Filed under: Analytics, Data, Datarati | Tags: Analytics, IBM, Optimisation
The emergence of new smarter systems which are interconnected and streaming real time information are presenting business and governments with a unique opportunity to transform decision making.
New opportunities to use this data to predict business outcomes, optimise old systems and spot trends before they happen are actually a reality.
Watch Video: http://www-03.ibm.com/press/us/en/presskit/27163.wss
YouTube has just released a new feature for its analytics platform that allows content owners to see how many unique visitors are watching their videos. While YouTube videos have long displayed their view counts to the public, until this point it hasn’t been possible to tell if the hits were coming from a wide audience or just a few devout fans who repeatedly watched the same clips.
To access the feature, visit the YouTube Insights page for the video in question and click ‘Show Unique Users’ under the ‘Views’ tab. A yellow line depicting unique views will appear alongside the standard green line showing absolute views, allowing you to easily gauge how many repeat hits you have. While most users will probably see fairly mundane trends, the most popular viral videos have statistics that are much more dramatic (I’d love to see the stats for this classic.)
Filed under: Analytics, Behavioural Targeting | Tags: Analytics, Bloomberg, Data, GoogleTV
Some time this month, advertisers will be able to bid for Bloomberg spots and watch them run on the network nationwide.
The spots will not rated by Nielsen, which can be deterrent for advertisers. But the Google platform allows advertisers to bid for time, and to receive second-by-second data on how their spots performed.
Before its recent deals–since April 2007–Google had been selling spots on Dish Network, reaching the satellite provider’s 13.8 million homes. Because Dish homes have set-top boxes, Google can obtain “census-level” data on viewing patterns down to the second. (Google also places some spots reaching some 25,000 cable subscribers in Northern California.)
But not all Dish homes are equipped for Google to obtain that type of viewing data. It has a subset that it believes is large enough (it won’t say how large) that allows it to extrapolate and provide ratings data reflecting what happens in all 13.8 million Dish homes.
(In that sense, it acts like Nielsen–albeit with a much larger sample size and one that has “hard” set-top box data to work with.) Going forward, the Dish subset will provide the basis for metrics Google produces for the national spots on TV Ads. Google’s complex algorithms will look at the performance of ads in the subset of Dish homes–perhaps between 2 million and 5 million of them–and project what takes place with viewership nationally.
Take NBCU’s Sci Fi Channel, which is in 93 million homes: Google will look at what the network’s viewers are watching in several million Dish homes, and then extrapolate it to provide the second-by-second data for what happens across the country. Google has a deal with Nielsen, where it can use Nielsen data to get demographic breakdowns for a channel’s audience–such as what percentage are men ages 18 to 49.
There is the potential hitch that the Dish subscriber may, in general, have a different profile than those who subscribe to cable or even satellite competitor DirecTV. So, if the sample is generated from Dish set-top boxes, it may not be representative of national viewing behavior. But Steib said Google has accounted for that, with sampling “done at such a scale that there is a high degree of statistical relevance.”
In addition to the opportunity to gather performance data, Steib believes networks will increasingly sign on to offer national inventory on Google TV Ads for a more immediate reason: revenue. The system offers the potential to drive sales on inventory that’s difficult to sell, he said, by offering the opportunity to tap into a new base of possible clients.
Filed under: Analytics, Behavioural Targeting | Tags: Analytics, Behavioural Targeting, MC Hammer
Want to hear MC Hammer talk Analytics and Behavioural Targeting??
You can’t watch this: http://www.youtube.com/watch?v=k6aBITJuSQA
Filed under: Analytics, Data, Multivariate Testing, Optimisation, Web Analytics | Tags: Analytics, Data, Optimisation, Web Analysts
Do you have an internal web analyst at your company? Are they integrated with your agency? Didn’t think so.
Quite simply, unless web analysts are integrated with your agency’s data, analytics and optimisation team – they will add limited to no value to your company.
As industry guru, Avinash Kaushik puts it… “If there is a “killer app” for getting context to your web analytics data it is tribal knowledge. Information about initiatives, marketing programs, website updates, changes, management re-org’s 
, server outages, ppc, direct marketing and on and on and on: Things that have a impact on your website.”
Web Analytics, and in turn Analysts, sit in a silo. This means that very often they have no idea about all the things that people are doing the site or to their acquisition strategies. So they look at all these numbers and metrics and trends and then like Tarot Card readers guess what the numbers mean!! A futile exercise.
There are lots of moving parts to a website and a lot of people involved in the holy exercise of creating something of value for your customers. It is a complex dance with many moving parts. . . .
The best way to get context is to seek out all the players and / or staying plugged into the various different processes to tease out vital context for your numbers.
“Ahhhh so you send out a big email blast five days ago!”
“You were running a multivariate test? Why did you not tell me in advance?”
“The shopping cart was down for nine hours! That explains so much, thank you. Kiss“
“We’ve adopted a new media mix model?”
“None of our last 6,000 campaigns were tagged! Pulling hair out!!!“
So on and so forth.
It’s time to step up to the plate and ensure your web analyst are working together with your agency to deliver you ultimate sales, revenue and profits!!
Filed under: Analytics, Data, Visualisation | Tags: Analytics, Data, Optimisation
Problem: How can we visualise the importance of data and actionable insights for each campaign? Something we can give our staff and clients to sell the true value of data, analytics and optimisation.
Solution: Jelly beans jar with a letter that reads:
“John sees hundreds of jelly beans in a jar. Will sees 142 blue, 127 white and 212 orange and that 20% of males 25 years and older dis-like the the taste of the black jelly beans, while 10% of women will eat all the red ones first and can then predict based on the data model, that their next likely choice of colour will be white”.
Call me to optimise your lollies: (02) 9016 1682
Filed under: Analytics, Segmentation | Tags: Analytics, Budgets, Customer Segmentation
Facing increasing market pressure, 60% of top-performing (”Best-in-Class”) companies plan to increase budgets for customer analytics and segmentation technologies next year to find more sophisticated ways of identifying key trends and commonalities among customers, according to an Aberdeen Group report. The increasing competition for high-value customers.
This week, Youtube will launch a new feature called HotSpots that allows video creators to monitor how viewings rise and fall within a video.
HotSpots plays a video alongside a graph that maps whether the audience is lower or higher than average for a particular length of video. When the graph goes up, the video is “hot,” and more viewers are watching — because there’s either less attrition or some viewers are fast-forwarding or rewinding to isolate a particular point in the video. When the graph goes down, the video is “cold” because viewers are leaving the video or skipping to another part of the content. Another service, Visible Measures, also measures this sort of audience engagement within a video, among other things.
And while pay-measurement services and optimizers can prove invaluable for bigger-budgeted marketers and media companies that need quicker hits and can spend more to get them, Insights has proven valuable to the large group of regular but nonprofessional video creators and uploaders. For YouTube, the benefit is clear: If you give users the tools to attract larger audiences, they’ll create more ad inventory.
Will Scully-Power
Data Director
Mark.
TechCrunch50 company FitBit (which demoed its health activity monitoring device live as well) put up this slide to underscore its point that obesity is a growing problem, particularly in the geek world. The slide shows how the distribution of T-shirt sizes at the Linux Symposium has shifted towards the XL and XXL side of the scale.
Will Scully-Power
Data Director
Mark.
Filed under: Analytics | Tags: Adobe AIR, Analytics, Google, Video Streaming
Ooyala, a video platform founded by two seasoned Google veterans, has launched a powerful new analytics backend for its service that it calls Backlot Analytics. The new analytics software allows content providers to get an extremely detailed data on their users’ viewing behaviors, helping them tweak their ad placement and future content selection. Backlot Analytics will be available as a native application in Adobe AIR, and will also work in the browser.
Included among the features in Backlot Analytics are graphs that detail exactly when users rewind or stop watching a video. Publishers can use this data to determine when to position their ads (for example, they might find that users are far more likely to drop off if there is an ad in the first thirty seconds of a video, but that there is little impact if the ad comes after one minute). Ooyala features a drag-and-drop interface that makes such adjustments easy – users can simply drag their ads to a new place in the video timeline, and the system will immediately adapt for future plays without having to render anything.
The new version also includes support for Geo- and Domain-reporting, as well as an API allowing developers to integrate the platform into existing management systems.
Ooyala launched in late 2007, and won Amazon’s AWS Startup-Challenge. The site has since partnered with thousands of media providers, including National Geographic, TV Guide, AOL, and Warner Brothers.
To keep costs low, Ooyala has partnered with numerous Content Delivery Networks (CDNs), buying their “excess bandwidth” for a bargain price. Excess bandwidth is typically available when there are lulls in Internet demand, and Ooyala says that these lulls come at a predicable hour: dinner time. Using using a patented polling technique, Ooyala “chases” dinner time (and other off-peak hours) around the world to minimize their bandwidth costs. But they have to stay on their toes – immediately after dinner there is always a spike that the company attributes to one thing: “Porn time”.
Will Scully-Power
Data Director
Mark.
