Case Study: Revolutionizing Ads Forecasting at LinkedIn – Achieving 10,000 Hours of Efficiency
Imagine spending entire days each week pulling together data from a bunch of different Excel sheets. Not fun, right? Well, that was the reality for LinkedIn's Ads Delivery team, a group of 70 dedicated professionals, who were stuck in a time-consuming, manual forecasting process. But we saw an opportunity to make things more efficient—and with a little creativity and some self-taught XAMPP skills, we did just that.
By building a custom web application, we completely transformed LinkedIn’s Ads forecasting process. The result? Over 10,000 employee hours saved annually, a more accurate quarterly revenue forecast than ever before, and a system that’s still running strong after nearly a decade. Let’s dive into how we pulled it off.
The Challenge: The Excel Nightmare
When you’ve got a team of 70 people all maintaining their own individual Excel sheets to estimate ad delivery and contract signings, things can get pretty chaotic. Managers and directors had to spend an entire day each week manually gathering and aggregating data, just to see if the numbers added up. The process was inefficient, prone to errors, and, let’s face it, not exactly the best use of everyone’s time.
The team needed a better way to manage their forecasting—one that would cut down on the manual work, improve accuracy, and help everyone work smarter.
The Goal: An Automated, Centralized Solution
The mission was clear: Build an automated system that could:
Bring all those scattered Excel sheets together into one centralized hub.
Make forecasts more accurate by using real-time data and historical trends.
Save a ton of time by eliminating the need for manual reporting.
But how do you take something so manual and make it automated without overcomplicating things? That’s where the fun part came in.
The Solution: From Excel Mess to Web Magic
We knew we had to build something simple yet powerful. After talking to the team, gathering feedback, and really understanding the pain points, I got to work. Here’s what we did:
Centralized the Data: We developed a web-based tool that pulled in real-time data from LinkedIn's CRM (for future deals) and Ads platform (for signed deals and delivery stats). No more messy Excel sheets!
Automated Forecasting: We set up automated projections based on historical close rates, delivery rates, and deal-specific adjustments. Now, the system could generate forecasts without any manual input (except for key adjustments, of course).
Designed for Humans: I made sure the interface was user-friendly. The team could easily input new data, see results in real time, and know exactly how the system was making its calculations. Transparency was key.
The Results: 10,000 Hours Saved
So, what happened after we rolled out the new system? Well, the team was able to ditch those old, time-consuming manual processes, and we saved over 10,000 employee hours per year. The new system not only improved accuracy—it made everyone’s job a whole lot easier.
Even better? The system’s still running strong today, delivering accurate quarterly revenue forecasts without all the headaches. Talk about long-lasting impact!
The Takeaway: Efficiency is Everything
When it comes to data, efficiency is key. By automating processes, centralizing data, and making it accessible in real-time, we were able to create a system that saved time, improved accuracy, and made everyone’s job a little less stressful. The key to this success was listening to the team’s pain points, finding a simple yet effective solution, and applying a little creativity.
If you’re struggling with manual processes and looking for ways to make your data work harder for you, it’s time to think about automation. Trust me, you’ll save way more time than you think.