AMA: LinkedIn Director Sales Strategy and Operations, North America, LTS, Zeina Marcotte on Revenue Forecasting

In order to forecast at the product level, you’ll need historical bookings broken down by product and/or open pipeline data by product on opportunities in your CRM. Below are some methods I’ve used for new product launches. Usually, I end up triangulating between a few of these options: Weighted Pipeline + Closed Won: this takes the bottoms up forecast from the field, multiplied by the probability and adds in the closed won bookings to date. This is as reliable as your sales team’s CRM Hygiene a ...Read More

The AI landscape is evolving quickly. My team has seen real wins in three areas: automating data hygiene, surfacing insights from unstructured data, and reducing the time spent on manual tasks. Before adopting any AI tool, you need solid data foundations. AI is only as good as the data you feed it, so ensure you have strong data hygiene and governance and the proper data access and permissions. I started by identifying the most time-intensive tasks and experimented there first. The use cases we' ...Read More

Depending on roles and levels each stakeholder may have a slightly different need but at the end of the day the main message remains the same: Where do we expect to land? Is there any risk? And do we need to change anything? First and Second Line Sales Leaders will need much more detail on the forecast. They will expect to discuss specific deals to see if any extra support is needed. They may want to dig into pipeline, pacing and other metrics to help them understand if they may have a gap to go ...Read More

Short-term and long-term forecasting serve different purposes and require different rhythms. Short-Term Forecasting usually focuses on the current quarter outlook. It’s necessary to give visibility to leadership and quickly course correct where needed. In quarter forecast should be reviewed at least biweekly and should include looking at metrics like churn and expansion assumptions on renewals, and pipeline, pacing and productivity data on acquisition and add-ons. Short-term forecasting is usual ...Read More

I’ll answer this question from a lens of long-range forecasting and planning. New product launches and market expansions require a different forecasting approach than your core business. I’ve historically separated our "organic" from "inorganic" revenue. Organic business: Forecast using our standard methodology—historical trends, pipeline analysis, sales productivity Inorganic business: Model new products and markets separately as an overlay with assumption-driven inputs. Build multiple scenario ...Read More

I triangulate between a mix of forecasting methods to predict churn. At LinkedIn, one of our leaders created a metric called Renewal Incremental Growth (RIG) that looks at both the churn and growth on an account. A RIG of 1 equals a flat renewal, a RIG below 1 had churn while a RIG above 1 had growth. The first method for forecasting churn involves leveraging historical RIG trends by segment, product line and pricing offer and assuming those same trends hold.   The second method I use for foreca ...Read More

The biggest forecasting challenge I faced occurred when market dynamics shifted suddenly. In the summer of 2022, we noticed customer behavior really changing. Interest rates were higher; customers were spending less. Our forecast was becoming less and less predictable. To help create more clarity, I built out a scenario model that would allow me to triangulate between multiple different potential outcomes. The model includes: Renewal Scenarios: Ability to choose between different fiscal year tre ...Read More