Using AI for sales forecasting

It's becoming far more difficult to predict sales accurately as markets become increasingly complex and volatile. But there's a new player in town that's changing the game: Artificial Intelligence.

AI is transforming business processes across the board, and sales forecasting is no exception.

Accurate sales forecasts aren't just nice to have – they're critical for making informed decisions, managing resources, and staying ahead of the competition.

But can AI really make a difference in your sales predictions? Let's dive in and find out.

What is sales forecasting? A definition:

According to HubSpot, sales forecasting is defined as:

“The prediction of what sales will be for a set time in the future. The best sales forecasts are in-depth reports that include metrics for what a salesperson, team, or company will likely sell weekly, monthly, quarterly, or annually.”

Sales forecasting is all about predicting future sales so you can make informed business decisions.

Here's a basic breakdown of what it involves:

1. Historical data analysis: Looking at your past sales figures to identify patterns or trends.
2. Market analysis: Examining industry trends, economic conditions, and competitor performance.
3. Sales pipeline analysis: Reviewing your current leads and prospects to gauge potential future sales.
4. Seasonal trends: Considering any seasonal fluctuations that might affect sales.
5. Adjustments for external factors: Factoring in any other elements that could impact sales, such as new product launches or changes in consumer behaviour.

Crucially, your forecasting methods need to be tailored around factors that are relevant to your business. This means that how you collect data and your analysis strategy are really important. If you want to make informed decisions that drive growth, then investing in robust sales forecasting processes will reward you significantly.

So we’ve established what sales forecasting is. Where does AI come into play?

Traditional sales forecasting tools vs AI tools

Sales forecasting is a data-centric process. There can be a lot of manual work involved, but usually sales teams will use tools to do a lot of the heavy lifting.

The folks at HubSpot say:

“Most sales forecasts draw from historical sales data and take demand fluctuations into account. While these can be created manually, most firms use some type of sales forecasting software to help automate the task.”

Traditional sales forecasting often relies on tools like:

• Spreadsheets: Programs like Microsoft Excel or Google Sheets help organize historical sales data, apply formulas, and perform basic calculations to predict future sales trends.
• Statistical software: These tools help identify patterns and correlations within the data, enabling more advanced analyses like regression models and time-series forecasting.
• Customer Relationship Management (CRM) systems: Consolidate customer interactions, sales activities, and pipeline information into a centralized place. This helps sales teams monitor prospects and leads, making it easier to predict future sales based on current customer behaviour and sales cycles.

While these traditional tools have been effective, they often require manual data entry and can be time-consuming to maintain. They may also struggle to handle large datasets or adapt quickly to market changes.

AI can radically transform the sales forecasting process - because it offers much more precise and efficient solutions.

Let’s take a closer look at how AI can impact sales forecasting.

Benefits of using AI for sales forecasting

Traditional forecasting often relies on historical data and human intuition. Generative AI takes it to the next level by processing more data points, considering external factors, and adapting in real time.

What’s more, natural language processing (NLP) allows AI to understand and analyse text data – think customer reviews, sales calls, or email exchanges. This means your forecasts can factor in qualitative data, not just numbers.

According to Forbes, AI enables sales teams to:

• make data-driven decisions
• plan better
• focus on the right stuff
• uncover opportunities at risk
• and get insights into sales processes

It’s important to note that generative AI isn’t a total replacement for traditional sales forecasting tools. Rather, it’s there to enhance existing tools, streamline processes, and improve accuracy.

Sales forecasting processes that can be enhanced with AI

Here are a few ways AI is especially helpful with sales forecasting:

Data analysis and pattern recognition 📊

AI thrives on data. It can process and make sense of complex, multi-dimensional datasets that would make a human analyst's head spin. Generative AI is also great at identifying trends and patterns that humans might miss, providing more accurate forecasts.

Example: A retail business uses AI to analyse its sales data. The AI identifies that sales for a particular product spike every November. Knowing this, the company can prepare for increased demand by adjusting its inventory and marketing strategies around that time of year.

Scenario analysis 💬

AI can simulate different scenarios based on various inputs (like market conditions or pricing changes) to see how they might affect sales.

Example: A car dealership uses generative AI to forecast how different pricing strategies could impact sales. The AI models various scenarios: a discount on a popular model, a promotional bundle, or a limited-time warranty offer. By simulating these options, the dealership can choose the most profitable approach.

Customer insights 🙋‍♀️

By analysing customer interactions and feedback, AI can help predict changes in customer behaviour, aiding in more precise forecasting.

Example: A subscription box service uses generative AI to parse customer feedback and engagement metrics. The AI reveals that customers are increasingly interested in eco-friendly products. The service can then tailor future boxes to include more sustainable options, potentially boosting sales.

Types of AI tools used for sales forecasting

The type of AI tool you choose to use for forecasting sales depends on two things:

• The specific sales forecasting tasks you want to use AI for
• How you want - or are able - to integrate AI into your current processes

Let’s break it down:

1. AI in existing sales tools

Many CRM platforms like Salesforce have AI capabilities, such as Salesforce Einstein. Other popular examples include Zoho’s Zia, Pipedrive AI and HubSpot’s Breeze. With tools like these, the AI is integrated into the platform and can analyse your sales data, forecast outcomes, and offer recommendations directly within the CRM. This path requires minimal setup.

2. Standalone AI platforms

Tools like ChatGPT or Claude can be used for specific tasks within sales forecasting, like:

• customer behaviour analysis
• generating predictive insights
• brainstorming solutions or actions to take off the back of analyses

You can then integrate the insights you generate into your existing processes. This method is more flexible but may require more effort to tailor to your needs.

3. Custom-coded AI

This involves developing AI models tailored to your specific needs. It requires significant expertise and resources. Best for larger companies and sales teams with unique requirements that off-the-shelf solutions can't meet.

Steps for successfully using AI for sales forecasting

1. Define your objectives
2. Start by identifying what you want to achieve with generative AI in sales forecasting. Are you looking to improve the accuracy of your forecasts, identify new sales opportunities, or optimise pricing strategies? Having clear objectives will guide your approach.
3. Choose the right AI tool
4. Pick a generative AI tool that suits your needs. If you're interested in, say, data analysis and pattern recognition, tools like Salesforce Einstein Analytics or Microsoft Azure Machine Learning can be beneficial. These platforms offer AI-driven insights and are user-friendly for those new to AI.
5. Collect and prep your information, properly
6. Just like baking a cake, the quality of your ingredients matters. Gather all relevant data – historical sales, CRM data, market trends, and even weather patterns if they affect your business. Clean it up and make sure it's consistent.
7. Teach your AI
8. Use your chosen AI tool to train a model on your data. This might involve customising the model to focus on specific metrics or outcomes important to your business. Many platforms offer pre-built models that can be fine-tuned to your specific needs.
9. Implement and integrate
10. Start using the AI model for your sales forecasting. Input current data to generate forecasts and compare them against actual outcomes to refine accuracy. Use the AI’s insights to adjust your sales strategies, and continuously feed new data into the system to improve its predictive capabilities.

Consistency is key to great AI-powered forecasting

AI-powered sales forecasting isn't a set-it-and-forget-it solution. It requires regular attention, updates, and refinement to deliver the best results.

Quality data leads to better predictions. Invest time in implementing a robust data collection process and a solid AI strategy. This will result in more accurate forecasts and will build credibility with stakeholders, too.

AI-powered forecasting takes time to reach its full potential. You can't expect miracles overnight. Be prepared to invest in your system and stick with it for the long haul!

Final thoughts

When you invest in AI for sales forecasting, your predictions become more reliable and your decisions more informed. Follow our 5 steps for success and implement them consistently to start seeing the benefits of AI-powered forecasting.

In summary, choose the right AI model for your needs, feed it clean and comprehensive data, and combine its insights with human expertise. Do this consistently, and I promise you'll see a marked improvement in your sales forecasting accuracy.

Remember, the future of sales forecasting is here – and it's powered by AI.