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.