To get ahead of this, MongoDB leadership sought to improve forecasting accuracy and rigor throughout the sales organization. But to do so, they had to find (and adopt) a new solution that could bring together all the underlying critical data, give them deeper visibility into the data, and help them enforce specialized workflows necessary to hold reps accountable to a forecast number. 

First, MongoDB RevOps and Sales leaders tried to address this problem using a combination of CRM, spreadsheets, and an older forecasting solution, but eventually determined that their previous forecasting tool’s architecture created significant impediments to solving their key requirements.

"Our forecasting process is complex. We need to blend actual usage data with future consumption projections submitted by over a thousand sellers globally. BoostUp is the only solution we explored that delivers the flexibility and performance we need to quickly and accurately roll up our forecast. It’s a critical component of our RevOps infrastructure," said Meghan Gill, Senior Vice President of Sales Operations at MongoDB.

Integrating various data sets and having access to flexible data visualizations had always been one of the MongoDB team’s requirements, but over the years they had built up substantial technical debt and an inflexible maintenance burden, with over 120 custom fields added to Salesforce. 

"We had to manage an overwhelming number of custom fields just to keep the system running, and at a certain point, that strains the efficiency of a business," explained Gill. “We were contending with a 15–30 minute data latency issue, which meant no real-time updates, something that’s incompatible with the needs of our consumption-based reps who need to view feedback and update forecasts immediately.”

That’s when MongoDB turned to BoostUp. 

BoostUp was able to create a meaningful impact to MongoDB’s forecast strategy in a fraction of the time, using Multi-Dimensional Forecasting (MDF) capabilities. The decision to select BoostUp came down to three primary factors: Hierarchies, Data and Architecture.

First, MDF gave them the ability to roll up revenue in ways that are completely aligned with their usage-based revenue model. This means they can instantly drill down into all layers of their complex hierarchy. A senior sales leader, for example, can drill down to any of their team members, the accounts they own, the underlying opportunities, and even the raw workloads that track the actual consumption. All of this data is presented in a single table in a highly intuitive, easy-to-explore interface. Additionally, the hierarchies are configured separately for each sales segment—Growth, Acquisition, Customer Success—each team gets their own specific hierarchy.

Second, BoostUp provided MongoDB with an infrastructure layer that brings together different data sets from various sources, allowing them to connect user, account, opportunity, and workload data into a single view, with real-time updates. The platform's flexible visualizations enable leaders and reps to analyze data from any Salesforce object, including standard and custom objects. The bi-directional data sync with Salesforce gives reps and leaders reliable, real-time feedback, enabling them to adjust commits immediately and ensures they can make and push edits back to Salesforce in real-time.

Third, unlike other forecasting solutions, BoostUp is not a UI layer on top of the Salesforce data model. Its modern and flexible architecture with a built-in data warehouse helped MongoDB eliminate the 120 different fields they previously needed to forecast, and reduced the technical debt they had built up. Using BoostUp’s configurability and self-serve capabilities, the MongoDB team was able to complete 95% of the implementation in just three months—compared to the typical 9–12 months they were accustomed to seeing with similar implementations—while addressing a range of modifications in minutes and days, rather than weeks. Each user profile and user role was given a customized hierarchy and view, ensuring that the right granularity of data was presented to each user. Forecasting workflows were also customized for each user profile and role. This level of responsiveness and agility were key to why MongoDB chose BoostUp.

A key reason for BoostUp’s flexibility is its modern data architecture that is built on MongoDB’s database. “Being able to customize and deploy Multi-Dimensional Forecasting to our customers requires precise modeling of their underlying schema and data objects”, says Amit Sasturkar, BoostUp’s CTO. “The flexibility and performance of MongoDB allows us to handle all revenue models, and all varieties of customers, with relative ease”.

“This was one of the most satisfying projects I have been a part of,” said Santosh Gowda, Senior Director, CRM Technologies. “We solved fundamental problems like forecasting workload run rates and pulling combined views of opps and workloads into a single pane of glass. This has been some of the best work, the best collaboration, and the best partnership we’ve had.”