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How to Create a Pivot Table in Excel: A Step-by-Step Tutorial

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How to Create a Pivot Table in Excel: A Step-by-Step Tutorial

The pivot table is one of Microsoft Excel’s most powerful — and intimidating — functions. Powerful because it can help you summarize and make sense of large data sets. Intimidating because you’re not exactly an Excel expert, and pivot tables have always had a reputation for being complicated.

The good news: Learning how to create a pivot table in Excel is much easier than you might’ve been led to believe.

But before we walk you through the process of creating one, let’s take a step back and make sure you understand exactly what a pivot table is, and why you might need to use one.

In other words, pivot tables extract meaning from that seemingly endless jumble of numbers on your screen. And more specifically, it lets you group your data in different ways so you can draw helpful conclusions more easily.

The “pivot” part of a pivot table stems from the fact that you can rotate (or pivot) the data in the table to view it from a different perspective. To be clear, you’re not adding to, subtracting from, or otherwise changing your data when you make a pivot. Instead, you’re simply reorganizing the data so you can reveal useful information from it.

What are pivot tables used for?

If you’re still feeling a bit confused about what pivot tables actually do, don’t worry. This is one of those technologies that are much easier to understand once you’ve seen it in action.

The purpose of pivot tables is to offer user-friendly ways to quickly summarize large amounts of data. They can be used to better understand, display, and analyze numerical data in detail — and can help identify and answer unanticipated questions surrounding it.

Here are seven hypothetical scenarios where a pivot table could be a solution:

1. Comparing sales totals of different products.

Say you have a worksheet that contains monthly sales data for three different products — product 1, product 2, and product 3 — and you want to figure out which of the three has been bringing in the most bucks. You could, of course, look through the worksheet and manually add the corresponding sales figure to a running total every time product 1 appears. You could then do the same for product 2, and product 3 until you have totals for all of them. Piece of cake, right?

Now, imagine your monthly sales worksheet has thousands and thousands of rows. Manually sorting through them all could take a lifetime. Using a pivot table, you can automatically aggregate all of the sales figures for product 1, product 2, and product 3 — and calculate their respective sums — in less than a minute.

2. Showing product sales as percentages of total sales.

Pivot tables naturally show the totals of each row or column when you create them. But that’s not the only figure you can automatically produce.

Let’s say you entered quarterly sales numbers for three separate products into an Excel sheet and turned this data into a pivot table. The table would automatically give you three totals at the bottom of each column — having added up each product’s quarterly sales. But what if you wanted to find the percentage these product sales contributed to all company sales, rather than just those products’ sales totals?

With a pivot table, you can configure each column to give you the column’s percentage of all three column totals, instead of just the column total. If three product sales totaled $200,000 in sales, for example, and the first product made $45,000, you can edit a pivot table to instead say this product contributed 22.5% of all company sales.

To show product sales as percentages of total sales in a pivot table, simply right-click the cell carrying a sales total and select Show Values As > % of Grand Total.

3. Combining duplicate data.

In this scenario, you’ve just completed a blog redesign and had to update a bunch of URLs. Unfortunately, your blog reporting software didn’t handle it very well and ended up splitting the “view” metrics for single posts between two different URLs. So in your spreadsheet, you have two separate instances of each individual blog post. To get accurate data, you need to combine the view totals for each of these duplicates.

That’s where the pivot table comes into play. Instead of having to manually search for and combine all the metrics from the duplicates, you can summarize your data (via pivot table) by blog post title, and voilà: the view metrics from those duplicate posts will be aggregated automatically.

4. Getting an employee headcount for separate departments.

Pivot tables are helpful for automatically calculating things that you can’t easily find in a basic Excel table. One of those things is counting rows that all have something in common.

If you have a list of employees in an Excel sheet, for instance, and next to the employees’ names are the respective departments they belong to, you can create a pivot table from this data that shows you each department name and the number of employees that belong to those departments. The pivot table effectively eliminates your task of sorting the Excel sheet by department name and counting each row manually.

5. Adding default values to empty cells.

Not every dataset you enter into Excel will populate every cell. If you’re waiting for new data to come in before entering it into Excel, you might have lots of empty cells that look confusing or need further explanation when showing this data to your manager. That’s where pivot tables come in.

You can easily customize a pivot table to fill empty cells with a default value, such as $0, or TBD (for “to be determined”). For large tables of data, being able to tag these cells quickly is a useful feature when many people are reviewing the same sheet.

To automatically format the empty cells of your pivot table, right-click your table and click PivotTable Options. In the window that appears, check the box labeled Empty Cells As and enter what you’d like displayed when a cell has no other value.

How to Create a Pivot Table

  1. Enter your data into a range of rows and columns.
  2. Sort your data by a specific attribute.
  3. Highlight your cells to create your pivot table.
  4. Drag and drop a field into the “Row Labels” area.
  5. Drag and drop a field into the “Values” area.
  6. Fine-tune your calculations.

Now that you have a better sense of what pivot tables can be used for, let’s get into the nitty-gritty of how to actually create one.

Step 1. Enter your data into a range of rows and columns.

Every pivot table in Excel starts with a basic Excel table, where all your data is housed. To create this table, simply enter your values into a specific set of rows and columns. Use the topmost row or the topmost column to categorize your values by what they represent.

For example, to create an Excel table of blog post performance data, you might have a column listing each “Top Pages,” a column listing each URL’s “Clicks,” a column listing each post’s “Impressions,” and so on. (We’ll be using that example in the steps that follow.)

how to create a pivot table step 1: enter your data into a range of rows and columns

Step 2. Sort your data by a specific attribute.

When you have all the data you want entered into your Excel sheet, you’ll want to sort this data in some way so it’s easier to manage once you turn it into a pivot table.

To sort your data, click the Data tab in the top navigation bar and select the Sort icon underneath it. In the window that appears, you can opt to sort your data by any column you want and in any order.

For example, to sort your Excel sheet by “Views to Date,” select this column title under Column and then select whether you want to order your posts from smallest to largest, or from largest to smallest.

Select OK on the bottom-right of the Sort window, and you’ll successfully reorder each row of your Excel sheet by the number of views each blog post has received.

how to create a pivot table step 2: sort your data by a specific attribute

Step 3. Highlight your cells to create your pivot table.

Once you’ve entered data into your Excel worksheet, and sorted it to your liking, highlight the cells you’d like to summarize in a pivot table. Click Insert along the top navigation, and select the PivotTable icon. You can also click anywhere in your worksheet, select “PivotTable,” and manually enter the range of cells you’d like included in the PivotTable.

This will open an option box where, in addition to setting your cell range, you can select whether or not to launch this pivot table in a new worksheet or keep it in the existing worksheet. If you open a new sheet, you can navigate to and away from it at the bottom of your Excel workbook. Once you’ve chosen, click OK.

Alternatively, you can highlight your cells, select Recommended PivotTables to the right of the PivotTable icon, and open a pivot table with pre-set suggestions for how to organize each row and column.

how to create a pivot table step 3: highlight your cells to create your pivot table

Note: If you’re using an earlier version of Excel, “PivotTables” may be under Tables or Data along the top navigation, rather than “Insert.” In Google Sheets, you can create pivot tables from the Data dropdown along the top navigation.

Step 4. Drag and drop a field into the “Row Labels” area.

After you’ve completed Step 3, Excel will create a blank pivot table for you. Your next step is to drag and drop a field — labeled according to the names of the columns in your spreadsheet — into the Row Labels area. This will determine what unique identifier — blog post title, product name, and so on — the pivot table will organize your data by.

For example, let’s say you want to organize a bunch of blogging data by post title. To do that, you’d simply click and drag the “Top pages” field to the “Row Labels” area.

how to create a pivot table step 4: drag and drop a field into the rows label area

Note: Your pivot table may look different depending on which version of Excel you’re working with. However, the general principles remain the same.

Step 5. Drag and drop a field into the “Values” area.

Once you’ve established what you’re going to organize your data by, your next step is to add in some values by dragging a field into the Values area.

Sticking with the blogging data example, let’s say you want to summarize blog post views by title. To do this, you’d simply drag the “Views” field into the Values area.

how to create a pivot table step 5: drag and drop a field into the values area

Step 6. Fine-tune your calculations.

The sum of a particular value will be calculated by default, but you can easily change this to something like average, maximum, or minimum depending on what you want to calculate.

On a Mac, you can do this by clicking on the small i next to a value in the “Values” area, selecting the option you want, and clicking “OK.” Once you’ve made your selection, your pivot table will be updated accordingly.

If you’re using a PC, you’ll need to click on the small upside-down triangle next to your value and select Value Field Settings to access the menu.

how to create a pivot table step 6: fine tune your calculations

When you’ve categorized your data to your liking, save your work and use it as you please.

Digging Deeper With Pivot Tables

You’ve now learned the basics of pivot table creation in Excel. With this understanding, you can figure out what you need from your pivot table and find the solutions you’re looking for.

For example, you may notice that the data in your pivot table isn’t sorted the way you’d like. If this is the case, Excel’s Sort function can help you out. Alternatively, you may need to incorporate data from another source into your reporting, in which case the VLOOKUP function could come in handy.

Editor’s note: This post was originally published in December 2018 and has been updated for comprehensiveness.

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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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