Churn Is the Enemy of Successful Web Analytics
One of the more serious challenges companies face as they seek to establish and mature their practice of analytics is “systems churn.” That is, companies regularly switch out web analytics tools and software, replacing old systems or adding new ones for new business units. For instance, a 2009 Forrester report found that the majority of organizations surveyed have had their analytics solutions in place for fewer than four years, and nearly half (46%) for fewer than three. We suspect that even higher percentages use several different tools.Given that analytics is a relatively new discipline, this may not sound like a big deal. In reality, it’s a formidable barrier to effective analytics. For one thing, it highlights that many companies haven’t embraced analytics as a standard part of the management toolkit. For another, constant “solution-hopping” suggests that analytics is all about the technology; if only you pick the right solution, you’ll gain magic insights across all your online properties and campaign and start making perfect decisions about how to boost traffic and engagement.
In fact, technology is almost always a secondary element in web analytics success. Companies have many solid options for measuring traffic and tracking user behavior – from free tools to highly advanced, enterprise-class systems. In our experience, focusing on sound requirements gathering, solid data governance, consistent definitions of the right metrics, establishing a “test-and-learn” mentality and building the right team are usually the more powerful enablers.
There are financial, strategic and technical reasons to avoid system churn. The addition of new tools and solutions can be expensive, and undercut overall technology ROI. Time spent on an internal (and fruitless) quest for the perfect software solution takes precious time and resources away from other critical needs and opportunities. Comprehensive software evaluations may prevent or distract organizations from the heavy lifting of establishing regular decision-making and performance review processes and defining precise strategic links between online operations and overall corporate goals.
On the technical side, data confusion (especially the dreaded “drowning” in huge volumes of data) often results when organizations switch solutions and have multiple tools in place across the company. If each business unit or product line is free to choose whatever web analytics platform or tool they like, inconsistent – and perhaps incoherent – measurements will exist for online businesses. Specifically, different systems generate different types of data for different processes or definitions (like customers or revenue). That disconnect means trends are harder to track and, more worryingly, can damage credibility of the analytics team with senior decision makers.
More practically, most companies that have implemented enterprise-class analytics packages are barely scratching the surface in terms of their capabilities. They’ve installed (or half-implemented) the software but rarely use them to their full potential, or in line with their unique needs. In other words, they’re buying new tools without having fully explored their existing ones.
It’s important to note that new or additional solutions can make sense if they meet very particular needs—such as search engine optimization and marketing, video tracking, or social networks—and if they are added to the mix carefully and strategically. For example, new tools should fit with pre-defined data parameters. The key is to avoid starting over with new metrics and definitions every time you try an allegedly superior tool or because there’s limited understanding of the solutions you have.
We believe, fundamentally, that analytics is a critical part of the performance management toolkit for all types of businesses. Put simply, any company that takes seriously the online component of their business (whether that component is mainly focused on marketing or customer service or sales) must take seriously the maturity of their analytics practice. Now is the time to pick one package (or, more likely, embrace and maximize the one you already have), look for incremental gains in the near term and start the long-term evolution toward greater analytics capabilities and processes. Once you recognize that analytics is here to stay, it’s easier to see that technology isn’t the only driver of maturity. The strategic approach, the investment in people and establishment of repeatable processes are the more important factors.
