When you hear the phrase “AI for sales forecasting” it feels like the solution is an algorithm to predict “the sales number”. This is only partially correct. While the ultimate goal is an accurate sales forecast, AI for sales forecasting requires artificial intelligence to be infused throughout the entire sales process.
Dependencies of an Accurate Sales Forecast
The foundation of an accurate sales forecast is a systematically built and realistic sales pipeline. To achieve this requires:
- A good understanding of the leads that need to be generated
- The ratio of leads to opportunities, opportunities to sale
- A tightly defined sales process for each of the leads and opportunities
- Understanding the attributes/segmentation of leads and opportunities that drive different sales cycles
The support of a realistic sales pipeline requires two things:
- Opportunity Details Readily Available - all correspondence, meeting dates/ meeting results, opportunity status update, lead funnel activities, etc.
- Good Sales Pipeline Visibility - an intuitive interface that allows you to visualize the pipeline along different categories and perspectives and provides the ability to drill down to the lowest level email and calendar item to understand qualitatively the nature of the opportunity.
Creating an Accurate Sales Forecast
Creating an accurate sales forecast requires working from the bottom up.
- Collecting all the detailed information about a lead or opportunity resulting from interactions
- Using the information to guide the next steps in the sales process
- Accurately reflect the next steps and opportunity status in the sales pipeline.
- Monetizing the sales pipeline, factoring in time drivers from two perspectives: 1)Typical time to close based on sales stage, opportunity attributes and factoring in patterns based on past opportunities that have closed; 2)Typical sales curve over time in the current quarter
AI for Sales Forecasting
To create an accurate forecast, AI needs to be infused across all the processes that support sales including:
- In the Marketing Funnel
- To enhance data collecting and interpreting detail information from the sales cycle, including emails, meetings, and website interactions
- In the sales pipeline to provide insights and recommendations into the buyer/seller journey to guide winning strategies
- To provide insights to support the sales manager/sales rep coaching process
- Numerically calculating the sales forecast
Lastly, AI for sales would fail if all it did was produce “A” sales number. There is more to be learned and more value created by having AI identifying the key drivers of sales and allowing the user to “war game” different outcomes by varying the basis and variables driving the AI forecast. AI for sales would also fail if it wasn’t imbedded into what a sales rep already does as a by-product of their current process. This also makes for a quick and effortless implementation.
Where Do We Go From Here
In the following weeks we will continue this blog theme “AI for Sales” by analyzing each of AI categorizes that support an accurate sales forecast as well as providing examples of how AI can identify key drivers and allow the user to “war game” results.
For a sneak preview of future blog content applied check out the following links:
- Easier pipeline updates for sales reps
- Comprehensive visibility in the pipeline
- Predictability in the sales forecast
* There are many types of sales situations and many different ways to apply AI depending on the situation. This blog post is focusing on AI for sales in a Business to Business.