Why Organizations Struggle With Digital Analytics (and How to Overcome the Struggle!)

It is hard for me to believe that I have been working in the digital analytics field for almost twenty years now! It seems like yesterday that I first learned about log files and putting snippets of JavaScript code on the website of the Chicago Mercantile Exchange in order to analyze visitor behavior. A lot has changed since then. Digital analytics has become much more sophisticated, the supporting technologies have matured, and businesspeople expect to see data for all digital actions taken by customers and prospects. But for all of the steps forward the digital analytics industry has taken, I continue to be amazed at how many organizations struggle to run successful digital analytics programs and implementations. I first encountered this way back when I worked for Omniture’s consulting group. I would go from client to client and see the same issues affecting each organization. These organizations had one or more of the following issues:

Digital analytics data was not trusted within the organization
Digital analytics teams were understaffed and unable to keep up with requests for analysis
Digital analytics implementations were set up incorrectly
Digital analytics teams weren’t knowledgeable enough on their analytics tool to get it to do what they needed
Business stakeholders didn’t know what data was available, how to access it or what it meant
Executives weren’t seeing the value of digital analytics within the organization

In most cases, the organization would choose to blame all of the preceding items on their digital analytics vendor. Even though I worked for the vendor at the time, I could honestly say that most of the issues above had nothing to do with the vendor or its products. Eventually, my role at Omniture was that of the “Wolf” in Pulp Fiction and I was sent out to help get these troubled organizations back on track. While it was easy for them to blame the vendor, I repeatedly found that there was a specific set of issues or behaviors that led to their digital analytics woes. I also learned over time some techniques that could be brought to bear to address the issues these organizations were facing. After I left Omniture, I went back to the client side and managed the digital analytics program at Salesforce. At the time, their analytics team was facing many of the same issues. This provided me with an opportunity to apply what I had learned in my “Wolf” role at Omniture in my own situation over a two-year period. For me it was a fascinating case study in how you can take an ailing digital analytics program and nurse it back to health. While there are no silver bullets, I found that many of the concepts I had applied to various Omniture customers did help our team at Salesforce and I have used the same techniques in my subsequent consulting career. Therefore, in this series of blog posts, I am going to share some advice and approaches to getting the most out of digital analytics in hopes that some of them help you in being more successful within your organization. Over the next few weeks, I will sequentially walk you through how to transform digital analytics at your organization. Even if you think you are perfect, I still think you will find some useful tips/ideas in these posts. Each post will provide concrete action items for you to take if you want to improve your digital analytics program. So, if you want to get more value from digital analytics at your organization, start by reading the first installment: Part 2: Is Your Team a Cost Center or a Profit Center? Part 3: The Dreaded SDR Part 4: Website Objectives Part 5: Why You Should Use Business Requirements Part 6: How to Create Business Requirements Part 7: Score Your Business Requirements Part 8: Requirements Driven SDR Part 9: Determining Your Development Needs Part 10: Know Your Tool! Part 11: Implementing New Items Part 12: Data Quality Part 13: Getting Executives to Care About Data Quality Part 14: Conducting Analysis Part 15: Supporting Analysis — Team Structures Part 16: Training End Users Part 17: Implementation Documentation Part 18: Implementation Governance Part 19: Building Executive Support Part 20: Managing Your Analytics Team Part 21: Conclusion