Thought Leaders in Big Data: Damon Ragusa, CEO of ThinkVine (Part 3)

Damon Ragusa: What we do is consolidate data from a lot of different sources. There are three V’s of Big Data—Volume, Velocity, and Variety. Variety is my favorite V. I think you get more value by integrating a larger variety of data to explain the thing you’re trying to understand than just more of the same. Fortunately in this world, we get a lot of both. We get a wide variety of data and we use some nifty algorithms that we’re able to connect all these data—demographic, behavioral, sales, digital click stream—to a modeling framework that allows us to understand and start to simulate how individual people carry out their interactions with the brand and media, and how they buy within a category.

Across all these data, we can start to get these great perspectives of how that happens at a very detailed level because we have so much granular data now. We can then start to play the game, if you will. We can start to understand, “If I didn’t run that campaign, what would have happened?” I can start to find the value of that campaign or the value of that one activity. I can start to connect the dots between the things that happen online where I can track, and the things that happen offline because I have a better understanding of how consumers behave. I can start to see when I’m spending dollars on certain areas associated with the sales based on what I know about how people behave, I can start to infer very accurately what’s likely to happen. It allows me to forecast what will happen.

We’ve talked to a lot of companies who have gone down this road of attribution but don’t build credibility around that attribution through a robust forecasting capability. If you can’t trust the methodology you use to deliver an attribution and to deliver an accurate forecast, then how can you trust the attribution piece? The attribution piece is something that is very difficult to prove. The only way to prove attribution is to create a test and test it. Most marketing departments spend $50 million to $500 million a year in marketing across a whole bunch of areas. The idea of creating a test and holding everything constant while you’re measuring this one thing is a near impossibility.

To understand how to develop and build credibility around those attributions, you should be able to forecast. That’s how we put it all together to create a very custom analytical environment for a brand within a category to understand how the shopper shops and how the marketplace works. We can integrate things like impact of economy and for some categories, impact of the weather. We want to make sure that we don’t want to confuse those things with the impact of marketing. Otherwise, we’ll tend to give marketing too much attribution. There’s a whole bunch of factors. The idea of our technology is to really reduce that complication and allow our customers to feel good about it by saying how well it can forecast.

This segment is part 3 in the series : Thought Leaders in Big Data: Damon Ragusa, CEO of ThinkVine
1 2 3 4 5