Sales forecasting empowers businesses to plan and allocate resources more effectively, better manage risk, maximize financial performance, and excel in customer satisfaction. By improving the accuracy of pipeline forecasting, organizations can fine-tune sales cycles, close more deals, and improve their finances. Advances in big data and machine learning (ML) have dramatically improved the accuracy of sales forecasting. In this article, we’ll explore why pipeline forecasting is so important, common barriers to accuracy, and how today’s organizations are using machine learning to deliver more accurate insights.
Why Is Sales Pipeline Forecasting So Valuable?
Sales pipeline forecasting helps companies predict the future performance of their sales pipeline. Accuracy in sales forecasting is crucial because it informs decisions related to optimizing sales efforts and companies depend on it to achieve financial goals.
Accurate sales forecasting results in better budgeting, helping teams to identify how much will be available to invest and where that spend is likely to generate the highest returns. In addition, by shedding light on the current state of the sales funnel, organizations can identify soft spots in the sales cycle and shore them up quickly. Sales forecasts are also essential for accurate budgeting, informing future hiring decisions, and identifying top sales performers as well as those who may require additional training or mentoring.
Pipeline Forecasting Methods
There are many sales forecasting techniques, but here are five of the most common methods and why each is valuable in the sales forecasting process.
1. Historical sales
This method involves examining historical sales data and analyzing trends over time. Identifying the impact of various external factors on past performance can help project the impact of similar events on future sales.
2. Current sales pipelines
This type of forecast focuses on present sales pipelines, incorporating factors such as the current stage of each deal, the potential value it represents, and the likelihood that it will close.
3. Lead values
Using this approach, historical sales data, such as average sales price and value per lead, is segmented by lead source. That data is used to create a forecast based on the potential value of each individual source.
4. Sales cycle length
Understanding the length of time it takes to convert a prospect helps businesses gauge how many deals they can expect to close within a specified time frame. A model based on sales cycle length can create more accurate projections as to the expected revenue for a reporting period and provide insights on how the sales cycle could be shortened.
5. Opportunity stage
When deal stages are clearly delineated, pipeline forecasting by opportunity stage is possible. Collecting data, such as number of appointments scheduled with qualified prospects, deals closed or missed, and outstanding proposals, can be used to calculate the potential value of the sales pipeline.
Build a Better Pipeline Forecast with Machine Learning
Because so much depends on the accuracy of sales forecasting, it’s crucial to include sufficient data—which is difficult for organizations that rely on traditional data analytics. Today’s machine learning models can be used to create faster, more accurate sales pipelines in three ways.
During the preparation stage, the data to be included in the ML model is identified and brought together into one place, most often a cloud data warehouse or data lake. This includes key information gathered from CRM, such as daily opportunities, who is working on each opportunity, and the actions taken to nurture the lead and close the sale. CRM data is supplemented with marketing campaign performance data and data from other sources to better understand the context of each lead. With relevant data identified and unified, model features that improve the accuracy of the model’s pipeline forecasting predictions can be created.
Once the data and model features have been identified, the ML model can be created. A significant challenge for many organizations is building models that adequately account for the volatility and the seasonality of their sales cycle. Selecting training data that’s representative of these fluctuations is essential for creating accurate predictions once the model is placed into production. ML models trained on diverse, representative data become more accurate over time as they learn the characteristics of deals that are likely to remain in forecast versus deals that are likely to be pushed or closed.
Once the model has been deployed, providing pipeline forecasts to the relevant departments, such as marketing and sales, can be done using a single dashboard. Ideally, this dashboard should enable stakeholders to visualize how the pipeline compares to previous quarters and provide additional context on sales performance. Detailed information such as insights into why particular deals have the predicted timeline can help the team understand and proactively address any underlying issues, such as a lack of marketing engagement or a failure on the part of the customer to hit key milestones.
Common Barriers to Accurate Pipeline Forecasting
While big data has created rapid advances in pipeline forecasting, many organizations are hindered by legacy systems and unable to take full advantage of the data they collect. Here are five common barriers to effective sales forecasting and how to overcome them.
1. Overreliance on CRM-based applications
While CRMs certainly serve a valuable purpose, they aren’t sufficient as an all-in-one solution for sales forecasting due to limited options for customization and the inability to incorporate additional data from third-party sources. For this reason, relying solely on these tools for sales forecasting creates an incomplete view of your sales cycle. Using a data platform that allows you to bring in data from a variety of sources, including a CRM, will enable you to work with all relevant data for better accuracy.
2. Poor quality data
Data gathered from the CRM is critical to creating high-quality sales forecasts. But much of this data is entered by sales representatives who may enter the data in different ways, each based on their own subjective experience. Absent a standardized process with consistently enforced guidelines for data entry across the sales team, low-quality data can quickly become a significant issue. For this reason, it’s important to educate team members on data best practices and enforce strategic data governance policies.
3. Siloed data
When data is spread across multiple departments and stored in disparate systems, creating a unified view of the sales pipeline is all but impossible. A cloud data platform brings all relevant data into one, single source of truth.
4. Legacy storage and compute resources
On-premises data storage and compute hardware weren’t designed to accommodate the massive amount of data we have available today. Creating accurate pipeline forecasting tools requires on-demand access to rapidly scalable storage and compute resources. Current cloud data platforms eliminate data storage constraints and resource contention, enabling organizations to realize the full potential of their data.
5. Lack of machine learning and analytics tools
Accuracy in sales forecasting requires using all data relevant for the question at hand. Modern machine learning and analytics tools can help organizations get the most value from their data. By quickly analyzing large amounts of data from various sources, these tools help sales and marketing leaders spot valuable opportunities for improvement and growth.
Enhance the Quality of Your Sales Forecasts with Snowflake
Better sales forecasts start with Snowflake. Designed to support all of your ML workflows with fast data access and elastically scalable data processing, the Snowflake Data Cloud with Snowflake Snowpark has all the capabilities data analytics and data science teams need for highly accurate pipeline forecasting. Bring all types of data into your ML models without complex pipelines and enjoy native support for structured, semi-structured (JSON, Avro, ORC, Parquet, or XML), and unstructured data. Augment model performance with shared data sets from your business ecosystem and third-party data from Snowflake Marketplace.