Enterprise-level digital measurement and analytics is developing at a swift rate. However, most web analytics solutions don’t deliver the capabilities necessary for many forms of advanced analysis.
By Gary Angel, president of Semphonic
If organisations are considering working on a digital measurement and analytics project, they need to be thinking about the long-term infrastructure necessary to support a truly integrated and efficient system of data collection, data warehousing, and personalisation.
The Problem
Trends in digital measurement have reached a crisis point in 2012. The reliance on SaaS vendors to provide aggregated reporting on digital marketing and the use of tags to collect user behaviour have been the two major trends driving digital measurement at enterprise level – both of which are now in a critical state.
Organisations have been given widespread access to Web analytics data through tagging systems which represented a momentous breakthrough in measurement data collection. Since the introduction of tagging, however, real drawbacks to this method have come to light.
The demand for customised tagging has gradually increased and has proven to be a significant challenge as customised tagging is both complex and cumbersome. Tagging is an ongoing (some say never-ending) process that’s often poorly supported by IT organisations that have little understanding of how tags function and how they relate to actual measurement.
As a result, the cost of switching Web analytics tools is raised to an almost prohibitive level, locking many enterprises nto measurement solutions they do not want.
Web analytics tools are also in a period of crisis. When a measurement vendor is collecting vast amounts of Web data for thousands of clients, it is hard to provide deep access to all that data. Vendors focus on providing the best set of reports that meet the needs of most clients – a kind of least common denominator approach to measurement.
As a consequence, Web analytics tools provide little or no access to detailed data, little or no customer level analysis, and little or no ability to do advanced analytics or customer-based testing.
Despite the fact that most Web analytics tools upgraded their data platforms in 2012 to support increased segmentation, these changes still leave them with little real customer analytics capability.
What’s the latest?
Enterprise analytics managers are responding to these problems and organisations have swiftly started to investigate either internal or cloud-based warehousing solutions that provide much deeper access and integration of the data.
Today’s warehousing technologies provides rich, flexible integration of virtually any form of data. That means unlimited tables, unlimited fields, multiple data types, flexible access paths, unlimited data transformation, and open tool access.
This makes it much easier to drive outbound services and if your organisation is interested in actually using Web data to drive personalisation, targeting, or CRM support, this outbound capability is critical.
This drive toward personalisation and improving the relevancy of customer communications online via warehoused analytics data has raised the table stakes on data collection.
We have a problem…
Getting data into a warehouse and exposing it to powerful query and analysis tools sounds like a panacea for all the problems associated with using Web data. Between big-data engines and powerful analysis tools, it is surely possible for organisations to exploit the full value of this data. Or is it?
There are two big challenges that face any technology designed to support digital marketing analytics. First, there’s the small question of getting the data in to the warehouse. Almost all existing digital data warehouses rely on data feeds from current tagging systems.
Not only do these systems carry-over the problems that have brought tagging to a crisis point, they introduce severe delays into the system. Web analytics data feeds operate on a daily basis so the data is one-day old before it ever hits your warehouse. If you’re building an infrastructure to support marketing personalisation, that’s simply unacceptable.
To improve relevancy, data is used to generate “micro-decisions” about what to show or offer the customer. Although these micro-decisions are based on rules that don’t need to be developed in real-time, it is essential that the data used to make each individual decision is available in real-time.
It’s easy to understand why. The single most important thing to know about a customer is “what they are doing right now.” So tempting as it may seem, your Web analytics tool is the wrong way to source your analytics warehouse.
Systems dedicated to providing real-time sourcing of Web data to the warehouse are beginning to appear, and organisations looking to create a robust infrastructure for the warehouse that extends beyond the next 12 months need to be looking beyond their Web analytics tool to an infrastructure specifically designed for the task.
Equally problematic is the question of how to understand digital data. Web analytics data – how visitors move from page to page on a Website – is thin gruel for most marketers. It is impossible to build effective marketing campaigns that focus on how many pages a visitor viewed or how long there visit lasted.
The problem is one of meaning, not of data access and the fastest warehouse in the world cannot solve it. If you don’t have a meaningful data model for incorporating digital data into your view of the Customer, your data warehousing effort is going to fail.
It is very likely that both your BI teams and your Web analytics teams have no idea how to build that data model. One knows warehousing and customer analytics and the other knows Web analytics tools. The gap in-between is far larger than you’d expect, and bridging that gap a critical part of a successful analytics warehousing program.
The future of digital measurement
The scope and sophistication of Web analytics tools have expanded dramatically, yet still deliver only a small percentage of the analysis capabilities necessary for segmentation, personalisation, or interesting site testing, and provide virtually no customer-level analysis.
Until now collection, aggregation, and segmentation/personalisation/testing have all been too generic, too focused on levels beyond the customer, and too siloed. As organisations embrace the analytics warehouse, there is an unprecedented opportunity to solve all these problems in new ways.
Organisations should be thinking about the long-term infrastructure necessary to support a truly integrated and efficient system of data collection, data warehousing, and personalisation.
It’s not too early to put the right infrastructure in place: an infrastructure that will provide robust data collection in a truly maintainable fashion and that will support real-time data collection and the robust digital data model necessary to take advantage of all that capability.
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