Bad Process Kills Good Analytics

  |  March 15, 2011   |  Comments

Process needs to set the analytical agenda within the organization, and then needs to exist to maintain the quality of the data that is being collected within the organization.

In many cases, organizations are still struggling to get the return on investment in their digital analytics that they were originally hoping for or could reasonably expect. Ten years on from when Web analytics started to go mainstream, why is that still the case? If we look at the possible reasons, they tend to lie in the "triumvirate" of technology, people, and processes.

A lot of organizations have access to Web analytics technology and have invested in it heavily over the years. The introduction of free services sparked by Google Analytics over five years ago means that it's cheap to acquire Web analytics technology. For organizations with more sophisticated requirements such as the ability to integrate Web data with other data sources and systems, the enterprise market satisfies those needs. The technologies have developed significantly over the past few years and provide richer analytics, particularly in the area of behavioral segmentation, than they did a few years ago. There are still areas that are not addressed well by Web analytics technologies; notably the attribution of acquisition channels. And while it's great that the technology providers are adding additional functionality, particularly in the social media arena, acquisition attribution is an area that it would be great to see some development in as well.

It wasn't that long ago that it was generally recognized that organizations were underinvesting in getting enough of the right type of people into their organizations. Avinash Kaushik's famous 10/90 rule he posted on his blog made the point admirably. We have seen organizations invest more in people more recently and significant Web analytics teams exist in many large advertisers or digital property owners. Investing in people remains a problem naturally for smaller organizations with smaller budgets and resources, but if at least it becomes part of someone's job, then it signals a degree of commitment.

To some extent, experience and qualifications remain a problem on the people side of things. Web analytics is still a relatively young marketing discipline and even the "veterans" in the industry have less than 15 years or so experience in the field. Again, this is evolving as organizations like the Web Analytics Association continue to develop education and certification programs. This will help to define "what good looks like" when it comes to Web analysts and provide a means of reference for organizations to assess the quality of potential staff and suppliers alike.

So while there are still opportunities for improvement in the areas of technology and people, I think that process remains the Achilles' heel of Web analytics in most organizations. Process is really about how the technology and people are applied within the organization to make a difference about the way the organization does business. The problem often begins with a lack of process around the setting of goals and objectives so that correct key performance indicators can be set. This is not an analytical process, it's a business process, and therefore is one that the business as a whole needs to buy into. This process operates at all levels from setting objectives for the channel as a whole, through to setting objectives for product development or down to individual campaigns. It's the process that sets the analytical agenda within the organization.

Processes then need to exist to maintain the quality of the data that is being collected within the organization. A lot of effort can be spent getting a new technology in or applying an existing technology to a new website, but it's vital to have processes in place to maintain the integrity of the data. Digital channels are never static, so continual effort is required to ensure that the data being captured reflects the latest developments. This means plugging analytical and measurement processes into the heart of the product development or campaign development processes and seeing data collection as being a core component of those processes rather than an afterthought.

The other important processes required are the ones that embed the data and insight into the decision making process. Optimization is all about "test, learn, and adjust" and the "learn" bit needs to be integral to that process. The challenge here is how to ensure that the analysts and their data are brought into the loop, and the challenge for analysts is to ensure that they can add value to the discussion. Part of the issue here might be about where analytical functions sit within the business and how they interact with their peers and colleagues. There are no easy answers to these organizational questions, but all the investments in technology and people will be undermined without consideration being given the way that the data and insights are capitalized upon.

In many cases, the hard investments in technology and people have been made, but the returns will be realized when the process issues are addressed as well.

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Neil Mason

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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