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You’re struggling to improve your sales forecasting. How can AI help you get there?

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Sales forecasting is a crucial skill for sales engineers, but it can also be a frustrating and time-consuming process. You need to analyze historical data, account for seasonal trends, adjust for pipeline changes, and factor in external variables. How can you improve your accuracy and efficiency without spending hours on spreadsheets and reports? The answer is AI.

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Sanjeev Somani

CEO @ Tribyl. Improve revenue conversion by eliminating guesswork and opinions.
Yishay Saban

Presales Solution Manager at CommBox
Ron Ezekiel

Visionary Leader | Customer Success, Technical success engineering ,Pre-sales & Technical Sales Expert | Driving…

1 What is AI and how can it help sales forecasting?

AI, or artificial intelligence, is the ability of machines to perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI can help sales forecasting by automating data collection and analysis, identifying patterns and trends, and providing insights and recommendations. AI can also help you reduce human errors, biases, and assumptions that can affect your forecasts.

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2 How to use AI for sales forecasting?

Using AI for sales forecasting can be done in several ways, depending on your goals, resources, and preferences. Pre-built AI tools or platforms that integrate with your CRM or sales software can generate forecasts based on your data and provide features such as scenario planning, what-if analysis, and forecast accuracy tracking. Alternatively, you can build your own AI model or algorithm using a programming language or framework such as Python, R, or TensorFlow for more flexibility and control over your data and logic. If you want to save time and effort, you can hire a third-party AI service or consultant to customize and optimize an AI solution for your specific needs and challenges.

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3 What are the benefits of using AI for sales forecasting?

Utilizing AI for sales forecasting can offer several advantages, such as improved accuracy, increased efficiency, enhanced visibility, and better decision making. AI can learn from your data and adjust to changing conditions, reducing the risk of overestimating or underestimating your sales potential. Additionally, AI can automate and streamline the forecasting process, saving time and resources. Furthermore, AI can provide more granular and actionable insights into your sales performance. Lastly, AI can help you make more informed and confident decisions based on data-driven forecasts, rather than relying on intuition or guesswork.

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Yishay Saban

Presales Solution Manager at CommBox
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If you want to enhance your Efficiency, go with AI. Why? 1. AI can automate time consuming tasks such as the data cleaning and preparation, analysis and more. That would save time so you can focus on strategic activities. 2. You can leverage the power of AI for market analysis and get great insights in no time. 3. Also, AI can generate multiple "what-if" scenarios based on different assumptions, helping you assess the potential impact of different sales strategies and market changes. 4. Churn prediction: With AI you can try to predict customer churn, helping you identify customers at risk of leaving and take proactive measures to retain them.

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Ron Ezekiel

Visionary Leader | Customer Success, Technical success engineering ,Pre-sales & Technical Sales Expert | Driving Exceptional Experiences | Strategic Innovator | Global Team Advocate
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Improved Accuracy/Data-driven Insights/Real-time/Enhance Sales Performance/helps in better Resource Allocation/Reduced Guesstimations /Save $$$/Adaptability to Change /Integration with Other Systems(Must be provided oob)

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Anjana Kaku Tyagi

Associate Director - Program Management - Data & AI Services
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There certainly are benefits of using AI for sales forecasting, BUT, the most important aspect of using AI for anything would be, to have trustworthy sales data that leads to trustworthy forecasts. Your DATA is key to confident data-driven decisions. So your asset is the data, not the sales forecast, as forecasts would only be as good as the data fed into the AI systems used for forecasting.

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4 What are the challenges of using AI for sales forecasting?

Using AI for sales forecasting presents a few challenges that must be addressed. Firstly, data quality must be ensured to produce reliable forecasts. This means the data must be accurate, complete, and consistent across sources and systems. Additionally, data privacy must be respected and relevant laws and regulations complied with, such as GDPR or CCPA. Lastly, data interpretation is important as AI can generate forecasts but cannot explain the logic behind them. Therefore, results of models and algorithms need to be interpreted and validated, as well as the limitations and assumptions they entail.

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Anjana Kaku Tyagi

Associate Director - Program Management - Data & AI Services
(已编辑)
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AI systems are black-boxes, they seldom explain the logic of arriving at the outcome and could be full of biases. In order to rate an AI system dependable, one needs to be able to see the results of tests run on the AI systems that show the confidence score summing up the ethics, biases etc. of the AI systems itself, and the logic that has been employed to arrive at the outcomes. Black boxes aren't going to be trustworthy anymore, since masses have come to realize that these systems have a potential to make as well as break them down.

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5 How to get started with AI for sales forecasting?

If you want to get started with AI for sales forecasting, there are a few steps you need to take. First, define your forecasting objectives and metrics. Consider what you're trying to achieve and measure, how often and far ahead you need to forecast, and the key factors that affect your sales outcomes. Second, assess your data availability and readiness. Examine what data you have, where it's stored, how clean and consistent it is, and the amount of data needed to train and test your AI models and algorithms. Third, choose the right AI method and tool for your needs. Decide if you want a pre-built AI tool or platform, build your own model or algorithm, or hire a third-party AI service or consultant. Lastly, implement and monitor your AI solution. Think about how you will integrate it with existing systems and workflows, evaluate and improve it over time, as well as communicate and report forecasts and insights to stakeholders.

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6 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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Sanjeev Somani

CEO @ Tribyl. Improve revenue conversion by eliminating guesswork and opinions.
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If you invited your prospect to the deal review, what would they say about the likelihood to close? - Did the rep uncover burning pain? - Did the value prop align to the decision criteria? - Is the differentiation relevant and compelling? - Are the right stakeholders involved and aligned? Using AI on sales activity data is barely sufficient. The future of AI-based sales forecasting lies in ensuring continuous alignment between the buying-selling motion. From prospecting until close. This requires knowing what IS working, why, and rinsing-repeating that in each interaction. Put another way, if your A.I. is that good, it should help INCREASE win rates, not just predict them based on historical data. ??

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Anjana Kaku Tyagi

Associate Director - Program Management - Data & AI Services
(已编辑)
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Certain questions that are looming the minds of data scientists irrespective of the industry they're in are - 1. How do you trust the data fed into the AI systems? Is the data confirmed to be trustworthy? What systems can we employ to ensure accuracy & completeness of data? 2. How do you choose the right AI method / tool for your needs?

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You’re struggling to improve your sales forecasting. How can AI help you get there?

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2

3

4

5

6

1 What is AI and how can it help sales forecasting?

2 How to use AI for sales forecasting?

3 What are the benefits of using AI for sales forecasting?

4 What are the challenges of using AI for sales forecasting?

5 How to get started with AI for sales forecasting?

6 Here’s what else to consider

Sales Engineering

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You’re struggling to improve your sales forecasting. How can AI help you get there?

1

2

3

4

5

6

1 What is AI and how can it help sales forecasting?

2 How to use AI for sales forecasting?

3 What are the benefits of using AI for sales forecasting?

4 What are the challenges of using AI for sales forecasting?

5 How to get started with AI for sales forecasting?

6 Here’s what else to consider

Sales Engineering

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