Note, this sheet was deprecated in v5 of the Standard Model base, and the functionality was built natively into the core Forecast sheet.
The Standard Financial Model is prebuilt with a flexible structure for forecasting growth that combines a growth rate, a sensitivity adjustment, and a manual adjustment, with a range of prebuilt growth rate curves available through the
Forecasting Method select dropdown. It also has a detailed revenue structure that calculates all revenues by cohorts, and allows for different average revenues by monthly cohort, with optionally different increases or decreases over time.
That said, for businesses with high-touch or irregular growth processes, or highly variable sales sizes, that structure is stressed to provide the variability that's needed to draw important insights about the business.
That's where the
Pipeline sheet comes in.
How it works
The sheet is prebuilt and linked from the input setup on
Get Started, and prelinked into the revenues section on
Forecast. The two big usecases are:
- Model an enterprise sales pipeline. The structure allows you to add in discrete expected customers or groups of customers, with inputs for liklihood to close, start date, # of a metric added per sale, contract length, and average monthly revenue
- Model a channel strategy. You can model a strategy where you sign channel partners or big enterprises, and then sell to their user bases or employees over time, and thus model the adoption and conversion within each channel explicitly.
Each row in the sheet can be used to represent a single customer, or the total customers or clients added in that relevant month.
You can use this sheet with or without using the average revenue inputs. One way to use it is to input the clients or channel partners in column B, select the appropriate metric from column D, and input the # of that metric that are added per client or partners in column F. For example, that would setup a situation where you sign a big customer that agrees to market you to their 1mm base of users. That creates a growth curve in that metric (base of users) that can then feed into the growth section - by choosing the appropriate dropdown from the
Forecasting Drivers - and used by the conversion and adoption section to show how you convert users from that user base over time. That works because the metrics from this automatically feed into the
Forecasting Drivers, thus are available for any of the other calculations in the model. In this case, you would not set an average revenue on the
Pipeline sheet, and instead use the prebuilt revenue calcs on
Forecast to calculate revenues. Details at Forecasting Drivers ›
Alternatively, you can calculate revenues directly on this sheet, and feed them into the revenues section on
Forecast. Each row also allows you to use the
Forecasting Drivers to create changes in average monthly revenue per cohort. The bookings, billings and revenues for up to two business segments are prelinked into
The inputs for
Pipeline are on this sheet. Each row has a number of inputs for:
- Client. Names for each client, or type of client, or total client added in each monthly cohort. The term "client" is an input, as well as any of the specific names.
- Metric. Select from the dropdown an operating metric you want to use to describe what each client adds. The available metrics are editable on
Hooksin the operating metrics section.
- Business Category. Select in the dropdown from the user-input business categories defined on
Forecast. These are used for any business category level reporting.
- Number per Client. This sets how many of the metric selected above are added per new client. This can be "1" if you are modeling the acquisition of a discrete customer, and revenues aren't tied to some other userbase; it could be 500, for example, for a per-seat license for an organization.
- Liklihood to close. The % input here allows you to create a probability-weighted revenue forecast. This matches a common methodology used in modeling enterprise sales funnels, to use the % liklihood that the customer will close at each stage, and adapts that here to use that liklihood in creating a revenue forecast. 100% means "definite, it's done", 0% means "no chance at all".
No common edits to this sheet other than inserting more rows to capture more clients or customers over time.