Digital Root

Bullet Time With Motion Video

Bullet time - the effect popularized in The Matrix - typically uses 24 still images stitched together to create a single from of video, or video-like dimensional motion. Working with the medium however, presents limitations when you want to tell a story using flowing motion and moving components.

A series of intelligent tools from Revisionfx introduces an amazingly simple, yet effective mechanism to achieve the effect of bullet time while still using video.

The particular tool, Twixtor intelligently combines sequence frames allow you to speed up or slow down the action to achieve the effect. These are two great examples of using lo-res video with Twixtor to generate cinematic effects with standard tools.

Experience Human Flight from Betty Wants In on Vimeo.

 

More Super Slow Motion [Water] - 550D from Rickard Bengtsson on Vimeo.

 

 

Using Search To Predict Hits

There are over 100MM internet searches performed daily. Keywords, phrases and numbers are collected and stored within the major search providers vast databases.

What if that search data could be used to predict trends before they happen?

In 2008 and 2009 Google researchers showed some success in trending flu cases using search data alone. However,  most studies since have been inconclusive, or statistically irrelevant.

A few researchers working with Yahoo! recently leapfrogged prior studies by formulating a set of statistical models that were comparable to baseline models.  The intent was to see if by just using search data, could they get close to well-informed models.  The first step was to observe the impact of time on the models. In the case of Movies, Games and Music, build up towards release showed significant traffic spikes, signaling consumer intent (or interest).

The conclusion of the studies showed that search data alone - automatically generated models predicted revenue/success for  Games, Music, and Movie releases with an overall range of 74%-93% accuracy. 

The regression (log) scatterplot below depicts the model visually. The dots represent predictive values, the line actual baseline. The closer the dots are to the line, the more predictive the regression is said to be. In this case, with Theatrical releases, the model had a near 80% accuracy for success based solely on search volume data.

 

The same models can't be used universally for all topics, products or brands. And these models may be improved upon by adding more variables, or using traditional methods of measurement.

The big deal here is that using something as simple as query data - future outcomes are meaningfully predictable. The reduction in cost and time in rendering the models is staggering.

In addition to search, mining unstructured internet data, social media and sentiment often reveals trends that weren't visible through traditional studies. When combined with Direct-Survey & Consumer Insight analysis, the planning capacity grows dramatically and informs marketers in a whole new, and often surprising way.