Elasticiti solved Big Data challenges to automate booking curve for leading financial website. The client needed an accurate revenue forecasting across direct and indirect revenue streams.
Numerous data sources and size of datasets overwhelmed the team’s technology and skillset.
Leverage the power of historical data in data warehouse
R for data munging
Tableau to visualize booking curves on per-client basis
A leading financial services website sells inventory via a direct sales model and programmatically in open auctions. The company wanted to better forecast its revenue, and in order to accomplish that goal, it needed to understand how revenue tracked, in the past, present and future.
Like all publishers, the company’s revenue doesn’t accumulate in a linear fashion; it accumulates episodically. In the beginning of the first quarter, its sales teams are selling into the first quarter, but by March, they’re selling into Q2 or even Q3. This made it difficult to accurately forecast quarterly revenue.
Additionally, the company wanted to know the implications on its programmatic revenue if conditions didn’t occur as expected on the direct side of the house.
Data Disparity. Client and revenue data is generated and housed in multiple systems, including Salesforce.com and Operative on the direct sales side, and PubMatic, Rubicon Project and AdEx on the programmatic side. All the data needed to be retrieved and normalized into a single program.
Lack of Granular Programmatic Data. Programmatic data was only available on a weekly level, and the client had little information on the actual pricing received.
Real-Time Data Difficult to Achieve. The data analysis team created spreadsheets to gain some insights, the process wasn’t scalable, and required significant manual input. That meant it was difficult to get insights on demand or updated in real time.
No Future or Historical Views. The spreadsheets didn’t allow a level of drilling, either in a future or an historical view, which meant the team couldn’t gain any significant insights.
The data analysis team engaged Elasticiti, who designed booking curves for the client, so that it could forecast both its direct sale and programmatic revenue, track changes that occur over time, and forecast the impact of unexpected events in direct sales revenue on programmatic earnings, and vice versa.
For the booking curves to be useful, Elasticiti needed to design models that tracked revenue year-over-year, as well as quarter-over-quarter. Additionally, booking curves can vary from client to client, even for the same order.
Elasticiti began by pulling historical data from Salesforce.com and Operative into R, which is well suited for normalizing disparate datasets, and for crunching data. Next it pulled data from PubMatic, Rubicon Project and AdEx using APIs.
Although the client had an abundance of data for its directly sold revenue, its programmatic data was much less granular. Creating a booking curve for the programmatic side of the house required a minimum of week-level views of prior volumes, as well as an analysis of the actual pricing involved, and the price settings. This required Elasticiti to make smart assumptions and to build a collaborative model assessed:
What is that strategy?
How short or long-term is that strategy?
What is it driven by?
To help the data analysis team share the insights throughout the company, Elasticiti fed the data model into Tableau, and created custom dashboards to display the booking curves.
Booking Curves for Accurate Forecasting. The company now has accurate booking curves to forecast its direct and programmatic revenue.
What-If Scenarios. Built in scenarios allow the company to predict implications of direct sales activities on programmatic revenue and vice versa.
Early Warning. The solution provides an early warning signal that earnings may not be what they anticipated. This signal allows them to take corrective actions, and advise the appropriate C-level executives.
“Anyone can make a forecast. But the real value of forecasting is a model’s ability to access the appropriate level of sophistication using data. And it needs to be agile. A model that takes hours to do isn’t as useful and one that’s running all the time and accurate,” said Ben Reid, CEO of Elasticiti.
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