Freezing panes will allow us to freeze certain sections of a spreadsheet - usually a header, and one or two columns, so that we can navigate around the rest of the sheet without getting lost.

Take a look at the four minute youtube video to learn this new skill, and don't forget to subscribe if you're interested in short weekly Excel tutorials!

A great example would be two numbers with a space, or maybe a column of names like "Eric, Andrews".

Before I knew about the Text to Columns feature, I used to go through these lists manually to move the data into a second column.

So I found a way to split up data using Text to Columns in just a couple clicks.

And by the end of this post, so will you!

Let's split some data using an example and sample data set.

Let’s get started!

Here is a list of numbers with a random space midway between each number.

So, we want to split the data into two columns, using the space as a split point.

**Split the data using the text to columns feature**

Highlight the data, go to the data tab, and click “text to columns”

Highlight the data, go to the data tab, and click “text to columns”

Our data is separated by space, so “characters such as commas or tabs separate each field” is a perfect description of our data.

So, in our example, let’s choose “delimited” data.

*if we had a comma separating the data, or tabs, or any type of character, we could also use that as a split point.

So, in our example, let’s choose “delimited” data.

*if we had a comma separating the data, or tabs, or any type of character, we could also use that as a split point.

Next.

In my red box, we see a preview of how the data will be split.

In my red box, we see a preview of how the data will be split.

It isn’t split into columns yet, so let’s click the “Space” box.

That means we want to split the data anywhere that there is a “Space”.

In the red square you’ll see our data is splitting correctly now (that little vertical black line shows where the data will break into two separate columns).

That means we want to split the data anywhere that there is a “Space”.

In the red square you’ll see our data is splitting correctly now (that little vertical black line shows where the data will break into two separate columns).

Click finish!

Boom! The data is split into two columns using the space as a split point.

Now you should be a pro at using the Text-To-Columns feature in Excel!

*To go deeper with the rest of Microsoft Excel, join the army of students on Udemy.com taking my video-based course: Become An Excel Power User in 2.5 Hours. Take a look below!*

]]>Now you should be a pro at using the Text-To-Columns feature in Excel!

Often we need to extract the story out of a large Excel data set; however, people often misunderstand how to tell most straightforward and simple story of the data using data visualizations.

The core purpose of visualizing data is to summarize it in a way to simplify the data, tell a story, and ultimately create intuitive insights.

To take a large amount of information and transform it into something you can understand in just a few seconds.

Simply visualizing data does not necessarily simplify it, if fact, sometimes it does the opposite.

We’re going to cover three of the most common ways to visualize data in Excel:

- Tables: data organized into columns and rows
- Charts: a data series visualized on a graph with a vertical and horizontal axis
- Dual axis charts: two data series visualized on a graph with a secondary vertical axis

Now let’s discuss how to choose when to use a chart (graph) vs. a table to present a data set.

Often we are tasked with summarizing a data set to help someone else understand it. Most often, we’ll have the option to present data in a simple Excel table, or visualize it in a chart.

In order to decide whether an Excel table or chart is appropriate, there are four things we must first consider:

- Big data or small data?
- Will your user ask for the underlying data?
- Have you underestimated your audience?
- What story are you telling?

As a rule of thumb, you don’t generally want to visualize small data in a chart.

Small data would be just a few data points, maybe a 5, 10, or 20 pieces of data.

Big data would represent hundreds, thousands, or even millions of records. Big data is impossible to understand without a chart or deeper analysis.

If the answer is yes, then you’ve created a chart that is less helpful than the actual data by itself.

If your end-user looks at a chart and tries to reverse engineer it to figure out the underlying data - you’ve been unsuccessful. Just give them the data upfront and don’t bother with a chart.

The next point to remember is not to underestimate your audience.

People are smart. And often the people that you’re presenting data to are the people you report to. Don’t oversimplify things.

A helpful way to remember that raw numbers are easy to consume is to look at the ESPN website.

ESPN is packed with data. All kinds of statistics. No charts. But the website is built for sports fans, not academics.

The thing to remember is that regular people can easily consume a tremendous amount of raw data.

**4. What story are you telling?**

The last piece is – what story are you telling?

Make sure your visualization transforms the data into a simple story.

**Example data set – should we use a table or a chart to visualize this data?**

The thing to remember is that regular people can easily consume a tremendous amount of raw data.

The last piece is – what story are you telling?

- Are you telling the story that revenue is going up?
- Are you telling the story that the margins are bouncing around?
- Are you trying to show a correlation?

Make sure your visualization transforms the data into a simple story.

So in our example, we have four records that contain a date, a sales number, and a gross profit number. We’re trying to decide whether to present this data in a table or a chart.

Which presentation is better?

Let’s think about our four questions:

Well, we’re dealing with small data here. And if we give our users the chart, will they ask for the underlying data? They probably will.

Have we underestimated our audience? Which presentation tells a complicated but more nuanced story?

And what is the story we want to tell?

The rising revenue is easy to see in a chart or a table; however, the volatility in the gross profit margin is easier to see in the table.

So in this case, I would use a table to present the data.

Now let’s learn how to visualize a data set using a basic Excel chart.

**Creating a basic chart**

In this section, we will visualize our sample data set into a basic chart.

To begin, let’s highlight our entire sample data set including the headers. Then, click the Insert tab and find the Recommended Charts option.

Which presentation is better?

Let’s think about our four questions:

- Big data or small data?
- Will your user ask for the underlying data?
- Have you underestimated your audience?
- What story are you telling?

Well, we’re dealing with small data here. And if we give our users the chart, will they ask for the underlying data? They probably will.

Have we underestimated our audience? Which presentation tells a complicated but more nuanced story?

And what is the story we want to tell?

The rising revenue is easy to see in a chart or a table; however, the volatility in the gross profit margin is easier to see in the table.

So in this case, I would use a table to present the data.

Now let’s learn how to visualize a data set using a basic Excel chart.

In this section, we will visualize our sample data set into a basic chart.

To begin, let’s highlight our entire sample data set including the headers. Then, click the Insert tab and find the Recommended Charts option.

This will quickly give you an idea of what different charts will look like.

Let’s select the third option - a clustered column chart.

Let’s select the third option - a clustered column chart.

Click ok.

Here is our initial Excel chart.

Here is our initial Excel chart.

The first thing I like to do when working on a chart is increase the size of my chart by dragging the corner down and to the right.

This makes it much easier for me to design.

This makes it much easier for me to design.

Now let’s look at some ways to make this chart more clear.

First, in the top left corner of the screen click add chart element and let’s add a title for our vertical axis.

Go to Axis Title, and select Primary Vertical.

Ok. The title I’ll add is ($ in 000s) which means “the financial numbers in this chart are in thousands”.

Double click the box on the vertical axis, delete the text and write "$ in 000s".

Double click the box on the vertical axis, delete the text and write "$ in 000s".

But I want the size to be bigger, so I’ll go to the home tab and increase the size to 12. Better.

Now our chart needs a title. Double click the chart title and let’s write “Sales & Gross Profit”.

Now double click the chart again. Is there anything else we should add?

Let’s look at Gridlines. Click add chart element, go to Gridlines.

Ok, maybe we switch to Primary Major Vertical. That will help the chart look a bit cleaner.

Let’s look at Gridlines. Click add chart element, go to Gridlines.

Ok, maybe we switch to Primary Major Vertical. That will help the chart look a bit cleaner.

Now we are done. This chart is ready to present.

It is a clean, simple story.

In our next section we’ll look at creating a dual axis Excel chart visualizing financial and web traffic data at the same time.

**Creating a dual axis chart**

In this section, we’ll learn how to design a dual axis chart.

A dual axis chart visualizes two types of data on the same chart.

The two different types of data must share a horizontal axis data type, which in this case, and most frequently, is time.

Each data type will have its own vertical axis, so we will have a primary and secondary vertical axis (“dual” axis!).

We’ll design a dual axis chart that visualizes two types of data on the same chart:

It is a clean, simple story.

In our next section we’ll look at creating a dual axis Excel chart visualizing financial and web traffic data at the same time.

In this section, we’ll learn how to design a dual axis chart.

A dual axis chart visualizes two types of data on the same chart.

The two different types of data must share a horizontal axis data type, which in this case, and most frequently, is time.

Each data type will have its own vertical axis, so we will have a primary and secondary vertical axis (“dual” axis!).

We’ll design a dual axis chart that visualizes two types of data on the same chart:

- Financial data
- Website conversion rate data

Both of these types of data occur during the same time, so we can plot them together on the same chart.

Our first step is to highlight the data set including the headers.

Next, go to the Insert tab and click Recommended Charts.

Our first step is to highlight the data set including the headers.

Next, go to the Insert tab and click Recommended Charts.

Now, click the All Charts tab, and we’re going to click the Combo chart, which is the last option.

This is a dual axis chart (a combo chart).

Now we must tell Excel which type of data needs its own vertical axis.

In our case, let’s click the Secondary Axis box next to conversion rate. Now click ok.

Now we must tell Excel which type of data needs its own vertical axis.

In our case, let’s click the Secondary Axis box next to conversion rate. Now click ok.

Now increase the size of the chart.

You can see here that the vertical axis on the left, our primary vertical axis, is showing a financial measurement.

While the vertical axis on the right, our secondary vertical axis, is showing the conversion rate.

Let’s first add a chart title. How about, “Financial Performance vs. Conversion Rate”.

While the vertical axis on the right, our secondary vertical axis, is showing the conversion rate.

Let’s first add a chart title. How about, “Financial Performance vs. Conversion Rate”.

Now let’s take a look at the scaling of the secondary vertical axis (conversion rate).

Is 0.0% - 3.0% the correct range?

Let’s adjust it to test other potential options.

Double click the chart.

Then, to the right, in format chart area, click the down arrow next to Chart Options.

This is the place where you can adjust many small design elements in a chart.

We want a very intuitive story, so designing the right chart can take some creativity and experimentation.

Lets choose Secondary Vertical Axis.

Is 0.0% - 3.0% the correct range?

Let’s adjust it to test other potential options.

Double click the chart.

Then, to the right, in format chart area, click the down arrow next to Chart Options.

This is the place where you can adjust many small design elements in a chart.

We want a very intuitive story, so designing the right chart can take some creativity and experimentation.

Lets choose Secondary Vertical Axis.

Now we don’t want fill, effects, size, we want Axis Options. Click on it once.

Now expand Axis Options by clicking the sideways triangle next to it.

Here you’ll see the minimum and maximum bounds.

The maximum is the highest value on the secondary vertical axis. 3%.

Let’s try adjusting it to 5%.

Click out of the box.

The maximum is the highest value on the secondary vertical axis. 3%.

Let’s try adjusting it to 5%.

Click out of the box.

The conversion rate like is out of place with the chart scaled at 5%.

I think we had it right the first time.

Let’s change back to 3%.

I think we had it right the first time.

Let’s change back to 3%.

Let’s try something else.

In the Units section below, we can also change the scaling.

We’re currently using 0.5% increments on our conversion rate axis. Let’s change it to 0.25% for the “Major” units.

In the Units section below, we can also change the scaling.

We’re currently using 0.5% increments on our conversion rate axis. Let’s change it to 0.25% for the “Major” units.

Click into another box to apply the changes (I clicked into the "Minor" box).

Now, let’s click out of the chart altogether.

Let’s add some more gridlines.

Double click the chart, now to navigate to Add Chart Element, Gridlines, and I think Primary Major Vertical Gridlines would be quite helpful. Click ok.

Now, let’s click out of the chart altogether.

Let’s add some more gridlines.

Double click the chart, now to navigate to Add Chart Element, Gridlines, and I think Primary Major Vertical Gridlines would be quite helpful. Click ok.

The dual axis Excel chart looks ready.

It tells a very interesting story about how the website conversion rates may have affected the profit margins of the business.

The chart tells a very complex story in just a few seconds.

**Conclusion**

Understanding when to use charts vs. tables, and how to build beautiful dual axis charts are critical in the business environment.

Luckily, these skills are straightforward and easy to learn – you should be a pro now!

Remember, when you have a data set, you must choose the best way to present the data.

You want to simplify the data and tell a story with it.

Don’t forget the four considerations!

**Tables **are great for small data sets (~10-20 pieces of data).

**Charts **work well to tell the story behind big data sets (hundreds or millions of records) with **two types** of data (ex: time vs. revenue).

**Dual Axis Charts** work well to tell the story behind big data sets with **three types** of data (ex: time vs. revenue vs. website conversion rates).

*To go deeper with the rest of Microsoft Excel, join the army of students on Udemy.com taking my video-based course: Become An Excel Power User in 2.5 Hours. Take a look below!*

]]>It tells a very interesting story about how the website conversion rates may have affected the profit margins of the business.

The chart tells a very complex story in just a few seconds.

Understanding when to use charts vs. tables, and how to build beautiful dual axis charts are critical in the business environment.

Luckily, these skills are straightforward and easy to learn – you should be a pro now!

Remember, when you have a data set, you must choose the best way to present the data.

You want to simplify the data and tell a story with it.

Don’t forget the four considerations!

- Big data or small data?
- Will your user ask for the underlying data?
- Have you underestimated your audience?
- What story are you telling?

Excel Sample Data - Download |

The data set is 2,350 rows, and 10 columns wide, for a total of 23,500 records. Here is a screenshot of the data in the file.

The data set is entirely made up, but mirrors the type of data that reflects the operations of a normal company. We have geographic data, product data, shipping data, and financial data.

**In the file, there are 10 different columns of sample data:**

- Category (product)
- State
- Region
- Ship Mode
- Size
- Color
- Order Year
- Quantity
- Sales
- Profit

This data set would be great for filtering and sorting, creating basic visualizations, dual axis charts, or using in an Excel Pivot Table.

Just for fun, I wrote some formulas to create the random Excel sample data set. Creating randomized data is actually quite easy. I often do it if I cannot find free data online that matches my needs.

Here is the link to download the Excel sample data set with functions included:

Excel Sample Data Creation - With Formulas! |

The only functions I used were:

- IF
- RANDBETWEEN
- ROUND

For example, the formula I used for the first column of sample data (Category) is:

=IF(ROUND(RANDBETWEEN(1,4),0)=1,"Table",IF(ROUND(RANDBETWEEN(1,4),0)=2,"Chair","Couch"))

RANDBETWEEN is pretty simple. You provide a maximum and minimum number, and the function will return a random number in between your inputs.

I use the ROUND function with RANDBETWEEN so that I can get a whole number.

With the IF function, I created a tree of IF-THEN statements which will return one of three possible options:

- Table
- Chair
- Couch

So, if the ROUND(RANDBETWEEN(1,4),0) produces a 1, then the output is a “Table”.

If the output is not a 1, the function starts over. If we get a 2, then the second IF-THEN statement gives us a “Chair”.

If we get anything else (a 1, 3, or 4), the second IF-THEN statement will produce a “Couch”.

So, we have basically 5 possibilities, and 3 of them will give us a “Couch”. So we should see couch about 3/5 or 60% of the time.

The rest of the data I built the same way, using very similar formulas.

It was a beautiful table that showed the profitability of a music festival at different numbers of attendees and price points.

The data table showed dozens of profit possibilities in one table, with the different price and attendee numbers laid out across the horizontal and vertical axes.

I thought to myself, “that looks complicated, maybe one day after working in Excel for years I’ll be able to do that.”

However, the reality is that these tables are quite easy to set up. By the end of this blog post you’ll be able to easily perform what-if-analysis.

What-if-analyses are used to evaluate complex “what if?” scenarios in Microsoft Excel.

When we perform a what-if-analysis, we can better understand the variability (risk) of results for a business or data model.

These types of analyses help us examine questions like:

- What happens to profit if we lower the price and spend more on marketing at the same time?
- What does profit look like at 10 different combinations of price & marketing spend fluctuation?
- At what price will we acquire exactly 1000 customers?
- What price do we have to charge at different numbers of customers to achieve profit targets?

These questions are very complex, but with what-if-analysis we can create reports and answer these types of questions in seconds.

We’re either changing inputs to solve for outputs, or changing outputs to solve for necessary inputs.

And to perform this type of analysis, we need to have a data model of some kind, say, a budget, with at least one “hard coded variable” (meaning non-formula, just a typed in number).

There are two main types of what-if-analysis methods:

- Goal seek
- Sensitivity analysis data tables

Let’s start with goal seek.

Goal seek allows us to solve inputs to achieve whatever outputs we decide we want. To start, we’ll need a simple data model with an inputs and an outputs section. Let’s review the following financial model for our table company in order to perform our analysis.

In the **Inputs Section**, we have our hard coded assumptions. Unit costs: sale price, units sold, manufacturing costs, customer acquisition costs (CAC). Fixed costs: rent.

In the**Outputs Section **we have a financial model that is driven by the inputs. In the model:

Our gross profit is $90,000 and our left over profit (net) is $15,000 at a 10% profit margin.

**Now let’s use goal seek to answer some questions**

Question 1: How do we get to a 20% profit margin?

We can approach this question in a variety of ways. In order to achieve 20% profit, we could change price, units, manufacturing costs, customer acquisition costs, or rent.

Let’s solve for different variables using goal seek.

Solving for price: What price gets us to 20% profit?

First navigate to the data tab, and on the far right you’ll see what if analysis. Click what-if-analysis and then goal seek.

In the

- Revenue = Sale Price * Units Sold
- Cost Of Goods Sold = Manufacturing Costs * Units Sold
- Marketing = CAC * Units Sold
- Rent = Rent

Our gross profit is $90,000 and our left over profit (net) is $15,000 at a 10% profit margin.

Question 1: How do we get to a 20% profit margin?

We can approach this question in a variety of ways. In order to achieve 20% profit, we could change price, units, manufacturing costs, customer acquisition costs, or rent.

Let’s solve for different variables using goal seek.

Solving for price: What price gets us to 20% profit?

First navigate to the data tab, and on the far right you’ll see what if analysis. Click what-if-analysis and then goal seek.

We have three boxes:

**Set cell:**the thing we’re trying to get. So in this case, it’s the 20% profit margin. So click the corner of “set cell” on the diagonal arrow and click the profit percent cell in the**Outputs Section**. Now click the arrow again to expand the Goal Seek box.**To value:**20%. We want to set the profit margin cell at 20%.**By changing cell:**we want to get to 20% profit by changing the price. So now click the corner of “by changing cell” on the diagonal arrow and click the price cell in the**Inputs Section**. Now click the arrow again to expand the Goal Seek box.

Click OK.

So you’ll see that we get 20% profit if we can just raise the price to $562. At this point, we can easily solve for this same 20% profit output by solving for other inputs, like: units sold, manufacturing costs, customer acquisition costs (CAC), or rent. Just follow the same steps.

**Let’s try one more example with goal seek**

Question 2: How can we get to $250,000 in gross profit?

In this scenario, the only variables that will change gross profit are price, units, or manufacturing costs (because gross profit = revenue - cost of goods sold).

Let’s solve for price using goal seek.

Solving for price: What price gets us to 250,000 in gross profit?

Navigate to goal seek again and fill in your three boxes.

We have three boxes:

Question 2: How can we get to $250,000 in gross profit?

In this scenario, the only variables that will change gross profit are price, units, or manufacturing costs (because gross profit = revenue - cost of goods sold).

Let’s solve for price using goal seek.

Solving for price: What price gets us to 250,000 in gross profit?

Navigate to goal seek again and fill in your three boxes.

We have three boxes:

**Set cell:**in this case, it’s the 250,000 gross profit. Select the gross profit percent cell in the**Outputs Section**. Now click the arrow again to expand the Goal Seek box.**To value:**$250,000. Just type in 250,000 manually here. We want gross profit to equal this number.**By changing cell:**we want to get to $250K in gross profit by changing the price. So now select the price cell in the**Inputs Section**.

Click OK.

So we would need to change the price to $1283 in order to earn $250,000 in gross profit.

Now we can easily solve for this same $250K in gross profit by solving for units sold. Just follow the same steps.

**Limitations & Summary of Goal Seek**

If we solve these inputs, and we actually knew this company, we’d immediately recognize what is realistic and what is not realistic. Maybe we just can’t drive marketing costs down, or it’s ridiculous for us to charge $1283 for our product.

But this analysis gives us an idea of what if would take and what the available options are for running the business.

However, the limitation of this type of analysis is that everything is held constant except your one input you are solving for.

It’s probably not reasonable to just raise the price to achieve the profit margin of 20%. But if you can raise prices a little, and lower manufacturing and marketing costs, maybe you can hit your target.

But Goal Seek won’t help you with this type of analysis.

For more complex questions with multiple moving pieces, we need to use the data table feature known as sensitivity analysis.

__2. What-If-Analysis Data Table (Sensitivity Analysis)__

Sensitivity analysis data tables show us what outputs we’ll achieve at combinations of multiple changing inputs.

So as we change the environment dynamically, how does the profitability of our company change?

Once we jump into our example, you’ll see that this concept is pretty easy.

**Let’s use a data table to evaluate whether or not we should raise the price of our product**

Let’s use the same table company financial model from our goal seek example.

Now we can easily solve for this same $250K in gross profit by solving for units sold. Just follow the same steps.

If we solve these inputs, and we actually knew this company, we’d immediately recognize what is realistic and what is not realistic. Maybe we just can’t drive marketing costs down, or it’s ridiculous for us to charge $1283 for our product.

But this analysis gives us an idea of what if would take and what the available options are for running the business.

However, the limitation of this type of analysis is that everything is held constant except your one input you are solving for.

It’s probably not reasonable to just raise the price to achieve the profit margin of 20%. But if you can raise prices a little, and lower manufacturing and marketing costs, maybe you can hit your target.

But Goal Seek won’t help you with this type of analysis.

For more complex questions with multiple moving pieces, we need to use the data table feature known as sensitivity analysis.

Sensitivity analysis data tables show us what outputs we’ll achieve at combinations of multiple changing inputs.

So as we change the environment dynamically, how does the profitability of our company change?

Once we jump into our example, you’ll see that this concept is pretty easy.

Let’s use the same table company financial model from our goal seek example.

Question 1: We’re thinking about charging more per table, but we know this may result in selling fewer units. We want a higher profit margin %. Our current profit percent is 10%. Should we do this?

So we need to build a table showing the profit margin % at different unit and price combinations.

And the key here is to make sure your assumptions are reasonable.

In our current financial model, we’re selling 300 units, at $500 per table. So we want to look at higher prices which will result in lower units sold.

So the maximum units should be 300, and we should look at lower numbers from there.

**How to set up the sensitivity analysis data table**

In order to populate the data table, you’ll need to set the table up like this, although you can format (colors, borders, fonts…etc.) however you want. I use borders and font colors to make it more clear.

So we need to build a table showing the profit margin % at different unit and price combinations.

And the key here is to make sure your assumptions are reasonable.

In our current financial model, we’re selling 300 units, at $500 per table. So we want to look at higher prices which will result in lower units sold.

So the maximum units should be 300, and we should look at lower numbers from there.

In order to populate the data table, you’ll need to set the table up like this, although you can format (colors, borders, fonts…etc.) however you want. I use borders and font colors to make it more clear.

The important elements are that you have:

You’ll need to fill in the assumptions with numbers that are reasonable, meaning they reflect a realistic possibility. You fill them in by just “hard coding” (typed in, no formulas) the assumptions you want to evaluate.

Now in the top left corner, link in the metrics that you want to sensitize, so in this case, it is profit %.

=Profit % Cell

- One assumption category along the top of the data table (units)
- A different assumption category along the left side of the data table (price)
- The top left corner of the table blank (labeled profit %)

You’ll need to fill in the assumptions with numbers that are reasonable, meaning they reflect a realistic possibility. You fill them in by just “hard coding” (typed in, no formulas) the assumptions you want to evaluate.

Now in the top left corner, link in the metrics that you want to sensitize, so in this case, it is profit %.

=Profit % Cell

Now inside the table, you have different assumption combinations.

The data that will populate the table will be the different profit percent for each combination.

**Now let’s perform the calculation to analyze (sensitize) profit % in our data table**

First highlight the whole table including the assumptions.

Now navigate to the Data tab, click What-If-Analysis and select Data Table.

The data that will populate the table will be the different profit percent for each combination.

First highlight the whole table including the assumptions.

Now navigate to the Data tab, click What-If-Analysis and select Data Table.

We have two input boxes:

**Row input cell:**this is the units assumption along the top row of the table. So click the corner of “row input cell” on the diagonal arrow and click the unit cell in the Inputs Section of the original model. Now click the arrow again to expand the goal seek box.

**Column input cell:**this is the price assumption left column of the table. Click the corner of “column input cell” on the diagonal arrow and click the price cell in the Inputs Section of the original model. Now expand the box again.

Now click OK.

Now quickly, I’m going to apply a percent format to the data table for newly populated profit percent data.

Here’s what it looks like.

Now quickly, I’m going to apply a percent format to the data table for newly populated profit percent data.

Here’s what it looks like.

Our original goal was that we wanted to raise our profit margin % by raising the price. Does this seem reasonable?

The results are interesting.

If we could raise the price from $500 to $600 per table, we could run a 17% profit margin at 200 units (a reduction of 100 units).

So with just a 20% price increase, we could withstand decreasing our unit sales 33% while still increasing our profit margins from 10% to 17%.

The conclusion? It looks like we could potentially find a solution in our data table that is reasonable in the real world.

I would recommend raising the price slightly as it seems the profit margin is not extremely sensitive to changes in unit sales.

**Limitations & summary of sensitivity analysis data tables**

The power of data tables comes in the ability to see dozens of possible outcomes at the same time. It helps to drive you strategically in a direction.

Very quickly, you will notice whether you should be pushing to raise the price knowing that unit sales will drop, or lower the price to increase unit sales. It can help you optimize your decisions.

And if we actually knew this company, we’d immediately recognize whether or not the results are realistic.

Maybe we are in a very competitive industry and cannot raise the price because our largest competitor sells the same table for $500.

But these data tables give us an idea of how the outcome we are analyzing will be impacted in a dynamic environment.

The limitation of sensitivity analysis data tables is the human component – the assumptions.

If your assumptions are not reasonable, then your results will not be informative.

**Conclusion**

What-if-analysis in Microsoft Excel is one of the fastest and most powerful ways to evaluate dozens of hypothetical situations at the same time.

These techniques are also easy to learn - you should be a pro now.

**Goal seek** is used to analyze one assumption when targeting an specific outcome.

When you need profit margin to be a specific number, and you are only planning to change price, use goal seek.

**Sensitivity analysis data tables** are used to analyze assumption combinations when targeting a specific outcome.

When you need profit margin to be a specific number, have the ability to change price and manufacturing costs simultaneously, then you should set up a data table to see what happens to profit in many “what if” situations at the same time.

*To go deeper with what-if-analysis and the rest of Microsoft Excel, join the army of students on Udemy.com taking my video-based course: Become An Excel Power User in 2.5 Hours. Take a look below!*

]]>The conclusion? It looks like we could potentially find a solution in our data table that is reasonable in the real world.

I would recommend raising the price slightly as it seems the profit margin is not extremely sensitive to changes in unit sales.

The power of data tables comes in the ability to see dozens of possible outcomes at the same time. It helps to drive you strategically in a direction.

Very quickly, you will notice whether you should be pushing to raise the price knowing that unit sales will drop, or lower the price to increase unit sales. It can help you optimize your decisions.

And if we actually knew this company, we’d immediately recognize whether or not the results are realistic.

Maybe we are in a very competitive industry and cannot raise the price because our largest competitor sells the same table for $500.

But these data tables give us an idea of how the outcome we are analyzing will be impacted in a dynamic environment.

The limitation of sensitivity analysis data tables is the human component – the assumptions.

If your assumptions are not reasonable, then your results will not be informative.

What-if-analysis in Microsoft Excel is one of the fastest and most powerful ways to evaluate dozens of hypothetical situations at the same time.

These techniques are also easy to learn - you should be a pro now.

When you need profit margin to be a specific number, and you are only planning to change price, use goal seek.

When you need profit margin to be a specific number, have the ability to change price and manufacturing costs simultaneously, then you should set up a data table to see what happens to profit in many “what if” situations at the same time.