What's the bestway to integrate Web analytics data with other forms of data? Last of a two-part series.
One thing about digital marketing: we aren't short of numbers. Typically, we have numbers coming in from our Web analytic systems, our PPC (define) consoles, our affiliate marketing systems, our ad-serving data, and so on. On average, a marketer may have five or six different data sources, if not more.
The challenge is to make sense of it all. The last column looked at what I call macro data integration. Macro integration is about pulling together data at the summarized level to relate different data sets together and spot trends and exceptions.
You want to be able to act far more tactically on data, however; so you'll want think about micro data integration as well. Micro data integration is when different data sources are integrated at a much more granular level. Often, this is conducted at the customer level.
Why would you want to think about micro data integration? For a number of reasons:
You may want to combine data from a Web analytics system on browsing behavior with on- and offline shopping data, for example, so you can be more specific and targeted in direct marketing activities. Or you may want to look at the long-term value of customers brought in by different acquisition channels.
Often the question is asked about the best place to integrate the data. Should data be imported into your Web analytics tool or a CRM (define) system or something similar? The answer is driven by your objectives and may involve both activities.
If the objectives are to improve the customer marketing processes, the best route will probably be to export certain data from the Web analytics system into the CRM system, as the CRM system usually drives the operation of the outbound marketing activity. The customer database or CRM system provides the total customer view; data from the Web analytics system will be just one component of that view.
Another reason you may want to export data from a Web analytics system into another database is because you want to analyze that data using other tools. Web analytics systems can report data in a variety of ways, but you may wish to conduct some more sophisticated statistical analysis using such tools as SAS, Clementine, SPSS, and the like. In some of the work we do, we process data using a Web analytics system to generate visitor level records, which we then look at using data-mining tools for interesting behavior patterns.
Other times, it may be useful to enhance data in a Web analytics tool by importing data from other sources, such as the marketing, customer, or product database. This is likely to be more useful when you need a site-centric rather than a customer-centric view. For example:
Thinking about what data to move requires careful planning. Different data sources have different data structures, and they won't necessarily fit easily together. Often, this means the data must be manipulated or transformed in some way so you can lay it alongside the other data.
The volume of data exported or imported is an issue as well. This also affects how often you integrate the data. Monthly? Weekly? Daily? Web sites generate huge volumes of data. It's often impractical and unwieldy to extract data in its rawest format. Think about what you want to do with the data, and create summarized variables if possible. If you want visit-based recency and frequency data in the customer database, for example, it's preferable to create a couple of summary variables, such as date of last visit and number of total visits, rather than import the whole customer's visit history.
The good news is Web analytics systems are becoming increasingly open and able to operate with other systems. The launch of WebTrends 8 and WebTrends Marketing Warehouse last month are steps in the right direction for making it easier for users to "micro-integrate" their data.
Till next time.
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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.
Hong Kong, May 5-6, 2015
Gartner Magic Quadrant for Digital Commerce
This Magic Quadrant examines leading digital commerce platforms that enable organizations to build digital commerce sites. These commerce platforms facilitate purchasing transactions over the Web, and support the creation and continuing development of an online relationship with a consumer.
Paid Search in the Mobile Era
Google reports that paid search ads are currently driving 40+ million calls per month. Cost per click is increasing, paid search budgets are growing, and mobile continues to dominate. It's time to revamp old search strategies, reimagine stale best practices, and add new layers data to your analytics.
May 6, 2015
12:00pm ET/9:00am PT