Month-on-Month Definition

Month-on-Month (or MoM) calculations mean comparing one period to the same period in the month before. You don’t necessarily have to cover a whole month, or be using data from this month – however in both cases you can.

For example, you could look at a:

• Month – as in all of August versus all of July
• Specific day – as in the first Monday this month versus the first Monday of last month
• Specific date (or date range) –as in the 14th-18th of this month versus the 14th-18th of last month
• Metric – as in how many leads you got this month versus how many leads you got last month
• Product – as in how much cheese you sold this month versus last month
• Page – as in how the average time on page compares this month to last month

Or any combination of the above. The key point is that you are comparing a time frame from this month to the equivalent time from last month.

While it is obvious, it should be noted that different months are different lengths. Therefore if you compare a month with 31 days to one with 30 days, there is going to be a discrepancy built-in.

To combat this, many platforms have a “last 30 days” or “last 28 days” option for date ranges. This means that you are not looking at a calendar month, but an approximately month-long period.

Last 30 Days vs Last 28 Days

Collecting stats for a full calendar month is useful for Year-on-Year comparisons. However, if you want to compare this month to the month before, the calendar month is a problem. Not only are months different lengths, but they also contain different amounts of weekdays and weekends.

This is important because many metrics change significantly between weekdays and weekends. For example, for businesses that deal with other businesses the majority of traffic will occur during typical work hours. Similarly, video on demand websites will often see spikes of activity on the weekend as people generally do not have to work.

This means that the number of weekend days in a month can significantly change performance. Because of this, looking back over 28 days and comparing it to the 28 days before creates a much better “month” to work with. With any 28 day period you always have the same number of each day of the week which eliminates the weekend bias that might otherwise occur.

Having said that, only one month each year is 28 days long – and it’s not even that length every year! This means that reporting on a 28 day period as a ‘month’ means that you are always underestimating any volume-based metrics.

This is why some platforms use 30 day comparisons instead. 30 days is how long a month is on average, so by looking at any 30 day period you can say – “that’s roughly how we do in a month”. However, 30 days can contain either 8 or 10 weekend days which can really skew results.

Ultimately there are problems and advantages to each type of ‘month’ reporting. Whichever you chose, just make sure you are aware of them.

How to Calculate Month-on-Month Change

The equation for change is:

Month-on-Month Change = ((Any Metric ÷ Same Metric from the previous month) -1) x 100

Alternate Equation

The Month-on-Month calculation we have provided is the most convenient to use, especially on a calculator. However, there are other ways to calculate Month-on-Month changes. They all come out with the same answer, but you input them in different ways.

The most commonly used Month-on-Month calculation is:

((This Month – Previous Month) ÷ Previous Month) x100

I personally don’t like this equation so much as if you are using a calculator then you have to input the second number twice.

If you want to work out a Month-on-Month change in Excel, then the equation is even simpler:

Month-on-Month Change =

This only works because Excel will turn this calculation into a percentage automatically. If you did this same calculation in a calculator you would get 0.

When should I use Month-on-Month calculations?

Month-on-Month calculations can be helpful when you are looking at performance and want to see if it is significant. For example, if you had a really good Friday for sales and you want to quickly check how good, you can compare it to the same Friday in the month before.

This sort of reporting can be useful to see if something you did recently made a difference. For example, if you ran a marketing campaign last month, and you want to know if this month is doing better because of it.

A month is a reasonable time frame to compare performance over because it’s long enough to get a good set of results, while not being too affected by seasonality. This depends on the month of course – in December people react differently to marketing than in January or November for example. But generally comparing this month to last month is a solid way of seeing if your stats are headed in the right direction.

Note: Any time you click “Compare to: previous period” in a platform (such as Google Analytics), and you’ve chosen a whole month as your time frame – you will be using Month-on-Month reporting.

If a metric is particularly affected by which month is it then a Month-on-Month comparison may not be appropriate. For example, advertising budgets are often set quarterly.

This means if you compare advertising revenue from the last month of a quarter to the middle month of a quarter you will typically see an increase. This is because ad agencies will often spend more at the end of a quarter to use up budgets that weren’t spent for whatever reason.

For big decisions, you should look at longer time frames. Depending on the type of metric this could mean Quarter-on-Quarter, or Year-on-Year comparisons.

Note: You cannot simply multiply Month-on-Month by 12 to get Year-on-Year calculations despite what some other Month-on-Month calculators suggest. For example, if you increase by 100% each month then you would get a 409500% Year-on-Year increase, not a 1200% Year-on-Year increase.

Who are Month-on-Month comparisons useful for?

As long as the metric you are looking at doesn’t suffer from the type of problems mentioned above, Month-on-Month comparisons are useful for almost everybody. This is because:

• It’s an easy to understand comparison
• It covers a decent period of time so the stats are reliable
• Monthly reporting isn’t too much of a burden for the people doing the reporting.

This last part is key. If you demand reports too frequently then you can get in a cycle where reports are just being produced for their own sake. Reports should in most cases drive actions. If you report on something weekly, it gives very little time to actually undertake any actions recommended from it – especially if large problems or opportunities are identified.

With monthly or quarterly reporting, there is enough time to build a robust report and make changes based on the findings.