Accurate forecasting, even in a downturn
Sometimes during earnings season, founders look at big corporations quoted on public markets and feel a pang of jealousy: each day brings another example of quarterly results that almost perfectly match those predicted months before.
That little feeling of “How do they do it?!” is more than just curiosity; it largely arises because whether your company is still in its early stages or you’re starting to look forward to an IPO, forecasting future results is a critical task.
Done well, forecasting can help boost further growth, such as by correctly identifying where to invest your resources or by wisely guiding investor expectations; it can also help avoid problems, such as the need for layoffs due to having had a poor understanding of hiring needs. Done badly, it can create big problems for everyone involved, from founders and investors to employees and customers.
Forecasting only becomes increasingly important when times are tough – such as those that economies around the world are currently experiencing. That’s why we wanted to give you a quick guide to help improve forecasting in your company, discussing different forecasting models, general best practices, and useful real-world examples.
Best practices for a solid foundation
Good forecasting starts with having a clear understanding of your business’s metrics and how they interact with each other. You need to develop a solid sense of how key performance indicators such as user growth, CAC, lead conversion, and others affect one another. (These, by the way, are also the major data points we at Uncapped use to determine if our fast, flexible loans would be a great way for your business to take its next steps toward growth.)
To track these KPIs, your business should have a data room that serves as a truth repository, the location where these metrics are received, stored and analysed. If your company has moved beyond its early days and has clear product-market fit, you’ll want to make sure the data handling is automated, so that the required data is being automatically pulled to the data room on a regular basis.
In early stages, this data room may be relatively simple, intended to forecast things as straightforward as marketing spend, conversion, and LTV. This is how Vetevo, a DTC petcare brand based in Berlin, first took off on its path to over 200K customers, leveraging a €210,000 loan into successful performance marketing and influencer campaigns that drove strong growth.
If you’re selling a physical product, forecasting can be a bit more complicated than it is for companies operating under a SaaS business model. After all, if important parts of your business are provided by third-party manufacturers or logistics experts, you’ll need to think about that as you build your forecast.
Pomabrush lives this on a daily basis, as forecasts aren’t only critical for the company’s internal operations but also for the company’s relationships with its production partners. As Pomabrush’s growth rates increased, they’ve had to accurately gauge how much product they’ll need in coming months, with the risk of either creating unsatisfying wait times for their customers or risking the business by tying up too much cash in their inventory backlog.
As your company matures and you have more historical data to work with, forecasting will become both easier, since that data can provide a more accurate view of how things will go in the future, and more complicated, since you’ll need better data analysis skills to properly manage the data.
Of course, the ultimate metric will almost always be revenue. By understanding your other KPIs, you should be able to see where potential efforts will pay off best: Are there obvious product improvements that need to be made? Could customer success do a better job of reducing churn? Are there upselling opportunities that the sales department could leverage quickly?
Just one thing before we move into more detail on creating the forecasting model that’s right for your business: while revenue almost always stands above other metrics in your forecasting, don’t lose sight of your gross profit margin. After all, if you’re pulling in more revenue but your cost of goods sold is negative (i.e., you’re spending more than you make!), increasing revenue would also mean increasing losses. So think through every aspect of your business; forecasts should help you shed light on the future, not lead you into dangerous pitfalls.
Determining and developing your forecasting model
In the early days, your company may stick to the most basic forecasting method: the straight-line model. This simply traces historical trends forward on the graph, assuming linear growth. While this can give a general idea of how things may go, it only provides a very static picture of the company’s future. With a straight-line model, the impact of things such as improved product-led growth, increased sales staff, effective affiliate programs, or any number of other factors that could accelerate growth simply aren’t taken into account.
In the digital age, though, few companies are satisfied with linear growth. Most founders and teams are instead looking for exponential growth, which means that your forecasting needs to be able to account for a more dynamic future. This can be done with a moving average model, which incorporates shifts in monthly performances and is thus better able to account for changing trends like those seen with increasing growth rates.
A moving average model can also be adapted using weighted averages, which is particularly useful when your forecasting model also needs to account for seasonal changes in your business. For example MORI, a leading babywear ecommerce brand, needed a model that could properly handle the fact that their Q4 sales are typically the highest of the year, while also anticipating the need to spend more cash in Q3 in order to prepare for that end-of-year rush. This meant developing a forecasting model that could properly predict shifts in not only cash flows, but also warehouse space and fulfilment logistics.
Beyond historical financial results, a good forecast will also take into account ongoing efforts in various departments: Do you have a new marketing campaign planned, in particular one that will reach the Ideal Customer Profile that you’ve recently identified? Did you recently add new members to the sales team, who are now up to speed and converting leads? Has your product received an update that’s already led to more stickiness and a reduction in churn?
As a general rule, improving your forecasting prowess means incorporating key data coming from various aspects of your business, evaluating their changing impact on your company’s performance:
- Product (updates, UX/UI…)
- Marketing (channels, geographies…)
- Pricing (billing frequency, offers…)
- Team (new hires, recruiting plans…)
Forecasting in a difficult business environment
If all you needed to worry about was your company’s internal metrics, forecasting might not be so difficult. But an accurate forecast also needs to take into account the macroeconomic context surrounding your company. Not only does that add more complexity to the calculations, it also adds more uncertainty.
After all, a challenging macro environment doesn’t necessarily mean that you should simply reduce your forecasts to go along with what everyone else seems to be thinking about the economy. Some of the biggest companies of the smartphone era – Airbnb, WhatsApp, Slack – were born and grew in the very difficult post-2008 macro environment. Still others may have started in other contexts, but prove to be particularly well-adapted to challenge-driven growth, such as Netflix or Dollar General.
Florence’s story is one that shows how an adverse macroeconomic environment can create a context where a business grows faster. Since their solution provides more healthcare worker availability, during the COVID-19 pandemic there was an even larger need for their services. While many companies were cutting back on expenses, Florence needed to increase theirs, opening new regional hubs and onboarding more caregivers. Being able to accurately forecast that shifting need, while also recognising that the traditional VC funding market was undergoing a major shakeup, was a big reason why they decided to top up the company’s bank account with an Uncapped advance that helped them boost revenues by 50%.
Thus one of the big questions you need to answer is exactly how something like a recession would impact your business. Would you gain customers but have difficulty with suppliers? Will you lose customers but also reduce headcount? Are you in a solid financial position that could let you acquire distressed competitors? Think about the big picture and the details, as both can have outsized impacts in a chaotic environment.
The bottom line
Let’s end with a few key takeaways to help you succeed in forecasting in any environment:
- Clearly understanding your product, business model, and customers will always be the starting point for any accurate forecast.
- As your business grows and you need to incorporate more data points to improve forecasting, make sure that you – or one of your collaborators – have the appropriate skills in advanced data analytics to manage the exercise.
- Forecasts can be used to find areas for improvement, whether in existing operations or in new markets. Putting in the work today can unlock the secrets to thriving tomorrow, no matter the broader economic context.
