If you're in sales, you know forecasting can be a huge pain. And in an increasingly tech-driven environment, best guesses and gut feelings aren’t cutting the mustard.
It’s time to embrace AI sales forecasting.
In this article, we'll explore how to leverage machine learning to predict sales more accurately. We’re talking anything and everything from how to collect and prep data, choose models, integrate forecasts into your CRM, and continuously improve predictions.
We’ll cover:
- What is AI sales forecasting?
- What are the benefits?
- Ways to get it done
Let’s get started.
What is AI sales forecasting?
You’re always trying to predict how much you’re going to sell, be it next week, next quarter, or next year. You could make an educated guess, consult the Magic 8 Ball, or try using AI.
AI sales forecasting uses machine learning models to analyze historical sales data and other relevant factors to predict future sales.
So, instead of relying on musical forces (and your friendly neighborhood Tarot reader), you’re relying on a different kind of magic — algorithms!
The benefits of AI sales forecasting
This tech is great for taking the guesswork out of sales forecasting. Relying solely on gut instinct or rudimentary spreadsheet models is a no-go.
AI offers a huge bunch of advantages for predicting future sales:
- Get more accurate predictions: AI algorithms analyze vast datasets to identify patterns and trends, leading to more precise sales forecasts. This helps businesses make informed decisions based on data, not just intuition.
- Give your team to tackle high-level tasks: Manual forecasting can be a real drag. AI automates this process, giving your team more time to focus on strategic initiatives and high-value activities like closing deals.
- Get holistic insights: AI combines insights from various sources — like your CRM, marketing data, and even economic indicators — to give you a comprehensive view of what's driving your sales. This holistic perspective helps you understand the full picture and make better-informed decisions.
- Explore different scenarios: AI-powered tools let you easily model different "What if" scenarios, so you can see how changes like pricing or marketing campaigns could affect your bottom line. This allows for more strategic planning and decision-making.
Here’s how to use AI for sales forecasting
Data collection and preprocessing
You can't train AI sales forecasting models without clean, relevant data. And we’re not just talking about last quarter's spreadsheets, oh no.
Before you take advantage of AI, you need to feed it the right information:
- Data sources galore: Don't just rely on your sales figures. Customer data, marketing stats, economic trends — the more diverse your data sources, the better your forecasts will be. This comprehensive approach gives your AI model a richer dataset with which to work.
- Clean and prep your data: Ensure your data is accurate and ready for analysis by removing duplicates, inconsistencies, and errors. Standardize formats and prioritize data quality so your AI model can work its magic.
- Extract relevant features: Identify and extract the key variables that influence sales performance, such as customer demographics, product features, and marketing campaigns. This helps your AI model focus on the most important factors.
Choose an AI model for forecasting
We’re not talking about the weather — AI can crunch massive datasets to generate accurate sales forecasts.
Let's dive into using AI for forecasting:
- Get your data flowing: Set up a system to seamlessly collect, clean, and feed data from your various sources into your chosen AI model. This keeps things running smoothly and ensures your model gets the most up-to-date info.
- Train and test different models: Not all AI models are the same. Experiment with a few different types, like regression or neural networks, to see which one works best with your historical data.
- Make your model work for you: Once you've found the winning model, integrate it with your business intelligence tools and dashboards. This way, you can easily access those valuable forecasts and use them to make smarter decisions about things like inventory, staffing, and promotions.
Analyze forecast results
Wondering how accurate your AI sales forecasts really are? Time to put them under the microscope!
Here’s how to do it:
- Check the accuracy: Use metrics like mean absolute error (MAE) and root mean squared error (RMSE) to see how close your forecasts are to your actual sales numbers. Remember, lower error means better accuracy.
- Compare forecasts to reality: Look at your forecasts over time and compare them to your actual sales. This helps you spot patterns, identify any weird outliers, and see how well your model is performing.
- Figure out what's driving your sales: Use AI to determine which factors, like pricing, marketing campaigns, or economic conditions, have the biggest impact on your sales. This will help you fine-tune your strategies and make better decisions in the future.
Integrate AI forecasting with CRM systems
Give your sales team a helping hand by connecting your AI forecasts directly to your CRM system.
This combo can help you in several ways:
- Bye-bye manual updates: Your forecasts will automatically sync with your CRM whenever the AI model is refreshed, saving you time and hassle. (Sorry about the 1% of you who actually find data entry meditative and soothing)
- Focus on the big wins: AI insights can help you zero in on the most promising deals and opportunities, so your team can prioritize their efforts.
- Coach your team like you mean it: Use AI-generated data to identify areas where your sales reps might need a little extra guidance, then offer targeted coaching to help them get even better.
Continuously update and retrain models
Remember, AI models are like plants — they need to be nurtured and fed to thrive. Keep your models in tip-top shape by regularly updating and retraining them.
Here’s what you need to know:
- Keep up with the times: Markets are constantly changing, so your models need to adapt. Retrain them regularly with fresh data to keep those forecasts accurate.
- Commit to continuous learning: Don't just set your AI models and forget about them. Treat them like living, breathing tools that need regular tweaking and fine-tuning.
- Get your team in the loop: Ask your sales team for feedback on the accuracy of the forecasts. Their real-world experience can help you tag areas where the model needs improvement.
More data sources mean more accurate predictions
To ensure your AI sales forecasting models are as accurate as possible, it's absolutely mission-critical to feed them a diverse range of data sources. Don't just rely on a single source.
Here are some ways to ensure a bit more data variety:
- Internal data is a must: Your CRM, ERP systems, and past sales records hold valuable information about your customers and sales patterns.
- External data adds an extra layer: Factor in market trends, economic indicators, and competitor activity to get a broader perspective.
- The more data, the merrier: Don't be afraid to throw in some social media buzz, weather patterns, or even industry-specific data. If you have a well-groomed, diverse data set, your AI model can learn and adapt better.
Validate models with historical data
Think of this as a time-traveling test drive for your AI model. By looking back, you can see how it would have performed in the past and make sure it's ready to predict what’s ahead.
- Give it a test run: Apply your AI model to historical sales data to see how its predictions stack up against what actually happened. If it starts getting things right, then you know it’ll be likely to get it right for future trends.
- Tally up the errors: Use fancy metrics like Mean Absolute Percentage Error (MAPE) or Root Mean Squared Error (RMSE) to quantify how accurate your model is.
- Learn and improve: Use this analysis to fine-tune your model and make it even better at predicting future sales. We’ve heard “practice makes perfect” a million times, but we mean it here!
Tailor models to different sales cycles
Not all sales cycles are the same. Some are quick and snappy, while others take time to simmer. Your AI sales forecasting model needs to be flexible enough to handle the unique rhythms of your business.
Here’s how to do it:
- Customize your model: Whether you sell to consumers or businesses, have a short sales cycle, or a long one, make sure your AI model's inputs and parameters are customized to fit.
- Consider the details: Think about things like the length of your sales cycle, the number of stages in your pipeline, your typical conversion rates, and any seasonal fluctuations.
- Keep it fresh: Your sales process will evolve, so make sure your AI model can evolve with it. Regularly review its performance and adjust it as needed to keep those forecasts on point.
Use AI insights for informed decision making
Don't leave your business decisions up to chance. AI sales forecasts give you a treasure trove of insights that can guide your strategic choices.
Here’s how AI helps you do some practical futurology:
- Get a glimpse into the future: Use AI forecasts to anticipate upcoming trends, allowing you to proactively plan for demand spikes or dips, ensuring you have the right resources at the right time. This allows you to be proactive rather than reactive.
- Make data-backed decisions: Instead of relying on gut feelings, let AI analyze complex data and provide actionable insights. Confidently prioritize products, allocate resources, and plan marketing campaigns based on solid numbers. You’ll know your decisions are supported by good, solid data.
- Stay adaptable in a changing market: The business world is constantly evolving, and AI can help you stay nimble. Keep your forecasts up-to-date and adjust your strategies quickly to stay ahead of the competition. Being flexible and adaptable beats rigidity every time.
Summing up
Now you’ve got the complete download on how AI sales forecasting works and how to make it happen.
Just remember, data is everything.
Garbage in, garbage out, so remember to feed your models quality info, keep improving them, and leverage the insights.
And something you can’t ignore — find the right tool for the job; otherwise, all the data in the world will amount to just a few diamonds in the rough!
Next steps
Tired of manually wrangling data and struggling to predict future sales accurately?
With Lindy, the premier tool for AI sales forecasting, you can ease up a bit and start using its cutting-edge features to get ahead of the competition.
With Lindy, you can:
- Get and preprocess data like you mean business Lindy automatically collects and cleans data from various sources, helping you make high-quality predictions.
- Thousands of ready-made integrations: Say goodbye to manual data entry and tedious spreadsheets. Lindy seamlessly integrates with your CRM, ERP systems, and other data sources, automatically collecting and cleansing data to ensure accuracy and consistency.
- Sophisticated AI models for accurate predictions: Lindy leverages advanced machine learning algorithms and natural language processing (NLP) to analyze your data and generate precise sales forecasts. It eliminates the need for guessing games and hunches, you know where decisions come from.
- Reporting as things happen: Integrate Lindy with your existing business intelligence tools to get up-to-the-minute forecasts and in-depth reports. Easily visualize trends, identify potential bottlenecks, and track your sales performance with a click of a button.
- Customization for your unique sales cycles: Lindy adapts to your specific business needs, allowing you to customize forecasting models based on your sales cycle length, pipeline stages, conversion rates, and other relevant factors.
- Feed it, and it will learn: Lindy constantly learns from your data and feedback, refining its models to ensure your forecasts are always accurate and up to date.
- As many templates as you need: The Lindy marketplace has a huge variety of ready-made templates. Can’t find what you need? It takes 30 seconds to make a new template from scratch, and you need absolutely zero coding knowledge to do it.
- The Lindy team: Since Lindy agents (“Lindies”) can tackle so many different tasks, it’d be great if you could pool them together so they could communicate, add up their brain power, and solve problems as a team. The good news is that they really can.
