AI tools improve sales forecasting by analyzing data, spotting trends and using predictive analytics to increase accuracy. The tools help SMBs make more informed decisions about improving the sales process and increasing revenue.
By the end of this article, you’ll know how to use AI for sales forecasting. You’ll also learn how to integrate AI into your forecasting process and choose the right AI software for your SMB.
What is AI sales forecasting software?
Artificial intelligence (AI) sales forecasting software uses machine learning algorithms and data analysis to predict future sales performance. Sales leaders can use this information to identify high-quality leads, estimate deal closure and optimize sales strategies.
Here are some examples of different AI tools that support sales forecasting:
Dear Lucy uses predictive analytics to monitor KPIs, improve pipeline management and enhance sales performance. These insights reveal trends, predict outcomes and pinpoint opportunities or risks.
Decide AI measures your data quality. It checks customer data for completeness, accuracy and validity to improve sales forecasts.
MeetRecord AI records and transcribes conversations with leads and prospects. It monitors engagement and talk-listen ratios to help determine the likelihood of conversion.
Explore more AI tools in Pipedrive’s marketplace.
How SMBs can use AI for sales forecasting
AI can fit seamlessly into your forecasting methods to help your team gather and maximize key data. AI predictive models analyze data and provide real-time predictions. Predictions adjust as new data comes in, allowing for quick strategy changes.
AI analysis can also integrate market changes and trends, freeing SMBs from the time-consuming task of manually tracking external factors.
Here are some ways businesses can use AI forecasting to support and improve sales operations.
Analyze large data sets to identify key sales trends
AI algorithms analyze large data sets that sales managers often don’t have the time or technical expertise to explore.
AI tools can scrutinize customer purchase history, seasonal trends and regional sales variations. These insights are crucial for accurate forecasts but can be challenging to research and evaluate manually.
Example: A small e-commerce business uses AI to analyze seasonal buying trends over multiple years. The AI model identifies spikes in demand throughout November and December, so the business stocks up on inventory ahead of time to meet demand and boost customer satisfaction.
Predict sales based on live data to make real-time decisions
AI analyzes live sales data (like website traffic and customer behavior) in real time. This means that sales forecasts are always up-to-date with the latest information so sales reps can quickly make informed, strategic decisions.
For instance, Pipedrive’s AI Sales Assistant analyzes your deals, contacts and emails. It finds the most valuable insights in your sales data. It then suggests ways to boost sales, such as identifying high-potential leads or the best times to follow up.
Example: A software company uses AI to track website visitors. When AI detects more traffic to a product page and more free trial sign-ups, it predicts higher conversions. The sales team then focuses on these leads, boosting their chances of closing deals.
Examine market conditions to find new sales opportunities
AI performs customer sentiment analysis and considers external factors like economic conditions and competitor activity. This information helps SMBs adjust predictions and strategies based on market shifts and competition.
Example: A clothing store uses AI to track social media trends and online searches. When AI spots rising interest in sustainability, the store increases its stock of eco-friendly clothing to stay ahead of the competition.
Optimize sales strategies to close more deals
AI examines patterns in sales cycles, customer objections and successful tactics to recommend adjustments. These suggestions help you optimize your sales strategy to get better results
Example: AI analyzes past sales data and finds that customers are more likely to buy after a personalized demo. It suggests prioritizing demos for similar leads, increasing conversions by 20%.
Identify high-quality leads to prioritize sales efforts
AI reviews customer interactions and buying behavior to find leads most likely to convert. As a result, sales teams can focus their efforts on the most promising prospects.
Example: AI identifies leads who have interacted with product demo emails and visited the pricing page multiple times. The software determines these leads are 30% more likely to convert. Sales teams can then prioritize these leads, increasing their chances of closing deals.
Improve forecasting accuracy to make more informed decisions
AI reduces human error and bases sales predictions on data-driven insights. This precision helps SMBs create more accurate sales forecasts, improving budgeting, staffing and resource allocation.
Example: A small SaaS company frequently overestimates revenue with manual forecasting. They get more accurate projections after using an AI tool, helping set realistic goals and allocate resources better.
5 steps to integrate AI for sales forecasting into your business
Integrating AI tools for sales forecasting provides more accurate projections. Adding AI to your daily sales workflows gives it access to the right data to generate tailored predictions for your business.
Follow these steps to seamlessly incorporate sales forecasting AI tools into your organization’s tech stack.
1. Assess data readiness
Data readiness involves verifying that your data is high-quality, accurate and accessible for analysis. It ensures AI tools have reliable data, leading to more accurate predictions and data-driven decision-making.
Here are some tips for assessing your data readiness:
Evaluate data quality | Check for completeness and consistency across data sources, such as customer data in CRMs and website engagement metrics from Google Analytics. Rectify errors to confirm data is accurate and up-to-date (use tools like Decide AI to help). |
Ensure data accessibility | Make sure data is easy to access and properly formatted for AI tools. For example, store data in a centralized database and make sure the database integrates with your AI tools. |
Establish data governance | Ensure there are clear policies for data management, including security and privacy protocols. Create policies defining data ownership, update access controls and make sure data storage complies with privacy regulations. |
Completing these steps will guarantee your sales data is clean, organized and consistent. AI models can then use this data to create accurate and reliable forecasts.
2. Set clear business goals
Setting clear goals helps you ensure that new AI tech aligns with your business objectives. It helps you focus on specific outcomes to optimize resources and drive tangible results.
Identify the specific objectives you want to achieve with your forecasting process, such as improving accuracy, increasing sales revenue or optimizing inventory.
Use the SMART goals framework to define specific and measurable goals.
Here’s an example of a SMART goal:
“Increase sales forecast accuracy by 10% within the next quarter by integrating AI tools to analyze historical data and current market trends.”
3. Train your sales team
Train your sales team to use AI so they know how to implement the software effectively. Understanding the tools will encourage adoption and remove guesswork, helping sales reps make confident, data-driven decisions.
Consider inventory management tools as an example. An AI-powered sales platform lets teams track inventory and customer demand in real time. With the right training, reps can keep shelves stocked with the right products to:
Fulfill customer orders by preventing stockouts and ensuring customers get what they need without delays
Enhance the customer experience by maintaining reliable product availability to avoid lost sales and frustration
Maximize profit margin by reducing excess inventory costs by focusing on high-demand items
An inability to use the tools effectively can lead to missed sales, frustrated customers and higher costs.
Here are some tips on how to deliver helpful training:
Offer hands-on experience | Conduct live demos and interactive sessions to help reps navigate the AI tool |
Encourage real-world practice | Let reps test the tool with real data to build confidence |
Provide ongoing resources | Share guides, tutorials and FAQs to reinforce learning and answer common questions |
Use existing tools and resources from the AI platform to support your training efforts. Look for interactive tutorials, community hubs and live support options. For example, Pipedrive’s AI Knowledge Base provides guidance and tutorials on all included AI features.
4. Integrate with existing systems
Effective integration ensures smooth data flow across your sales platforms. This provides accurate, up-to-date information for forecasting.
For example, integrating AI forecasting tools with your CRM means that the AI software can analyze:
Customer demographics, interactions and engagement history
Deal stages and pipeline progression
Sales pipeline trends and historical performance
Lead scoring and conversion probabilities
Inventory levels and demand forecasts
With these insights, the AI can generate forecasts that account for customer trends and behavior. Sales reps can make better decisions about lead prioritization and sales strategies to close more deals.
Here are some tips for integrating AI forecasting tools into your existing tech stack:
Select compatible tools | Choose AI forecasting tools that integrate with your existing CRM, email marketing and other sales tools |
Collaborate with your IT team | Work with your IT team to ensure AI tools can pull and analyze data from your current systems |
Test the integration | Run pilot tests to ensure smooth data flow between systems |
Once you’ve integrated your systems, train all users, monitor performance and adjust as needed for smooth adoption.
5. Monitor and refine your AI tools
Monitor the software to ensure it runs efficiently and provides accurate forecasts. If you identify discrepancies or inaccuracies, make adjustments.
For example:
If AI overestimates sales in a specific region, adjust the model to factor in local economic data or historical trends
If conversion rates are low, you might adjust the model to improve lead scoring and increase the chances of closing more deals.
Here are some ways to track AI performance:
Monitor forecast accuracy | Compare AI predictions to actual sales outcomes to ensure consistency and reliability |
Track key performance metrics | Watch metrics like conversion rates, sales revenue and average deal size to gauge AI tool success |
Gather feedback from employees | Speak to sales reps and other team members to assess usability and identify areas for improvement |
How to choose the right AI sales forecasting tools for your SMB
The right AI tools will have the features and integrations you need to create accurate forecasts. It will support your business needs and be accessible to all sales reps.
Here are three key steps for selecting the right sales forecasting AI systems for your business.
1. Define your business needs
Clarify how you want AI to support your sales forecasting efforts. Knowing what you want helps you choose tools that align with your needs and sales processes.
For example, if customer churn rates play an important role in your forecasting, you need a system that tracks user engagement, subscription patterns and renewal data.
If you choose an AI system without these features, your forecasts will lack crucial business insights.
Here’s how to define your business needs before choosing AI forecasting software:
Identify goals. Pinpoint your key sales objectives, such as increasing revenue or improving conversion rates.
Assess challenges. Identify obstacles that prevent achieving your goals, such as difficulty tracking specific metrics.
Determine essential features. Pinpoint features that help achieve your goals, like analyzing customer preferences to increase conversions.
Evaluate AI features by researching which tools meet your needs. Request demos or trials to test their functionality and ensure they align with your business goals.
2. Consider user-friendliness and accessibility
Choose an easy-to-use system so your team can implement it without needing technical expertise. A simple platform interface helps you use the software to its potential to ensure accuracy and efficiency.
Consider a user-friendly dashboard that imports historical sales data and generates forecasts with one click. This simple functionality allows salespeople to interpret trends, reduce manual data entry errors and streamline decision-making without delays.
Get organized with your free sales pipeline excel template
Here are some tips on finding user-friendly systems:
Look for no-code or low-code solutions. No-code/low-code software allows users to customize platforms without programming skills. For example, you can use drag-and-drop functionality to create a custom forecasting dashboard.
Check for automations and integrations. Choose tools that connect with your CRM, ERP and accounting software to reduce manual data entry.
Evaluate the user interface and experience. Look for easy navigation and clear visualizations that simplify sales data.
Pipedrive offers a good example of a simple interface. The sales CRM is easy to navigate and has custom dashboards that allow you to display key information at a glance.
Pipedrive’s AI tools also align your forecasting efforts with key CRM data. For example, Pipedrive’s AI Sales Assistant identifies patterns, recommends high-potential deals and suggests next actions to prioritize.
The software also uses OpenAI to generate deal status summaries. These summaries provide an overview of deal interactions, giving you and your team the insights to take prompt action.
3. Confirm your budget (but consider ROI)
Clarify your budget when choosing AI software to ensure you don’t overspend. Set a clear spending limit for the software, whether monthly or annually, and select tools within that range.
When creating your budget, consider the long-term return on investment (ROI) of using the software. Time savings, better sales accuracy and informed decision-making are a few examples.
This approach helps you evaluate the tool’s potential for better business outcomes over time.
For example, AI forecasting software predicts customer churn so you can spot at-risk accounts early. Early detection lets you improve customer retention by offering personalized discounts or proactive support.
As a result, you increase renewals and improve your ROI.
Final thoughts
AI forecasting helps you make better decisions by analyzing data, spotting trends and improving accuracy. Finding user-friendly AI tools and implementing them effectively streamlines forecasting, supports data-driven decisions and drives long-term success.
To learn more about how Pipedrive’s simple CRM can support your AI forecasting efforts, sign up for a 14-day free trial. Use live customer data to inform your projections and make real-time decisions to improve your bottom line.
