You asked us for a faster way to create charts, so we are excited to share a new version of our chart builder designed with speed in mind.
To open the new chart builder
At the top right side of your dashboard menu, click Charts
Next, click Create Chart at the top right side of your dashboard menu
To edit an existing chart, click Edit on the top right side of the chart you wish to edit.
For new charts, you should see the following screen and a prompt to choose a Metric from the drop-down menu.
Metrics are the data points you want to analyze in RJMetrics, such as Revenue, Average Order Value, or Average Time Between Purchases.
Tip: If you’ve connected RJMetrics to your Google Analytics, you can include that data in your dashboard by scrolling down to the bottom of the Metrics drop-down and choosing Google Analytics.
Time Period: Once you have chosen a Metric, you can select a time period to show or just select All-Time to show all available data.
Interval: You can segment the data by Year, Quarter, Month, Week, or Day.
Tip: When using a specific date range for your Time Period, make sure your start date is at the beginning of your Interval and your end date is at the end of your Interval. For example, setting a Time Period from January 1st to March 1st and choosing a monthly Interval will show March as a datapoint, but ignore every day in March except March 1st. In that case you should make your Time Period from January 1st to March 31st.
Perform Cohort Analysis: A cohort analysisallows you to group together customers based on a common date (e.g. customer's registration month) and chart each group’s metrics against each other. This tells you if your company is getting better at creating better customers. To learn more about cohort analysis, check out cohortanalysis.com.
To create a cohort analysis, click Perform Cohort Analysis.
Next, choose the Cohort Date, which can be the date they joined or the the date they placed their first order.
Next, choose the time Intervalwhich will depend on how much time your data spans across.
After that, choose a Time Period. Here you can choose from
Moving Cohorts - A good start. It lets you specify a certain number of cohorts to compare against each other and makes sure they have enough data to be relevant.
Specific Cohorts - Good if you want to measure customers who joined during specific times, such as the holiday season.
All Cohorts - Bring in all possible cohorts from any date until now.
Lastly, give the cohorts a duration and choose which table to get cohort members from. If at any time, you want to revert back to the normal chart builder, click Exit Cohort Analysis.
Next, choose which type of chart you want to use. There are six options.
Line Chart - Used to plot metrics over time (e.g. Revenue per month)
Bar Chart - Used to compare values for categories of a metric (e.g. Revenue per product)
Stacked Bar Chart - Used to compare multiple related categories of a metric (e.g Revenue per product, with each level of the bar showing orders by quantity size) Pie Chart - Used to view categories of a metric as a percentage of the total (e.g. Revenue per product) Data Table - Simple display for showing exact values in a table. It’s great for when you have lots of categories and want to maintain readability of each item. You can scroll down data tables in the dashboard. Scalar Value - Used to highlight a very specific metric on your dashboard to generate action in your organization (e.g. Orders today) or highlight a goal that was or was not yet achieved (e.g. Revenue for the year)
Perspectives make analyzing the data easy. The standard perspective shows your data as it is, but others can show you where it’s heading or spot trends. Here are some example of how perspectives can show you different things.
Amount Change vs Previous Period - Shows the amount of change from one interval to the next. Useful for measuring the rate of change in fast growing metrics. There are also perspectives to compare the Interval to one year ago and also a percent rate of change.
Cumulative - Shows the ongoing total amount of the metric over the interval. This is often used to analyze total customers or orders and plan for future capacity.
Percent of First Value - Show the data as a percentage of the first value. This is helpful in measuring the effectiveness of specific actions. Below is the aggregate revenue data.
Now here is the same data broken out by customer referral source using Group by and shown with the Percent of First Value perspective. It helps connect actions with effects that may not have had a large change in their amount, but a great change in their percentage.
Incremental Event Probability - Shows how likely a future event is based on history. For example, select the metric Number of orders and Group by Customer purchase number. This shows us if a customer places x number of orders, how likely are they to place one more? The chart below shows the increasing probability that a hacker news user will submit additional links. Over 90% of users will continue to submit once they have reached their 5th submission. Use incremental event probability to measure your organization's stickiness
Filters allow only certain data into the visualization. This can be helpful when
Evaluating individual acquisition channels
Eliminating test data from the analytics
Removing outliers (e.g. your average order size is $40 and someone just placed a $1,000,000 order).
Analyzing metrics on order types (e.g. you have many small and large transactions and few in between. Filtering out the small or large orders allows you to find insights hidden when grouping them all together).
To create a filter, click Add a New filter condition
You should see a filter item appear like this
Use the drop-down menus and text box to create the filter. Here are some examples:
order_total Greater Than 100 will allow only order_totals that are greater than 100
customer’s referral_source Equal to Paid Search will only allow in data that came from customers acquired from Paid Search
customer’s referral_source Not Equal to Paid Search will only allow in data that did not come from customers referred from Paid Search
You can even use wildcards (% or _) with a Like statement. _ will match any single character and % will match multiple characters.
affiliate’s name Like B% will only allow in data from customers whose name starts with B
affiliate’s name Like _ake will only allow in data from customers whose names are something like "Jake," "Rake," or "Bake" but not "Drake" or "Blake"
Click Apply when you are done to see the changes in the chart.
Adding multiple filters allows tight control of the chart’s data. By default, all filter conditions must be true for a piece of data to be included, but you can create OR relationships by editing the Filter Logic text box.
Filter Logic Examples
(([A] AND [B]) AND [C]) - All filter conditions must be true
([A] OR [B] OR [C] OR [D] OR [E]) - Any condition can be true
(([A] AND [B]) OR [C]) - Both A and B must be true, or just C needs to be true
((([A] AND [B]) OR ([C] AND [D]))) - Both A and B must be true or both C and D must be true
The Group by feature allows you to group data by values of a category. This can be helpful in analyzing the data for things like referral source or customer location.
To add a Group by, first click on the Group by tab next to the Filter by tab.
Click on the dropdown menu and select the category you want to group data by. Below we’ve chosen to group by customer’s referral_source.
The data has not changed yet, because we need to specify the which values we want to compare. If you start typing, RJMetrics will try to guess which values you want to add and autocomplete your entry. For our example, we typed Paid Search and pressed Enter.
Now our chart only shows data from Paid Search. Let’s add another in the same text box: Organic Search
Now we can compare our customer referrals from each source. Let’s add the rest by clicking Add All, which will plot lines for all other values.
Now we’re comparing monthly revenue by customer referral source.
A common way to use Group by is to now go back up to the top of the Chart Builder and set the Interval to None set the Chart Type to Bar Chart. Now you can compare the totals of each category value (in our case, revenue segmented by customer referral source).
Take note of the sort bar
With the sort bar, you can sort and even limit the number of results shown to the most relevant to your metric.
You can go back to the old chart editor by clicking Use the Old Editor at the top of the Chart Builder screen.
Composite Charts are not yet supported by the New Chart Builder.