Choosing the Right Chart for Your Insightly Dashboard

When we build a chart, we're usually trying to answer a question:

  • How many deals did we close last quarter?
  • Are we on track to meet our goal this year?
  • Who are our top ten salespeople?

When the chart type is set to bar, column, range bar, range column, bubble, donut, pie, candlestick, ohlc (high Low), or waterfall, users can define the color scheme of the chart. Via Chart Properties, users can select Series Colors to choose from available color themes.

  • Color settings are retained when cards are saved and also if the chart type for the card is changed.

  • If additional series are added to a chart after custom colors are chosen, Insightly will assign custom colors for the added series.

  • When cloning a dashboard card, color settings will be cloned as well. 

If you already know the goal of your chart, you might want to check out Juice Analytics' Chart Chooser. It's a tool to help you select the right chart for your analysis.


What was the revenue by month for last year?
How are our incoming leads trending over the last six months?

Line charts are the best option to visualize trends over time. Think about stock market charts: Dates across the bottom (the x-axis), values down the side (the y-axis).


How is Joanne doing in closing sales compared to Bethany and Chuck? 
Which marketing flyer is bring in more customers: the discount coupon or the free nail file voucher?

Line charts, bar charts, or a combination of the two can display similar data together to see if there's a pattern or correlation that helps us find (usually) a winner.

Comparisons by region

Where is our business coming from?

Use maps to visualize revenue sources from around the world. You can choose from global, continental, or country maps.

Comparisons along a trendline

How many projects did we complete in each category in each month?

Stacked area charts are a way of merging separate line graphs into one chart. The area below each line is shaded a different color so that the differences can be compared easily.


Is there a correlation between a customer's first purchase amount and their total number of transactions with us?

With a scatter plot chart, you can plot the intersection of two different values with points that may show you a trend that you might not spot otherwise.



How close are we to meeting our closed project goal for the week?

Gauges are like the speedometer on a car. You set the goals when you set up the chart. As the value you've set grows, the higher the needle moves toward your final goal.



How many deals have closed for each lead source? 
How many opportunities are open in each category?

A distribution shows you how things break down by a category or label. Vertical bar charts and scatter plots are most commonly used for this scenario.


How much of our total business comes from new customers versus repeat customers? 
How much are we spending on customer acquisition by lead source?

The classic pie chart, or its cousin the donut chart, displays a breakdown of a total. The funnel chart is also used for this purpose; it's great for displaying the number of items or total value in each stage of an Insightly pipeline.

The power of these charts is that you don't even need to display a numerical value. A quick glance at each slice of the pie gives you an understanding of the big picture.

Compositions along a trendline

What were our sales for each month and how did they break down by category?

Stacked bar charts allow you to see trends as well as the composition of each data point. They are basically a combination of the bar graph and a pie chart, but without the circles.


Comparison of three or more quantitative variables

How did customers rate us on our five-question survey?

Radar charts cover this one. This one sounds a little tricky. Basically, you can map out three or more values in a pattern that looks like a spiderweb. Ideally, you'll have another radar chart with the values you'd like to see. You can then compare the shapes to see if you're close to your goal for each data point.

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