Browsing posts in: Analytics

How to choose a digital analytics vendor

We’ve got a problem in the analytics space. It has to do with vendor discovery.


See, the complexity and use cases for digital analytics has changed dramatically over the last ten years. Digital analytics has gone from being an interesting side project for the IT department to becoming, at leading organizations, an absolutely critical competitive tool for understanding customers, being the marketing department’s eyes and ears, and guiding overall business strategy.

Yet, the way most companies – particularly larger ones – select digital analytics vendors today has hardly changed. This has led me to the conclusion that the way most companies are choosing their digital marketing and analytics partners is now profoundly flawed. Many companies are simply not getting the maximum business value out of their solutions that they could, often because of a selection mismatch. Here’s how to fix it.

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Christmas in May: Forrester Web Analytics 2014 Wave reactions


For the digital analytics community, Forrester Wave season is sort of like the NFL Draft. Your opinion about its accuracy (or even relevance) usually depends on what team you’re rooting for. This year, of course, is no exception. The Forrester 2014 Web Analytics Wave is now published – and now #measure will descend upon it like a pack of wolves.

There were a lot of interesting aspects in this new Wave – both in terms of what it says, and what it doesn’t. Overall, I think it’s a fair report about web analytics. But then… it’s been a while since I just had a discussion about web analytics. Having been through the Wave (and Magic Quadrant, and other analyst report) process a couple of times now, here’s some initial analysis.

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Where have all the on-premise web analytics solutions gone?


Being an on-premise web analytics user is a lonely place these days. First, Webtrends announced that it has “no plans” to update its on-premises solution in the next year, focusing instead on its SaaS-based Analytics 10 platform. (Keep in mind that the most recent on-prem version, 9.2, is now almost three years old.) Then, just last month, IBM  announced that it’s end-of-marketing the on-premise web analytics solution inherited from the Unica acquisition in 2011 – the venerable NetInsight. More and more, it seems that large companies looking for on-premise, enterprise-class digital analytics solutions will be left out of the incredible innovation seen in most of the marketing analytics industry.

What’s behind this swift decline of the on-premises analytics market? And more importantly, what does the shift to SaaS (Software as a Service) solutions mean for – and about – the rest of the marketing analytics industry? Here are my thoughts.

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Missing the point, in real-time

It’s become almost cliche by now in the marketing analytics community to harp on the crucial differences between reporting and analysis. But we keep coming back here because too many marketers are doing lots of the former, and not nearly enough of the latter.


This theme applies directly to a popular demand in the digital analytics world: real-time analytics. Market demand has made real-time capabilities table stakes today for any serious analytics platform. But to what end? What are marketers actually doing with the real-time data at their fingertips?

While not dismissing some valuable applications of real-time analytics, it’s my view that many analytics teams today are either over-emphasizing real-time data, misusing it entirely, or letting it actively hinder the development of a data-driven culture in their organization. Here’s why.

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Why you’re not really “data driven” – and how to start

In today’s marketing environment, you’ll find few organizations that do not claim to be “data driven” in some way. We hear it all the time, in every industry, at every conference and by every speaker. Everyone is “data driven” today. Except… that they’re not.


Poke under the covers, and as we all know, you’ll soon discover that many, perhaps most, companies are still no where near as smart with data as they claim. In most organizations, the HIPPOs are still firmly in control – though some of them have begun to learn the right lingo.

Why? It’s pretty straightforward: it turns out that being data driven is actually really, really hard. Creating an operational culture built on constant validation with data and course-corrections based on performance does not happen overnight. Even the “leanest” startups find it challenging to achieve the discipline required. Large companies, especially those with strong pre-existing operational cultures, tend to struggle mightily with it, and have a bad habit of relying on consultants.

Of course, the benefits of a vigilant “data culture” can be immense. In many industries (not just in technology!), we see many examples of companies that have successfully used the smart application of data and analytics to become more effective, efficient and competitive. In our business, we constantly work with business and marketing teams to become more data-driven, and I’d recommend four key steps to get started.

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