{"id":17332,"date":"2020-11-26T10:51:57","date_gmt":"2020-11-26T09:51:57","guid":{"rendered":"https:\/\/chartmogul.com\/blog\/?p=17332"},"modified":"2023-08-11T11:15:52","modified_gmt":"2023-08-11T09:15:52","slug":"forecasting-mrr-revenue","status":"publish","type":"post","link":"https:\/\/chartmogul.com\/blog\/forecasting-mrr-revenue\/","title":{"rendered":"A Simplified Method for Forecasting MRR Growth"},"content":{"rendered":"\n

The spreadsheet used to make the charts included in this blog post can be found <\/em>here<\/em><\/a>.<\/em><\/p>\n\n\n\n

Ugh! It\u2019s budget season.<\/p>\n\n\n\n

How many meteorologists does it take to accurately<\/em> predict the weather?<\/p>

Nobody knows, it\u2019s never been done.<\/p><\/blockquote>\n\n\n\n

I\u2019ve built a lot of revenue and growth models in my life. It\u2019s not something that I particularly enjoy, but it\u2019s something that I think I do a pretty reasonable job of. It\u2019s sort of like being the weather forecaster \u2014 perfection unattainable.<\/p>\n\n\n\n

One thing I do enjoy is going back to look at the models that I\u2019ve built in the past. It\u2019s fun to have hindsight on assumptions that you made in your model. It\u2019s also sometimes humbling to look at models that were outrageously aggressive. <\/p>\n\n\n\n

I just reviewed a forecasting model that I built 6 months ago where actual MRR is going to beat the modeled prediction for MRR a month early. In this case, a 2-month period with substantially lower than normal churn caused the model to miss the mark. The funny thing is that even with hindsight, we can\u2019t really put our finger on the reason for those 2 months having abnormally low churn.<\/p>\n\n\n\n

So what is the best way to model MRR growth for SaaS businesses?<\/h2>\n\n\n\n

I once built a model, and the response I got to it was very lackluster It\u2019s not complex enough<\/em>.  On another occasion I was told $15M ARR is the number I want, work the model back from that<\/em>.<\/p>\n\n\n

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To a large degree MRR is a vanity metric for CEOs and a yardstick for venture capitalists.<\/p>\n Tweet this quote<\/a>\n <\/blockquote>\n\n\n\n

If we are being honest, to a large degree MRR<\/a> is a vanity metric for CEOs and a yardstick for venture capitalists. Because of that truth, MRR forecasts and goals many times get set based on emotion, a nice round number<\/em>, or what you should do because that\u2019s what other companies of your size have done. There\u2019s nothing wrong with that, but this model should be a good sanity check.<\/p>\n\n\n\n

So let\u2019s dive in. There are probably 1001 ways to model out a forecast for MRR. What I\u2019m going to suggest is a very quick and dirty way that seems to work best for me, while pointing out some of the pitfalls that I\u2019ve encountered. <\/p>\n\n\n\n

To start with, I\u2019m going to assume that your business or product has at a minimum a few years of history. Should that not be the case, you will need to try a different approach. I\u2019m also going to assume that you use a product like ChartMogul<\/a> that allows you to have an in-depth understanding of your historical MRR changes by month.<\/p>\n\n\n\n

Simplicity<\/h3>\n\n\n\n

The model that I like to use is very simplistic. It\u2019s based on month-over-month growth rates of MRR. The reason I pick a model that is so simplistic is that I fundamentally believe that for every assumption you make in a model, the greater the error introduced into the model, many times in a compounding way.  <\/p>\n\n\n

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For every assumption you make in a model, the greater the error introduced into the model.<\/p>\n Tweet this quote<\/a>\n <\/blockquote>\n\n\n\n

The guy that told me It\u2019s not complex enough<\/em>, what he really meant was he wanted something analogous to a zero-based budget model that predicted MRR growth based on growth channels, organic and paid \u2014 starting from the ground up. <\/p>\n\n\n\n

That model started at the top of the funnel estimating conversion rates on websites and multiple app stores for organic traffic. Likewise, for paid acquisition, it estimated conversion rates on advertising through various channels. Then once a customer converted, it accounted for retention \/ expansion \/ contraction of users across the various platforms we had. Had the model worked it would have been beautiful, but it didn\u2019t<\/em>.  <\/p>\n\n\n\n

Our funnel was very complex: we were aggressively ramping up our advertising and we didn\u2019t have a good enough understanding of the paid acquisition funnel metrics. I think a model like this might work for more mature companies that have a lot of data, measure and track that data, and have the time and patience to build out the model. But most SaaS businesses don\u2019t have this luxury.<\/p>\n\n\n\n

There are 5 components that make up MRR<\/a>. You could build your model at that layer (new business, expansion, contraction, churn, and reactivation) but frankly that\u2019s five things that could go wrong. Each one of them is a complex thing to model in and of itself. Or you could even go a layer lower, modeling the things that make up each of the five components \u2014 but again \u2014 that is destined for error without a lot of effort and reliable historical data.<\/p>\n\n\n\n

\"MRR<\/a><\/figure>\n\n\n\n

I start my forecasting models by first building out the history of the month-to-month growth rate of MRR. Step 1 is to export my historical MRR data from ChartMogul into a spreadsheet.<\/p>\n\n\n\n

History<\/h3>\n\n\n\n

Knowing the history of a business will help you understand the present and predict the future.  This should help you understand a few possible trends.<\/p>\n\n\n\n

  1. Does the company see seasonality in its growth?<\/li>
  2. Has the company noticed a slight decline in growth rate as it grows?<\/li>
  3. Are there any other anomalies that stand out?<\/li><\/ol>\n\n\n\n

    I like to not only look at the history by year, by overall average, but I also like to do a weighted average. What happened last year is probably more indicative of future growth than what happened 5 years ago. Usually, a picture is worth 1000 words.<\/p>\n\n\n\n

    \"Historical<\/a><\/figure>\n\n\n\n

    In this fictitious example a few things stand out:<\/p>\n\n\n\n

    1. It does indeed look like the growth rate is reducing over time. As the company ages, the growth has slowed \u2014 it\u2019s a lot harder to grow big numbers than small numbers.<\/li>
    2. April 2020 stands out like a sore thumb. I\u2019m sure you can guess what happened there. I wouldn\u2019t expect future Aprils to take on this same behavior, however, your business may have some sort of impact in general in 2021 and beyond that may need to be accounted for.<\/li>
    3. While it\u2019s not super obvious, it looks like there might possibly be some seasonality in growth?<\/li><\/ol>\n\n\n\n

      If we dig into this data a bit further by normalizing the data on an annual basis, a few things stand out even further.<\/p>\n\n\n\n

      \"Normalized<\/a><\/figure>\n\n\n\n

      What we see is that there indeed seems to be some seasonality.  December through March have historically been the better growth months while July through September have been slower months. That said, the seasonality isn\u2019t extreme such as some companies that do all of their business in the months of November and December. <\/p>\n\n\n\n

      I should point out that I like to use shades of grey to represent years, using a very light shade as you go further back. The reason for this is that when I look at trends I want the more recent years to stand out more \u2014 I\u2019m less interested in trends from 2016.<\/p>\n\n\n\n

      Future<\/h3>\n\n\n\n

      The next thing I do is look to the future, carefully<\/em>. What are the things that the company is doing that might impact your growth rates? Are there major product improvements or changes in go-to-market strategies?<\/p>\n\n\n\n

      This is probably where a lot of models go sideways. They become overly optimistic about what the future holds. As I mentioned before, the company growth rate<\/a> tends to slow down as a company ages up until it\u2019s about 12 years old where it then stabilizes<\/a>. <\/p>\n\n\n\n

      So as you build your model and you begin to estimate future monthly growth rates keep in mind that gravity \u2014 or in this case the size of your company \u2014 is pulling against you.<\/p>\n\n\n\n

      \"Annual<\/a>
      Source of Data: SaaS Capital<\/a><\/figcaption><\/figure>\n\n\n\n

      At this point, you could dig deeper if you desire. Is there seasonality in the components that make up your MRR, such as in your New MRR or your Churn MRR? But that\u2019s for another day.<\/p>\n\n\n\n

      Scenarios: 30-60-90 Model<\/h2>\n\n\n\n

      I don\u2019t know who introduced this methodology to me, possibly Brad Feld<\/a>, but it\u2019s something that we used regularly in my last company and it was a really good way of framing forecasts and budgets. Here is the way that it works.<\/p>\n\n\n\n