The 10/20/70 rule for Achievable Web Analytics Success

Published by Eric T. Peterson on June 3, 2007 All posts from Eric T. Peterson

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:

  1. 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.
  2. 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:

  1. 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
  2. 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.

I know, I know, I said to hire people and everything would be fine. It will be fine, but with process, you can be better than fine. You can make money. Piles of it.

So if you think your company is not following the 10/20/70 rule, here is my humble recommendation for you to consider:

  1. Take whatever technology you have already deployed, until you’re good at web analytics the technology doesn’t really matter
  2. Gather your key site stakeholders together
  3. Ask them to share their experience and understanding of web analytics thus far
  4. 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”
  5. Pick any typical site process such as launching a new campaign or deploying a new page or micro-site
  6. Diagram the process you picked in step #5, highlighting decision points, tasks, and sub-tasks
  7. Determine where measurement fits in the diagram you produce
  8. Ask yourself if measurement, reporting, and/or analysis always happens in the places you’ve identified
  9. If not, ask yourself if the lack of measurement, reporting, and/or analysis results in the stakeholder concerns discovered in step #4
  10. 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:

  1. If you have spent $10 of every $100 on technology, are you successful in getting most of your questions answered?
  2. If you answered “yes” to question #1, are you sure you’re asking the right questions?
  3. If you’re trying to spend $90 of every $100 on people, how exactly is that going for you?
  4. If you answered “great” to question #3, are you sure you’re not paying too much?
  5. If you have great technology and great people, what is your web analytics ROI?
  6. If you don’t know the answer to question #5, why not?
  7. 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?
  8. If you answered “too much reporting and meetings” to question #7, why do you think that is?
  9. If you have thought about the process of doing web analytics, do you have a checklist or business process diagram for the core processes?
  10. 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.

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  • Steve

    Aside: Gotta love timezones – Eric and Judah both publishing during my early evenings. Perfect!

    I only disagree with one area in what you have here and what Avinash mentioned with his 10/90 rule. Mainly because it’s a problem for us.

    Analytics is pretty much useless without the corresponding ability to change anything.

    I appreciate it may be implied, but for some of us, it’s a killer. WA is emphatically not my main job, even so it takes months of subtle effort to effect even small positive changes out of the analysis I do provide.

    I’ll start getting cynical again…. ;-)

  • eric

    Steve: I’m heading back to my own timezone right now but I can vouch for Judah the “night owl!” Crazy huh?

    Ironically you’re not disagreeing with me at all when you remind us that the “ability to change anything” is critical and fundamental. I said this at Emetrics (twice, so I must believe it):

    “If you’re not running controlled experiments, you’re not doing web analytics!”

    Controlled experimentation is a CRITICAL web analytics process, one that I outline in my white paper “The Web Analytics Business Process: Making the Case for a Process-Driven Approach to Web Site Measurement” (download here) But simply having people (i.e., 10/90) doesn’t create the mandate to run experiments and make changes. More often than not, hiring people in the absence of a clear PROCESS to make changes only aggravates the situation: You’ve spent money on technology and people, but aren’t seeing any positive ROI. Oops!

    You are not alone, and that is the problem. IMVHO 10/20/70 is the solution.

    Thanks for writing and I look forward to your cynicism!

  • daniel markus

    Hi Eric,

    Very interesting post. Food for thought, like always. What is hard for me to follow is the 70%. Defining and deploying the process seems a one time effort. Therefor I tend to stick with Avinash 10/90 rule.

    Process is necessary, but how do you envision continuously spending 70% on the process?

    You mention 70% of time and effort. But spending on process is not mutually exclusive from technology and people. Unless you mean hiring specialists to define and deploy the process.

    When the process is in place, the analysts will spend the majority of their time on analysis and experiments. If the process is ill defined the analysts will spend time plugging holes with producing data and interpreting standard reports.

    But when the process is in place you end up in the 10/90 situation again, where the smart people periodically review the process.

  • eric

    Daniel: Perhaps what is confusing is the definition of “process” and I would encourage you to download my white paper on this subject from:

    When I say “process” I mean the multitude of things every organization needs to accomplish to be successful with web analytics: assigning ownership of web analytics, defining business objectives, hiring people, determining an overall strategy for web analytics inside the organization, etc.

    When you say “when the process is in place” I think perhaps you’re taking something for granted. Last week, in Europe, I only heard from a single company that had “the process in place” (Postbank) and I would offer that even they have struggles with the effective usage of web analytics elsewhere in their organization.

    In my experience, often times the processes necessary to be successful with web analytics are only poorly defined. In these situations, as I said, the bright people you hire with Avinash’s $90 will struggle, be less effective internally than desired, or simply take a new job.

    Again, I hope you’ll ready my white paper and thank you sincerely for your comment!

  • Rene

    Hi Eric,
    As we had the opportunity to discuss in Amsterdam last week-end ;-), I believe that your rule (70/20/10) is a declination of Avinash’s. Who’s creating and managing processes? People, bright people.


    Ps I’ll publish soon the video with Bryan, Avinash and you speaking about the 70/20/10 rule at the last Emetrics.

  • eric

    Rene: I respectfully disagree with your assertion that 10/20/70 is derivative of the 10/90 rule. In his original post, Mr. Kaushik cites:

    “Required investment in “intelligent resources/analysts”: $ 90.”

    The implication is that a company should spend $90 of every $100 on ** analysts ** who can run the applications. He goes on to say …

    “2) Cancel the contract with your favorite expensive analytics vendor and take that $50k or $100k or $200k and: 1) Hire a smart analyst for between $50k to whatever maybe your areas great salary 2) Put the rest of the money in your pocket.”

    … which reinforces the idea that ** an analyst is always the solution to the problem **. As you, Aurelie, and I discussed on the way to dinner Friday night, a surprising number of companies ** don’t actually need an analyst as badly as they need a “web analytics project manager” **, a position that few companies currently have.

    I’m not saying that analysts aren’t required — far from it — but I don’t think that the majority of analysts are hired into situations where they are free and able to completely re-design how web analytics fits into normal business process. That’s why I say if you rely solely on bright people you might end with some very frustrated people; conversely, if you focus your efforts on creating the right process, the bright people will thrive (and Avinash’s sage guidance on spending will be fruitful!)

    However, the point of 10/20/70 is that it’s not how you spend your ** money **, it’s how you spend your ** time **. This is a fundamental yet subtle difference between AK’s guidance and mine on the subject of “how to be successful with web analytics.”

    As always, I’m willing to be wrong about this issue. I would love to hear from (more) of you who read my blog and who are currently in “analysts” roles in your organization. The questions I’d love to discuss are “Does your company allow you to be truly effective?” and “How much web analytics process have you been successful in implementing in your organization?”

    Thanks again for your comment, Rene, and I look forward to seeing the video from the WAG event.

  • Judah

    The challenge with the 90/10 guideline is when you spend major bucks on a tool. Say you spend a cool $1M US. You then spend $9M on people? At 50k a pop that’s 180 people (and no web analysts work that cheap). Say you spend $100k for the tool, that’s $900k on people. At $50k a pop that’s 18 people. Good luck getting the expense allocation or finding that number of resources, especially if the business is “new” to web analytics.

    What about when you spend zero on a tool, doesn’t that mean you spend zero on the people or 100% of the cost, which is nothing. In this case how would one derive the 10% to frame the rule?

    So the idea of segmenting, deriving, and/or deconstructing the 90/10 rule to apply to different classes of businesses and across different existentialisms (like time) makes sense.

    Perhaps a 10/20/30/40 time-based rule? 10% of time selecting and extending the tool, 20% managing, 30% on analysis (part of process), and 40% on controlled experimentation and outcomes measurement (part of the process)? Just riffing right now. I sense a forthcoming blog entry…

  • eric

    Judah: Good points about the spending model … I know Avinash was just trying to provide good guidance but it’s not easy to apply those numbers to any but the most simple models. That’s why I chose to focus 10/20/70 on ** time ** not money.

    I mean, you can make more money, right?

    In terms of your 10/20/30/40 … analysis and controlled experimentation (and reporting, and tagging, and data validation, and, …) are all part of the PROCESS which is why I think Bryan was so right when he said “70 percent is process.” Again and again it is the processes that are so important … as I’m sure your employer can appreciate — if they lose you to some cool new startup consulting firm, they sure don’t want to lose all the hard work and effort you’ve done (and they’ve paid for …)

    I don’t know about another blog entry (I’m so backlogged as it is!) but let’s plan on drinks when I’m in Beantown week after next and hash out the solution over several Sam Adams.

    As usual, thanks for commenting!

  • Rene

    Hi Eric,

    I understand your position and I should have selected my words when commenting (sorry but english is my third language). What I meant was more that it is an evolution of Avinash’s rule. As Judah says, the 10/90 rule dosen’t make sense in all the situations. Your rule seems more logical and indeed you speak about time/effort while Avinash speaks about money. From my perspective Avinash’s rule has helped to show the market that products weren’t the objective neither the silver bullets; even if the rule is not rocket science, it has helped the market to go a step further and put the focus on what happens once you’ve got a WA tool. Your rule goes a step further and brings processes to the arena, thus a more structured framework.

    Hope that this makes more sense ;-)

    Cheers from Paris,


  • eric

    Rene: I should be so lucky to have a third language ;-) Your comment makes sense, and you’re correct. Avinash’s 10/90 rule reiterated the guidance that many people had been giving for years — JupiterResearch, Forrester, Jim Sterne, Jim Novo, Bryan Eisenberg, many others — that it was not the tools but how the tools were used.

    10/20/70 builds on that but again is a summary of what some pretty smart people have been saying for quite some time. And the message is still the same — it’s not the tools, but rather the processes through which bright people use the tools to create success in the business.

    Enjoy Paris and please give my regards to Aurelie!

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I recently had a client pose an interesting question related to their shopping cart. They wanted to know the distribution of money its visitors were bringing with them to each step of the shopping cart funnel.

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A Guide to Segment Sharing in Adobe Analytics
Tim Wilson, Partner

Over the past year, I've run into situations multiple times where I wanted an Adobe Analytics segment to be available in multiple Adobe Analytics platforms. It turns out…that's not as easy as it sounds. I actually went multiple rounds with Client Care once trying to get it figured out. And, I've found "the answer" on more than one occasion, only to later realize that that answer was a bit misguided.

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Currencies & Exchange Rates
Adam Greco, Senior Partner

If your web analytics work covers websites or apps that span different countries, there are some important aspects of Adobe SiteCatalyst (Analytics) that you must know. In this post, I will share some of the things I have learned over the years related to currencies and exchange rates in SiteCatalyst.

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Linking Authenticated Visitors Across Devices
Adam Greco, Senior Partner

In the last few years, people have become accustomed to using multiple digital devices simultaneously. While watching the recent winter Olympics, consumers might be on the Olympics website, while also using native mobile or tablet apps. As a result, some of my clients have asked me whether it is possible to link visits and paths across these devices so they can see cross-device paths and other behaviors.

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The 80/20 Rule for Analytics Teams
Eric T. Peterson, Senior Partner

I had the pleasure last week of visiting with one of Web Analytics Demystified's longest-standing and, at least from a digital analytical perspective, most successful clients. The team has grown tremendously over the years in terms of size and, more importantly, stature within the broader multi-channel business and has become one of the most productive and mature digital analytics groups that I personally am aware of across the industry.

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Ten Things You Should ALWAYS Do (or Not Do) in Excel
Tim Wilson, Partner

Last week I was surprised by the Twitter conversation a fairly innocuous vent-via-Twitter tweet started, with several people noting that they had no idea you could simple turn off the gridlines.

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Omni Man (and Team Demystified) Needs You!
Adam Greco, Senior Partner

As someone in the web analytics field, you probably hear how lucky you are due to the fact that there are always web analytics jobs available. When the rest of the country is looking for work and you get daily calls from recruiters, it isn't a bad position to be in! At Web Analytics Demystified, we have more than doubled in the past year and still cannot keep up with the demand, so I am reaching out to you ...

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A Useful Framework for Social Media "Engagements"
Tim Wilson, Partner

Whether you have a single toe dipped in the waters of social media analytics or are fully submerged and drowning, you've almost certainly grappled with "engagement." This post isn't going to answer the question "Is engagement ROI?" ...

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It's not about "Big Data", it's about the "RIGHT data"
Michele Kiss, Partner

Unless you've been living under a rock, you have heard (and perhaps grown tired) of the buzzword "big data." But in attempts to chase the "next shiny thing", companies may focus too much on "big data" rather than the "right data."

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(503) 282-2601

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