When the solution now called Omniture Insight became part of the Omniture Suite last year, marketers gained the powerful opportunity to integrate existing web (SiteCatalyst) data with other data points, including offline data. Many Omniture customers have already taken advantage of this opportunity and are proving that the “sky is the limit” as far as data they can integrate and utilize in this fast, visual analysis tool.

Although there are some ready-to-use implementations that meet common Key Business Requirements (KBRs), each Omniture Insight installation is at least somewhat different, dictated by the unique business needs and situations of the client.

Let’s briefly take a look at three integrations of offline and online data and discuss the dataset architecture that was employed and business needs that were solved – we’ll refer to the relationship between the levels of data as the “schema.”

1) Live Online Ad Integration

One popular use of Omniture Insight is to combine SiteCatalyst click stream data with live online ad impression information. In this case, ad impression information can either be loaded as an offline data source (a data file coming on a recurring basis from your ad server) or collected live, in real-time.
Using one of several methods to tie the SiteCatalyst cookie ID to the ad server cookie ID, ad impression and click data can either be combined within the “Visitor”, or another level of data for a “Customer” can be added above the “Visitor” level.

The former (combining “Ad Visit” and “Ad Impression” data) is the most common, and is illustrated below.

Dataset schema with Ad Visits

Dataset schema with Ad Visits

A “Visitor” could have one or many “Ad Visits” (business-defined periods of activity with online advertising.) Each of these Ad Visits could have one or many “Ad Impressions.” Any attribute of an Ad Impression can be collected and made available for analysis and for selection – attributes like “banners with red text” as opposed to those with blue text, ads on one site versus another site, ads in one size or placement versus another.

Also included in the dataset, as illustrated in the schema, are the standard SiteCatalyst elements, like Visits and Page Views.

As a result, a selection of an ad attribute to which a set of Visitors was exposed quickly makes available the information related to their on site Visits, Page Views, Conversions, etc.

Conversely, a selection of certain Conversion attributes, products purchased, or other site-based attributes yields the opportunity to analyze ad attributes that contributed the most – or the least – to those events.

2) Offline Retail Data

Another growing use of Omniture Insight is for offline retail data. At its simplest view, you might consider the web-related levels of data we’re accustomed to (“Visitor -> Visit -> Event”) and their retail-world parallels (“Customer -> Transaction -> Item”.) A Customer to your retail store or chain might have one or many Transactions. Each of those Transactions might have one or many Items.

As in the above example, each level of the dataset can have its own attributes that can be selected or analyzed. For example, you might have information on a Customer’s loyalty type, level or status, demographic information, geographic location, and more. For Items, you might have information on brand/supplier, price, cost, and margin, and location in store.

Example main levels of a retail dataset schema

Example main levels of a retail dataset schema

Selecting a segment of Transactions (for example, transactions that included chocolate candy bars) allows quick analysis of what other Items were most commonly in the basket in those Transactions (“basket analysis”.) Selecting the same segment of Transactions and then selecting latent transactions – before or after – allow insight into what Customers purchased in prior or subsequent Transactions, and how long it was between those Transactions.

Again, any attribute at any level can be selected or analyzed for insight into its relationship with or impact upon other attributes at other levels.

3) Combined Offline Retail & Online Marketing Data

One of the most exciting areas of integration has been at the intersection of this online and offline data.

One such example includes a full combination of online and offline data – with online Visitor IDs “keyed” to offline Customer IDs to allow a comprehensive view of the customer.

Web data (green in the below schema) is collected in SiteCatalyst and available to web channel analysts in that tool, but is also sent to Omniture Insight to be combined with all other offline & online data for the Customer.

Combining Web, Ad, and Retail data in a schema

Combining Web, Ad, and Retail data in a schema

In many of these cases, Offline/Online data includes:

  • All Web site traffic (Visits, Events, Conversions, etc.)
  • All Customer demographic and loyalty data
  • All Online Ad impressions, attributes, etc.
  • All Offline (Direct Marketing) impressions for the Customer
  • All resulting Transactions (which, for this implementation, occur offline) and Items

In one combined analysis tool, business users are able to analyze on or make selections based upon Items in the final purchase, margin in that purchase, promotions applied to the purchase, location of the purchase – as well as attributes of the Customer: the ad or direct marketing exposures the customer had, and what they experienced and/or reserved on the website prior to the final purchase.

Imagine the power of full analysis and attribution across channels from first impression through conversion, and beyond – all in one tool.

If you believe you could benefit from the offline data integration or high data volume visual analysis capabilities by adding Omniture Insight to your suite, talk to your Omniture consultant or account manager today.

Have a question about anything related to Omniture Insight?  Do you have any tips or best practices related to Omniture Insight you want to share?  If so, please leave a comment here or send me an e-mail at mhalbrook@omniture.com and I will do my best to answer it here on the blog so everyone can learn. (If you prefer, I won’t use your name or company name.) You can also follow me on Twitter @MichaelHalbrook.

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6 comments
Daniel Lum
Daniel Lum

Truly, the powerful visualizations provided by Insight allow users to immediately infer meaning to make quick business decisions that improve overall business performance. Insight accepts data from any source, including data warehouses and business intelligence tools. Technology is slowly increasing expectations of data centres and databases, hence the Data Centre Design and Building industry is progressing at a very fast pace.

Michael Halbrook
Michael Halbrook

Dave, Thanks for your comment/question. In response, a couple of the methods we've used before to tie our cookie ID to the ad server cookie ID include: 1) Including an Omniture tracking image call in the tag (js or iframe) for each ad impression, and exposing the ad server cookie value to the Omniture tracking image. 2) Placing an ad server "outcome" or "spotlight" tag on high-traffic pages of the site and using it to expose the ad server cookie value to the Omniture tracking tag in place on that page. Hope that helps. Those are the most common/successful methods we've used to date.

Dave
Dave

You mention several methods to tie the SiteCatalyst cookie ID to the ad server cookie ID. Can you explain what these methods are?

Michael Halbrook
Michael Halbrook

Thanks for the feedback, Kiran. As you know, there's a lot we'll be able to show off regarding loading call data (call centers, etc.) as well. We have a lot of ground to cover in helping people realize the power of Insight.

Kiran Ferrandino
Kiran Ferrandino

Michael - I really like your examples of the three datasets to show the kinds of scenarios/challenges that Insight can meet. It's kind of amazing when you think about what can happen when companies can achieve if they can do some of the offline data tie ins and bring it into Insight. Not making light of the challenge that companies face in doing some of that offline visitor identification and mapping - but what a sweet spot when they can make that investment and get it right and load it into Insight. Great read. Looking forward to seeing how present complex topics like this.