Welcome to Vertical Acuity’s Corporate blog and our first post.  Through our blog we will begin to interpret the industry data (starting with the Music industry) we receive from our subject level analytics platform and our measurement partners.  Future posts will highlight artist related case studies, industry trends, as well as discuss key topics such as measuring consumer engagement.  Before we move on to these and other topics, I feel it’s more important to start our first blog post with a discussion around what Vertical Acuity has termed Semantic Analytics, or Subject Level Market Intelligence, as well as its’ practical applications and background.

Vertical Acuity defines Semantic Analytics as, “The analysis of related website content and anonymous user behavior data with respect to any subject appearing within a single webpage, website, or group of related sites”. A subject can be defined as a brand, a product, an artist or album, a travel destination, or even a car model – any individual item that can be tracked on the Internet. By tracked, we mean actual measurement of how many people are reading the subject, where they are coming from, and how much time they spend with that subject across multiple websites, etc. You can think of it as a normalized approach to web analytics, also referred to as the semantic web when looking at the relationships between related subjects. Subjects and subject relationships can be rolled up into categories and sub-categories – the brand Coca-Cola is a soft drink or beverage. The Apple ipod can be categorized under consumer electronics and further under portable music players, and the artist 50 Cent and his albums are categorized within the Hip-Hop genre.

By breaking down a webpage into its primary subjects (what people are researching, reading, listening to and watching) and aggregating web analytics data at the subject level with additional sources of data such as consumer sentiment or demographics, a 360 degree view of a companies products, digital assets, and brands can be created across like sites within an industry category. To create a complete picture of online product and brand performance, six components must be measured:

1. Occurrence – how many times does a product or brand appear and on what types of sites and pages

2. Demographics – who is viewing the product or brand and where are they located (Geographics)

3. Velocity – what is the growth rate of a product or brand being mentioned, as well as consumed (actual views), and on what types of pages

4. Engagement – how many seconds to people remain engaged with a product or brand

5. Reach – how many people is a product or brand reaching during a given period of time

6. Location – where does a product perform the best

Once this information is consolidated, it can be used in a number of ways, including: improved targeting of content and advertising, measuring consumer engagement and advertising effectiveness (think measurement of ad latency), predicting products, brands, artists, etc. that are close to the ‘tipping point’, understanding which search terms are used to find products from both search engine traffic and network traffic – the list goes on.

A good illustration of how using semantic analytics data, in the context of market intelligence, could improve targeting can be illustrated in a recent article on Clickz entitled, “The Trouble with Audience Measurement”. I think the article effectively highlights one of the major problems in the industry by citing an automotive targeting example. The author discusses an example where an automotive company wants to reach women reading about autos who are in the middle of the purchasing cycle, and upon selecting a site to advertise on finds out the sites audience is 60% male after running a Nielsen report. Why does measurement stop at the site level? Because of panel sizes, lack of data, technical approach?

There are a number of reasons online intelligence stops at the site and category level, and I will cover those in future posts, but the key point I want to make with this reference is around targeting automobile models that have a higher percentage of female readers rather than websites. Why can’t we produce information that shows which makes and models have a higher percentage of engaged female readers across automotive sites and then target those subjects? Or maybe only those female readers that are actively engaged with certain makes and models across sites. Why can’t we ‘cherry pick’ those audiences based on subject level data trends versus picking sites based on a 60/40 demographical split? Site Owners should have the ability to slice up their site by key subjects, use the data to find the right audience segments, and stop wasting impressions on a 60% gender breakdown of their sites content. We believe the end result for many websites would be an increase in advertising rates while allowing marketers to reach the exact buyer profile they want to reach.

Future posts will discuss this and other areas in more detail, but our next post will look at an example subject performance profile for the artist Notorious B.I.G and provide some insight into his online performance from our beta measurement network.