Brener and Yaniv’s first startup, Carambola, uses natural language processing to help media companies automatically optimize ad content and increase revenue. Earlier this year, Carambola was sold to the Fire-Arc Group for an undisclosed sum.
Their experience with Carambola gave the pair a first-hand look at the complexity involved in turning traffic into ad revenue and how that complexity often betrays media brands. With an ad revenue chain consisting of so many constantly changing and interplaying links, it’s simply too much data for human eyes to effectively oversee.
Yet, unlike other industries and departments, publisher monetization teams have little tooling to help them monitor and make sense of their data. Between the web market and the app market, it’s an industry valued at over $333B - with the vast majority of that value coming from ads - and they’re working on glorified Excels and basic KPI dashboards!
This is what oolo set out to change.
The mission was clear provide the technological wherewithal to automatically monitor data and ensure revenue health across a variety of channels.
In practice, it would mean helping media companies:
The increase in the amount of data maintained by organizations for business decisions is leading more and more companies to adopt smart, automated technologies.
These tools, for the most part, are industry-agnostic and designed to suit the different data types of different industries. They are not optimally calibrated for each and every industry and the uniqueness of its data relationships. For many industries and operational departments, therefore these tools are inadequate - lacking the necessary context and calibration to produce the desired results. So manual monitoring remains the norm and the power of AIis never unlocked.
Media is one of these industries, and its operations teams, who work and deal with a highly dynamic and complex environment, still do not rely on advanced tools to get a clear picture of real-time risks and opportunities.
That’s why oolo takes a decidedly different approach - coming pre - loaded with the necessary industry context, business dependencies, and operational controls. What’s more, oolo comes with all the relevant data metrics already relationally mapped.
This allows oolo to make the leap from an academic instrument - surveying statistics - to an operational solution - telling a story. It’s also what allows the company to automate investigations when anomalies are detected - pinpointing the responsible data hierarchy and tracing the issue to its root cause.
The company estimates that routine revenue leaks and operational inefficiencies cost media companies as much as 7% of their total ad revenue. With oolo, that figure shrinks to nearly zero.
"It’s clear to everyone that manual monitoring is ineffective and will be replaced by something smarter and faster in the coming years, "explains Yuval Brenner. “We’re in position to spearhead that market transformation with our solution specifically adapted to this world.”
Early in the development of oolo, Brener and Yaniv made the decision that oolo would not just be a research tool. They were intent on building a business enablement solution. To do this, the company would need to build a strong technological backbone with world-class predictive modeling. Currently, the company says its ad revenue and delivery forecasts are 30% more accurate than Google's. No small feat for a young start-up!
For oolo, it all comes together to create an effortless and error-proof monitoring framework. It never gets tired, never takes its eyes off the road, never misses a thing, never floods the user with irrelevant or insignificant alerts, and always catches problems/opportunities at their earliest expressions.
That’s the secret to the company’s rapid growth and why it’s become so popular with big name brands like Major League Baseball and Voodoo. It’s not just another tool, it’s an integrative strategic solution to help improve business results.
