One of the most common challenges facing companies is the desire to make better decisions regarding inventory management, production investments, or growth plans based on sales forecasts. While one can look at past sales performance, that information often provides too little assurance of an accurate and realistic forecast of future sales.
In the recent releases of Microsoft Dynamics 365 Sales the possibilities for sales forecasting continues to expand. In this post, we'll explore how sales teams can improve their forecasts.
Why sales forecasting?
Sales forecasting helps an organization to:
- Identify potential risks in the sales pipeline.
- Evaluate sales performance against set goals and adjust the focus as needed.
- To anticipate sales and the resulting allocation of personnel and resources.
- To make projections that influence strategy and investments.
Insights from sales forecasting enable quick strategic decision-making. CEOs want to forecast demand for each product in order to implement strategic business transformations, COOs want to allocate people and resources efficiently, and CFOs need insight into future cash flow to develop financial plans.
The importance of sales forecasting should not be underestimated, and organizations very often struggle to develop accurate sales forecasts that truly support strategic decisions.
Dynamics 365 Sales Forecasting
With Dynamics 365 Sales Forecasting Microsoft introduced a new set of capabilities that enable organizations to create and manage native bottom-up sales forecasting processes.
To fully understand this, some explanation of the interface is needed. The first thing you’ll notice in the new interface is that the sales figures are organized hierarchically. In the video, you’ll see a hierarchical breakdown by sales manager. The system automatically displays a total per team, followed by the totals for each individual salesperson, which are then aggregated up to the next higher hierarchical level.
There are standard 3 different hierarchies are possible:
- according to the structure of the sales organization (via the Manager field),
- according to sales territory structure,
- according to the classification of products
The next thing that stands out is that salespeople can categorize their opportunities into a “forecast-category” and that subtotals are calculated automatically for each category. These forecast categories are, of course, configurable. To help you understand this overview, we’ll explain the default “forecast categories” add:
- Quota: This is the target amount assigned to a specific salesperson, region, or product category. The progress bar is calculated based on this target amount.
- Committed: These are the open opportunities where one can great confidence in that they will be implemented.
- Best-case scenario: Confidence in these current opportunities is rather low.
- Pipeline: It is uncertain whether these opportunities will materialize (low confidence).
- Omitted: Opportunities in this category are not included.
- Won: This shows the total number of opportunities won.
- Lost: This shows the total number of missed opportunities.
The roll-up is typically calculated based on. Estimated Revenue of the opportunities presented by the Estimated Closing Date are contained in the filter. More complex calculations based on related information under the Opportunity are also possible—for example, to calculate totals by product category based on Opportunity Products associated with an Opportunity.
Even More Accurate Predictions Thanks to Data and Artificial Intelligence
Premium Forecasting helps salespeople and managers improve the accuracy of their forecasts. To achieve this, premium forecasting uses AI-driven models that analyze historical data and the current sales pipeline to predict future revenue results.
Data from “snapshots” or instantaneous records
AI models need data to learn. The data required to make better forecasts consists of snapshots of the sales pipeline. A snapshot freezes the forecast data at a specific point in time. The frozen data includes aggregated column values, manual adjustments, and underlying record fields that directly impact the forecast. You can use these snapshots to see how the forecast and the underlying data have changed over time. Snapshots are created automatically every day. Thanks to these snapshots, it becomes possible to track how sales opportunities evolve from one moment to the next.
The “Trend Chart”shows how the sales pipeline has evolved over time. Per forecast category becomes a trend line shown in this chart.
The “Flowchart” provides a visual representation of how the forecast changes between two points in time (i.e., the flows between the various forecast categories). Managers can use flowcharts to zoom in (i.e., drill down) on the specific deals that contributed to the increase or decrease in forecasts, allowing them to monitor their teams and coach them on improving their forecast accuracy.
