Difficulties of sales forecasting
According to research shared by Forrester, 79% of sales teams miss their sales forecasts by at least 10%. Other research suggests that only 28% of deals that make it to close were forecasted accurately; the rest of the time, the final timeline or sale amount looked markedly different from what was projected.
Many sales professionals would look at these numbers and nod knowingly. At its best, sales forecasting helps businesses create clear and documented strategies for the future. At its worst, it’s an endless treadmill that keeps sellers running after targets that are unattainable — and unrealistic.
Read more below.
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Difficulties of sales forecasting
Why is sales forecasting so difficult?
If the sales industry knows forecasting is tough, what are the challenges of sales forecasting that keep getting in the way of real success? There are many obstacles to clear, from messy historical data to subjective seller biases getting in the way of an accurate prediction of how any given lead might behave in the future.
At Collective[i], we believe that modern technology provides a way forward for any seller looking for easier, more accurate sales forecasting. In the spirit of embracing that better way, let’s take a look at why organizations are so serious about sales forecasting, what causes bad forecasting, and what the future of prescriptive, AI-enabled sales forecasting looks like in practice.
How do organizations benefit from accurate demand forecasting data?
The benefits of accurate forecasting are numerous, including better financial planning, strategic resource planning, and benchmarking for sales team success. Creating a dependable forecast isn’t an easy task, but it’s worth investing in because it provides a clear roadmap for the future as organizations make important decisions about financing, hiring, product research and development, and marketing.
Here are a few of the top benefits of good sales forecasting:
Better financial planning
When companies can predict how much revenue they’ll earn and the sources bringing it in, they can better manage cash flow to keep the business healthy. Finance leaders can secure better terms on loans and lines of credit when they can reliably document their business’s financial health and prospects moving forward.
Better resource management
Financial stability also contributes to better decision making surrounding resource management. If an accurate sales forecast suggests that one type of product will account for a large portion of revenue, it helps company leadership plan accordingly and invest more resources in supporting that line of business. They may choose to prioritize new hires that can support those incoming revenue streams, or pull resources away from streams that aren’t expected to perform as well.
Better go-to-market strategy
On a basic level, accurate sales forecasting helps sales teams stay focused on their goals. They can pursue the best opportunities for revenue growth and focus on actions that are more likely to close deals. This perspective extends to marketing, which can be focused on supporting sales by developing campaigns and content to produce more leads around more promising types of customers.
With the “why” set, let’s look at “what” goes into forecasting to make it deliver on these benefits.
What are the factors affecting forecasting?
In order to better address the reasons that forecasting sales can be difficult, it’s important to get clarity on every factor that can contribute to an accurate — or inaccurate — sales forecast. These forces are at work outside and inside every organization.
External factors of forecasting
We’ve written in the past about the pitfalls of forecasting based only on historical sales data. No spreadsheet can factor in the complicated web of factors outside of any company’s control that could play a role in future sales. Think of these external factors as a lesson in chaos theory; even the most accurate sales forecast could crumble in the face of changes in any of them.
Let’s say a thermostat manufacturer has a great track record of success selling standard digital thermostats to apartment complexes, property managers, and retailers. As consumers broadly start to expect more functionality in line with “smart” thermostats, this company’s standard offering might not maintain its perceived value moving forward. These shifts in sentiment can play a big role in the accuracy of sales forecasts on a long-term basis, but current events can also cause sudden shifts.
The labor market
Especially for B2C brands, trends in the labor market can have a serious impact on customers’ ability and willingness to spend. If unemployment is high or wages are stagnant, that could mean less disposable income and a drop in sales. But this is also a factor for B2B brands; a tight labor market could have a ripple effect on a company’s plans to purchase materials for new projects, for example.
Broad market forces
Inflation. Stock market movements. Oil and gas prices. These are all constantly shifting market forces that can send shockwaves through industries that might not even seem to be at their whim. If oil prices skyrocket and the tourism industry sees a hit due to high airfare costs, a travel agency might see a dip in vacations booked. From there, an insurance company selling travel policies could see a dip. That might tighten marketing budgets so an advertising agency sees a loss of revenue.
Believe it or not, the weather has a similar impact on a variety of industries. Droughts, storms, and everything in between can cause spikes in sales of things like roofing supplies and car windshields — or plateaus.
Any of the above factors might cause a loyal customer to tighten their budget for spending, making it more difficult to accurately project how much they’ll spend moving forward based on what they’ve spent in the past. Or none of those factors could play a role and a new executive with different priorities could lead to the same effect.
The key to accurate forecasting isn’t to try to predict when the next stock market drop is going to hit, but to stay nimble and react accordingly when that does happen. Click here to learn more about how modern sales teams are utilizing neural networks to adapt to changes in the market and pivot their sales activities with Collective[i].
Internal factors of forecasting
While it might seem like the external factors of forecasting are more uncontrollable and therefore account for more forecasting inaccuracies than internal factors, that’s not necessarily the case. According to a 2016 study by the Harvard Business Review, 85% of executives blame internal factors for shortfalls in their company growth. Those factors include:
The labor market
Yes, this can be both an external and internal forecasting factor. When organizations can’t hire the skilled professionals they need, it can have a domino effect. The customers might be there to buy, but the product or service isn’t there to sell.
For businesses selling products, inventory is everything. Certain products might be available in abundance, but labor shortages, supplier shortfalls, or any number of other contributing factors might mean there’s less of a given product than originally planned. Sales forecasts based on the presumption that sellers will be selling a certain amount of a given product can fall apart without the inventory to back them up.
Product and services pricing is rarely stagnant for long. As organizations evolve their pricing models, sales forecasts need to keep up. Increases or decreases in pricing (including sales that pop up out of nowhere at marketing’s urging) have an obvious impact on the underlying calculations of any sales forecast.
New revenue streams
For starters, it’s impossible to predict what sales will look like holistically for a time period if sales doesn’t know about a new product or service line that will launch into the market. But even beyond the obvious impacts, there’s a big challenge in allocating resources; a new product line might take sellers’ attention away from other, historically successful revenue streams. The net impact of those changes can be difficult to predict.
Any of the above factors can be impacted by an organization’s access to working capital. Financing can be driven by external factors like increased interest rates or market fluctuations that make it easier or more difficult to access funding, but internal decisions about spending can cause a ripple effect. If there’s more or less capital available to spend on developing and launching a new product, that will change what sales might look like later.
Why is it difficult to forecast sales?
Forecasting sales is difficult because both external and internal factors come together to create an increasingly complicated set of data upon which to build predictions of the future. Building a forecasting model that can account for changes in the market or internal matters relating to staffing or inventory is a tall order with plenty of room for error.
On another level, however, there are some common human elements that can make accurate forecasting a herculean task. So, what are the difficulties of forecasting that might be similar from organization to organization?
- Subjective bias: There’s no accounting for taste, they say, and they’re right. Especially when “taste” in the sales forecasting world speaks to the individual opinions and instincts sellers bring to the table. One seller might know something about a customer that’s impossible to quantify, but tells them a deal is sure to close by the end of the month. Instinct is a tricky element to weight in any forecasting model.
- Data upkeep: A sales forecasting tool is only as good as the information it has available to it. Collecting information like sales histories, interactions with leads, and even external market data is a time-consuming task. When humans are responsible for seeing it through, mistakes can be made. Updates can be skipped. Forecasts can easily be built upon faulty information.
- Management challenges: The way human managers decide to utilize sales forecasts can be a major problem associated with forecasting. Once a forecast is made and agreed upon by leadership, some managers may turn that forecast into a target that sellers have to hit, no matter what. That, in turn, can create a vicious cycle of sellers spending energy on deals that simply aren’t going to close in an effort to hit their numbers. That leads to underperformance, rendering future forecasts just as futile — or moreso. This is one of the primary disadvantages of forecasting the old fashioned way.
What are the causes of bad forecasting?
The most frequent causes of bad forecasting are messy CRM, siloed data, and blind spots in buyer behavior. Keeping CRMs up-to-date with accurate customer information is a time-consuming process that’s fraught with human error. Likewise, the data that sales forecasting tools rely on might be available, but ends up compartmentalized and inaccessible. And one of the most pervasive problems in sales forecasting as it’s been done traditionally is sellers not knowing what they don’t know. With so many factors coming into play for any sales forecast, it’s easy for sales teams to overlook information they simply don’t have, or to rely too heavily on historical data that’s not actually reliable.
Here’s a closer look at the three primary causes of bad forecasting — and how they can throw a wrench in the works for sales teams.
Data entry is a tedious process no matter who does it. Keeping up-to-date records of what’s happening with every deal has long required diligent work. That starts with the basics: customer contact information, potential deal values, and assumed timelines for closing sales. But it’s also important to record every interaction with a lead, both on a personal level and when it comes to engagement on emails and other forms of marketing and sales communication.
In many ways, a CRM then becomes a problem rather than a tool to help sellers make forecasts and close deals. Even the best-kept CRM can fall victim to random accidents — the CRM fails to update when a seller moves back an estimated close date, so a sales manager continues to expect the deal to move forward according to the old timeline. And accurate or not, time spent manually managing CRM data is lost time, full stop.
Imagine a scenario: A sales team relies on a CRM to manage their deals, an email automation platform for lead nurturing, and regular inventory reports from operations for a clear look at what’s available to be sold. Because each of these sources of data are managed by different teams — CRM by sales, email automation by marketing, and inventory by operations — they were each purchased and implemented independently of the other. There’s no connection between them, so it’s up to sales managers or even individual sellers to cross-reference on the fly.
This is a common situation across industries and organizations, and it has obvious drawbacks. When a seller has to take the manual step to review a lead’s engagement with marketing emails to score them appropriately within the CRM, there’s room for error. When a seller mistakenly pulls up a dated inventory report in preparing an SOW for a new potential customer, there’s potential for misalignment.
Bringing all of these disparate pools of data together can be a monumental task as it is — made even more complicated depending on what sales forecasting tool a sales team uses to interpret that data.
Another scenario to consider: A seller has been working hard to close a huge deal for the last three months. The process to get them from initial conversations to a contract ready to execute took only two weeks, and the enthusiasm of the buyer gave the seller the impression that a close was imminent. But each week, another touch-base call leads to the same result: someone higher up has the contract on her desk and we’re just waiting for her to sign it. The initial energy was enough for the seller to not want to give up on the deal, but what information are they missing? It turns out that a spending freeze was put in place at the executive level, and the seller’s contact doesn’t even know about it.
This illustration just goes to show that in any forecast, there’s information a seller simply can’t learn independently. They might be at a dead-end with a buyer who isn’t actually the right person to be speaking with. A competitor could be at an even later stage with the same prospect, and the buyer is just biding time until their preferred deal goes through. What sellers do know can go a long way in helping them prioritize deals, but what they don’t know can sometimes lead them to a dead end.
Overcome the challenges of forecasting with Collective[i].
These causes of bad sales forecasting are prevalent across organizations and industries. When sellers are forced to make misinformed guesses about their selling activity and then do whatever it takes to make those guesses a reality, it doesn’t benefit anybody. That’s why Collective[i] set out to develop the first truly prescriptive sales forecasting tool that removes the challenges of forecasting and provides sellers and management alike with accurate predictions and actionable recommendations to drive revenue.
Here’s how we do it:
We automate everything about managing CRM, from activity and contact capture to reporting and workflows. Less time in the CRM means more time selling.
By integrating sales team’s historical data across platforms, our machine learning tech is able to provide daily forecasts with the best next steps for any deal.
Our neural network of data from a growing collective of sales organizations provides insights into global buyer activity and can make networking recommendations to get to decision makers.
By letting artificial intelligence tackle the biggest forecasting challenges facing sales teams, Collective[i] empowers sellers to focus only on the actions that will drive revenue. The result is a full forecasting transformation that emphasizes the human elements of sales and empowers sellers to punch above their weight. Our forecast accuracy of up to 98.6% is just a bonus.
Ready to see what predictive sales forecasting could look like for your business? Click here to explore Collective[i].