A look at Omniture's updated product roadmap and other trends involving analytics vendors.
A couple of weeks ago I met up with executives from Omniture here in the U.K. to get an update on the company's products and product roadmap. After all its acquisitions over the past 18 months, followed by the integration of various operations, a number of product launches, and the rebranding of some businesses, it was useful to get a perspective of Omniture as it stands and where it's headed.
After having talked for a couple of hours, it seems to me that Omniture has most of the bases covered. SiteCatalyst offers core Web reporting and analysis capabilities. Data integration requirements are managed through the Genesis program with dozens of partners covering most digital marketing disciplines. Optimization capabilities are offered through the integration of the Offermatica and Touch Clarity into the "Test and Target" service. High-end analytical requirements are part of the Discover offering including the Visual Sciences product rebranded as Discover onPremise. And at last, we see one of the benefits of the Instadia acquisition through the launch of Omniture Survey, which allows the integration of survey responses with Web analytics.
Having been through company and product integrations in the marketing services industry, I understand the challenges involved in bringing together a mishmash of different services and cultures into something that looks like a coherent product line up. From what I have seen, Omniture has done a pretty good job. The presentation of the services makes sense and you can see how they can deliver against an organization's needs as they evolve and grow.
This is not meant to be an ad for Omniture. But I came away from that meeting thinking that if Omniture can be used as a proxy for the Web analytics industry, then the industry is at an interesting point in its development. Over the past couple of years, some vendors broadened from the core application of Web reporting, either through acquisition (e.g., Omniture) or by being acquired themselves (e.g., Unica's NetTracker). The question is: where next? Will Web analytic tools develop into enterprise-level systems capable of supporting multi-channel analytics or will they remain a "point application" for the digital marketing channel?
Under the model for hosted Web analytic systems, everything's fine if you want to analyze and manipulate the data within the system itself. Generally, tools are getting better at analyzing and reporting Web data and the systems are getting easier to use. The challenge comes when you want to either report or analyze the data in a different way or even using a different tool.
Let's take an example.
Everyone knows that the last-click attribution model for measuring campaigns is naïve. Advertisers want to better understand the relationships between different digital channels and their impact on an eventual conversion. In most tools, the standard model is to use the last click with an option of using the first click to allocate a conversion to a channel. In some cases you can also allocate the conversion equally across all channels involved. If the advertiser wants to look at different ways of attributing conversion to marketing channels in a typical hosted environment, then the advertiser would need to get the data out and analyze it separately. This then presents another challenge.
Hosted Web analytics systems generally offer an all-or-nothing approach to export the data. You can export the topline reports into Excel or something similar and that's it. Or you can also have all the raw clickstream data. It's like saying you can have the drips from the tap or you can stand in front of the hose and get soaked. Few organizations are equipped to handle raw clickstream data, which is why they opt for a hosted service in the first place. There's too much noise in the data and a Web analytics system manages and processes the data into something that can be analyzed and reported. But in the process, sometimes it dampens the noise too much so it's hard to see what's going on. The example of the campaign attribution is one example.
Organizations increasingly want their Web analytics systems to offer the ability to deliver clean, summarized but granular data. WebTrends has made progress in this direction with the Visitor History File. It allows you to regularly export a series of attributes against each visitor that comes to the Web site. It includes, for example, the first campaign that attracted the visitor, the last campaign, the total number of visits, and so on. It doesn't solve all the problems but is a step in the right direction.
Increasingly organizations will be looking for tools to integrate data more easily into other marketing or corporate systems so that they can understand all the customer touch points. It will be interesting to see how the industry responds. Will it see itself as a solution for digital marketing only or will it be an important component of the broader mix?
Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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