Disadvantages of sales forecasting
Critical decision making based on relevant, timely market data — that’s the competitive edge sales forecasting gives successful business owners. Similar to how investors read changes in the stock market, business owners and leaders rely on forecasts to make “investments” in the form of quarterly and annual business plans. And with so many factors affecting sales forecasting, there’s a lot of room for error when developing sales strategies.
Sales teams spend a lot of time arguing the merits of different types of sales forecasting, but it’s not as often they acknowledge the difficulties of sales forecasting. What happens when all the data analysis about sales over a given period of time doesn’t give teams the competitive advantage they hoped for?
What are the disadvantages of sales forecasting?
Whether a forecast is accurate or not, it can be difficult to truly evaluate the quality and impact of a forecast before it’s too late. When a quarter goes well and meets or exceeds its forecasts, sales managers will tout the advantages of sales forecasting in terms of keeping sellers accountable and helping businesses plan — but did the forecast really have an effect on how sellers reached their goals? That’s not to mention the many forecasting models that were inaccurate, the time and resources dedicated to information that didn’t add up in the end, and when a projection fell flat. There’s a dark side to sales forecasting when it’s not providing measurable value: sales teams might not even realize their efforts are in vain.
There’s a high cost —and time investment — to committing enough resources to conduct long-term, data-driven sales forecasting. And even if all the manual data collection and analysis could be synthesized with effective technology, at what price? Veteran sales experts, for example, have often quibbled about the expense quantitative analysis requires. Why not just rely on trusted sales techniques and know-how? While the arguments on the advantages and disadvantages of sales forecasting can vary, the science of forecasting comes with a price tag — but it’s well worth the investment if it provides the kinds of insights that help leaders make better decisions.
What are the risks of forecasting?
The risks of forecasting include bad data not accurately reflecting the status of certain deals, the potential for error in manual management of that data, and incomplete data that doesn’t give sellers a full view on what could or could not lead a deal to close. Perhaps the biggest risk in forecasting — sales-related or not — is the unforeseen changes that upend what the statistical data has to say.
We can list all the factors affecting forecasting, identify trends and patterns, and then still miss these dynamic changes in the market to no fault of the people running the forecast. Consider these the rogue waves that we know exist but present few warning signs. In sales forecasting, this might be an economic upheaval — the 2009 housing bubble, or the more recent pandemic. If buyer behavior starts changing dramatically in response to bigger market movements even the best-prepared forecasts from a month ago might not be worth anything today.
Why is sales forecasting not always reliable?
Many historical methods of predictive sales forecasting aren’t reliable because faulty and incomplete data limits any predictive model’s ability to project into the future. Forecasting will always have inherent instability, because the market changes. Blame it on the obvious rogue waves when they occur, but there are also unpredictable elements that affect every prediction, a disadvantage of sales forecasting for any old-school forecasting approach. This is why modern sales teams are turning to agile, prescriptive forecasting technology that can notice these major shifts and incorporate them into real-time forecasts.
In historical forecasting models, there are two types of bias that can easily take any projection off track: qualitative bias and quantitative bias.
Qualitative bias stems from opinion-based methods of sales forecasting, like a seller’s individual opinion about their opportunities or an industry expert’s predictions of how the market will move in the future. Sales forecasters often have to spend time arguing and pinpointing any possible benefits of qualitative forecasting because of the fairly obvious disadvantages involved when relying on expert opinion. People make errors — why rely on their predictions? Even with success of the Delphi Method, or the proven grassroots predictions of sales team veterans, there will be implicit bias for any qualitative forecast. Certain sellers will naturally give more weight to some opinions over others. It’s a known risk.
Quantitative bias, on the other hand, has to do with ways that limited data sets can set an inherent bent to any projection. Take a look at how many different types of quantitative sales forecasting methods exist, and then add the types of variables included in each, and you get many subsets of data. Quantitative bias exists when analysts favor certain data over others — when they look for answers they want to exist and fail to include data that counters their perspective.
What are the limitations of a sales forecast?
If sales forecasts are limited by sellers’ biases and the inherent biases found in limited data sets, a modern solution needs to allow sales teams to work the way they want to while increasing the amount of data being considered, visibility into complex deals, and adaptability to changes in the market. Enter Collective[i].
We use real-time machine learning to take data from a growing neural network that pulls in data inside and outside of our customers’ organizations, streamlining that info into collaborative Virtual DealRoomsTM that help selling teams work together to meet and exceed their revenue goals. Our prescriptive forecasts are focused not just on accurate projections, but on helping sellers make informed decisions and actions. The result? Clarity for the entire business around how to seize every opportunity and make the right action at the right time.
Click here to get a demo of Collective[i] and see it for yourself.Explore Collective[i]