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Beyond Web Analytics:  Analytics and Reporting for Real Business Results

Overview:

Online content of all forms is exploding. In parallel, user expectations and reliance on this content have set the stage for accelerated growth in this market. Delivery of online content has become a legitimate and increasing proportion of bottom-line revenue for both “new” and traditional businesses-model companies. One has only to look at Microsoft, which generated over $150M in 2007 from movie downloads alone 1 and imagine the new revenue streams yet to be realized by new media companies such as YouTube and Face Book. Driven largely by the arrival of Web 2.0 and the available of enterprise-level bandwidth, the online content phenomena has spawned a host of enabling technologies and business models focused on helping companies deliver new, diverse and compelling types of content ever faster to an increasingly hungry global audience. This phenomena is responsible for revitalizing the content delivery market projected to reach $4B in just a few years. From iTunes to Netflix movie downloads, maximizing the return on investment of the ongoing stream of online content is mission-critical.

Companies have implemented new ways to produce, transport, store and make available all sorts of exciting new content and applications – from streaming video and live Flash to interactive chat sessions. What has lagged behind, however, are solutions that provide content owners effective ways to understand the “who, what, when, where, why, and how” of content usage, which are critical components to monetization content usage. Solutions offered by web analytics and Content Delivery Networks (CDNs) fall far short of delivering meaningful analytics and reporting on content delivery , which are unable to answer critical questions such as:

  • How long is any one video clip is watched?
  • Which users downloaded my least trafficked content?
  • What region is responsible for the most content downloads?
  • What ads generate the most revenue over time?

Answering these questions may seem simple, but it requires accessing and merging together multiple sources of extremely high volumes of data. Data as diverse as content server log files, network traffic control data, and content application log files, all notorious for complex data formats, lack of standards, and huge volumes of data (think terabytes/day or hundreds of thousands of records generated/second).

Web analytics solutions are built around analyzing a single data source – clickstream – and are thus missing most user interactions with the “new” content that has been promoted by the growth of Web 2.0, such as video and Flash. Enterprise content owners and content service providers are left to cobble together a mix of traditional business intelligence/analytics tools, web analytics, and even enterprise search tools. But, this mix of “old” and “new” technologies doesn’t solve the significant analytical challenges:

  • Scale: Handle massive amounts of data over time
  • Flexibility: Seamlessly integrate diverse, “weird” data formats
  • Speed: Offer fingertip access to meaningful results based on this data.

These challenges must be solved before organizations can turn content into sustainable revenue streams. Because understanding the details of content usage over time drives the ability to deliver differentiated service offerings as well as maximizing the return on investment into online content delivery.

Traditional Business Intelligence: Business Intelligence (BI) typically relies on database technologies, which break down when dealing with very large and very diverse data sets. These high volume data sets can cause extreme latency issues, with analysis taking days, weeks or even months, rendering the intelligence meaningless to the business user. It is also inflexible in handling dynamic, changing data sources or complex data.

Web Analytics: There are a host of web analytics solutions that can offer insights into web traffic logs, but typically involve special coding placed on corporate web pages, and often require a predictive path for further drill-down. What these solutions don’t do is offer an un-biased view across all web traffic, and the ability to combine and relate the web log data to other data sources, such as streaming content logs, customer data, marketing campaign or lead/sales tracking data bases. These solutions have implemented complex, heavy infrastructure to handle a single stream of data (HTTP – click-stream data), but this can’t handle the multitude of content application data streams.

Advanced Enterprise Search : Technology advances now offer faster, easier ways to search across enterprise databases and networks to locate data entities. However, enterprise search today is far from offering analysis and insights about these data streams.

The New Data Environment:

Monetizing content requires an understanding of how your customers interact with all of your content — not just easy to reach segments. The more you know about user behavior, the better you can deliver what they want. As customer and prospect interactions increasingly happen over the network and the Internet, companies need to track behavior in this cyber environment, which is far more complex, faster, and generates more data points than ever before.

And, online user interactions are no longer limited to HTTP transactions. Instead, users are accessing a wide range of online applications and services which generate a unique data streams. Users move seamlessly between online interactions, such as:

  • Viewing a Flash demo to review a new product
  • Streaming a TV episode
  • Clicking on a banner ad
  • Placing an eCommerce order
  • Downloading a music clip
  • Viewing a web page

Each interaction touches different network components (web servers, live media servers, ad servers, etc), and produces complex, diverse, and extremely large, streams of data detailing customer interactions (Figure 1).

 

Figure 1: Dynamic websites offer a wide range of possible user interactions, each of which connect with different network components, illustrated here. To obtain a comprehensive view of user behavior requires that each of these interactions be combined and analyzed together – over time.

Click for larger image

The sheer volume and complexity of the data generated from these multiple user interactions make it impossible to efficiently put the data into a database, a requirement of traditional BI solutions. Too often, the different pieces of data required to paint a complete picture of user behavior is either discarded, accessed for “point in time” analysis, stored and ignored, or sampled for a limited historic view.
As a result, businesses make decisions about online content offerings — the real basis of monetizing online content investments — based on a limited understanding of how users and customers interaction with the content.

Key Question: A user goes to your website and clicks on a special promotional 2 minute video clip. Will your web analytics solution show you how long they spent viewing the clip?

Answer: No. Web Analytics solutions track the HTTP data only for pages that you have inserted a special code. But, even with the code in place, you would only see the number of times the clip was accessed, but it would not include any metrics on viewing time, including average viewing times. This data is only available if you have access to the media server log files responsible for streaming the content. Web Analytics solutions can not track this data.

Data Value Chain: Answering the Important Business Questions

Data that is transformed into rich, timely information fuels successful businesses. The most valuable information, however, reflects the joining together of multiple sources of data, over time. Tracking how customers and prospects access and use online content can only be obtained by combining the various data sources generated by both online and offline processes. Each data source adds a dimension to understanding the patterns of behavior, with the highest business value derived from a complete multi-dimensional view of customer behavior as shown in Figure 2.

   

What type of information can be achieved at each of these dimensions, and who benefits?

Click here to view a detailed table...

 

Meaningful reports and results obtained from data increase dramatically as single streams of operational data are joined with multiple customer data sources, permitting business users at the highest levels to better understand key factors driving both short and long-term profits and revenue.

While the benefits of combining multiple data sources to optimize content monetization are self-explanatory, the practical implementation is unattainable due to the nature of network and content traffic data – complexity, volume, and lack of standards.

The Skytide Process:

The Skytide Analytical Platform™ presents a new way of processing data that makes possible this merging of data sources, even with volumes of data reaching terabytes and billions of records daily. Because Skytide analyzes data directly, without the need to store data in a database, it drastically increases the ability to perform meaningful analytics and reporting on the data that traditional Business Intelligence (BI) solutions can not deal with. The Skytide breakthrough technology enables business intelligence in previously prohibitive areas for BI, due to that technology’s dependency on database centric technologies and storage.

According to leading market analyst, Dennis Drogseth, vice president of Enterprise Management Associates, “Traditional Business Intelligence solutions lack the ability to deliver dynamic, real-time information on very large heterogeneous data sets. A new generation of business analytics, such as the Skytide Analytical Platform, will allow companies to bridge these high volumes of structured and unstructured data sets with speed and power for on-the-fly information without the overhead of a data warehouse.”

The content delivery market is a key area where Skytide has quickly gained market share because of its ability to answer this sector’s business imperative to monetize content delivery.

At a high level the Skytide process follows these seven steps:

  1. Skytide connects to each data source where it resides, and data is pulled into the Skytide engine for processing.
  2. Standard or user defined parsers are applied to data, transposing it “on-the-fly” into an XML structure. A Skytide library of standard parsers covers tens of formats, which can be quickly extended for new or proprietary formats in a matter of hours. With parsers in place, this is an automatic, near-real-time process.
  3. Data flows into pre-defined multi-dimensional models, or “cubed”, which reduces data volume without losing data fidelity.
  4. Models are instantly available for analysis and reporting.
  5. Queries are built using Skytide’s unique modeling point-and-click Designer environment, which can be performed by non-technical business users without IT involvement.
  6. Reports are automatically generated through a wide range of presentation layers: Corda interactive dashboards, CrystalReports, JasperSoft, Excel, etc.
  7. Incremental updates are automatically performed, providing users near real-time reports.
  8. Ad hoc queries and reports can be quickly created to allow further drill-down or slicing through the data aggregated in analytical models.

A Key Question Revisited: A user goes to your website and clicks on a special promotional 2 minute video clip. With Skytide, can you track viewers of your promotional video even if this video represents a “long-tail” (a video with very few users on watching it)?

Answer: Yes. Skytide can provide viewing statistics on any content and any URL and not just the most popular ones. With Skytide you no longer need to worry about millions of individual users that you might want to track. Skytide can not only provide all of the details of the interaction times, it can also show you top geographic regions where the clip was viewed, specific times of the day, what players were used to view the data, and identify top errors that may have occurred.

Summary

Companies across all key vertical industries are struggling to deliver and justify the investments they have made in online content delivery. Customers, end-users, and prospects all expect to have fingertip access to content via the Internet for entertainment, for informed purchase decisions, for educational value, and to process requests. Up to this point, the focus of Web 2.0 applications has been in delivering the actual content or enabling the application. Increasingly, organizations are now trying to figure out how to monetize this investment. This monetization requires detailed understanding of how and why content is being used by customers and prospects.

Skytide delivers a way for organizations to maximize the value of the highly diverse volumes of data generated as a result of online content delivery. For enterprises delivering their own content, or CDN providers, Skytide offers a seamless way to transform streams of traffic control data into valuable information, information that helps monetize on online content, and drive revenue.