AI, Excel

Combine ChatGPT with Excel and Become Irreplaceable

Imagine you work in the finance department of a supermarket, and your boss asks you for an analysis of which product promotions were successful this year and which ones weren’t.

If you’ve never done this type of analysis before, it might seem overwhelming.

But what if I told you that combining ChatGPT with Excel could make your job much easier and help you become irreplaceable at work?

In this blog,I’ll walk you through how you can use ChatGPT and Excel together to simplify your workflow.

I’ll share practical examples that you can apply right away, along with a trick that lets you leverage ChatGPT without sharing any confidential data.

Ready to boost your productivity? Let’s dive in!

Watch my YouTube Video

In this video, I explain how you can use ChatGPT to 10X your Excel skills as a finance professional.

Click on the GIF to watch it.

Boost your Productivity by Using ChatGPT with Excel

Here are the steps on how you can increase your productivity by 10x:

#1: Identifying KPIs Without Sharing Confidential Data

The key to using AI like ChatGPT without compromising confidentiality is the clever use of data headers.

For example, you have an Excel file that contains product names, revenue from last year and this year, as well as details about promotions during those years.

Instead of uploading the whole dataset to ChatGPT, you simply provide the headers: “Product,” “Revenue Last Year,” “Revenue Current Year,” etc.

This way, ChatGPT can understand the context without accessing sensitive numbers, which helps keep your data secure.

After you provide the headers, you can ask ChatGPT to suggest relevant KPIs for analyzing the promotion performance.

#2: Choosing the Right KPIs for Promotion Analysis

After inputting the headers, ChatGPT suggested several potential KPIs, including:

Promotion Effectiveness
Revenue Growth Rate
Promotion Spending Growth Rate
Revenue to Promotion Ratio
Incremental Revenue per Unit of Promotion

Each of these KPIs tells you something different about how the promotions performed, but I found the last one particularly interesting because it helps identify how much additional revenue was generated for every dollar spent on promotions.

I then asked ChatGPT how to calculate this KPI in Excel.

#3: Getting the Right Excel Formula from ChatGPT

ChatGPT provided a detailed explanation and even gave me an Excel formula to calculate the incremental revenue per unit of promotion.

What I found most useful was that ChatGPT also assumed certain columns for the data (e.g., “Revenue Last Year” in Column D).

I double-checked my Excel file to ensure that these columns matched, and they did, so I used the formula directly.

After entering the formula, I formatted the results, and just like that, I had the incremental revenue per unit of promotion for each product.

This KPI allowed me to quickly see which promotions were effective and which weren’t.

#4: Making the Data Speak: Conditional Formatting

But numbers alone can be overwhelming, especially if you have dozens of products to analyze. That’s where conditional formatting in Excel becomes a powerful ally.

I asked ChatGPT how to highlight the best and worst-performing promotions, and it suggested using conditional formatting.

With conditional formatting, I applied a color scale to the KPI column, making it easy to see which products performed well (in blue) and which did poorly (in red).

For instance, it was immediately clear that while walnuts had a high return on promotion, sodas didn’t perform as well.

#5: Visualizing the Results with Graphs

If you’re dealing with a lot of data, it can be challenging to communicate your findings effectively.

To make the data more digestible, I asked ChatGPT which graph would be best to represent the KPI data for all 50 products (SKUs).

It suggested a few options, such as a bar chart, scatter plot, and heat map.

Based on my experience, I decided to go with a scatter plot because it clearly shows the relationship between promotional spending and revenue performance.

ChatGPT even guided me step-by-step on how to create the scatter plot and label each point with the corresponding product name, making it easy to identify which products were overperforming or underperforming.

#6: Interpreting the Graph for Insights

Once the scatter plot was ready, I wanted to understand what it meant for each product. I used ChatGPT again, asking how to interpret the graph.

The AI provided a helpful explanation that showed me which products had high promotional spending but low returns and vice versa.

For example, I noticed that walnuts were performing exceptionally well—we didn’t spend much on promoting them, but they generated a lot of additional revenue.

On the other hand, chicken promotions didn’t fare as well. I even filtered the data to focus on just the meat category, allowing me to compare beef and chicken directly.

Practical Tips for Your Next Analysis

Use Headers Only: Always provide headers instead of confidential data when using AI tools.
Conditional Formatting: Make large datasets easier to understand by color-coding key metrics.
Visualize with Scatter Plots: Use scatter plots for clear visual representation, especially when analyzing multiple products.
Ask ChatGPT for Explanations: Don’t hesitate to ask ChatGPT how to explain complex metrics to your boss or team.

Watch my YouTube video on how you can use ChatGPT to 10X your Excel skills as a finance professional by clicking here.

Wrapping Up

In just a few minutes, we used ChatGPT to identify the right KPIs, calculate them, visualize the results, and even interpret the data.

By combining ChatGPT with Excel, you can not only make data analysis easier but also become more valuable to your organization.

With this method, you will quickly deliver insights that drive better decision-making.

If you want to continue improving your skills and stay ahead in corporate finance, I have something for you.

I’ve created a free 5-day email course covering how to get the most out of AI tools like ChatGPT. Click here to enroll!

FAQ

Q: How can I use ChatGPT with Excel without compromising data security?
A: You can use headers instead of actual data, which allows ChatGPT to understand the context without accessing confidential information.

Q: What are the best KPIs to analyze promotion performance in Excel?
A: Some effective KPIs include Promotion Effectiveness, Revenue Growth Rate, and Incremental Revenue per Unit of Promotion.

Q: How can I visualize KPI data effectively in Excel?
A: Scatter plots, bar charts, and heat maps are great for visualizing relationships between data points, especially for promotion analysis.

Q: How can I highlight the best and worst promotions in Excel?
A: Use conditional formatting to color-code KPIs, making it easier to see which promotions performed well and which didn’t.

Q: How does ChatGPT help explain complex metrics?
A: ChatGPT can provide clear explanations and even suggest how to present metrics to your boss or team in an understandable way.

Excel

Top 10 Underrated Excel Formulas You Need to Know

Excel can feel like magic when you truly get a grasp of its many capabilities.

Sure, everyone talks about VLOOKUP and PivotTables, but there are other, often overlooked, formulas that can be absolute game-changers in finance.

Here’s a personal take on my top 10 underrated Excel formulas—ones that I’ve found invaluable time and again in my career.

Let’s dive in and explore what makes each one special.

Top 10 Underrated Excel Formulas You Must Know

Here are the 10 underrated Excel formulas that have saved me countless hours and helped me advance in my career:

#1: MATCH

During one of my budgeting exercises, I had to quickly identify which of our cost centers were causing variances. Using MATCH, I could instantly figure out which row contained each cost center—saving me hours of scrolling through long lists. By locating the position of data points, MATCH makes subsequent analyses easy. Imagine finding a specific product’s revenue for a particular month—MATCH can feed into INDEX to help pinpoint that value. In my experience, it’s like having a compass pointing you exactly where you need to look.

Formula: =MATCH(“Product A”, A2:A10, 0)

Why It’s Underrated: MATCH gets less attention compared to its famous sibling VLOOKUP, but it’s a more flexible way to locate items within a range.

Finance Example: Suppose you need to find the position of a certain revenue stream within a list of monthly categories. MATCH is perfect for this.

#2: INDEX

When I had to reconcile revenue reports and needed detailed numbers across different sheets, INDEX became my savior. It felt like a treasure hunt where I could finally say, “Aha, that’s the value I need!” INDEX can return any value from a range when combined with MATCH. This combo is far more versatile than VLOOKUP because it works left, right, up, or down—making it incredibly adaptable in financial analysis. It’s saved me countless times when I needed to extract data that just didn’t fit in VLOOKUP’s rigid structure.

Formula: =INDEX(B2:G10, 4, 3)

Why It’s Underrated: INDEX often flies under the radar, yet it’s the powerful partner to MATCH that allows you to pull data from anywhere in your table. Additionally, using INDEX with MATCH—often called the INDEX-MATCH formula—is more powerful than VLOOKUP. Unlike VLOOKUP, INDEX-MATCH can look left as well as right, and it doesn’t require the lookup column to be the first column in the range, providing greater flexibility in financial analysis.

Formula for INDEX-MATCH: =INDEX(B2:B10, MATCH(“Product123”, A2:A10, 0))

Finance Example: Let’s say you want to return the actual revenue value for a certain product in a particular month.

#3: XLOOKUP

A couple of months ago, I had to do a spending analysis by vendor. With XLOOKUP, I felt like I had finally upgraded from a flip phone to a smartphone – everything just clicked. XLOOKUP is simple yet powerful—it can look in any direction, doesn’t require sorting, and can handle errors gracefully. Once I started using it, I realized how much time I’d been wasting with workarounds. Now, my lookup needs are far more seamless.

Formula: =XLOOKUP(“Vendor123”, A2:A10, B2:B10)

Why It’s Underrated: XLOOKUP is newer and often overshadowed by older lookup formulas. However, it’s like VLOOKUP on steroids.

Finance Example: Looking for the expense amount associated with a vendor code? XLOOKUP can do it—no matter where that column sits.

#4: IFERROR

When preparing management reports, the last thing I wanted was for an executive to see a #DIV/0! error. IFERROR made my sheets “executive-ready”—flawless and smooth. Instead of ugly error messages in your spreadsheets, IFERROR allows you to present clean results. I love it for creating presentable outputs without needing manual intervention.

Formula: =IFERROR(A2/B2, 0)

Why It’s Underrated: Handling errors without complex nesting is underrated for clarity and peace of mind.

Finance Example: When performing division to calculate ratios, you might get #DIV/0! if the denominator is zero. Use IFERROR to avoid this.

#5: CONCATENATE

During one project, I needed a way to differentiate between similar product lines across multiple fiscal years. CONCATENATE made my work feel organized and clear. CONCATENATE helps build context into reports by combining relevant information. I often use it to create unique keys or descriptive labels.

Formula: =CONCATENATE(A2, ” – FY”, B2)

Why It’s Underrated: Combining text strings doesn’t get much attention—but for reports and summaries, it’s so handy.

Finance Example: Joining a cost center name with a fiscal year to make unique identifiers.

#6: SUMIF

Whenever our team had to segment revenue into regions for presentations, SUMIF made the work almost too easy—just a quick entry and the numbers were right there. Instead of manually filtering data, SUMIF adds precision. It’s a go-to for me when summarizing sales or expenses by categories.

Formula: =SUMIF(A2:A10, “North”, B2:B10)

Why It’s Underrated: SUMIF is the quiet achiever when it comes to conditional sums—especially powerful in finance.

Finance Example: Summing sales only for a particular region or sales rep.

#7: COUNTIF

While analyzing overdue accounts, COUNTIF helped me identify just how many customers were falling behind—highlighting trends that needed immediate action. Knowing the frequency of specific events is critical—COUNTIF has given me insights into data trends that were otherwise easy to miss.

Formula: =COUNTIF(B2:B10, “>1000”)

Why It’s Underrated: COUNTIF is often overshadowed by SUMIF, but it’s incredibly useful for counting occurrences based on conditions.

Finance Example: Counting how many times a budget variance exceeded a certain threshold.

#8: ISBLANK

During an end-of-quarter close, I relied on ISBLANK to find missing entries in our reconciliation. It’s one of those tools that prevent costly errors from slipping through. This formula is a handy checkpoint. When I’m validating data, ISBLANK helps me ensure that all necessary information is entered before I run complex analyses.

Formula: =ISBLANK(A2)

Why It’s Underrated: Identifying gaps in data is crucial, but ISBLANK is often overlooked.

Finance Example: Checking if certain cells have been filled out before proceeding with calculations.

#9: SUBTOTAL

One time, during a management review meeting, we filtered out a department’s expenses to see what remained. SUBTOTAL saved the day—and saved me from having to redo calculations. Unlike SUM, SUBTOTAL changes dynamically as you filter—making it perfect for ad hoc reporting. It’s my preferred choice when I need clarity on filtered financial data.

Formula: =SUBTOTAL(109, B2:B10)

Why It’s Underrated: SUBTOTAL is more flexible than SUM, especially when dealing with filtered lists. One of the advantages is that if you have in-between other SUMs—such as in a big list with a lot of SUMs in between—SUBTOTAL allows you to avoid counting the subtotals. Additionally, you can do different types of subtotals, either totaling all of the visible lines or the invisible ones.

Finance Example: Calculating total expenses only for visible rows after filtering.

#10: GETPIVOTDATA

In preparing a dashboard for executives, I needed numbers to dynamically update from PivotTables. GETPIVOTDATA became my go-to—once I understood it, it was like my PivotTables were feeding me answers directly. GETPIVOTDATA allows you to extract specific values without manually navigating the PivotTable. It has made many of my dashboarding tasks faster and more accurate.

Formula: =GETPIVOTDATA(“Revenue”, $A$3, “Segment”, “East”)

Why It’s Underrated: People often avoid GETPIVOTDATA because it seems complex, but it’s incredibly efficient for pulling key data out of PivotTables. One of the common problems with PivotTables is that when they feed into a report, any movement in the PivotTable can result in errors in the reporting. With GETPIVOTDATA, it doesn’t matter if the PivotTable changes in terms of format, colors, or lines—it will always bring back the correct value.

Finance Example: Extracting the total revenue figure for a particular segment from a Pivot Table.

Last Words

These formulas might not be the first ones you learn, but they’re definitely worth mastering.

They can make a huge difference in how efficiently you work and how much insight you can gain from your data.

Whether it’s saving hours of manual work or adding layers of precision to your analysis, these underrated Excel formulas have been game-changers for me, and I hope they will be for you, too.

Give them a try next time you’re working in Excel—you might just find they’re the key to unlocking the full power of your spreadsheets.

FAQ

Q: Why should I use INDEX-MATCH instead of VLOOKUP?

A: INDEX-MATCH is more flexible because it allows you to look up values in any direction, not just to the right. It’s also more robust if your data layout changes, unlike VLOOKUP which can break if columns are moved.

Q: What makes XLOOKUP better than VLOOKUP?

A: XLOOKUP is like an upgraded version of VLOOKUP. It can search both horizontally and vertically, doesn’t require sorting, and can handle errors more gracefully, making it a more versatile choice.

Q: How can IFERROR improve my reports?

A: IFERROR helps you avoid ugly error messages like #DIV/0! by replacing them with a custom value. This makes your reports cleaner and more professional, especially when sharing with others.

Q: When should I use CONCATENATE?

A: CONCATENATE (or its modern counterpart, TEXTJOIN) is useful when you need to combine text from different cells. It’s great for creating unique identifiers or adding context to data in your reports.

Q: What’s the advantage of using SUBTOTAL over SUM?

A: SUBTOTAL is more flexible because it only sums the visible rows when you filter your data. This makes it perfect for ad hoc analysis, as you don’t have to adjust your formulas when filtering data.

Excel

Python in Excel Is Now Available

The expectation from us, the finance pros, is that we need to do much more than just keep spreadsheets in order.

We’re expected to dive deep into data, build financial models, and predict what’s coming next.

Excel has been our go-to for years, but let’s face it – handling big datasets or automating complicated processes can feel a bit limiting.

That’s where Python in Excel steps in.

As of now, Python is built right into Excel.

You get the familiar Excel interface, but now, with the power of Python to manage more complex tasks, analyze huge datasets, and automate things we used to do manually.

If you’re in finance, this is a game-changer. It’s like taking your Excel skills to the next level, making your work faster and more efficient.

Python Has Just Arrived in Excel

Python’s integration with Excel is more than just a new feature – it’s a breakthrough for finance professionals. With Python, you can easily handle larger datasets that would typically slow Excel down.

This allows for more efficient data processing using powerful libraries like pandas and NumPy.

Repetitive tasks like updating reports or reconciling data can now be automated, freeing valuable time for more strategic work.

When it comes to complex financial models, Python simplifies advanced calculations such as Monte Carlo simulations or risk models that would be difficult in traditional Excel.

The integration also enhances how we present data. Python’s robust libraries, like matplotlib and seaborn, provide better options for customized and clearer visualizations, making it easier to communicate insights to stakeholders.

Beyond that, Python expands Excel’s limitations, offering tools for more advanced analysis that Excel’s built-in functions just can’t handle.

In addition to all this, Python brings machine learning capabilities directly into Excel, allowing you to create more accurate forecasts and predictive models for revenue, expenses, and market trends- going far beyond traditional methods.

Python in Excel GIF - Microsoft — Preview of Python in Excel – Credits to Microsoft

Why Are The Python Libraries Important for Finance?

One of Python’s greatest advantages is its vast ecosystem of libraries that are designed for specialized tasks.

Some of the key libraries that finance professionals can leverage within Excel include:

pandas

The most popular Python library for data manipulation and analysis. With pandas, you can quickly analyze, clean, and structure data into formats that are ideal for financial modeling.

NumPy

Perfect for handling large datasets and performing mathematical operations, including linear algebra and statistical calculations.

matplotlib and seaborn

These libraries allow you to create a wide variety of visualizations, from simple bar charts to more complex financial graphs and charts.

scikit-learn

A machine learning library that can help finance professionals build predictive models to forecast trends or make data-driven decisions.

Faker

Useful for generating synthetic datasets that can be used for testing financial models or training machine learning algorithms.

statsmodels

A library that allows for advanced statistical modeling, perfect for regression analysis and time-series forecasting in finance.

How to Install Python in Excel

One of the key advantages of this integration is that you don’t need prior programming experience to get started.

Plus, with ChatGPT by your side, you can rely on it to help with coding, troubleshooting, and understanding Python concepts.

Let’s begin with step-by-step instructions for installing and configuring Python in Excel for finance professionals.

Installing Python in Excel

Microsoft has made it simple to access Python directly from Excel, but to get started, you need to ensure that your Excel version supports Python integration.

Python in Excel is part of Microsoft 365, so having a subscription to the latest version of Excel is necessary.

Here’s how you can get Python running in Excel:

Step 1: Check Your Excel Version

Python in Excel is available in Microsoft 365 Excel (Windows version). If you are using older versions like Excel 2019, or Excel for Mac, Python support may not be available.
To check your version, open Excel, click File > Account, and verify your subscription details. Ensure that you are using the latest build.

Here is a screenshot of how to see your version:

This is Microsoft’s official website, and Python in Excel is available.

This is the official website of Getting Started with Python from Microsoft, which explains how to install it.

As for 22 September 2024, this feature is not available on the following platforms.

Excel for Mac
Excel on the web
Excel for iPad
Excel for iPhone
Excel for Android

Step 2: Using Python with the =PY() Function

Once Python is available, you can start writing Python code directly in Excel cells using the =PY() function. For example:
- In any cell, type =PY(“print(‘Hello, Python in Excel!’)”) and press Enter. This will execute the Python code in Excel.

Setting Up the Python Environment in Excel

Excel uses Anaconda to manage Python, a distribution widely used in data science and financial analytics.

Anaconda is a popular open-source distribution of the Python and R programming languages, designed specifically for data science, machine learning, and large-scale data processing.

Anaconda comes with pre-installed libraries such as pandas, NumPy, and matplotlib – key tools for handling large datasets, performing mathematical calculations, and generating advanced visualizations.

Here’s some of the packages/libraries available out of the box:

pandas: Perfect for working with tabular data like financial statements and transactional data.
NumPy: Useful for complex numerical calculations often needed in financial modeling.
matplotlib: A flexible library for visualizing data in charts and graphs.
scikit-learn: For applying machine learning techniques to forecast financial trends.

You can find more details on the Microsoft website.

Troubleshooting Installation Issues

If you run into issues while setting up Python in Excel, here are some common problems and how to solve them:

Python in Excel Feature Not Available:
- Ensure you are using Microsoft 365 and that Excel is fully updated to the latest version.
- If Python in Excel is not available, try joining the Office Insider program, which gives early access to features.
Libraries Missing:
- If you find that certain Python libraries aren’t available, it’s because Python in Excel comes with a predefined set of libraries. At present, you cannot install additional libraries manually. However, the pre-installed libraries cover most FP&A use cases (pandas, NumPy, matplotlib, etc.).

You can check what are the most current libraries by running “%pip list” from a Python formula.

Errors When Running Python Code:
- Python code errors can happen if there are syntax mistakes or missing dependencies. For beginners, leveraging ChatGPT to check the code can be helpful. Simply paste the error into ChatGPT, and it can guide you on how to resolve the issue.

Next Steps: Exploring the Power of Python in Excel

Now that you’ve successfully installed Python in Excel, you’re ready to start exploring its powerful capabilities.

Remember, ChatGPT is here to assist you as you begin using Python for advanced financial analysis, visualizations, and automation.

This was just the beginning… if you want to master Python in Excel, get your copy of the guide now!

Last Words

Python’s integration with Excel is a monumental advancement for finance professionals.

It’s no longer just about crunching numbers and creating simple models; it’s about automating tasks, handling big datasets, and gaining deeper insights faster than ever before.

With Python, you can scale your financial analyses and reporting without needing to be a coding expert.

This is an essential skill if you want to secure your job and advance in your career.

FAQ

Q: What makes Python in Excel such a game-changer for finance professionals?
A: Python’s integration into Excel allows finance professionals to handle larger datasets, automate repetitive tasks, and perform more complex financial modeling that would be difficult using Excel alone. It enhances Excel’s capabilities, making it a more powerful tool for financial analysis.

Q: Do I need programming experience to use Python in Excel?
A: No, you don’t need prior programming experience. Python in Excel is designed to be user-friendly. With built-in libraries and tools, even those new to coding can quickly get up to speed. The “Python in Excel for Finance” guide offers step-by-step instructions, making it easy for beginners to start.

Q: What are some examples of tasks I can automate with Python in Excel?
A: You can automate tasks like updating financial reports, reconciling data, generating financial forecasts, and running advanced simulations. These automations not only save time but also reduce the chance of errors, allowing you to focus on more strategic work.

Q: Which Python libraries in Excel are most useful for finance professionals?
A: Key libraries like pandas, NumPy, matplotlib, and scikit-learn are extremely useful. pandas helps with data manipulation, NumPy handles complex calculations, matplotlib creates visualizations, and scikit-learn enables advanced machine learning for financial predictions.

Q: How can I get started with Python in Excel?
A: In the beginning, make sure your version of Excel supports Python, which is available through Microsoft 365. Follow the steps to install and configure Python in Excel, and then explore the built-in functions like =PY(). Furthermore, the “Python in Excel for Finance” guide can help you master the process and unlock the full potential of this powerful integration.

Excel

Top 21 Excel Formulas You Need to Know

Excel is more than just a spreadsheet tool; it’s a powerful ally in managing data, making decisions, and visualizing information.

Whether you’re in finance, project management, or any field that relies on data, knowing how to use Excel formulas can make your work easier and more accurate.

In this guide, I will provide you with my Top 21 Excel Formulas and give you a glimpse into how they can boost your productivity.

Top 21 Excel Formulas

Here’s a preview of 10 of the 21 formulas you will find inside the PDF:

#1 SUM: The Basics of Adding Up

The SUM formula is one of the most used in Excel, and for good reason.

It allows you to add up all the numbers in a specified range, making quick calculations a breeze. For example, using =SUM(A1:A9) will sum all the values from cell A1 to A9.

#2: AVERAGE: Finding the Mean Effortlessly

Need to calculate the average of a set of numbers? The AVERAGE formula has you covered.

This formula takes all the numbers in a range and finds their mean.

For instance, =AVERAGE(B1:B9) will give you the average of the numbers in cells B1 through B9.

#3: ROUND: Simplify Your Numbers

The ROUND formula is perfect for when you need to round numbers to a specific number of decimal places.

If you want to round the value in cell C1 to two decimal places, you would use =ROUND(C1, 2).

#4: MIN: Finding the Lowest Value

When you’re looking for the smallest number in a range, the MIN formula is your go-to.

By using =MIN(D1:D9), Excel will tell you the minimum value found within cells D1 to D9.

#5: MAX: Identifying the Highest Number

Conversely, the MAX formula helps you find the largest number in a range.

Typing =MAX(D1:D9) will reveal the maximum value in the specified range.

#6: COUNT: Counting Numeric Cells

If you need to count how many cells in a range contain numbers, the COUNT formula is what you need.

For example, =COUNT(F1:F9) will count the number of numeric values in cells F1 through F9.

#7: INDEX: Locate Data in a Table

The INDEX formula is incredibly useful for retrieving a specific value from a table based on the row and column number you specify.

For instance, =INDEX(K1:L10, 2, 3) will return the value in the 2nd row and 3rd column within the range K1.

#8: MATCH: Pinpointing Positions in a Range

The MATCH formula helps you find the position of a specific value within a range.

If you want to know where L1 falls within the range M1 to M9, you’d use =MATCH(L1, M1:M9, 0).

#9: VLOOKUP: Searching Vertically in a Range

VLOOKUP is a powerful formula for finding a value in the first column of a range and returning a corresponding value from another column.

For example, =VLOOKUP(G1, H1:H10, 2, FALSE) searches for G1 in the first column of H1 to H10 and returns the value from the second column.

#10: HLOOKUP: Searching Horizontally Across Rows

Similar to VLOOKUP but used for rows, HLOOKUP helps you search for a value in the top row of a range and returns a value from a specified row.

For instance, =HLOOKUP(I1, J1:S2, 2, FALSE) finds I1 in the top row of J1 to S2 and returns the corresponding value from the 2nd row.

#11: COUNTIF/COUNTIFS: Counting Specific Conditions

COUNTIF and COUNTIFS help you count cells that meet specific conditions in a range.

For example, =COUNTIF(N1, “>5”) counts the number of cells with values greater than 5.

#12: COUNTA: Counting Non-Empty Cells

The COUNTA formula is used to count non-empty cells in a specified range.

Use =COUNTA(O1) to count all cells that contain data in the range from O1 to O9.

#13: SUMIF/SUMIFS: Conditional Summing

SUMIF and SUMIFS allow you to sum values based on criteria.

For example, =SUMIF(P1, “>5”) will add up the values greater than 5 in the range P1.

#14: IF/Nested IFs: Conditional Logic

The IF formula performs logical tests and returns different values based on the condition.

For instance, =IF(R1>5, “Yes”, “No”) checks if the value is greater than 5. Nested IFs and IFS handle multiple conditions, such as =IF(test1, true, IF(test2, true, false)).

#15: AVERAGEIF/AVERAGEIFS: Conditional Averages

AVERAGEIF and AVERAGEIFS calculate the average of values that meet certain criteria.

Use =AVERAGEIF(Q1, “>5”) to find the average of numbers greater than 5 in the specified range.

#16: Logical Operators (AND, OR): Combining Conditions

Logical operators like AND and OR are used to combine multiple conditions in Excel.

For example, =AND(S1>5, S2<2) returns TRUE only if both conditions are met. Meanwhile, =OR(S1>5, S2<2) returns TRUE if either condition is satisfied.

#17: CONCATENATE: Joining Text Strings

The CONCATENATE function combines multiple text strings into one.

For example, =CONCATENATE(T1, ” “, T2) merges the first name and last name into a single cell.

#18: LEFT/RIGHT: Extracting Text from Strings

LEFT and RIGHT functions are used to extract characters from the start or end of a text string.

For example, =LEFT(U1, 5) retrieves the first five characters, while =RIGHT(U1, 5) captures the last five.

#19: MID: Extracting Middle Text

The MID function allows you to extract characters from the middle of a text string.

For instance, =MID(V1, 7, 7) returns seven characters starting from the 7th character.

#20: TEXT: Formatting Numbers as Text

The TEXT formula converts numbers into text using a specified format.

For example, =TEXT(V1, “MMM-YY”) converts a date like 7/02/2024 into the format Feb-24.

#21: IFERROR: Handling Errors Gracefully

IFERROR returns a custom result when a formula generates an error.

This is useful for cleaning up errors in calculations, ensuring that your spreadsheet outputs are clear and error-free.

Get your free copy of the Top 21 Excel Formulas PDF

Why Are Excel Formulas Important?

Excel formulas have a big role in making sure financial models are accurate, which is key when you’re dealing with forecasts, budgets, and valuations.

In finance, where every number counts, these formulas help keep everything precise and consistent.

When it comes to reporting, Excel formulas make life easier by automating repetitive tasks.

Get my high-quality PDF of the Top 100 Excel Tips to improve your productivity.

This means you can create reports faster and with fewer mistakes, which is crucial when senior management needs them on time.

Formulas also help you make better decisions. CFOs depend on solid data to guide their choices, and Excel gives you the tools to analyze trends, compare different scenarios, and see how the company is really doing.

This insight is invaluable when making strategic decisions.

In terms of risk management, Excel formulas allow you to run what-if scenarios and stress tests, helping you spot potential risks before they become problems.

Being able to plan for different outcomes is a huge advantage in finance.

Lastly, using Excel formulas ensures that everyone on your team is on the same page.

It keeps things consistent, so everyone’s working with the same data and methods.

For finance pros, getting comfortable with Excel formulas isn’t just useful—it’s essential for doing the job well.

So, don’t wait any longer; grab your copy of formulas and increase your work efficiency!

Get your free copy of the Top 21 Excel Formulas PDF

12 Excel Charts You Probably Don’t Know

Here is everything you need to know about the charts:

#1: Heat Map

Description: Shows patterns, variances, and densities of data.
How to do it in Excel: Use conditional formatting or third-party Excel add-ins.
Use Cases: Analyzing risk distribution, financial performance across regions, or investment opportunities.

#2: Sunburst Chart

Description: Displays hierarchical data as a multi-level pie chart.
How to do it in Excel: Insert tab > Hierarchy chart > Sunburst.
Use Cases: Visualizing sales source or expense categories

#3: Box and Whisker Plot

Description: Highlights the central tendency, dispersion, and skewness of data.
How to do it in Excel: Insert tab > Charts > Box & Whisker.
Use Cases: Salary distribution across departments, Price distribution across products, Sales distribution across regions.

#4: Pareto Chart

Description: Identifies the most significant factors in a dataset.
How to do it in Excel: Insert tab > Histogram > Pareto Chart.
Use Cases: Identifying major causes of financial discrepancies, prioritizing cost reduction efforts, or analyzing revenue sources.

#5: Histogram

Description: Shows how many times each value appears in the dataset.
How to do it in Excel: Insert tab > Charts > Histogram.
Use Cases: Distribution of asset returns, income level distribution, or expense categorization.

#6: Treemap Chart

Description: Visualizes parts of a whole by size and color.
How to do it in Excel: Insert tab > Hierarchy chart > Treemap.
Use Cases: Allocating resources, analyzing portfolio composition, or visualizing revenue streams.

#7: Sparkline Chart

Description: Provides a quick overview of data trends without detailed axis information.
How to do it in Excel: Insert tab > Sparklines.
Use Cases: Tracking financial metrics over time or monitoring project progress.

#8: Radar Chart

Description: Compares multiple quantitative variables.
How to do it in Excel: Insert tab > Charts > Radar.
Use Cases: Performance analysis across different financial metrics, comparison of subsidiaries performance, or analysis of competitor profiles.

#9: Funnel Chart

Description: Represents stages in a process.
How to do it in Excel: Insert tab > Charts > Funnel.
Use Cases: Sales process analysis, conversion rates, or identifying potential leaks in processes.

#10: Scatter Plot

Description: Illustrates the relationship between two numerical variables.
How to do it in Excel: Insert tab > Charts > Scatter.
Use Cases: Identifying correlations between financial variables or risk-return analysis

#11: Speedometer chart

Description: Visualizes performance or progress on a dial
How to do it in Excel: Insert tab > Charts > Radar
Use Cases: Monitor KPIs, assess project progress, track budget in corporate finance

#12: Fill-up Chart

Description: Visualizes completion progress with fillable segments
How to do it in Excel: Insert tab > Pie chart > Donut chart
Use Cases: Visualize project, goal, or survey progress

Get your free copy of the Top 21 Excel Formulas PDF

Excel vs. Power BI

Which one should you use and when?

Choosing between Excel and Power BI can be tricky when it comes to data analysis.

Here’s a straightforward comparison to help you decide:

When to Use Excel

Excel is your go-to tool for quick calculations, making it perfect for simple, on-the-fly math tasks.

It’s also ideal when you’re dealing with small data sets, typically those with less than 100 lines.

If you need to perform ad-hoc analysis, such as quick investigations or one-off tasks, Excel excels in these scenarios.

It’s also great for basic visualizations, like straightforward charts and graphs, and works well for individual projects or small teams.

Get your free copy of the Top 21 Excel Formulas PDF

When to Use Power BI

On the other hand, Power BI shines when you’re working with large or complex data sets.

It’s built to handle big data with ease, making it perfect for more extensive data analysis.

Power BI is also excellent for real-time analysis, offering up-to-the-minute insights that are crucial in dynamic environments.

If you’re working on a collaborative project that spans multiple departments, Power BI’s design is geared towards teamwork.

Additionally, it’s the tool of choice for creating advanced, interactive visualizations and is particularly powerful in predictive analytics and forecasting.

Final Thoughts

Mastering Excel formulas isn’t just about getting the job done – it’s about doing it better, faster, and with more confidence.

From simple calculations to advanced data analysis, these formulas are essential tools that can help you tackle any data-related task with ease.

Even if you’re just starting with Excel or looking to refine your skills, understanding these formulas will undoubtedly give you an edge.

So, dive into my Top 21 Excel Formulas and practice to see how they will transform your workflow.

If you want to learn to use AI for Excel and become a proficient Excel user, then this is the right course for you. Even as an Excel and AI expert, I learned a lot with her course, and she made it really easy to follow along.

This course teaches you how to use AI tools like ChatGPT and Copilot, as well as the new AI-driven functions, to unlock Excel’s full potential.

Get your free copy of the Top 21 Excel Formulas PDF

FAQ

Q: Why should I learn Excel formulas if I already know the basics?
A: Learning Excel formulas takes your skills to the next level. While the basics help you get by, formulas allow you to automate tasks, analyze data more effectively, and make better decisions based on accurate information. It’s a way to work smarter, not harder.

Q: How do I know when to use Excel versus Power BI?
A: Excel is best for quick calculations, small data sets, and simple visualizations. If you’re dealing with larger, more complex data or need real-time analysis and advanced visualizations, Power BI might be the better choice. It’s all about matching the tool to the task.

Q: What are some common mistakes to avoid when using Excel formulas?
A: Common mistakes include not double-checking your formulas for errors, using absolute versus relative references incorrectly, and not keeping your data organized. Always review your work and make sure your formulas are applied correctly to avoid mistakes.

Q: Can Excel formulas be used for collaborative work?
A: Yes, Excel formulas can be used in collaborative environments, but they require clear communication and consistent practices among team members. For more complex, collaborative projects, tools like Power BI might offer better features for teamwork.

Q: How can I learn more Excel formulas beyond the basics?
A: You can learn more by practicing with real data or exploring Excel’s built-in help feature. There are also many tutorials and guides available online that can help you master more advanced formulas and techniques.

Excel

12 Excel Charts You Probably Don’t Know

Visualization tools are crucial for transforming raw data into insightful, actionable information in data analysis.

Excel, one of the most widely used data analysis tools, offers a plethora of chart types that cater to different analytical needs.

However, as Excel offers many functionalities to visualize your data, there are some charts that you didn’t know existed or still haven’t tried out.

Today, I will analyze 12 Excel charts that you probably don’t know, each with its unique capabilities, how to create them in Excel and practical use cases for finance.