When you're creating and designing dashboards in Planhat you're going to be visualising how your data is going to look. Planhat has a wide range of chart types that will bring your vision to life 😃.

This chart type is used to compare categories of different groups. For this example, we have used the multi-horizontal bar chart to view the number of tasks that are "not yet due" and tasks that are "overdue" for each CSM. In this case,

This chart type is useful if you're looking to display sub-groups that come under different categories. For this example, we're displaying the total amount of conversations that each CSM has been involved in, along with the source of the conversations.

Use the "Bar Chart (multi)" when you want to look at how the second category changes within each section of the first category. For this example, we're using the multi-bar chart to view the distribution of MRR across our product phases and account owners. Immediately we can see that all of our "Churn Risk" belongs to "Niklas" (I'm sure he's doing a fantastic job!).

The horizontal bar chart is a commonly used chart type that's great for displaying a wide range of data. For this specific example, we're using it to display our total MRR split by the account owner.

The bar chart is another commonly used chart type that you will be able to utilise to display a wide range of data. For this particular example, we wanted to display the largest customers and the bar chart was the perfect chart type for the job 😄.

Every dashboard has to have a pie chart right? 🥧 - For this example we're using the pie chart to display our total conversations split by conversation type. Another great chart type that makes it incredibly easy to see large disparities in the data. On a final note, everyone loves pie 🥧.

Radar charts are great! they're good for displaying a large number of variables and they make it easy to spot commonalities and outliers in the data. For this example, we're displaying our total churn amount split by churn reason. The radar chart has made it easy to see that most of our churn has come from either a change in leadership or they haven't been satisfied with the support (this is just demo data, our support team is great 😄) .

Doughnut charts are commonly used because they make it easy to compare sets of data and they require minimal explanation. I'm sure you will find many uses for this chart type 👍! To give you an example, we've used the doughnut chart to display how much customer engagement each team member has had.

There are several options when visualising data over time in Planhat. Below we explain what they do and how to use them.

As the name suggests, the Cohort Bar Chart is great for displaying data in cohorts. A good example of this is displaying your data by quarter. For example, we wanted to see the amount of churn there has been for each quarter and what the churn reasons were.

Cohorts in Cohort charts are based on a date value related to the data object you are visualising. For Company level data, options are things like the date the Company was created or its Renewal date. For the Conversation level, data options are when the Conversation took place or last updated etc. All Company level date options are available for other objects as well.

Metric data cannot be aggregated in Cohort Charts. If you want to view aggregated Metrics, see the TimeSeries charts below.

The Face/off chart allows you to select a metric and break it down by segments, using either some property "split by" and/or filters from the data module. Read more here!

Time Series charts enable you to aggregate your metric data into days, weeks, months, quarters or year buckets to see the development over time.

This chart type solves a number of use cases and is valuable in different ways when analysing an individual customer's behaviour as well as all customers in your portfolio.

There are 3 ways to think about Time Series data so we have provided some examples below explaining what they do and how to build them:

2. You may instead want to see the average daily number of Logins during each week, so instead you would aggregate by "week (daily average)". This will take the total for the period and divide it by the number of days in the period.

3. Alternatively, you might also want to see the average number of Activities by a customer during a week, in which case you would aggregate by "Week Sum", and choose "Avg All" or "Avg Values" in the Values section. This will take the total number of Activities in the period and divide it by the number of Customers included in the chart.

You could also view the Average daily activities per Customer by selecting Week (daily average) and Values "Avg (all)" or Avg (values):

Stacked Time Series Bar charts are just like time series bar charts, but they also allow you to set your own value buckets to view the evolution of a metric's cohort-based composition over time. So, like their un-stacked counterparts, they'll still allow you to aggregate metric data into days, weeks, months, quarters or year buckets to see the development over time, but give you the option to view this trend across sub-bar cohorts determined by value ranges of any metrics, including health categories, logins, and ARR. Simply create these buckets or segments using the "Custom Colours" settings.

The most salient use case here relates to the health score: let's say I want to see how my portfolio of companies, broken down by health score, is changing monthly, with each column showing the number of companies in my portfolio. To build this for yourself, simply choose value as count, then select week/month/quarter (daily average). I simply select the health score as my metric, with the month (daily average), showing "count" values and then specify the value-range colours I desire. Here, to match the health colours, I classify 0-3 as red, 3-7 as yellow and 7-10 as green.

📌 Note: The values from-to also include the referenced value. So e.g., setting 0-3 means including 0, 1, 2, 3.

📌 Note: even though health scores are always whole numbers, there will be decimals here. That's because we want the column totals to show the count of companies in each bucket (and therefore in my whole portfolio) over the whole month, rather than at a single point in time. Naturally, if a company's score changes so it moves into a different bucket, then both these buckets would have 1 fewer/more company for a portion of the month, resulting in a decimal value.

📣 Pro tip: the Stacked Time Series Bar widget also allows you to replicate the Company360 health trend (showing bars for each week's score, coloured accordingly) for a single company, in pages. Simply filter the view above for any company, using "sum" rather than "count". This is particularly useful if you want to share this trend view with other stakeholders.

For all the details on the Stacked Time Series Bar, check out this article 📚

The Top Chart is a quick and simple way to view the top 'X' results for any given data point. For this example, we're viewing our top 5 accounts based on their total ARR.

Exactly as the chart type suggests, you can display a top list of your data based on a data point of your choice. For this example, we're viewing our top 10 accounts that have the highest ARR amount. What's great about this chart type is that we can also pull in other data to see how our top accounts are performing. Immediately we can see from the image below that our third-largest client has a very low health score 😭!

This is a standard table, use this chart type to display the relationship between 2 different sets of data. For example, below we are showing the Count of Customers in each Phase by CSM.

KPI Tiles display the count, sum or average of a selected data point which is then displayed as a whole figure. KPIs make it easy for you to display key figures on your dashboard.

📌Important to note: If you come across the chart setting "Split by Category" or "Group by", it's important to know that you can select any of the fields in the list but if you want to use a custom field, it's likely that you will only be able to use a field that has one of the following data types:

The main reason for this is that the data types mentioned above have their data stored in an array and there are a set number of defined values. If you were able to split by a text field, you would typically end up with a chart that's unmanageable and unreadable because there will be many unique values.