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What are the most common reasons for inaccurate sales forecasting?

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Sales forecasting is a crucial process for sales management, as it helps to plan resources, budgets, and strategies based on expected revenue and demand. However, sales forecasting is not an exact science, and many factors can affect its accuracy and reliability. In this article, we will explore some of the most common reasons for inaccurate sales forecasting, and how to avoid or mitigate them.

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Faisal Khan

Scaling Revenue & Market Share in SaaS & Tech | Director of Sales | Building High-Performing Teams
Darren Joseph Bayley

Director of Sales & Subject Support at AQA
Ellie Ryan

Revenue Focused | Enterprise & Mid-Market Sales Leader In Growth Startups | Invested in People First

1 Human bias

One of the main reasons for inaccurate sales forecasting is human bias, which can distort the data and assumptions used to project future sales. Human bias can take many forms, such as overconfidence, optimism, anchoring, confirmation, and recency. For example, a sales manager might overestimate the probability of closing a deal based on a positive interaction with a prospect, or a sales rep might ignore negative feedback that contradicts their initial impression. To reduce human bias, sales managers should use objective criteria and evidence to evaluate sales opportunities, and encourage feedback and collaboration among sales teams.

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Faisal Khan

Scaling Revenue & Market Share in SaaS & Tech | Director of Sales | Building High-Performing Teams
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I've been there too, inflating my sales pipeline based on just a gut feeling. Lucky for me, I've worked with some ace Sales Leaders who've shown me the ropes, guiding me towards making my forecasting more realistic and spot-on. Letting the team loosely feed data into the CRM is a surefire way to end up with pie-in-the-sky forecasts that'll come back to bite you at the end of the quarter or year. So, keep a tight ship! Implement KPIs for CRM data entry. Set your deal closure expectations to 30, 60, or 90 days. And when you're moving those deal stages, be brutally honest with yourself. Stick to the hard facts and ditch those 'feelings'-based assumptions. It's all about keeping it real and factual in the world of sales forecasting!

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Darren Joseph Bayley

Director of Sales & Subject Support at AQA
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Seller bias, whether it manifests as overly pessimistic or overly optimistic forecasts, is an issue that should be addressed over time during business review sessions. As a sales leader, through getting to know my team, I developed a relatively good understanding of their mindset and behaviors, which allowed me to work with them to challenge and refine their forecasts progressively. The ultimate goal being to assist them in improving their forecast accuracy over time. Taking the time out to look back and reflect upon forecast accuracy at month and quarter end was really helpful in helping the team grow as sales professionals.

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Ellie Ryan

Revenue Focused | Enterprise & Mid-Market Sales Leader In Growth Startups | Invested in People First
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Not understand the buying process is the biggest flux I see in accurate forecasting when winning the business. In many cases, the AE didn’t ask the right questions or the main contact may not know their own internal buying process. Ways to control this: - Multi thread - Test your champion - Ask detailed questions about the buying process & create a mutual action plan together - Don’t be “so” optimistic with forecasting. - Have multiple points (prospect led & company led urgency) & understand the impact on both ends if timelines aren’t met

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Ajay Gupta

Data Driven Digital Transformation | Agentic AI | Autonomous Enterprise | GenAI |
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The following are the key reasons. 1. Driven by intuition than data. 2. Trusting customer loyalty over-delivering real value. 3. Wrong interpretation of market research data. 4. No or bad safety net planing.

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Bill L. Brown

SVP of Sales & Partnerships at Devtron | 15+ Years Expert in High-Growth SaaS | Driving 8 and 9-Figure ARR | Advisory Board Member | Speaker | Coach | Digital Transformation Leader | ENTP
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Nailing sales forecasting? It's an art and a science, aiming for spot-on revenue predictability and growth. But here's the real deal - Boost your success by nailing three things: Master the '3 Whys' with stakeholders - Why is it needed? Why us? Why now? Without these answers, your forecast risks being off-target. Align with the customer's buying vision and journey. Then gain agreement on the joint timeline, and you're on track. Implement a model like MEDDIC for sharper predictability. Sort your deals into 'realistic', 'stretch', and 'pull-in' categories. That's your ticket to forecasting accuracy.

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2 Data quality

Another reason for inaccurate sales forecasting is data quality, which refers to the accuracy, completeness, consistency, and timeliness of the information used to generate sales forecasts. Data quality can be affected by various factors, such as human errors, system errors, outdated data, missing data, and duplicate data. For example, a sales rep might enter incorrect or incomplete information about a deal in the CRM system, or a sales manager might use old data that does not reflect the current market conditions. To improve data quality, sales managers should implement data governance policies and procedures, and use data validation and cleansing tools.

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Lluis Font

?? LinkedIn Top Voice in Artificial Intelligence and CRM ?? Business Development Manager ?? Consultant & Trainer ?? Interim Management??Generative AI for Companies ?? Sales ?? Marketing
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More data, best forecast. Obviously, if we are not in a big market change... ncreased Precision: A larger dataset allows for a more detailed analysis of historical trends and patterns, leading to more precise forecasts. Better Insights: More data points can provide deeper insights into customer behavior, market dynamics, and potential risks, aiding in more informed decision-making. Improved Models: Advanced forecasting models often perform better with larger datasets, enabling more accurate predictions. Enhanced Forecasting Intervals: With more data, you can often extend your forecasting horizon, providing a longer-term view of trends and opportunities.

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Jonathan Ruest, MBA

Head of Sales and Business Development - Americas at Georg Neumann GmbH
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A 2016 Study by IBM found that bad data quality contributes to $3.1 Trillion in losses annually from the US Economy alone. While data quality is of significant importance to forecasting, it is imperative to also look how you are implementing the data in your business activities. - Do you have the organizational structure to implement a good data strategy? - Do you have the right technology? - Will this data lead to benefits to your business or is it just data for the sake of data? Once you improve your data quality, learning what to do with it is a critical next step.

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3 Market changes

A third reason for inaccurate sales forecasting is market changes, which are external factors that influence the demand and supply of products or services. Market changes can include economic fluctuations, consumer trends, competitive actions, regulatory changes, and technological innovations. For example, a sales forecast might be inaccurate if a new competitor enters the market with a lower price or a better offer, or if a regulatory change affects the legal or ethical aspects of the product or service. To account for market changes, sales managers should monitor and analyze the market environment, and adjust their sales forecasts accordingly.

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Ajay Gupta

Data Driven Digital Transformation | Agentic AI | Autonomous Enterprise | GenAI |
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Change in market dynamics cannot be controlled, however, no plans for such scenarios are fatal. A good sales strategy invests in anticipating well and doing fallback planning. Policy changes due to changes in Government, and geopolitics equations are beyond control parameters as well.

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4 Sales process

A fourth reason for inaccurate sales forecasting is sales process, which is the set of steps and activities that sales teams follow to identify, qualify, nurture, and close sales opportunities. Sales process can affect sales forecasting accuracy in several ways, such as the length, complexity, and variability of the sales cycle, the alignment and coordination of sales roles and responsibilities, and the effectiveness and efficiency of sales methods and tools. For example, a sales forecast might be inaccurate if the sales cycle is longer or shorter than expected, or if the sales team is not following a consistent and standardized sales process. To optimize sales process, sales managers should define and document the sales process, train and coach sales teams on best practices, and measure and improve sales performance.

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5 Forecasting method

A fifth reason for inaccurate sales forecasting is forecasting method, which is the technique or model used to estimate future sales based on historical data, current data, and future assumptions. Forecasting method can affect sales forecasting accuracy in different ways, such as the level of detail, the degree of complexity, and the margin of error. For example, a sales forecast might be inaccurate if the forecasting method is too simple or too complex, or if it does not account for uncertainty and variability. To choose the best forecasting method, sales managers should consider the purpose, scope, and resources of the sales forecast, and compare the advantages and disadvantages of different forecasting methods.

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6 Forecasting frequency

A sixth reason for inaccurate sales forecasting is forecasting frequency, which is the interval or period at which sales forecasts are updated and reviewed. Forecasting frequency can impact sales forecasting accuracy by affecting the relevance, timeliness, and responsiveness of the sales forecast. For example, a sales forecast might be inaccurate if it is updated too infrequently or too frequently, or if it is not reviewed and revised regularly. To determine the optimal forecasting frequency, sales managers should balance the trade-off between accuracy and effort, and align the sales forecast with the business goals and cycles.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most common reasons for inaccurate sales forecasting?

1

2

3

4

5

6

7

1 Human bias

2 Data quality

3 Market changes

4 Sales process

5 Forecasting method

6 Forecasting frequency

7 Here’s what else to consider

Sales Management

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What are the most common reasons for inaccurate sales forecasting?

1

2

3

4

5

6

7

1 Human bias

2 Data quality

3 Market changes

4 Sales process

5 Forecasting method

6 Forecasting frequency

7 Here’s what else to consider

Sales Management

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