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Analytics calculations for different periods of time are incredibly important for evaluating the key performance indicators in a business. Without these period analytics calculations, companies couldn’t make important, data-driven business decisions. This piece dives into the common forms of growth and value calculations that businesses may look at.
Year over Year (YoY) growth is the calculation of the growth this year as compared to the growth last year. Typically, this is represented as a percentage. For example, if a company grew revenue from $10,000 to $12,000, this would be a 20% growth rate for the year. Growth rate is incredibly important for a business to understand its overall trajectory. Venture-backed businesses are often measured by their growth rate on a year over year basis. Additionally, SMB and fortune 500 companies alike rely on growth rates to understand how well their business is doing compared to industry benchmarks.
Year over year growth rate is calculated as (THIS_YEAR_METRIC - LAST_YEAR_METRIC)/LAST_YEAR_METRIC * 100
Below is a YoY growth rate calculator, which automatically calculates growth rates given current year and previous year inputs. Additionally, the calculator can be helpful for calculating any period vs any period growth rate.
Month to date is calculating a total up to the current day. For example, if you had sales on Jan 1 of $200, Jan 2 of $300 and it’s Jan 3 today, the MTD calculation would be $500. Month to date excludes the current day, as it is yet to be complete.
Below is a month to date calculator, allowing you to put in values for each day in that month and get the total up to that date.
A quarter to date calculation involves adding up all the days from the start of this quarter to the current day. For example, in Q2, if sales were $1,000 in April, $2,000 in May, and $500 so far in June, QTD would be $3,500. Be aware that a QTD calculation could be the calendar year, or the fiscal year, which can get confusing.
Below is a quarter to date calculator, allowing you to put in totals by month in a quarter and get the quarter to date total.
A year to date calculation is the total amount up until this point in the year. For example, if January sold $2,000, February sold $1,000, March sold $3,000, and April currently has sold $500, then the YTD calculation would be $6,500. Again, be aware that YTD could be for a fiscal year or calendar year, which could change the calculation significantly.
Below is a year to date calculator, allowing you to put in totals by month in a year and get the year to date total.
It is incredibly important for businesses to calculate these values and maintain these as internal metrics for comparison. By utilizing data like this, teams can evaluate if initiatives are working. For example, if YoY growth has gone down, that could be a great way to identify that something has gone wrong in sales, marketing, customer success, or product. While not necessarily a clear indicator of what may be going wrong, these metrics can help inform that something may be going wrong. For diving into exactly what is going wrong in a business, KPI tracking is typically the best way to do that. These calculations can be considered some of the most important KPIs, but shouldn’t be considered the only KPIs.
In general, YoY growth will be a more helpful metric as compared to month to date, quarter to date, or year to date. The primary reason is that it’s helpful to have a comparison when looking at a metric. For example, $100,000 for a year could be great revenue for a small business, but would probably be disastrous for a big company like Google. Everything is relative to expectations, and business metrics are no different. That being said, sometimes it is helpful to compare a current period to a previous period, which MTD, QTD, and YTD are helpful for. A team can then go back and calculate the previous period metric to calculate period over period growth. In general the calculation for period over period growth is:
(SUM(CURRENT_PERIOD) - SUM(LAST_PERIOD))/SUM(LAST_PERIOD) where LAST_PERIOD_END_DATE = CURRENT_PERIOD_START_DATE-1LAST_PERIOD_START_DATE = CURRENT_PERIOD_START_DATE-(CURRENT_PERIOD_END_DATE-CURRENT_PERIOD_START_DATE)
In general, key performance indicator charts are going to be best for these values. KPI charts enable teams to see a metric as an aggregation and even compare those metrics against previous periods. There are a few types of KPI charts as well.
A regular KPI chart shows a key performance indicator (KPI) in a single value.
A KPI trend chart shows the total for a period as compared to the previous period, automatically calculated for you.
A KPI trend chart with lines shows the total grouped by a particular period; in this case, it’s grouped weekly.
There are many tools on the market for calculating these period analytics, but Explo is one of the premiere options for easily enabling internal and external stakeholders to calculate their own KPI metrics. Within Explo, there is a Report Builder product line that has a built-in KPI chart and line chart options to be able to easily plot things like Year over Year growth, or Month to date calculations. With minimal training, a team member can have access to a dataset and calculate a KPI, selecting a start date from an intuitive dropdown.
Within Explo’s Explore product line, a team can easily setup a KPI chart, a KPI trend chart, or KPI trend chart with lines in a simple point-and-click interface.
Explo makes calculating these metrics as easy as possible.
Throughout this piece, we have gone through what these metrics are, how to calculate these metrics, and wonderful tools to use to calculate these metrics on an ongoing basis.
With the advent of AI and Large Language Models (LLMs), it will only become even more accessible for teams to calculate and utilize these metrics on a daily basis, as many tools, including Explo, have begun to incorporate AI into the user experience. AI can automate the ingestion of a lot of this data, standardize that data, and output that data into helpful visualizations. Hopefully this piece helped to clarify the importance and usefulness of these metrics to continue to help with the ever growing data-driven workplace and prepare you for the future of data analysis.
