Sales forecasting is an integral part of the business. It allows for better financial planning, which is essential for achieving growth and customer satisfaction.
If SaaS businesses want to make wiser, more informed decisions and improve their sales, then checking out sales forecasting strategies is a good start.
Let’s take a look at what sales forecasting is, its benefits, and the various strategies you can utilize in your business.
What is sales forecasting in SaaS?
SaaS sales forecasting involves collating past sales and company performance data to predict future sales patterns. This report typically includes various sales data from the business that can be analyzed and reviewed to assist companies in making smarter choices based on previous data.
“Sales forecasting is estimating a company’s sales revenue for a specific future period, most commonly a month, quarter or year. It’s a prediction of how much a company will sell in the future. Accurate sales forecasting enables companies to make informed business decisions about resource allocation and budgeting.”
— Salesflare
Sales tools can make the sales forecasting process more accessible, allowing you to view data and spot patterns more clearly. For example, CRM platforms like Salesflare feature an in-depth dashboard so you can visualize data for better insights.
Another method that allows for more customization and advanced forecasting data is to use an Excel or Google Spreadsheet file. This template provided by Salesflare is a great example.
Apart from traditional sales forecasting that relies on data like leads, sales, revenue, Incorporating elements like project forecasting within these tools can further enhance the ability to predict and manage future sales effectively, aligning with broader project management goals, as well as expenses.
It’s an important step in helping the business to refine product development, risk management, and other business processes. It allows sales teams to consistently review the effectiveness of their strategies and adjust accordingly, ensuring campaigns stay on-track to meet goals and KPIs.
The clever part about sales forecasting is that the more accurately you can predict demand, the better you will be at meeting those demands and exceeding customer expectations. So you’re always one step ahead.
What are the benefits of SaaS sales forecasting?
Let’s look at some of the key benefits of SaaS sales forecasting.
Help with decision-making
Utilizing sales forecasting can assist with business decisions in the future, as it acts as a guide for what could potentially happen. It may also help with budgeting and risk management in the company.
For example, by refining their sales forecasts, one SaaS company was able to determine which accounts had the biggest potential. With this information, they could then make critical decisions about how much of the sales team’s time and budget should focus on those accounts and what additional support the team would need to close deals.
What we found: So often the problem a client sees is the symptom rather than the cause. The underlying problem in this case was customer service. Not bad customer service, in fact there was a healthy focus on keeping customers happy. The real issue was that all customers were treated the same, regardless of their size or potential. This meant that all customers received personalised time and attention with the sales team at their beck and call.
Source: SalesUntangled
What did this have to do with forecasting and funnels? Not surprisingly the biggest £ opportunities came from the largest clients. But with limited time to spend nurturing relationships and networking the account, the nuance of decision-making was being lost. There will always be some surprises in forecasting so if you’re going to focus on accuracy then do it for the top 50% of revenue.
As a result, the team increased call numbers on key customers by almost 20%, which let to improved lead quality and a double-digital increase in forecast accuracy.
When you have a good understanding of what could happen in the next few months, it can make you feel confident in your business decision-making and better prepare you for the next steps. You can more proactively react to upcoming demands, market fluctuations and customer trends.
Set more accurate targets
You can use sales forecasting to set more accurate sales and revenue targets in the immediate and long term. This is because your targets are data-backed, meaning you’ve been able to utilize your historical sales data to give you a more accurate reading on future events.
Improve sales
SaaS sales forecasting can help improve your business’s sales performance dashboard. By reviewing historical data, you can make informed choices about your future, leading to an increase in sales and revenue.
Research from Aberdeen Strategy & Research shows that businesses with accurate sales forecasts are 7-10% more likely to witness year-on-year sales growth and 2x as likely to be at the top of their field.
Additionally, monitoring the customer satisfaction score provides crucial insights into customer approval and expectations, helping to refine forecasting models and strategies. This approach complements data-driven strategies by adding a dynamic layer of customer interaction that can be crucial for standing out in a competitive market.
Better resource allocation
Another benefit of sales forecasting is that it powers better resource allocation. By planning future situations and potential scenarios, you can get to the crux of what your business will want and need when the time comes.
You can better manage those resources. That includes budgeting, staffing, materials, infrastructure, and warehouse management.
This SaaS start-up used sales forecasting in tandem with a marketing budget analysis to forecast demand and predict revenue. Using the template below, they were able to look ahead and determine sales rep time in line with growth goals.
This allowed them to determine how many leads to send each rep each month to hit their target revenue. As a result, they grew their team and could route 100 warm leads to each sales rep each month, generating 15,000 monthly trials and $40,000 of new MRR.
Improve customer relationships
Sales forecasting can improve customer relationships. The knowledge it provides of previous customer behavior can be used to personalize the future customer experience.
It allows businesses to tailor their approach, building on customer relationships. The aim of the game is to get customers to make repeat purchases, and sales forecasting may help with that!
For example, by integrating your CRM with your sales forecasting tool, you’ll get access to data like customer interactions, purchase history, and social media activity. Using these as the basis for your sales forecast will help you make customer-centric predictions, such as customer lifetime value, and identify cross-selling and upselling opportunities. You can also analyze customer service interactions and purchase history to create personalized offers that drive sales and engagement.
Challenges in SaaS sales forecasting
Here are some of the challenges in SaaS sales forecasting.
Uncertainty in the economic landscape
Like many businesses, SaaS companies are affected by the economic landscape. This means that when the climate is doing well, sales may increase, and they may experience more business from companies wanting to invest in software.
However, when there are drops in this climate, as part of their sales strategies, businesses may be more likely to want to hold back on new technology as part of their sales strategies. Unexpected highs and lows can be hard to predict, even with solid sales forecasting data. Because they are just that, unexpected.
Scaling up
Forecasting may be more tricky when scaling a business because tech companies tend to scale quite quickly. So, even with data-backed sales forecasting plans, businesses need to be ready to adapt efficiently if anything changes within the organization.
This will enable them to accommodate their growing customer base better and allow them to keep on track with their sales targets.
Clean data
“Clean” data is one of the most important tools when forecasting sales. You need access to clear, easy-to-read data that spans a wide range of time and people. If data is found to be inaccurate or downright impossible to interpret, it can negatively affect the SaaS sales forecasting process.
Inaccurate data can undermine sales forecasting, leading to faulty strategic decisions. For instance, payroll mistakes can introduce significant errors in financial data, leading to flawed assumptions and misguided forecasts, thereby complicating strategic decision-making.
Similarly, inaccuracies in inventory tracking can skew resource allocation models and result in overspending or stock shortages, which disrupt operations and financial planning.
This means businesses need to work on gathering and maintaining high-quality data year after year. This will help with sales forecasting and improve other business areas, such as marketing and sales reporting.
Law and regulations
It seems that new regulations and laws are constantly cropping up in a bid to protect consumers in the Internet age. This can also impact data gathering, as SaaS businesses that fail to comply with newer regulations such as GDPR may find themselves in a tricky spot. Outbound calling techniques, email marketing, and paid social marketing require a business to follow GDPR rules.
To further secure compliance and enhance the reliability of data used in sales forecasting, implementing digital signature technology can ensure that all data transactions are authenticated and legally binding, aligning with global regulatory standards.
It’s important for organizations to stay up to date with the latest laws and regulations to ensure they’re doing right by themselves and their customers.
Integrations
SaaS sales forecasting may require some new tools or software. However, integrating these with existing CRM systems can prevent challenges. In fact, according to research from Korn Ferry, only around 30% of teams say their sales technology stack is closely integrated with other applications like their CRM.
For precise forecasting, businesses must invest in software that seamlessly flows with their existing CRM software.
Businesses should be careful, though, to avoid the trap of SaaS sprawl, which involves having countless SaaS applications without tracking costs.
6 SaaS sales forecasting strategies
Now that we’ve examined some of the benefits and challenges of SaaS sales forecasting, it’s time to examine some of the most popular sales forecasting strategies.
1. Mapping the customer journey
Businesses are always looking for ways to better plan the customer journey. But what is the customer journey? Well, it’s how the customer moves through the stages of buying from your business.
This is from the first point of contact, such as landing on your website, to all interactions and touch points in between until they reach the final stage of the journey: purchasing a product or service.
Ultimately, you want these customers to return repeatedly to create repeat customers. Businesses need to ensure that each touch point on the customer journey is as seamless as possible.
Customer journey mapping is a key strategy that many businesses may want to focus on, as there’s doubt that creating happy, loyal customers is good for business.
Businesses can segment customers into categories using the data they have collected and target them based on various factors, such as specific pain points or customer behavior patterns.
This customer-first strategy can be very successful, as not only are you utilizing your own customer data, but you’re able to forecast future behaviors and try to predict how the customer will move throughout their journey. The hope is that by rectifying some of the customer pain points, more and more will make it to the end of the customer journey and make a purchase.
2. Real-time personalization
Real-time personalization is another SaaS sales forecasting strategy that can help manage and exceed customer expectations. You can examine elements such as the customer’s time on your website, the bounce rate, when they request a demo, and abandoned carts.
By analyzing all of this data, you’re learning about your customer behavior and the times at which you can make the experience more personalized. For instance, when they abandon their cart, you can send them an email reminding them to check out.
This real-time personalization can be key to boosting sales and building customer relationships. You can then begin to adjust elements of your website that just aren’t working or change other parts of the customer experience to better cater to them.
3. AI-backed forecasting
Sales forecasting tools can automate many steps in the sales forecasting process, making it easier and more manageable for businesses. Those tools powered by AI just add to the ease of the process, and you can complete all kinds of tasks using AI, from data analysis to written reports.
AI models can sift through all your business data and extract and analyze the parts most beneficial to your sales forecasting strategy. In this way, it saves you time because you are being shown only the most important, relevant information that is useful for the forecasting process.
To further enhance strategic planning and resource allocation, companies can also utilize frameworks such as the GE McKinsey Matrix, which helps prioritize investments by comparing the potential and performance of various business units.
This can mean that your SaaS sales forecasting is more targeted too, focusing only on the steps that are going to improve your business processes and generate more sales in the long run.
4. Qualitative data forecasting
This forecasting technique uses qualitative data, which can be useful when you don’t have a ton of existing data to work from. This data can be gathered through various means, such as through a competitor or marketing research conducted by your business.
You can also conduct a market survey and send it to people with pop-up ads or their email inbox. You could also rely on salesforce polling, where you speak to your sales reps and gather data on customer buying habits through their feedback.
This qualitative data can then be analyzed and used to create a SaaS sales forecasting model based on predictions from the data, which can be more of an educated guess.
So, what does this look like in real life? Let’s say your company is launching a new conference call service. As this is a new service, you don’t have access to any historical data, but you can look at the market and competitors.
You analyze the providers currently dominating the market and know that the global economy is on a downward trend. Your introductory plan is twice as expensive as competitors, so you need to understand whether your go-to-market strategy is foolproof.
In this case, gathering qualitative data would give you insights into whether you should proceed as planned. By conducting market research and surveying potential customers, you can determine factors like how much businesses plan to spend on new tech tools in the future, what features they’d be willing to pay more on, and whether they would consider your service.
5. Causal forecasting
Causal forecasting is where solid data is mixed with qualitative data methods, utilizing both what you know for certain and gathered data to create a sales forecasting strategy.
It’s all about making the most of the data you have from previous years while also considering the outer climate and what other businesses and/or experts are saying.
This is beneficial because it means you’re not putting all your eggs into one basket and just trusting one particular data source.
It’s also particularly beneficial for SaaS companies as it takes into account the industry’s nuanced dynamics more so than historical forecasts do. It better identifies the impact of new trends and industry innovations and helps you optimize resources.
Look at Netflix as an example. The streaming platform used a casual forecast that considered factors like the content library, user experience, content cost, and engagement metrics. Using a casual forecast, they introduced AI-powered recommendations to improve customer retention rates and grow their subscriber base.
6. Use A/B split testing
A/B split testing is where you try two different strategies on the same audience and see which performs better. Typically, you’d switch to the best-performing strategy as soon as it’s feasible. It can be one of the most effective ways to spend your budget in the right places.
This also works for SaaS sales forecasting, as you can test out two different methods to see which one comes out more accurately after the year ends. With this, you can go forward with this method in the future or put it up against another method to see which comes out on top.
For instance, try the customer journey mapping strategy to piece your sales forecasting process together. You may also use AI to generate another process. You can then compare the two and see which one had a more accurate outcome and positively impacted your sales.
Using SaaS sales forecasting strategies in your business
SaaS sales forecasting can be made much easier when you use the right strategy for your business. Each company is different, with each target being personal to them. It’s vital that the strategy you use is going to be the most effective for your wants and needs.
Newer businesses may need to rely on qualitative data methods more to achieve the results they want, whereas more established companies have the luxury of relying on customer journey data to guide them into the future. Either way, you must choose the right method for your business.
