A Framework for Implementing Web Analytics

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In my previous column, I talked about how to increase our web analytics effectiveness. But in order to truly get the most value out of web analytics, it's important to have a holistic approach to implementation. Today I'd like to delve deeper into a framework for implementing web analytics.

Now web analytics is nothing new to digital marketers. In fact, most brands and agencies have been doing web analytics for years. If you're one of those marketers, then ask yourself honestly the following questions:

1. Do you know the purpose of looking at certain metrics?
2. Do any of your web analytics reports tie to your business objectives?
3. Do you have any insights after reading your Google Analytics or Omniture report?
4. Do you know what actions to take after you read your reports?

My guess is you answered no to more than one of these questions. That's because most companies treat web analytics as a reporting tool, while it's really an approach to accountable and data-driven digital marketing. A complete web analytics implementation should fundamentally change the way digital marketing is done in your organization.

According to the Web Analytics Maturity Model below by Stephane Hamel, a clear direction is needed in all the following areas for web analytics to be successful: Management/Governance, Objectives, Scope, Team and Expertise, Process and Methodology, and lastly Tools and Technology.

Before I talk about each area of the framework, let me first show you an example of how most companies approach web analytics. Consider the evaluation below:

Now what happened here? The marketing director probably went to some digital marketing conference and saw the wonders of Omniture or Webtrends. As he gets back to the company, he mandates the digital team should immediately evaluate and purchase a web analytics tool. Hence the focus is all about how to choose the best tool, and how to magnify scope by tagging anything and everything under the sun. But as this company rolls out the shiny new tool, no one is clear on the business objectives of what and why they're tracking. Since it's a technical tool, the IT team gets slapped with the weekly reporting duties. But the IT team has no clear methodology in reporting the right metrics for the marketing team. And most importantly, no one is clear on a governance model on how insights and recommendations will be executed. As a result, this company's web analytics effort ends down the road of data puke. Where reports are produced with no purpose and no action.

Instead, the correct approach for implementing web analytics should address all dimensions as depicted below:

It's important to take baby steps in each and every area before growing too fast in one:

1. Governance: An organization should set clear ownership and guidelines to tracking, reporting, analysis, and action. The RACI model (who's responsible, accountable, consulted, and informed?) can be used here to be crystal clear whose neck to choke when things go wrong.
2. Objectives: Clearly define business objectives of every digital asset. For example, what's the purpose of my website? Is it for lead generation or consumer engagement? Then define analytics metrics that reflect if my objectives are being met. In this case, if I'm after lead generation, then I might look at the conversion rate for media channels. However, if I'm after consumer engagement, then I'll define measure goal achievement such as watch a video or click a share button.
3. Scope: Too many companies want to tag everything when they just start with web analytics. Instead, be clear about what your objective is, and then set the scope to match each campaign/project. The best practice is to start with a single digital asset, whether it's a campaign site or brand site. Then expand to the entire digital ecosystem.
4. Team: This area is the pain point for many organizations. Too often, the web analytics role is filled by IT. Yes, IT can help in adding the tracking code, but do they really know your digital marketing objectives? Hence, it's important to have at least one single analyst dedicated to actually look at the data and coming up with insights. This is the single most important aspect of a successful analytics implementation.
5. Methodology: Unfortunately, there isn't a "best practice" way on what methodology you should use to set tracking metrics and implementation. There's only "your" way, because the methodology used should fit closely with the organization's business objectives. Hence, tracking and measurement methodology should evolve closely with the team's experiences.
6. Tools: Lastly, we can focus on identifying the right tool. From a core functionalities perspective, there aren't that many striking differences among the web analytics tools out on the market (e.g., GA, Baidu Tongji, Omniture, Webtrends, CoreMetrics, etc.) But if we look at it from a long-term data integration strategy perspective, there are some implications to data ownership and integration options.

As you can see, web analytics is not just a technical tool. Doing it correctly can greatly enhance our digital marketing efforts. So I recommend everyone to carefully consider all the aforementioned factors in order to make an informed decision on web analytics implementation.