How to forecast sales projections more effectively

Has your company ever missed its quarterly or annual sales projections? This is one of the top reasons why C-Level executives are let go from growing companies. It often happens inside companies where forecasting is treated more like a math exercise and the underlying growth model for the business isn't strong enough. That's why on this episode of How To SaaS, I talk about how executive teams can forecast sales projections more effectively, while still setting ambitious targets. 

If you prefer audio, listen to the podcast episode here:

This is actually a very common problem that plagues a lot of companies. And the reason is that with investor pressure and funding comes the responsibility of growing the company faster. Quite frankly, however, a lot of that pressure is self-inflicted, because we're incredibly optimistic and bullish on how fast we can grow our company. And we often overestimate how quickly we can get to the next plateau of our growth.

And so this creates a vicious cycle, executives overestimate how quickly they can grow. They end up at a board meeting with missed sales targets, the investors are left no choice but to ask really difficult questions. And if this happens quarter after quarter for many quarters in a row, eventually C-level executives are let go and a new management team is brought in.

Bottom up forecasting is better for most companies

That's why the number one thing I like to preach to management teams in this case is to stop forecasting as a top-down exercise. Instead, it's better to use a bottom-up approach.



A top-down approach looks at macroeconomic trends, market data and extrapolates down to the company level to figure out how much of that market the company can capture. If you've ever seen an episode of Shark Tank, you'll often see entrepreneurs say something like, this market is worth $4 billion annually. Even if we capture just 1% of that market, we'll be a huge company. This is an example of a top-down approach and this is often a recipe for disaster. Unless you're a company that is massive and operates on a global scale, macroeconomic data often has little correlation to how exactly the company will perform. Yes, knowing your market data is important. It's just about when you introduce it into your forecasting process.

So for example, if you're Coca-Cola, and you're trying to forecast your annual revenue targets, one of the most important data points for you would be, what is the annual volume of carbonated beverages sold and consumed? This type of data point can actually help Coca-Cola because they can say, hey, we have 30% of the market share. So based on that volume, here's a reasonable expectation of how much our market share in terms of revenue will be of the overall carbonated beverages market.

However, if you are a smaller firm, let's say you're just starting out as an alternative beverage company that is trying to enter into the carbonated beverage space, that macro data is really not that helpful. Instead, micro data points can help you establish a far better forecasting model.

So for example, it would be important to know, how many stores can you actually sell your beverages in? It would also be important to know on average, how much volume will each store move? This allows you to forecast revenue expectations as the number of stores that carry your product grow. This is what's called a bottom-up approach. And this is what a lot of companies fail to use, because they're being far too optimistic in terms of how fast they can grow.

1. Forecasting is not a math exercise

Which is a critical point here, because forecasting is actually not just a math exercise. We are trying to achieve a specific goal with forecasting.

We would like to have a model that accurately predicts sales bookings. We still want to challenge all revenue team members with stretch targets, not unachievable ones, and we want to ensure investors are being promised numbers that we are actually capable of delivering on. These are the main reasons why a bottom-up approach can serve most organizations much better than a top-down approach. Using market data is important, but we'll get to that in the later stages of building out our model using a bottom-up approach.

2. Understand the inputs of your revenue model

If we're going to use a bottom-up approach, we need to understand all the different inputs of our revenue model. This is where questions like how many MQLs are being generated per channel? How many MQLs can each sales rep handle per day or per week or per month? And how much can we upsell to our existing customer base? Become very important in trying to figure out how much can we actually grow this business in the next year. And there are hundreds of data points that we can gather to build out this overarching revenue model for the entire business.

When we look at new MRR, there are a bunch of inputs that marketing and sales need to contribute. When we look at expansion MRR, both marketing sales, along with customer success and product need to work together to figure out, what can we upsell and cross-sell to our existing customer base. And then when we look at attrition MRR or retained MRR, we need to completely understand what is driving people away from our product and how can we get them to stay longer, and what are the different inputs involved.

What this means is, each revenue sub-team, function or department needs to understand all the activities that contribute into the core inputs that ultimately drive revenue. And this involves building a data-driven culture where every activity is measured, every activity is tracked all the way through from the top of the funnel to the bottom of the funnel, to know how revenue is being driven across the organization.

3. Identify opportunities to scale using market data

Once we've built our revenue model and understand the different inputs involved, now we can introduce market data and ask questions like, how much can each channel be scaled with more spend? How much demand is out there? What is our TAM/SAM? And how many more people can we reach? How many more sales reps do we need per 100 MQLs? What new channels and markets can we open up with more investment? As you can see, we've reintroduced market data into our model just at a later stage. And the reason for that is, now we can make a more informed decision, given that we truly understand all the different inputs of the business and how the market data can be used to scale even further.

What this means is, market and scaling data layers on top of our existing model. And we use a business case and ROI based approach to evaluate all the different opportunities. A very simple example would be, let's say we go to 10 trade shows a year and we drive 100 MQLs collectively, and help us close 10 big deals per year. We can then go out and make a list of all the other trade shows we can be attending, how much each trade show would cost and forecast the revenue from each trade show based on the average number of leads and deals that we close based on our historical data. This is where the market data and the historical data and the model of the company come together into one place to help us create a better model.

As you do this for every channel and for every activity, you can almost create a ranked list of potential activities that you can actually scale with more investment. And then based on the investment that each item requires and the ROI that each item can drive, we can make decisions on which initiatives get priority in our budget and our forecasting. As we make a decision on which initiatives we want to prioritize, we can ultimately roll them into our budget and forecast, which is grounded in the truth based on our input model and market data to combine them together into one forecast that we are actually capable of hitting.

4. Build sensitivity into your model

Once all of this work is complete, the most important step is to build sensitivity into your model. What I mean by this is to account for multiple different scenarios that can play out depending on which of your variables or inputs go your way and which ones actually don't. And this will happen. Not every variable that you're counting on will bounce your way. So a sensitivity analysis is critical to setting expectations the right way. A simple way to approach this is to use red, yellow and green scenarios. Where red is a conservative estimate, yellow is the middle ground, and green is incredibly optimistic based on if all the variables go your way.

And then when you present to the investors, present the yellow scenario as your projections for the following year, and walk them through the entire input model along with the market data, along with the business cases and the ROI-based decisions that you've made. And say, this is why we think the yellow scenario is a stretch target, and at the same time achievable to help us take this business to the next level.

Now here's the part that most people miss. Your investors are your partners. They want you to hit your sales projections because that means the company's growing at an expected rate. It's far worse for the investors to project ambitious sales targets, because when you miss, it actually impacts their portfolio performance as well. So if you come to the table with all the information, all the inputs and all the data, and walk them through why you arrived at the forecasts that you did, and present a moderate scenario, instead of an overly ambitious one, you're far more likely to get buy-in from your investors.

So your job as a fiduciary of the business and a representative of the company to the board is to make sure that the board understands why you're making the kind of forecasts you are, all the different inputs and variables that can impact your business results, and how you're going to actually be able to achieve the targets that you're promising. What this means is, while hitting sales targets is about operational efficiency, and actually being good at product sales and marketing, it is just as much about accurate forecasting and expectations management.

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