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The Ultimate Guide to Improving Sales Forecasting Accuracy
The Ultimate Guide to Improving Sales Forecasting Accuracy
January 10, 2024
9 min read
Topics covered in this article
With today’s big data analytics, machine learning, and AI technologies, correctly estimating companies’ potential future revenues is no longer a mystical art that can only be guessed by luck. With the help of continuously developing technologies, accurate sales forecasting not only helps to precisely determine targets but also helps managers identify where and how sales reps are losing deals.
In this article, we are going to explore what sales forecast processes look like, how to do sales forecasting, why accuracy is vital, and how it can be improved through implementing sales forecasting best practices.
Why Sales Forecasting Accuracy Matters
"Sales forecasting is not about predicting the future; it's about making informed decisions today." - Chris Voss, author of Never Split the Difference: Negotiating As If Your Life Depended On It
Sales forecasting is a strategic corporate planning tool that not only aims to predict future sales, but also other key performance indicators such as customer demand, product performance, and market trends. It's a multi-layered process critical for making informed decisions in various business areas such as inventory management, workforce planning, financial budgeting, and marketing strategies. The main goal? To anticipate future market conditions and align the company’s operational and strategic plans accordingly.
The implications of effective sales forecasting are far-reaching. It allows companies to optimize their inventories – avoiding both surpluses and shortages – and helps align production plans with market demand. Accurate forecasting supports marketing teams in creating targeted campaigns, helps sales teams set realistic targets, and informs financial planning to ensure sustainability and growth.
The importance of accurate sales forecasting cannot be overstated. Inaccurate forecasts can lead to significant business disruption. For example, tech giant Cisco Systems faced a staggering $2.2 billion inventory write-down in 2001 due to an overestimation of demand caused by an inaccurate sales forecast. This not only led to direct financial damage but also affected the company's credibility in the market.
Similarly, BlackBerry's failure to accurately estimate the declining demand for its smartphones in the early 2010s resulted in a significant loss of market share and left the company with excess inventory.
Understanding Sales Forecast Accuracy
"The accuracy of your sales forecast is directly proportional to the quality of your data and the sophistication of your process." - Tim Berry, author of The Profit Zone: How Strategic Business Planning Leads to Success
To understand the driving factors of accuracy, let’s explore the process of how to make a sales forecast:
- Data collection. Gathering historical sales data is the basis for creating forecasts. In this step, past sales are analyzed to identify trends and seasonal fluctuations that are critical to accurate predictions.
- Market analysis. Researching current market trends involves examining overall market conditions, including consumer behavior and preferences. This analysis helps to anticipate market shifts and adjust sales strategies accordingly.
- Assessment of industry drivers. Identifying factors impacting the industry is about understanding external influences such as technological changes, regulatory developments, and economic conditions.
- Pipeline evaluation. Analyzing the current sales pipeline involves reviewing ongoing deals and their potential to close. This helps to predict short-term sales and understand the performance of the sales team.
- Predictive analysis. The use of AI and machine learning for future sales insights uses advanced algorithms to create sales forecasts based on complex data patterns. This approach can provide more accurate and nuanced forecasts than traditional methods.
Data quality is a key pillar for accurate forecasts. The more data is available, the better the accuracy can be improved. These include:
- CRM software. Provides detailed customer interactions and sales histories.
- ERP systems. Provide insights into operational data and resource management.
- POS systems. Record real-time sales data essential for understanding current sales trends.
- Market research reports. Provide insights into industry-specific trends and consumer behavior.
- Field sales reports. Provide first-hand information from sales representatives on customer interactions and market feedback.
- Customer feedback. Collected through surveys, reviews, and interviews, and provides insights into customer satisfaction and preferences.
- Competitive analysis. Examines competitors’ strategies, market positioning, and performance.
- Economic indicators. Includes broader economic data such as GDP growth, unemployment rates, and consumer spending patterns.
- Social media analysis. Provide real-time consumer sentiment and trends.
- Industry news and publications. Provide insights into industry trends, regulatory changes, and technological advances.
The Realistic Level of Accuracy
When evaluating the precision needed for your enterprise's financial projections, the level of accuracy will be a crucial element. The necessary degree of exactitude varies depending on the forecasting maturity and scale of the company.
For a newly established startup, with many unknown factors and no rich historical data available, hitting a forecast within a range of 20-30% is realistic.
When a company reaches a more mature state and has a deep pool of data available, getting the forecasts accurate is crucial. Businesses in this stage usually aim to be within 5% of their predicted numbers.
Risks that may Impact Accuracy
Sentiment Risk. This refers to objections from crucial stakeholders, often only emerging in the final phase. For instance, a last-minute concern from a key decision-maker can unexpectedly stall a deal that seemed certain. An early response to these objections can significantly improve forecasting accuracy.
- Relationship Risk. Relying on a single point of contact or having a fragile connection with the buyer can be risky. Imagine a scenario where the sole contact leaves the company or changes roles, leaving the sales team without a reliable liaison. Building strong, diversified relationships protects against such uncertainties.
- Dis-engagement Risk. Insufficient engagement from key decision-makers or buyer personas indicates a lack of commitment or interest. For example, if senior executives are consistently absent from meetings or unresponsive in communication, this is a warning sign that the business may not be as solid as planned.
- Out of Sales Process Risk. Ignoring critical issues or not completing important steps on time can derail a deal. Imagine critical compliance issues not being discussed, leading to last-minute revelations and jeopardizing the deal. Following the sales process ensures that all necessary items are addressed.
Overcoming such challenges and risks is particularly important to ensure that the accuracy of sales forecasts can be maintained at the highest possible level.
To improve the accuracy of sales forecasting, objective metrics must be tracked and best practices implemented. These approaches focus on establishing concrete, quantifiable measures to enhance the reliability of forecasts. A key practice is setting and monitoring weekly and quarterly pacing against weighted targets. This strategy involves defining specific attainment targets for each period, providing a clear measure of the sales reps' progress on their quarterly forecasts. By breaking down the quarter into smaller segments, companies can better understand and manage sales performance by recognizing who is ahead or behind and having early, actionable conversations about deals and forecasts.
Another best practice is to review the historical accuracy of commitments and forecasts. By tracking for each sales rep the percentage of revenue generated in previous quarters from their commit and best-case deals, you can better assess the health of the current forecast. This historical perspective helps to understand the likely outcome based on past performance.
In addition, it is critical to assess the coverage gaps in the pipeline for the current and upcoming quarters. Early monitoring of pipeline coverage is a good indicator of performance in the final quarter. Using historical data to calculate the pipeline conversion rate in the quarter allows managers to estimate the necessary pipeline volume each agent needs to meet its forecast.
Using KPIs to Enhance Accuracy
To improve forecast accuracy, it's essential to adapt the performance indicators (KPIs) of the forecast to the respective context. Segmenting forecasts by geographic region takes into account different work cultures and sales expectations and uses historical profit rates for accuracy. Different forecasting methods are recommended depending on company size, e.g. a 30-day cycle for small companies that focuses on deal volume, a deal-by-deal approach with intuitive estimates for medium-sized companies, and in-depth, weekly reviews for enterprises.
Key KPIs for forecast accuracy include Pipeline Coverage, with a general guideline of keeping three to five times the forecast volume in the pipeline, adjusted for different regions. It's crucial to monitor the forecast categories and their changes and to understand the dynamics of deals that are likely to close or are at risk. It's also recommended to focus on the age of opportunities and know that deals that exceed the average length of the sales cycle are less likely to close.
How AI Improves Accuracy
By integrating data from CRM software, ERP systems, POS systems, and market research reports, AI algorithms can analyze patterns and trends that humans may overlook. AI can sift through CRM data to identify buying patterns, customer preferences, and the length of sales cycles to make predictions about future sales trends.
A major advantage of AI in forecasting is its ability to process large amounts of data quickly and accurately. When analyzing POS data, for example, AI can identify sales trends in real time and link them to external factors such as market changes or advertising campaigns. This comprehensive analysis helps to create more accurate sales forecasts.
Using the right data sources and implementing AI-based predictive sales forecasting tools, an estimation accuracy of up to 95% can be achieved, far exceeding the forecast precision of manual estimation methods.