would be ready for data analysis. There are numerous techniques available for data analysis. Data visualization can also be used in order to project the data in a graphic format. Correlation or Regression Analysis which are well-known statistical models can also be used for data analysis.

Communication

While data analysis may seem like the last step of the process, the findings of data analysis need to be communicated in a structured way to the end users. The end users may want the findings in a particular format. This is where some of the techniques of data visualization such as table and charts can prove quite useful as they can communicate the message quite succinctly. Colour coding and other tools can help you simplify it and enable you to communicate the findings more effectively.

Process of Data Analysis with Excel:

When it comes to data analysis with Excel, here’s how you go about it:

• Data collection
• Data Cleaning
• Data Exploration (using Pivot Table)
• Data Visualization

Let’s get started…

Data Collection:

• In order to get started with data analysis, the first step is to collect information on the variables in a systematic way. This kind of a process will help us find answers to the important questions and assess the results.
• Data collection part is vital because it ensures the accuracy of the data so that decisions related to the data turn out to be valid.
• Data collection is also useful because you have a baseline with which you can measure and you also get a target where you aim at reaching.
• As regards Excel, it is possible for you to collect and import data from a diversity of data sources. Your data sources could be:
  • Web Page
  • Microsoft Access database
• Let’s look at the practical example as mentioned below to see how we can collect data from various sources:

1. Extracting Data from Web Page

• It is possible that you would need the data that is refreshed on a website.
• For doing so, you can effectively use different Excel features. For instance, you can import data from a table on a website into Excel using a feature called Excel Web Query.

Step-by-Step Process to Extract Data From Web Pages:

Step 1: Open a workbook with a blank worksheet in Excel.
Now, go to DATA tab on the Ribbon -> Click on From Web. You would be returned to the New Web Query dialog box as illustrated in screenshot given below.

Step 2: Enter the URL of the website from where you want to import data, in the box next to Address and click Go.
In this example, we will extract data from the URL given below:
https://www.basketball-reference.com/playoffs/NBA_2018.html

Step 3: Click the yellow icons to select the data you want to import. Having done that, click the Import button after you have selected what you want.

Step 4: Click Import data, specify where you want to put the data and click Ok. Arrange the data for further analysis and/or presentation.

Output:

You can also collect data from other sources such as the following:

• From Microsoft Access Database
• From Files like csv, txt and xml
• From SQL server

Data Cleaning

• Data cleaning is all about finding out and correcting the errors in the dataset. It also includes replacing the incomplete or inaccurate parts with the correct ones.
• In Excel, you can clean data by using the techniques given below:
  1. Removing duplicate values
  2. Removing spaces
  3. Merging and splitting columns
  4. Reconciling table data by joining or matching

1. Removing duplicate rows:

• When you have large chunks of data, it is possible to have some duplicate rows. It would be advisable to filter for unique values first in order to confirm that the results are what you want before you remove duplicate values.
• Fortunately, Excel comes with an in-built feature to remove duplicate values from a table. With it, you can remove the duplicate values from a given table based on selected columns.

Let’s understand by an example:

Step 1:
Follow these steps to remove duplicate values: Select data –> Go to Data ribbon –> Remove Duplicates

2. Removing Spaces:

• It is possible that the data you have in Excel may contain leading, trailing, or multiple embedded space characters. These characters can sometimes cause unexpected results when you sort, filter, or search.
• However, you can use the Trim function in Microsoft Excel in order to remove all spaces from text except for single spaces between words.

Step 1:
Enter the formula =TRIM (A1) in the adjacent cell C1 and press the Enter key.

Step 2:
Select cell C1 and drag the fill handle down to the range cell that you want to remove the leading space. Then you can see all cell contents are extracted with all leading spaces removed.  Please see the screenshot:

3. Merging and Splitting columns

• In Excel, it is common to merge or split two or more columns into one or split one column into two or more columns.
• For example, you may want to split a column that contains an address field into separate street, city, region, and postal code columns.
• For this task, we will make use of Table To Column Function.

Step 1:
Go to Data tab, in Sort & Filter Group. Click on the Text to Columns.
Then choose radio button: Delimited (to split the address) and click on next button like the screenshot given below:

Step 2:
Click and put a tick on the “Space” check box because our data delimiter is “Space”. When you click on it, you will be able to see the data being separated in the data preview box.
Then Click on the Next button.

Step 3:
Click on destination to choose the location where you want to split the text and Click on the “Finish” button.

Step 4:
You can see that the text from one cell in column A has been split into the column B:F as shown below.

You can also use this feature for additional common values that may require merging into one column or splitting into multiple columns include product codes, file paths, and Internet Protocol (IP) addresses.

4. Reconciling table data by joining or matching

• Excel can also be used for finding and correcting matching errors when two or more tables are joined. This may entail reconciling two tables from different worksheets.
• For example, you can use it to see all records in both tables or to compare tables and find rows that don’t match.
• Here, function vlookup() would help to perform this task.
• Vlookup(): It searches for a value in the first column of a table array and returns a value in the same row from another column in the table array.
• Let’s look at the table below (order and Customer). In Order table, we want to map city name from the customer tables based on common key “Customer ID”.
• Here, function vlookup() will enable us to perform this task.
• Go to Formula tab -> in Function Library click on Lookup & Reference -> click on Vlookup.
• Now, We´ll use the VLOOKUP function and type this formula into E3.
• Vlookup Syntax:
  • Lookup_value : Key to lookup
  • Table_array : Source_table
  • Col_index_num : column of source table
  • Range_lookup : are you ok with relative match?
• For our example:
  • Lookup_value –  A4
  • Table_array –  I : L
  • Col_index_num – 4
  • Range_lookup – 0
• This will return the city name for all the Customer id 1 and post that copy this formula for all Customer ids. Please see the screenshot given below:

Data Exploration Using Pivot Table

• Data Exploring is the vital process of performing initial investigations on data in order to find out patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.
• Why it matters so much is that you can make use of exploring data and make sense of the data you have. You can then figure out what questions you want to ask and how to frame them, as well as how best to manipulate your available data sources to get the answers you need.

Pivot Table:

• Excel’s Pivot Table is a summary table that lets you count, average, sum, and perform other calculations according to the reference feature you have selected.
• Let’s Create Pivot Table for the table given below:

Step 1:
To show Region and Product wise sum of premium, we will create a pivot table as follows:
Select table (A3:E13) -> Go to Insert tab, in the tables group, Click on Pivot Table.
Then select Existing worksheet Location where you want the Pivot Table.

Step 2:
Now, you can see the Pivot Table Field List panel, which contains the fields from your list. All you need to do is to arrange them in the boxes at the foot of the panel. Once you have done that, the diagram on the left becomes your Pivot Table.

As shown in the screenshot, you can see that we have arranged “Region” in row, “Product id” in column and sum of “Premium” is taken as value. Now you are ready with pivot table which shows Region and Product wise sum of premium. You can also use count, average, min, max and other summary metric.

Data Visualization:

• As exploring data is quite important, data visualization as a technique through which we can explore data also becomes vital for us.
• Data visualization is the presentation of data in a pictorial or graphical format. The reason why such a graphical format matters is that it becomes easier for decision makers to see analytics presented visually. In other words, they can grasp difficult concepts or identify new patterns far more easily.
• In Excel, there are 2 features (Charts and Pivot Charts) which are most popular for data visualization.

Charts:

A simple chart in Excel can say a lot more than a sheet full of numbers. As you’ll see, creating charts is quite easy.

Let’s create Simple Line Chart by executing following steps:

Step 1:
Select the range A1:C11 -> On the Insert tab, in the Charts group, click the Line symbol.

Step 2:
Now, to create Line Chart, click Line with Markers as shown in the screenshot.

Pivot chart:

A pivot chart is the visual representation of a pivot table in Excel. Pivot charts and pivot tables are connected with each other.

Go back to Pivot Tables to learn how to create this pivot table.

Let’s create a Pivot Chart:

Step 1:
Click any cell inside the pivot table -> On the Insert tab, in the Charts group, click Pivot Chart.
Then the Insert Chart dialog box appears. Click OK to create pivot Chart.

In the screenshot given below, you can find the pivot chart.

Once you have created the pivot chart, you can customize it to your particular needs to communicate your desired message by filtering chart attributes and changing chart types.

1. PowerPivot

Excel has limitations of 1048576 Rows which means you cannot analyze more than 1048576 rows of data.

And this is where Powerpivot comes in…

Power Pivot is an Excel Add-on that was first introduced in Excel 2010, and gives you a chance to import, merge and prepare data from more data sources at once.

You can import many tables from many different sources (SQL, Azure, Oracle, Excel, Access,…) into Power Pivot and then you can relate all this data to one another.

It means that you can build a Data Model containing multiple data sets from multiple different sources and by connecting them acquiring the ability to analyze them all in one Pivot Table.

Learn More about Power Pivot :
https://support.office.com/en-us/article/power-pivot-powerful-data-analysis-and-data-modeling-in-excel-a9c2c6e2-cc49-4976-a7d7-40896795d045

2. ToolPak

While developing complex statistical or engineering analyses, you can save steps and time by using the Analysis ToolPak.

All you need to do is to provide the data and parameters for each analysis, and the tool uses the appropriate statistical or engineering macro functions to calculate and display the results in an output table. Some tools generate charts in addition to output tables.

ToolPak Provides 19 various features (like Correlation, Covariance, Histogram, Regression and many more…) for data analysis.

Learn More about ToolPak:
https://support.office.com/en-us/article/use-the-analysis-toolpak-to-perform-complex-data-analysis-6c67ccf0-f4a9-487c-8dec-bdb5a2cefab6

End Note

It’s common knowledge how Excel is imperative for businesses in their day-to-day operations. However, not many businesses are aware of the potential of Excel for data analysis.

Since data analysis is crucial for businesses, it’s paramount that businesses leverage the power of Excel for data analysis. The more effectively you can use Excel, the more insights you can gain out of data analysis which you can utilize in enhancing your business.

There are other options such as Python, R Language or rapidminer that you can capitalize upon for data analysis as well. There are many tools that you can use for data analysis. However, each one will require a particular kind of expertise that you may or may not have. Therefore, data analysis with Excel is the simplest and yet one of the most effective data analysis solutions.

Do share your valuable feedback and comments regarding this blog.

In the world of technology, data is how machines communicate. It’s a language of numbers and metrics; a simple, yet hauntingly complicated system that gives us non-machines many headaches.

Microsoft Excel helps us break this language barrier and turn raw numbers into ideas, trends and insights.

Analyzing data in Excel is also useful when you need to transform your data into charts and other visuals. These are invaluable tools that help you see the story behind the numbers and highlight your best assets and most significant shifts in reports to clients and stakeholders.

When it comes to creating effective charts for your data, seeing is really believing. Very few managers have the time to analyze data with Excel by hand. Charts make the insights jump off the screen.

This discussion will look at how analysts, researchers, and managers can use Excel to transform their data into helpful charts and stunning visuals.  It is the most effective way to detect trends, patterns, outliers, and other notable events in your data sets. In this blog you will learn:

• How To Analyze Data in Excel?
• How to Use ChartExpo Add-in for Data Analysis in Excel ?
• An Overview Of Excel
• How to Carry Out the Data Analysis Process?
• What is the Importance of Data Analysis for your Business?
• How does Excel Help Digital Marketers in Data Analysis?

How To Analyze Data in Excel?

Navigating through data can feel like a nightmare. It can be quite tricky to explore and process data when dealing with large chunks of it all at once.

After all, not all data is useful or relevant. To make matters worse, data in its raw form can often cause more confusion than clarity.

Before you can derive any sort of actionable intelligence from your data, it needs to be gathered, filtered, cleaned, visualized, analyzed, and reported. All of these steps form the data analysis process.

The data analysis process can be different each time you perform it. Unique obstacles and challenges can arise that make conclusions difficult to obtain. Thus, it is best when you have dynamic solutions to handle all of the unexpected bumps in the road.

Excel’s robust toolset offers an excellent kick-start to the process. Not only is it a convenient way to collect, arrange and organize data, but you can also perform complex computations and visualize the data with some basic charting options.

How to analyze data in Excel? From your spreadsheet data, you may even be able to glean some basic insights. In other words, you can perform some rudimentary analysis right from your spreadsheets, before charting or digging deeper into the numbers.

How to Use ChartExpo Add-in for Data Analysis in Excel ?

Excel has a lot of benefits to business owners and marketers. It is a convenient tool for collecting and organizing data. However, it does have its limitations. One of these deficiencies is in charting. Excel only offers a small handful of charts for marketers to use.

As we’ve covered, being able to display data as a chart is extremely valuable. It can save you hours when it comes to detecting insights and understanding the story behind the raw numbers.

So how do you get the most out of your data with this lack of charting options? With the ChartExpo add-in for Excel, you can gain new functionality from Excel.

ChartExpo adds over 80 different Excel visualizations. Many of these charts are designed specifically for digital marketers and PPC advertisers.

Manually analyzing data requires a lot of surfing through spreadsheets. You may even need to have some coding abilities. ChartExpo for Excel makes it simple to gain insights from even a complex spreadsheet.

To get started with the ChartExpo add-in:

• Open your Excel application.
• Open your spreadsheet and click INSERT from the top toolbar. From the drop-down menu, you will see My Apps.
• Click on My Apps and then click to See All.

• If you’ve already added ChartExpo for Excel, you’ll see it available. Otherwise, you’ll have to visit the Microsoft Office Store and download the free plugin.
• Once you’ve installed ChartExpo, select the add-in and click on the Insert button.

• By clicking insert, you’ll add ChartExpo to your Excel environment.
• You’ll be asked to log in with your Microsoft account. If you don’t have one, you can create one for free.
• It’s worth mentioning that you are only required to log into your Microsoft Account the first time you access ChartExpo. Each time you use the plugin after, you will not have to undergo this step.

• At this stage, you are ready to begin using ChartExpo. You have access to the entire library of charts and visualizations. Click on the start button to continue.

• Click Create Chart to get started.

• Next, you’ll see a list of different chart categories. You can select any group to expand it and see the individual charts available under that category.

Let’s say you are running a Google Ads campaign and you want to analyze your ad impressions by time. You can select the Pay-per-click (PPC) category and then choose the Dayparting Chart.

• Once you create the chart, you’ll see a sample Dayparting chart that looks like this:

• By clicking Explore Sample Data, the data will be automatically added to your spreadsheet.

• You can change the data in this table to reflect your campaigns’ data. After adding your own data, click on the Create Chart From Sheet Data Option

• This will change the Dayparting chart to reflect your own data.

With this PPC chart, you can see what days of the week and hours of the day generate the highest impressions. Dark squares reflect times when impressions are at their highest, while lighter squares are times that perform poorly for impressions.

You can use these insights to:

• Stretch your campaign budget: You can change when and how often your ads appear in order to take advantage of this Dayparting schedule. This can help you stretch your budget to its limits by only showing ads at high-value times.
• Maximize your impressions: Impressions can reflect times when your target audience is most active. By increasing bids at these times, you can improve the likelihood that your ads are shown during peak times.
• Time your strategies: Your Dayparting data can also be used to inform your other strategies. When your ads are seeing lots of activity, it could mean valuable times for your social posts, content updates and other tactics.

Remember, if you face any issues with ChartExpo library in Excel 2013, make sure to install office service pack 1 on Windows.

An Overview Of Excel

Excel is a software tool developed by Microsoft with the core function of making spreadsheets. However, the application can do much more than just make spreadsheets.

There is a vast selection of tools, settings and other options. No matter how you want to present or organize your data, there are options available to help you.

This selection includes pivot tables, a macro programming language (called Visual Basic for Applications or VBA), powerful calculation capabilities and much more.

As part of the Microsoft Office Suite, Excel is compatible with every major operating system — Windows, macOS, Android, iOS — and some lesser-known ones.

Most users have Excel Basic, the standard application. There are other, more expensive options, which have even greater capabilities.

Over the years, Excel has become the go-to method for organizing and presenting data because of its smooth, practical and flexible interface.

You’d be hard-pressed to find a business today that doesn’t use Excel to some degree, mostly because of how integral data management has become for all types of companies.

The reason that Excel is so popular is because of how many different ways it can be used. Even if you aren’t a master of VBA or know how to set up complex computations in your spreadsheets, you can still get a lot of use from this program.

It is truly a vital, versatile and powerful tool for all types of data.

How to Carry Out the Data Analysis Process?

To extract the most value from your data, you need to consider how data analysis actually works. Here is an overview of the step-by-step process to analyze data with Excel.

1. Specifying Data Requirements

To perform effective data analysis, you need to set the requirements of the data. This means establishing the structure and categories of the data that will be pertinent to your analysis.

For example, if you analyze your marketing audience, your data requirements may include things like their age, income, location, etc. These requirements will dictate what data needs to be collected.

2. Data Collection

Once you have defined the variables and organized them into categories, you need to collect all of the relevant data related to these areas. Your data needs to be complete and as accurate as possible.

Ultimately, it is up to you to source your data correctly and ensure its relevance, quality and accuracy. The information still needs to be filtered and cleaned.

3. Data Processing

After you collect the raw data, you need to organize it for further analysis. You must structure the data into proper categories. This is the stage when you need to enter the data into a spreadsheet or develop some type of data model to arrange the information logically.

Organizing the data in this fashion makes it possible to filter and clean the data.

4. Data Cleaning

The data you have organized may look nice and neat, but it is likely incomplete and may contain errors or duplicate items. Data cleaning is the process of reviewing your collected data and fixing any errors or inaccuracies you may find.

The cleaning process will depend on the type of data you’ve gathered. For example, if it is financial data, you can simply sum up totals and make sure they match your records. This is a pre-review step that is important for establishing the truth and reliability of your data points.

5. Data Analysis

Once your data passes through all of the phases above, it is ready for analysis.

You can manually perform the data analysis process by physically examining each row and column of data and comparing the totals and recognizing patterns and other correlations. If your data set is extensive, this is extremely challenging and can border on the impossible.

That is where data visualization tools come into play. By charting the data, you can visually see the patterns, outliers, trends, etc. It is an extremely quick method for gaining a deeper understanding of your data.

6. Communication

While the analysis of the data may seem like the final step, you still need to be able to share and communicate your findings. You may need to report your data insights to stakeholders, clients, team members or other parties.

Whoever is reading your data needs to be able to reach and understand the same conclusions. Sometimes, the data may be too complex and difficult to explain without the help of charts and other tools to simplify the information and communicate the findings effectively.

What is the Importance of Data Analysis for your Business?

When running a business, or even just managing a Google Ads account, it is often difficult to know where to start, where to go and the best way to get there. The Digital Age has produced so many new audiences, channels, strategies and other options that are worth exploring.

Our world has become so data-driven. It is crucial to be able to make sense of your marketing data, no matter what type of business you operate.

Hidden in this data is everything you need to learn how to reach your target audiences, make better products, construct stronger ad messages, increase your marketing ROI and so much more.

The problem is that all of this data is a jumbled mess of numbers coming from all different sources. It’s a massive, tangled ball of string (attached to several other equally-tangled balls of string).

Data analysis unravels this mess and reveals the insights obscured by the chaos.

While every path is valuable, some are more fruitful than others. The importance of data analysis is discovering the most promising course of action.

You identify your best and worst performers and use this information to optimize your strategies and maximize your returns.

How does Excel Help Digital Marketers in Data Analysis?

Sales is a numbers game and, thanks to the Internet, marketers have more numbers than ever to deal with. This is why data has become so crucial in the Digital Age. However, it is important to realize that data is not valuable unless you can analyze it and extract insights.

These insights are what make Excel and data so helpful to digital marketers. Digital marketers can use Excel to develop and track different strategies that generate new customers.

Here are all of the ways that Excel and data analysis are helpful to digital marketers.

• Budgeting

Before you do anything in your digital marketing, you have to answer the budgeting questions. How much money can you afford to spend on each strategy? Think about your entire budget. How much do you allocate towards paying writers for SEO content assets? How high will you set your budget for PPC and Google Ads?

An Excel spreadsheet can quickly arrange your cost-related data and show where and how your budget is utilized. You can save a lot of time by using Excel to track your costs. By having all of your expenses in one place, it is easier to measure how your money is being spent and whether the returns are worth it.

After all, it’s not just about what you are spending, but also what you are getting in return. A pillar of successful digital marketing is being able to optimize how you spend your budget to achieve the best possible results.

• Maintain a Blog Calendar

It is important to your customers that you keep in contact with them regularly. You want to avoid long lapses in content or sudden bursts of posting.

Using an Excel spreadsheet, you can easily build an editorial calendar, which will be extremely useful for scheduling the writing and uploading of web content for your pages. It is easier for most people to stay on track when they have a calendar to work from.

Keeping all of your information in the same space makes it easier for you to keep it connected and stay on track. Many teams use Excel spreadsheets to delegate tasks and decide who will do what, when and where.

• Tracking SEO Results

Tracking SEO keywords is vital to successful organic search marketing. There are many keyword research and analysis tools that will help you select better search targets and track their performance.

With an Excel spreadsheet, you can take this data and easily compare different keywords. You can also track costs and measure the ROI of each prospective keyword target.

At the end of the day, there is no better way than Excel to collect, organize, track and save data regarding keywords and costs.

• Maintain Social Content

Social media is a mandatory strategy for building and cultivating your audience. Similar to having a blog calendar, you can also use Excel to schedule and organize your Tweets, Facebook posts, Instagram Stories, LinkedIn discussions, etc.

More importantly, you can collect your social media metrics across these various platforms and organize them into a single spreadsheet. This allows you to have full visibility of how each platform performs and contributes to your overall social media marketing strategy.

You can use this data to determine which social media platforms are providing the best results, as well as what types of posts work best on which site. If you also track when each post is published, you can use this spreadsheet to determine the best times and days to publish posts.

• Tracking Google Ads

Google provides many ways for you to track how effectively your ads are working for you and how many clicks and conversions the ads are generating. Excel is, once again, helpful in collecting and organizing these various metrics.

A common way that PPC marketers use Excel is to set up a table of costs versus clicks. This simple analysis can provide a baseline of your costs versus returns. From there, you can dig deeper into conversions and the actual revenue created from these clicks.

After all, just because you’ve generated lots of clicks doesn’t necessarily mean that your strategies are profitable. Excel can help you determine what ad messages are actually producing profits in your PPC campaigns.

• Generate and Track Leads

You can create a traffic/lead tracker with Excel to monitor how many website visitors took that next step and expressed interest. This could be clicking through to your website, subscribing to a newsletter, etc.

Lead generation is a huge part of sales. Excel can help you organize each lead based on their position in your funnel. By analyzing how much of your traffic is developing into viable leads, you can determine whether you’re wasting money or not on your prospects.

In other words, making spreadsheets can help you assess what is working for your business and what isn’t.

• Monthly Market Metrics Reports

Seeing your marketing efforts payoff is one of the most rewarding feelings. With an Excel spreadsheet, you can keep track of all of your investments and returns in one place. This will keep your costs in check and ensure that each strategy you implement helps your business.

Excel can help you perform a “monthly marketing metric report.” This simple spreadsheet will show your key metrics month-to-month. Sometimes, strategies change. What worked last month is not guaranteed to achieve the same returns this month.

Tracking this data monthly will ensure that you are always on top of any significant changes in your marketing data.

FAQs:

What is the best way to analyze data in Excel?

Excel is not only for tabular data, tabular data analysis becomes difficult if there are many rows and columns. Best way to analyze data in Excel is through visualizations.

What is the data analysis tool in Excel?

ChartExpo add-in is to visualize your data with better insights. You can use this as a tool in Excel to give your data analysis a new angle for improved understanding.

How do you analyze large data in Excel?

You can use quick analysis tool in excel for larger data sets but if you convert your data to awesome visualization , it will become much easier to analyze large datasets. ChartExpo library have wonderful visualization collection for almost all types of large dataset analysis.

Wrap Up

Unfortunately, data and numbers are so much more complicated than they ever have been in the past. Excel helps you collect, organize and manage the complexities of your data.

Hands down, it is one of the most important tools for a data analyst and for digital marketer who want to visualize their data in better way.

That said, there are some limitations when using Excel, particularly when it comes to charting options.

This is where ChartExpo becomes exceptionally valuable to marketers and pay-per-click advertisers. By adding over 80 different charts and visualization options, you have a lot more freedom with presenting your data.

When you select the perfect chart to visualize your data, it makes discovering valuable insights and reporting results so much easier.

If you are already analyzing data with Excel, you can improve your efforts with just one click by downloading the ChartExpo add-in.