Introduction
As successful entrepreneurs and business leaders know, numbers aren’t everything. Understanding and utilizing forecasting methods that account for factors beyond raw numbers is vital for business success. When it comes to sales forecasting, using a mixture of different forecasting methods provides a more comprehensive view, improving accuracy and guiding your company effectively.
What Is Qualitative Sales Forecasting?
Qualitative sales forecasting is an estimation method using expert judgment to provide insights into future outcomes. These methods rely less on hard numbers and more on experience, expertise, and instinct. They transform insights and opinions from experienced management, employees, and consultants into numerical sales forecasts.
Examples include:
- Forecasting how well an upcoming marketing campaign will perform
- Predicting new product sales success
- Accounting for industry innovations and policy changes
- Anticipating shifts in consumption patterns
Qualitative methods prove valuable when future sales are expected to differ significantly from prior periods. These techniques include:
- Utilizing consultant expertise
- Gathering sales rep projections
- Surveying customers about product needs
- Consulting distributors about market trends
How Is Qualitative Forecasting Different Than Quantitative?
The primary distinction lies in subjectivity versus objectivity. Qualitative forecasting is subjective, while quantitative relies strictly on objective calculations.
As Dave Guggenheim, IS Researcher for Digital Strategy and Data Analytics, describes it: qualitative forecasting is like discovering a “tell” in poker — not statistically certain, but it indicates present and future states.
Quantitative forecasting depends solely on historical numerical data to predict sales trajectories without factoring in opinions. Companies identify trends from past data and derive formulas for future forecasts. These forecasts have clear supporting data — for example, if sales grow 4% annually, predict next year using that rate. Quantitative methods also help predict seasonal spikes.
Why You Need Qualitative Forecasting
Qualitative methods help leadership understand ambiguity that quantitative forecasting creates. They work best alongside quantitative forecasts, providing a comprehensive picture accounting for marketing changes and customer trends.
Key scenarios where qualitative forecasting excels:
- Unexpected environmental events — A record cold front could boost heater sales beyond historical patterns
- Lack of historical data — New companies rely almost exclusively on qualitative methods initially
- New products/services — Expanding into new markets or territories requires qualitative predictions
- Comprehensive understanding — Hard numbers alone provide limited insight; qualitative methods fill gaps
The Pros & Cons of Qualitative Forecasting
Pros
- Quick execution without elaborate statistics
- Breakdowns by product, customer, territory, or salesperson
- Predictive ability from experienced leadership and customer interactions
- Flexibility using non-numerical data sources
- Accounts for factors quantitative methods miss (economic decline, supply shortages, competition)
- Useful with inadequate or ambiguous data
Cons
- Requires significant time and resources
- Lower accuracy compared to some quantitative methods
- Risk of group opinion swaying (unless using Delphi method)
- Possibility of lacking consensus
- Risk of selective perception ignoring conflicting information
- Sales reps and leadership may be overly optimistic or pessimistic
Types of Qualitative Forecasting Methods
Leadership Opinions
Best for: Businesses with limited resources needing insights from multiple departments
This method gathers opinions from company leadership across departments (marketing, accounting, sales). Each expert provides insights creating a comprehensive perspective. In larger companies, outside analysts participate; in smaller businesses, owners meet individually with supervisors.
Two approaches:
- Group discussion reaching consensus
- Independent estimates from leaders, then averaged
Monthly or quarterly updates accommodate changing market conditions.
The Delphi Method
Best for: Long-range forecasting in markets expecting significant changes or external disruptions
The Delphi Method eliminates group-think influence. A panel of experts completes questionnaires individually and anonymously. An outside coordinator aggregates responses and reshares them anonymously. The process repeats 2-3 times until consensus emerges, reducing forecast variability.
Key advantages:
- Participants give insights without fearing reprisal
- Gradual reduction in response variability
- Only the coordinator knows all team members
- Anonymous feedback encourages critical thought
Sales Field Opinions
Best for: Industrial equipment companies determining production volumes or similar industries with expensive products
Also called grassroots forecasting, this method gathers composite opinions from sales representatives — those closest to customers. Sales reps hear objections firsthand, understand demand shifts before numbers reflect them, and provide territory-specific insights.
Each salesperson provides:
- Future sales opinions
- Estimated volume numbers
- Customer responses to new products/services
Responses are averaged to develop forecasts with territory-level breakdowns.
Customer Surveys (Market Research)
Best for: Companies gauging acceptance and purchase likelihood of new products or features
Customers know what they want. Surveys conducted online, by phone, or in person reveal customer perceptions of your product, marketing campaigns, and brand, plus upcoming product needs. Statistical analysis of results creates total sales demand forecasts and tests hypotheses about consumer behavior.
Utilize Qualitative Sales Forecasting Methods for Better Insights
Whether lacking hard numbers or needing deeper insights, qualitative forecasting methods serve companies well. Using multiple techniques provides a complete picture enabling better sales direction and informed company decisions.
