The 10/20/70 rule for Achievable Web Analytics Success
In San Francisco during the “Guru Breakfast” event Rene Dechamps asked a question about the importance of process to web analytics. This is clearly something I believe to be tremendously important (quit my job, printed business cards, etc.) and Bryan Eisenberg commented that “web analytics was 10 percent technology, 20 percent people, and 70 percent process …”
Rene said he would post the video he took of this conversation soon, but suffice to say Avinash Kaushik, Jim Sterne and I all agreed with Bryan. Process is very important to web analytics, but the importance of process is often overlooked.
Recently a reporter got me thinking about these numbers, so I would like to formally propose an update to beloved guru Kaushik’s widely quoted 10/90 rule. I call it the 10/20/70 Rule for Achievable Web Analytics Success. Here is what it says …
- Our Goal: Highest value from our investment in web analytics
- Percent of time and effort spent on the selection and deployment of their technology platform: 10%
- Percent of time and effort spent on the hiring and allocation of really smart people: 20%
- Percent of time and effort spent on the process of actually “doing” web analytics, leveraging both technology and people: 70%
- Bottom line for Achievable Success: It’s the process
The explicit recognition of the value of process resolves some of the issues people have with Kaushik’s original proposal, two of which include:
- It is very difficult to spend $90 of every $100 on “intelligent resources/analysts” given the extreme dearth of available talent relative to the number of jobs currently open in the market today. Even Kaushik’s former organization (Intuit) is, to the best of my knowledge still looking for his replacement, several months later, ironically highlighting the difficulty of finding good talent.
- Unless you’re gonna go the Google Analytics route (spending $0 on technology) and hire inexpensive resources to install the software (likely not one of the GAAC partners, although I’m not entirely sure what they charge) you’ll be hard pressed to spend $10 of every $100 on software license and implementation.
Now, I obviously agree with Avinash’s emphatic call to hire smart people. I’m a huge fan of dedicating resources to web analytics projects and have been since 2004 when JupiterResearch published my report Web Analytics: Spending, Staffing, and Vendor Selection. You need bright people to run your web analytics applications and to analyze data (although you may not need the people you think you need … more on that in another post at another time!)
But I think that the right way to frame the right approach to web analytics is not in terms of how you spend your valuable money, it’s how you spend your valuable time. So the 10/20/70 rule updates Kaushik’s rule by applying the appropriate emphasis squarely on the processes involved in “doing” web analytics.
Remember, you can always make more money, but it’s hard to make more time. Fortunately, some pretty bright people seem to agree with me.
Consider this: The technology involved is largely the same, especially at the level of need that most companies currently have from their web analytics solution. And while people are a good proxy for true process, in my experience too great of a dependence on people can cause two substantial problems:
- If the people are not the right people, the organization may not realize there is a problem until a great deal of money has been spent and a great deal of time has been wasted
- In my recent web analytics survey (results coming very soon!) we found that HALF of all respondents having web analytics experience had considered taking a new job in the last six months
So in the absence of process, some companies end up hiring unqualified people, hiring the wrong people, or hiring people who jump ship when the next best offer comes along. Certainly this is not the case with all companies, but until your organization has clear expectations about the goals for your investment in web analytics and how you plan to achieve those goals, technology and people will only get you so far.
So if you think your company is not following the 10/20/70 rule, here is my humble recommendation for you to consider:
- Take whatever technology you have already deployed, until you’re good at web analytics the technology doesn’t really matter
- Gather your key site stakeholders together
- Ask them to share their experience and understanding of web analytics thus far
- Document the gaps, looking for statements like “concerns about data accuracy”, “problems with data collection”, “not getting the right reports”, “reports are not actionable” and “concerns about how effectively we’re using web analytics tools”
- Pick any typical site process such as launching a new campaign or deploying a new page or micro-site
- Diagram the process you picked in step #5, highlighting decision points, tasks, and sub-tasks
- Determine where measurement fits in the diagram you produce
- Ask yourself if measurement, reporting, and/or analysis always happens in the places you’ve identified
- If not, ask yourself if the lack of measurement, reporting, and/or analysis results in the stakeholder concerns discovered in step #4
- If so, add measurement, reporting, and analysis to your diagram and make sure to follow the new diagram/process/checklist every time!
Still on the fence? Here are some questions for you to consider:
- If you have spent $10 of every $100 on technology, are you successful in getting most of your questions answered?
- If you answered “yes” to question #1, are you sure you’re asking the right questions?
- If you’re trying to spend $90 of every $100 on people, how exactly is that going for you?
- If you answered “great” to question #3, are you sure you’re not paying too much?
- If you have great technology and great people, what is your web analytics ROI?
- If you don’t know the answer to question #5, why not?
- If you have dedicated analysts on your staff, what percentage of their time do they spend generating reports and attending meetings vs. producing analysis and managing experiments?
- If you answered “too much reporting and meetings” to question #7, why do you think that is?
- If you have thought about the process of doing web analytics, do you have a checklist or business process diagram for the core processes?
- If you answered “huh?!” to question #9, call me.
Obviously my clear bias is for companies to invest in the process of doing web analytics. But professionally I have spent a great deal of time looking at this problem from all possible angles. And every time the answer is the same: The companies that invest their time refining how they actually “do” web analytics get more out of their efforts than companies who simply invest their money.
As always I welcome your comments and criticism.