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Trailing Twelve Month & Time Series Data
Trailing Twelve Month & Time Series Data

Learn more about what trailing twelve months means, how it's calculated, and why we use it in the Growth Engine.

Jessica Nash avatar
Written by Jessica Nash
Updated this week

Trailing Twelve Month (TTM), also known as Rolling Twelve Month, is a method of aggregating time series data.

A time series dataset is where the datapoints are indexed by date, so for example the data that is collected in the CRM is date stamped, allowing for them to be analysed and displayed visually.

A TTM period consists of data for twelve consecutive months, so that every period contains an instance of every month. This is done to reduce effects of seasonality in the metrics.

For example, the period of “April 2023” will contain the actual data for the twelve months preceding April 2023 to provide a consolidated view of the months April 1st 2022 to March 31st 2023.

Let’s have a look at an example with some dummy data, and for this scenario let's consider this sales data is for an ice cream distributor.

Month

Revenue

Jan-22

37000

Feb-22

48000

Mar-22

51000

Apr-22

65000

May-22

93000

Jun-22

115000

Jul-22

147000

Aug-22

185000

Sep-22

186000

Oct-22

85000

Nov-22

28000

Dec-22

15000

Jan-23

45000

Feb-23

51000

Mar-23

47000

Apr-23

68000

May-23

91000

Jun-23

110000

Jul-23

150000

Aug-23

200000

Sep-23

187000

Oct-23

90000

Nov-23

32000

Dec-23

28000

The revenue in the table is indexed by month, hence the graph that's produced by this table is a Time Series visual. Analysis of Time Series data is important for understanding underlying trends such as seasonality and growth rates in sales.

As the product is highly dependent on weather, there's a noticeable peak during warmer months; this would likely be a trend that you'd be able to observe every year. Looking at actual revenue figures and the graph as it is, it's not so obvious whether there's revenue growth due to the skewing that seasonality causes.

Let us now aggregate the data into TTM periods to form the following table and graph:

TTM Period

Revenue

Dec-22

1055000

Jan-23

1063000

Feb-23

1066000

Mar-23

1062000

Apr-23

1065000

May-23

1063000

Jun-23

1058000

Jul-23

1061000

Aug-23

1076000

Sep-23

1077000

Oct-23

1082000

Nov-23

1086000

Dec-23

1099000

It's now more apparent that the company has been experiencing consistent revenue growth over the previous twelve TTM periods. This graph shows the total revenue in that TTM period, but by dividing by 12 it's possible to get an average monthly revenue for that period. This graph has around half the amount of datapoints compared to the previous graph, but it still contains the exact same information, just displayed in a specific format.

Throughout the Growth Engine Dashboard (coming soon!), TTM is used for visualising metrics to help you in better understanding your growth rates. It's the default setting for visualisations; however, you can change it to show your actual 12-month data instead by selecting the 'Monthly' tab.

When you first start using your CRM, you might not have enough data for the graphs to be aggregated into the TTM periods. Until the system detects that you have reached the threshold of data, your visuals will be using actual figures, not aggregated.

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