Media such as mobile channels, social networks, blogs and Twitter have increased the demand for online analytics that go far beyond “traditional” web analytics. The mobile evolutions and the focus on a holistic and multi-channel marketing approach have consequences for businesses. They range from new metrics to the integration of customer analytics solutions. And let’s not forget the impact on the amount of data that customer-centric and data-driven companies now have to their disposal. In a white paper I recently read from Bloor Research (dating from earlier this year), this topic is tackled: how to manage all our web data “to gain business insights” (what ultimately matters) in a world where we are increasingly “drowning in online data”.

As we have written previously on this blog (read here), data are playing an increasing role in the cross-channel marketing approach businesses need to adopt to serve the omnichannel customer. Customer data, if well used, lead to information and insights that help businesses to serve their customers and nurture their leads better. But as you could read on this blog yesterday, a single view on the customer (and thus data) is key to provide an integrated customer experience and personalized interaction approach in function of the customer’s digital footprints.

According to Bloor Research, there are essentially two different types of web-based data that you might want to analyze.

One is click stream analytics, as Bloor says “usually associated with user behaviour in conjunction with shopping carts on retail web sites”, and the other, the research company says “is the sort of analysis that occurs in Web 2.0 environments where it is more a question of understanding customer interests”.

Complex analytics will need to be deployed in real-time as social networking sites get more sophisticated

But what is seldom covered, according to the paper, are “the complications that arise when you want to combine the sort of web-based data under discussion within conventional analytic environments”.

The report, looks at the challenge from different angles, including connectivity, latency and data volumes. Regarding social networking, Bloor says that social media, in which the identification of ‘influencers’ is important, “tend to have quite simple real-time query requirements (for example, “other people you might know”) and/or more complex analytics where there is little need for low latency”.

Bloor Research states that “in the future it is likely that more complex analytics will need to be deployed in real-time as (social networking) sites get more sophisticated and competitive”.

Related post: The multichannel urgency: go where your customers are and get integrated now